Comparison of the noise spectral parameters of the recordings picked up by the microphone with different directional patterns placed at different positions.
\\n\\n
More than half of the publishers listed alongside IntechOpen (18 out of 30) are Social Science and Humanities publishers. IntechOpen is an exception to this as a leader in not only Open Access content but Open Access content across all scientific disciplines, including Physical Sciences, Engineering and Technology, Health Sciences, Life Science, and Social Sciences and Humanities.
\\n\\nOur breakdown of titles published demonstrates this with 47% PET, 31% HS, 18% LS, and 4% SSH books published.
\\n\\n“Even though ItechOpen has shown the potential of sci-tech books using an OA approach,” other publishers “have shown little interest in OA books.”
\\n\\nAdditionally, each book published by IntechOpen contains original content and research findings.
\\n\\nWe are honored to be among such prestigious publishers and we hope to continue to spearhead that growth in our quest to promote Open Access as a true pioneer in OA book publishing.
\\n\\n\\n\\n
\\n"}]',published:!0,mainMedia:null},components:[{type:"htmlEditorComponent",content:'
Simba Information has released its Open Access Book Publishing 2020 - 2024 report and has again identified IntechOpen as the world’s largest Open Access book publisher by title count.
\n\nSimba Information is a leading provider for market intelligence and forecasts in the media and publishing industry. The report, published every year, provides an overview and financial outlook for the global professional e-book publishing market.
\n\nIntechOpen, De Gruyter, and Frontiers are the largest OA book publishers by title count, with IntechOpen coming in at first place with 5,101 OA books published, a good 1,782 titles ahead of the nearest competitor.
\n\nSince the first Open Access Book Publishing report published in 2016, IntechOpen has held the top stop each year.
\n\n\n\nMore than half of the publishers listed alongside IntechOpen (18 out of 30) are Social Science and Humanities publishers. IntechOpen is an exception to this as a leader in not only Open Access content but Open Access content across all scientific disciplines, including Physical Sciences, Engineering and Technology, Health Sciences, Life Science, and Social Sciences and Humanities.
\n\nOur breakdown of titles published demonstrates this with 47% PET, 31% HS, 18% LS, and 4% SSH books published.
\n\n“Even though ItechOpen has shown the potential of sci-tech books using an OA approach,” other publishers “have shown little interest in OA books.”
\n\nAdditionally, each book published by IntechOpen contains original content and research findings.
\n\nWe are honored to be among such prestigious publishers and we hope to continue to spearhead that growth in our quest to promote Open Access as a true pioneer in OA book publishing.
\n\n\n\n
\n'}],latestNews:[{slug:"stanford-university-identifies-top-2-scientists-over-1-000-are-intechopen-authors-and-editors-20210122",title:"Stanford University Identifies Top 2% Scientists, Over 1,000 are IntechOpen Authors and Editors"},{slug:"intechopen-authors-included-in-the-highly-cited-researchers-list-for-2020-20210121",title:"IntechOpen Authors Included in the Highly Cited Researchers List for 2020"},{slug:"intechopen-maintains-position-as-the-world-s-largest-oa-book-publisher-20201218",title:"IntechOpen Maintains Position as the World’s Largest OA Book Publisher"},{slug:"all-intechopen-books-available-on-perlego-20201215",title:"All IntechOpen Books Available on Perlego"},{slug:"oiv-awards-recognizes-intechopen-s-editors-20201127",title:"OIV Awards Recognizes IntechOpen's Editors"},{slug:"intechopen-joins-crossref-s-initiative-for-open-abstracts-i4oa-to-boost-the-discovery-of-research-20201005",title:"IntechOpen joins Crossref's Initiative for Open Abstracts (I4OA) to Boost the Discovery of Research"},{slug:"intechopen-hits-milestone-5-000-open-access-books-published-20200908",title:"IntechOpen hits milestone: 5,000 Open Access books published!"},{slug:"intechopen-books-hosted-on-the-mathworks-book-program-20200819",title:"IntechOpen Books Hosted on the MathWorks Book Program"}]},book:{item:{type:"book",id:"1956",leadTitle:null,fullTitle:"Phytochemicals - Bioactivities and Impact on Health",title:"Phytochemicals",subtitle:"Bioactivities and Impact on Health",reviewType:"peer-reviewed",abstract:"Among the thousands of naturally occurring constituents so far identified in plants and exhibiting a long history of safe use, there are none that pose - or reasonably might be expected to pose - a significant risk to human health at current low levels of intake when used as flavoring substances. Due to their natural origin, environmental and genetic factors will influence the chemical composition of the plant essential oils. Factors such as species and subspecies, geographical location, harvest time, plant part used and method of isolation all affect chemical composition of the crude material separated from the plant. The screening of plant extracts and natural products for antioxidative and antimicrobial activity has revealed the potential of higher plants as a source of new agents, to serve the processing of natural products.",isbn:null,printIsbn:"978-953-307-424-5",pdfIsbn:"978-953-51-5194-4",doi:"10.5772/2373",price:139,priceEur:155,priceUsd:179,slug:"phytochemicals-bioactivities-and-impact-on-health",numberOfPages:402,isOpenForSubmission:!1,isInWos:1,hash:"bca0d717264e92e4863937bdcf16e06b",bookSignature:"Iraj Rasooli",publishedDate:"December 22nd 2011",coverURL:"https://cdn.intechopen.com/books/images_new/1956.jpg",numberOfDownloads:57743,numberOfWosCitations:70,numberOfCrossrefCitations:23,numberOfDimensionsCitations:96,hasAltmetrics:1,numberOfTotalCitations:189,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"February 3rd 2011",dateEndSecondStepPublish:"March 3rd 2011",dateEndThirdStepPublish:"July 8th 2011",dateEndFourthStepPublish:"August 7th 2011",dateEndFifthStepPublish:"December 5th 2011",currentStepOfPublishingProcess:5,indexedIn:"1,2,3,4,5,6",editedByType:"Edited by",kuFlag:!1,editors:[{id:"61446",title:"Prof.",name:"Iraj",middleName:null,surname:"Rasooli",slug:"iraj-rasooli",fullName:"Iraj Rasooli",profilePictureURL:"https://mts.intechopen.com/storage/users/61446/images/1890_n.jpg",biography:"Iraj Rasooli was born in 1960 in Ahar, Iran. He received his BSc in Microbiology from Shivaji University (India) before obtaining his MSc and PhD in 1992 and 1998 respectively in Microbiology from Bombay University (India). He joined Shahed University in 1993 as an assistant professor where his focus has been on research activities on biological properties of essential oils from medicinal plants and he has published a number of papers in a number of notable national and international journals. He was awarded Razi festival prize by the president of Islamic Republic of Iran in 2002. Professor Rasooli went to the University of Calgary (Canada) in 2002 for his sabbatical where he accelerated his level of knowledge of molecular biology at Prof. Anthony Schryver’s laboratory. To date he has published 86 full length papers on bioactivities of essential oils and molecular microbiology. 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The magnetic resonance imaging (MRI) method is successfully used for monitoring progress in therapy after vocal fold cancer surgery or for monitoring of the implanted cartilage in legs or arms, and/or the process of regeneration in different tissues, etc. In the case of the open-air MRI device, a weak magnetic field (up to 0.2 T) is usually generated by a pair of permanent magnets. Between these magnets, the gradient system consisting of 2 × 3 planar coils is situated together with the RF receiving/transmitting coils surrounding the tested object [1]. Slices of a tested object are selected in 3D coordinates by a gradient system consisting of planar coils parallel to the magnets. A rapidly changing current flowing through the gradient coils produces significant mechanical vibration [2, 3] causing blurring of images of thin layer samples and acoustic noise significantly degrading the speech signal recorded simultaneously during MR scanning of the human vocal tract [4, 5]. Acoustic noise has always negative physiological and psychological consequences on the exposed person depending on the noise intensity and time duration of noise exposure [6]. In order to minimize these negative factors, this work is focused on mapping of energy relationship between vibration and noise signals measured in the MRI scanning area and its vicinity with the final aim to choose the proper scan sequence and its parameters—repetition time (TR), echo time (TE), orientation of scan slices, etc. Apart from real-time recording of the vibration and noise signals, the sound pressure level (SPL) was measured by a sound level meter using frequency weighting to match human perception of noise. The measured data and recorded signals were further processed off-line—the determined energetic features were statistically analyzed and the results were compared visually and numerically.
\nAs mentioned above, the open-air MRI device is primarily used in medical diagnostics, so designation of three planes formed by x, y, and z axes follows medical terminology used for human body planes [7]. The plane dividing the body vertically into ventral (anterior) and dorsal (posterior) parts is called a coronal (frontal) plane. The second vertical plane dividing the body to left and right sides is a sagittal plane. The horizontal plane that divides the human body into superior (upper) and inferior (lower) parts is called a transverse (cross-sectional) plane. During sequence execution, the gradient coil pair corresponding to the chosen scan orientation is activated, it consequently vibrates, and acoustic noise is radiated in the surrounding air. Two basic types of sequences called spin echo (SE) and gradient echo (GE) arising from MRI physical principles [8] are preferred in this type of MRI device. The volume size of the tested object/subject is another important factor having an influence on the intensity of the produced vibration and noise in the scanning area of the MRI device. A tested person/sample/phantom as a part of the whole vibrating mechanical system changes the overall mass, stiffness, and damping by loading the lower gradient coil structure in the patient’s bed.
\nIf the vibration and noise signals are recorded during MR scanning, interaction with the stationary magnetic field B0 in the scanning area must be eliminated; otherwise, the quality of the acquired images would not be preserved. It means that the vibration sensors placed in the MRI scanning area with the static magnetic field cannot contain any part made from a ferromagnetic material. In MRI equipment, working with a weak magnetic field the interaction problem can be solved by a proper choice of the measuring device and its arrangement. Usually, it is sufficient to locate it in an adequate distance from the noise signal source outside the magnetic field area. Since the noise intensity as well as its spectral properties depends on the position of the measuring instrument, the recording/measuring microphone must have high sensitivity, an appropriate pickup pattern, type of the microphone, and a position in regard to the central point of the MRI scanning area (distance, direction angle, working height). The best solution is to use a microphone with a variable pattern having two diaphragms that share a common back plate. Such a microphone behaves as two back-to-back cardioid microphones. If one membrane is connected to a constant polarization voltage and the second one is polarized by a variable voltage, principally any directional pattern can be created. Basic omnidirectional, figure-of-eight, and cardioid patterns corresponding to both same voltages of the same polarity, the opposite polarity, and one zero voltage are represented in an ideal form by a polar equation:
\nwhere A = 1, B = 0 for omnidirectional, A = 0, B = 1 for figure-of-eight, and A = 0.5, B = 0.5 for cardioid directional patterns.
\nThe noise distribution in the scanning area of the MRI equipment and its neighborhood has to be mapped prior to the selection of the proper recording microphone location. C-weighting was used for SPL measurement to accommodate the objective noise intensity to the subjective loudness at high sound levels. The C-weighting filter frequency response in s-domain is given by the equation
\nwhere f1 = 20.6 Hz, f2 = 12,194 Hz, and 20 log G = 0.062 dB [9]. To get the transfer function of the digital IIR filter, the frequency scale is warped by the bilinear transform from s-plane to z-plane
\nThe sensors measuring vibration signals are placed inside the MRI scanning area where the basic stationary magnetic field of the MRI device is present together with the superimposed pulse magnetic field generated by the gradient system as well as the high voltage field originated during activation of the excitation RF coil. These fields would disturb a signal picked up by the sensor from ferromagnetic material or damage electronics integrated with the sensor [10, 11], which can be avoided using the vibration sensor with a piezoelectric transducer. The sensor must have good sensitivity and maximally flat frequency response with the frequency range covering the vibration and noise harmonic frequencies that fall into the low band due to frequency-limited gradient pulses. As a similar frequency range can be found in basic processing of speech signals, it is very important in the case of 3D scanning of the human vocal tract by MRI with parallel recording of a speech signal [5].
\nThe mentioned requirements imposed on the vibration sensor can be met by the sensor for acoustic musical instruments [12]. Its first usage in the magnetic field environment must be preceded by a calibration procedure and a measurement of its sensitivity and frequency response. The measured frequency response is used to determine a correction curve for filtering of the picked-up vibration signal and consecutive linearization operation that has effect on correctness of all analyzed spectral properties determined from the vibration signals—see the block diagram in Figure 1. The correction filter is proposed by a standard procedure of second-order shelving filter design [13]:
\nBlock diagram of processing of the picked-up vibration signal.
For the sampling frequency fs, the polynomial filter coefficients a0,1,2 and b0,1,2 are derived from three input parameters—gain G, mid-point frequency fc, and quality factor Q—in the following manner:
\nSeveral methods can be used to determine the energy of a periodical signal:
The standard root mean square (RMS) is calculated from a signal x(n) in a defined region of interest (ROI) with the length of M samples
\n
The absolute value of the mean of the Teager-Kaiser energy operator OTK [14] is used to calculate the energy EnTK
\n
The frame energy is estimated by the first cepstral coefficient c0 or the autocorrelation coefficient r0 after processing the signal x(n) in frames using NFFT-point FFT to compute magnitude spectrum and power spectrum |S(k)|2,
\n
For basic visual comparison of spectral properties of the recorded vibration and noise signals, the periodogram representing an estimate of the power spectral density (PSD) can be successfully used. The basic spectral properties can be determined from the spectral envelope, and subsequently, the histograms of spectral values can be calculated and compared. They also include the basic resonance frequencies FV1 and FV2 and their ratios, and the spectral decrease (tilt-Stilt) as the degree of fall of the power spectrum calculated by a linear regression using the mean square method.
\nThe supplementary spectral features describe the shape of the power spectrum of the noise signal. The spectral centroid (Scentr) determines a center of gravity of the spectrum—the average frequency weighted by the values of the normalized energy of each frequency component in the spectrum
\nThe spectral flatness (Sflat) is useful to determine the degree of periodicity in the signal, and it can be calculated as a ratio of the geometric and the arithmetic mean values of the power spectrum
\nThe spectral entropy is a measure of spectral distribution. It quantifies a degree of randomness of spectral probability density represented by normalized frequency components of the spectrum. The Shannon spectral entropy (SHE) can be calculated using the following formulas:
\nThe performed measurements were focused on analysis of vibration and noise conditions in the scanning area and in the neighborhood of the open-air MRI equipment E-scan Opera by Esaote S.p.A., Genoa [15] located at the Institute of Measurement Science, SAS, Bratislava. The experiments were realized in four steps: in the preliminary phase, the calibration was carried out, and the sensitivity and the frequency response of the used vibration sensor were determined. Next, the noise was measured using different directional patterns of the pickup microphone and the influence of the pickup pattern on the spectral properties of the recorded noise signal was analyzed. Then, the main vibration and noise measurement and recording experiment were realized. The recorded signals were subsequently processed and statistically analyzed. Finally, a detailed analysis of the influence of chosen scan parameters on the time duration of the used MR sequences and on the quality factor of the MR images was performed with the aim to find a suitable setting to minimize exposition of the examined persons to noise and vibration.
\nThe calibration and measurement experiments were realized with the help of the main devices: the Audio Precision System One including two programmable input and output channels for simultaneous measurement of electrical signals from the vibration sensors mounted on the Vibration Exciter ESE 201 located at the Institute of Electronics and Photonics, FEE&IT SUT, Bratislava. As a reference sensor, the accelerometer KD35a from the company Metra Mess- und Frequenztechnik was used. The sensor sensitivity of this standardized accelerometer is guaranteed, and it operates over a frequency range from 50 Hz to 10 kHz. Three types of vibration sensors having good response in the lower audio frequency range up to 2 kHz were tested within this work:
Cejpek SB-1 with the thin circular brass disc of 0.25-mm thickness and 27.5-mm diameter designed primarily for pickup of a musical sound of a contrabass (further called as “SB-1”),
Shadow SH-SB2 double bass pickup with two disc transducers of 0.5-mm thickness and 22.5-mm diameter (further called as “SB2a,b”),
RFT heart microphone device HM 692 comprising a piezo-electric element integrated in the 1-mm thin aluminum metal cover with 30-mm diameter (further called as “HM692”).
The sensors were mounted on the plate of the vibration exciter as shown in the detailed photo of the arrangement of the sensors in the right part of Figure 2. The output voltage for supply of this exciter and the signal from the calibrated sensors were checked parallel by the digital oscilloscope Rigol DS1102E. Two types of the parameters of the vibration sensors were measured and compared in our experiment:
relative sensitivity at the reference frequency fref = 125 Hz,
frequency response in the range from 20 Hz to 2 kHz at the chosen output voltage of the vibration exciter (UexcBa0 = 360 mV).
Principle block diagram of the used calibration and measurement method together with a detailed photo of practical mounting of the sensors on the plate of the vibration exciter.
Dependence of the sensor’s sensitivity on the excitation voltage for all three sensors is presented in Figure 3a. The reference voltage sensitivity Ba0 of the SB-1 sensor was determined from this graph. Comparison in Figure 3b shows that the measured frequency responses of SB-1, SB2ab, and HM692 are rotated by a slope of about −20 dB per decade with respect to the frequency response of KD35a. As the reference KD35a is an acceleration sensor, it emerges that the remaining three sensors are velocity ones. The calculated inverse frequency response of the SB-1 is drawn by the magenta dashed line together with the correction frequency response obtained by shelving equalization that is plotted by the cyan dot-dash line in Figure 3c. The effect of this shelving filter on the time-domain vibration signal, its frequency-domain periodogram with chosen spectral features, and the spectrogram can be seen in Figure 4.
\nGraph of: (a) measured sensors’ sensitivities, (b) frequency responses in the range 20 Hz to 2 kHz measured and recalculated in [dB], and (c) correction frequency response for the SB-1 sensor linearization using the shelving filter (b): fref = 125 Hz, UexcBa0 = 360 mV, Ba0 = {3.69 (KD35a), 12.9 (SB-1), 5.65 (SB2ab), and 2.45 (HM692)} mV/m s−2.
The vibration signal picked up by the SB-1 sensor without/with the applied shelving filter (left/right set of graphs): selected 150-ms ROI of the signal together with the calculated RMS value (a), corresponding periodogram including the spectral decrease-tilt (b), and spectrogram calculated from the whole 8-s duration of the vibration signal (c); Q = 0.115, fc = 120, G = 30, and fs = 16 kHz.
Acoustic noise measurement in the MRI neighborhood was realized in the directions of 30, 90, and 150°, at the distance of 60 cm from the central point of the scanning area, and at the height of 85 cm from the floor—see the principal arrangement photo in Figure 5. In this noise recording part of the experiment, the pick-up Behringer dual-diaphragm condenser microphone B-2 PRO with switchable cardioid, omnidirectional, or figure-of-eight pickup patterns was used—see the directional patterns from the manufacturer’s specification sheet in Figure 6.
\nPrincipal arrangement of acoustic noise recording in the vicinity of the scanning area of the open-air MRI device Opera: the pickup microphone situated at 30, 90, and 150°.
Example of directional patterns: cardioid (a), omnidirectional (b), and figure-of-eight (c) for the Behringer condenser microphone B-2 PRO.
Subsequently, the spectral properties of the recorded noise signals were analyzed using the mentioned three microphone pickup patterns. The obtained results are presented for visual comparison in Figure 7 and summarized in numerical form in Table 1; the output statistical parameters of the supplementary spectral features are shown in Figure 8.
\nComparison of spectral envelope values in [dB] of the noise signals with different directional patterns of the pickup microphone placed at different positions: histograms for omnidirectional, cardioid, and figure-of-eight patterns—signals recorded at 90° (a) and histograms for signals recorded at 30, 90, and 150°—with the cardioid directional pattern (b).
Microphone pickup pattern/position | \nAt 30° | \nAt 90° | \nAt 150° | \n||||
---|---|---|---|---|---|---|---|
Signal RMS [−] | \nStilt [deg] | \nSignal RMS [−] | \nStilt [deg] | \nSignal RMS [−] | \nStilt [deg] | \n||
Omnidirectional | \n15.3 | \n−16 | \n13.5 | \n−15 | \n14.2 | \n−13 | \n|
Cardioid | \n15.2 | \n−11 | \n13.3 | \n−10 | \n14.0 | \n−4 | \n|
Figure-of-eight | \n14.1 | \n−18 | \n13.1 | \n−13 | \n13.0 | \n−9 | \n
Comparison of the noise spectral parameters of the recordings picked up by the microphone with different directional patterns placed at different positions.
Supplementary spectral properties of the recorded noise signals with different directional patterns of the pickup microphone—(a) omnidirectional, (b) cardioid, and (c) figure-of-eight; box-plots of the basic statistical parameters in the upper graphs, corresponding histograms of values of the spectral centroid, flatness, and Shannon entropy (in the lower set of graphs); signal recorded at 90°.
The acoustic noise SPL was measured using the multifunction environment meter Lafayette DT 8820. In the first step, the dependence of the SPL noise values on the distances DX was mapped. The measuring device was located successively at the distances of {45, 50, 55, 60, 70, 80, 90} cm from the central point of the scanning area, at the height of 85 cm from the floor (between both gradient coils), and in the direction of 30° from the left corner near the temperature stabilizer, producing majority of the background noise SPL0—see the experiment arrangement photo in Figure 9. Comparison of the resulting SPL values obtained during execution of two basic SE and GE types of the MR scan sequences with the background noise SPL (with no sequence running) is presented in the graphs of Figure 10.
\nArrangement photo of SPL noise measurement and parallel recording of noise and vibration signals of the open-air MRI device Opera: (1) RF knee coil with a spherical water phantom, (2) vibration sensor, (3) pick-up microphone, (4) SPL noise meter, and (5) principal angle diagram of the scanning area.
Mapping of the acoustic noise SPL at different distances DX = {45, 50, 55, 60, 70, 80, 90} cm from the middle of the scanning area of the MRI device for SE/GE sequences: (a) comparison of the SPL values with those of the background noise (SPL0) and (b) box-plot of their basic statistical parameters.
Within the scope of our main experiments, the baseline measurement and recording of the vibration and noise signals were carried out during the execution of the MR scan sequences. For noninvasive testing of the subject/object, usually two basic classes of scan sequences are used to take MR images of human body parts with high quality:
high-resolution (Hi-Res) sequences using the basic SE/GE MRI scan methods [16],
special 3D sequences used for building or reconstruction of 3D models of biological or botanical issues [17].
Five types of MR scan sequences were tested in total in the investigated MRI device Opera: SE 18 HF, SE 26 HF, GE T2 (as a typical representative of the “Hi-Res” class), SS-3Dbalanced, and 3D-CE [15]. For each of these scan sequences, different settings of the scan parameters are analyzed:
orientation of scan slices TORIENT = {Coronal, Sagittal, Transversal}—see visualization of the energy features of the vibration and noise signals in Figure 11,
echo times TTE = {18, 22, 26} ms—compare the numerical results in Table 2,
repetition time TTR = {60, 100, 200, 300, 400, 500} ms—documented by comparison of the basic statistical parameters calculated from the vibration and noise signals in Figure 12,
mass of the object inserted in the MRI device scanning area {testing phantom/lying person}—see graphical comparison of the mean values of the energy and basic spectral properties of the vibration signal in Figure 14.
Visualization of energy features of the vibration and noise signals for different slice orientations: {coronal, sagittal, transversal}: (a) signal RMS together with noise SPL values, (b) mean Enc0, (c) mean Enr0, and (d) mean EnTK; used Hi-Res SE scan sequence with TE = 18 ms and TR = 500 ms.
Sequence1 | \nVibrations (SB-1) | \nNoise2 SPL (C) [dB] | \n|||
---|---|---|---|---|---|
Signal RMS[−] | \nEnTK [−] | \nEnc0[−] | \nEnr0[−] | \n||
TE = 18 ms | \n31.5 (1.53) | \n4.32 (0.67) | \n0.044 (0.002) | \n23.04 (4.7) | \n61.5 | \n
TE = 22 ms | \n34.6 (2.11) | \n4.96 (1.02) | \n0.040 (0.003) | \n24.03 (8.5) | \n62.5 | \n
TE = 26 ms | \n36.0 (2.27) | \n5.75 (0.85) | \n0.055 (0.004) | \n24.40 (9.3) | \n63.0 | \n
Comparison of the mean energetic parameters of the vibration signal and the acoustic noise SPL (together with std. values in parentheses) for different settings of the TE time.
Used Hi-Res SE-HF scan sequences with TR = 500 ms and sagittal orientation.
Measured at the distance of DX = 60 cm and the angle of 30°, SPL0 = 56 dB.
Visualization of energetic relations of the vibration (upper set of graphs) and noise (lower set) signals for different TR times; {60, 100, 200, 300, 400, 500} ms—basic statistical parameters of: (a) Enc0, (b) Enr0, and (c) EnTK; used Hi-Res GE-T2 sequences with TE = 22 ms and sagittal orientation.
The slice orientations as well as the TE and TR parameters were set manually to perform measurement and comparison in the range enabled by the current sequence [15]. Practical realization of the last part of the experiment consists in placing a testing phantom or a head and a neck of a lying person in the RF scan coil between the upper and lower gradient coils of the MRI device. While the total weight of the used testing phantom in the first part of the experiment was 0.75 kg, the weighs of one male and one female voluntary person lying on the patient bed of the MRI device were approx. 80 and 55 kg.
\nThe multisignal measurement comprised real-time recording of the vibration signal by the piezoelectric sensor located inside the scanning area of the investigated MRI device and of the acoustic noise signal using the microphone in its proximity, and the additional measurement to check the noise SPL. In this part of the measurement, the microphone stand with the Behringer dual-diaphragm condenser microphone B-2 PRO was placed together with the SPL meter at the distance of DX = 60 cm, and the 140-mm diameter spherical testing phantom filled with doped water [15] was placed inside the knee RF coil. The SB-1 sensor [12, 18] was used to pick up the vibration signal inside the scanning area of the MRI Opera device. Practical position of the sensing disc was on the surface of the plastic holder of the bottom gradient coils, as can be seen in the arrangement photo in Figure 9. The stored recordings were further processed in order to evaluate and compare the measured signal properties. All the noise and vibration signals were recorded with the help of the Behringer Podcast Studio equipment. The signals with duration of about 15 s sampled at 32 kHz were next processed in the sound editor program Sound Forge 9.0a.
\nThe chosen type of the scanning sequence and the values of the resulting basic scan parameters (TR and TE) have significant influence on the scanning time. These parameters can also be changed manually, but their final values depend on the setting of the other scan parameters—number of slices, slice thickness, number of used accumulations NACC of the free induction decay (FID) signal [8, 16], etc. Practical demonstration of the acquired MR images with increasing quality factor (QF) shows greater range of visible details in the images for three different MR scans of the human vocal tract in Figure 15.
\nThe console program “ESAMRI” of the MRI device control software [15] was used to carry out the following two parts of the analysis and comparison:
Influence of the basic setting of scan parameters on the final quality factor of MR images and on the time duration TDUR of the scan sequence execution for:
different slice thickness of {2, 2.5, 3, 4, 4.5, 5, 10} mm—the predicted QF values are presented in Table 3 for the scan sequence Hi-Res SE18 HE,
different repetition times of {60, 100, 200, 300, 400, 500} ms together with NACC—see visualization of the graphical results using the “Hi-Res” sequences of SE and GE types in Figure 16, and TDUR values in Table 4 for both Hi-Res sequences types,
increased number of applied accumulations of the FID signal: NACC = {1, 2, 3, 4, 5, 6, 7, 8, 10, 16}—the predicted values of QF and TDUR are shown numerically in Table 5 for the scan sequence Hi-Res SE18 HE.
Comparison of the predicted QF and TDUR values for “3D” types of MR scan sequences—numerical matching of the results for the changed number of FID signal accumulations and different number of 3D phases using:
Parameters1 | \nSlice thickness [mm] | \n||||||
---|---|---|---|---|---|---|---|
2 | \n2.5 | \n3 | \n4 | \n4.5 | \n5 | \n10 | \n|
QF [−] | \n17 | \n21 | \n26 | \n34 | \n38 | \n43 | \n85 | \n
Influence of the slice thickness on the predicted quality factor of the MR image and on the time duration for the scan sequence Hi-Res SE18 HE (TR = 500 ms, NACC = 1).
TDUR = 1 min 39 sec in all cases.
NACC [−] | \nTR [ms] | \n|||||
---|---|---|---|---|---|---|
60 | \n100 | \n200 | \n300 | \n400 | \n500 | \n|
1 | \n0:14 | \n0:22 | \n0:41 | \n1:09 | \n1:20 | \n1:39 | \n
8 | \n1:35 | \n2:37 | \n5:12 | \n7:46 | \n10:20 | \n12:55 | \n
16 | \n3:08 | \n5:11 | \n10:20 | \n15:29 | \n20:38 | \n25:47 | \n
Dependence of the time duration TDUR [min:sec] on setting of TR and NACC parameters—merged values for both Hi-Res sequences of SE and GE types; slice thickness = 4.5 mm.
Parameters | \nNACC [−] | \n|||||||||
---|---|---|---|---|---|---|---|---|---|---|
1 | \n2 | \n3 | \n4 | \n5 | \n6 | \n7 | \n8 | \n10 | \n16 | \n|
QF [−] | \n14 | \n20 | \n24 | \n28 | \n31 | \n34 | \n37 | \n40 | \n44 | \n56 | \n
TDUR [min:sec] | \n0:14 | \n0:26 | \n0:37 | \n0:49 | \n1:00 | \n1:12 | \n1:24 | \n1:35 | \n1:58 | \n3:08 | \n
Influence of the number of FID signal accumulations on the predicted quality factor of the MR image and on the time duration for the scan sequence Hi-Res SE18 HE (TR = 60 ms and slice thickness = 10 mm).
Parameters | \nNACC [−] | \n|||||
---|---|---|---|---|---|---|
1 | \n2 | \n3 | \n4 | \n8 | \n16 | \n|
QF [−] | \n59 (102) | \n84 (144) | \n103 | \n118 (204) | \n167 | \n237 | \n
TDUR [min:sec] | \n3:14 (5:36) | \n6:25 (11:04) | \n9:37 | \n12:48 (22:00) | \n25:34 | \n51:05 | \n
Influence of the number of FID signal accumulations on the predicted quality factor of the MR image and on the time duration for the scan sequence SS-3D balanced (TE = 10 ms and TR = 20 ms) and 3D phases = 24 (for 42 phases, the values are in parentheses).
Parameters | \nNACC [−] | \n|||||
---|---|---|---|---|---|---|
1 | \n2 | \n3 | \n4 | \n8 | \n16 | \n|
QF [−] | \n134 (79) | \n189 (122) | \n231 | \n267 (137) | \n378 | \n534 | \n
TDUR [min:sec] | \n1:04 (9:53) | \n2:00 (19:44) | \n2:56 | \n3:52 (29:35) | \n7:36 | \n15:04 | \n
Influence of the number of FID signal accumulations on the predicted quality factor of the MR image and on the time duration for the scan sequence 3D-CE (TE = 30 ms and TR = 40 ms) and 3D phases = 8 (for 72 phases the values are in parentheses).
The performed calibration and frequency response linearization of the piezoelectric vibration sensor enables precise pick-up of vibration signals in the environment of a weak stationary magnetic field and a high-voltage RF signal disturbance that is observed in the scanning area of the MRI device.
\nOur measurements have shown an inverse relationship between the diameter of the used sensor and the minimum frequency of the vibration picked up from the measured surface. The sensor HM692 with a massive aluminum microphone capsule used in phonocardiography had the lowest sensitivity and caused the greatest decrease of the maximum frequency. The calibration of the SB2 sensor was carried out in parallel for both pickup elements. The measured frequency responses SB2a,b are practically identical with nonlinear decrease in the range of low frequencies from 35 to 100 Hz—see the frequency responses in Figure 3a. In 3D scanning of the human vocal tract [4, 5, 19], the MRI device generates the acoustic noise of frequencies in the range from 25 Hz to 3.5 kHz that is similar to the basic frequency range of speech signals. For this reason, the SB-1 sensor was chosen for its greatest size allowing the best low-frequency sensitivity.
\nComparison of noise spectral properties recorded for different types of directional patterns of the pickup microphone yields the best recording conditions for the cardioid pattern (minimum spectral decrease as shown by the obtained results in Table 1). On the other hand, dispersion of the spectral envelope values is similar for all three analyzed pattern types as can be seen in histograms in Figure 7a. Comparison of different microphone positions has shown that at 30°, the background noise from the MRI temperature stabilizer degrades the recording (see the signal RMS values in Table 1) and the direction of 150° is a bit unnatural from the point of view of an examined person lying in the MRI scanning area. Therefore, the direction chosen as the best for noise and speech signal recording was in the main horizontal axis of the MRI device (at 90°). In addition, at this position, the lowest values of the noise signal RMS were measured and the smallest dispersion of the spectral envelopes was observed—see the green dash-dot line in Figure 7b.
\nThe results of a detailed measurement of the acoustic noise intensity at different distances from the central point of the scanning area for the SE and GE “Hi-Res” sequences are presented in Figure 10. The GE sequence produces noise with a slightly higher intensity, then the SE one (approx. 3-dB difference in the nearest location of 45 cm from the center of the scanning area) and variation of the SPL values depending on the measuring distance is also greater as seen in the box-plot graph in Figure 10b. The minimum distance was set to 45 cm in order to eliminate interaction of metal parts of the SPL meter with the static magnetic field of the MRI device. If the SPL meter was placed near the center, the field homogeneity would be disrupted and the warning message on the MRI control console would be followed by disabling to run any scan sequence by the software system [14]. The maximum measuring distance was set to 90 cm where the measured MRI noise was masked by the background noise originating from the temperature stabilizer. In the middle of the investigated measuring distances, the SPL values were similar for both types of MR scan sequences, so the working distance of 60 cm was used for all further measurements.
\nNext investigation of the recorded vibration and noise signals was aimed at the influence of the choice of the slice orientation on the energy of the produced vibration and noise signals. This effect is large—the maximum can be found in the sagittal plane and the minimum in the transversal plane for the vibration signals, and in the coronal plane for the noise signals—see the column charts in Figure 11. Therefore, the remaining experiments used only the sagittal orientation.
\nIn accordance with our previous research [12, 18] the current experiments confirm the influence of the TE and TR times on the vibration and acoustic noise properties. The TE time extension causes fall of the final signal energy as documented by raised all the four determined vibration energetic parameters as well as the achieved SPL noise values in Table 2. The influence of the TR time determining the basic dominant frequency can be seen in box-plot graphs in Figure 12. This visualization of the basic statistical parameters obtained from analysis of vibration and noise signals shows the highest values of all energetic parameters for the shortest TR times (60 or 100 ms).
\nComparison of energetic relations of the vibration and noise signals for different sequence types brings ambiguous results and shows only small differences—see three bar-graphs in Figure 13. The 3D sequence “SS-3Dbalanced” differs from the remaining sequence types by reverse behavior: while the Enc0 and Enr0 parameters indicate the minimum values, the EnTK achieves the maximum ones (see the graph in Figure 13c). This situation can be caused by the minimum settings of the TE and TR times that were used for the “Hi-Res” types to be comparable with the “3D” types with slightly atypical values being out of the normal range of use although the control software enables their setting [15].
\nComparison of energetic relations of vibration and noise signals for different sequence types: {Hi-Res SE-HE, Hi-Res SE-HF, Hi-Res GE-T2, SS-3Dbal, 3D-CE}: (a) mean Enc0, (b) mean Enr0, and (c) mean EnTK; in all cases, the sagittal slice orientation was used.
Next comparison of energetic relations of the vibration and noise signals for different objects placed in the scanning area of the MRI device shows a relatively high effect of the mass put upon the bottom plastic holder of the gradient coils. The effective weight of the person exerting a pressure on the bottom plastic holder of the gradient coils attenuates the vibration pulses partially. The mass effect is demonstrated by increase of the vibration signal energy based on Enc0 parameter with its maximum for the lying male person with the weight of 80 kg (see the bar-graph in Figure 14a). It is also demonstrated in the spectral properties of the vibration signal as shown by lower spectral decrease in Figure 14a and by shift of the first two dominant frequencies toward higher values—see the mutual FV1,2 position in Figure 14c.
\nComparison of mean values of the energy and basic spectral properties of the vibration signal for different objects placed in the scanning area of the MRI device: (a) energy Enc0, (b) box-plot of basic statistical properties for the spectral decrease values, and (c) mutual values of the frequencies FV1 and FV2; used Hi-Res SE-HF scan sequences with TE = 18 ms, TR = 400 ms, and sagittal orientation.
Examples of MR images of the human vocal tract obtained with different values of the quality factor: (a) scan sequence Hi-Res SE26 HF (TR = 500), slice thickness = 4.5 mm, QF = 100, (b) scan sequence Hi-Res SE26 HF (TR = 500), slice thickness = 7.5 mm, QF = 196, and (c) scan sequence 3D SSF 30 (TR = 10), slice thickness = 9.4 mm, and QF = 398.
Influence of the TR time and the number of FID signal accumulations on the predicted image quality factor for the Hi-Res sequences of—(a) SE and (b) GE type; slice thickness = 4.5 mm.
Results of the preliminary analysis of influence of the slice thickness document that its increase has a positive effect on the predicted quality factor of MR images—compare the values in Table 3. Next comparison shows a positive influence of increase in the TR time on the quality factor, and this effect is more pronounced when using the SE sequence type—see the left graph in Figure 16. This figure also documents significant dependence between the applied number of FID signal accumulations and the predicted QF value. Also, in this case, the increase of QF is more distinctive for the SE sequences. Values in Table 4 describe the influence of TR and NACC values on the final time duration of the executed scan sequence. While the increased TR causes only moderately greater overall time duration, the changed NACC parameter has comparably higher influence on the final time duration. This effect is also shown in a detailed comparison of numerical results for different NACC values in Table 5. For the “Hi-Res” sequence types, the increase of the parameter NACC from 2 to 16 results in about 2.8 times greater value of QF but 6 times greater than that of TDUR. For the “3D” sequence types, the increase of the resulting time duration is also affected by the choice of the number of 3D phases (equivalent to the number of slices with selection of the slice thickness for the “Hi-Res” sequences) in parallel as shown in Tables 6 and 7.
\nAcoustic noise measurement in the vicinity of the investigated open-air MRI device yielded the maximum sound pressure level of about 82 dB(C) at the distance of 45 cm from the central point of the MRI scanning area for the GE scan sequence with short TE and TR times and the sagittal orientation of scan slices. For examination of other parts of the human body (leg, arm, etc.), the head is not inserted directly between the upper and the lower gradient coils, so the noise level is much lower as documented for different distances in Figure 10. Finally, the scanning times for the mostly used 3D or Hi-Res sequences are in general less than 15 minutes (typically about 3–5 minutes depending on the chosen number and thickness of the slices)—exposition of the examined person and his/her hearing system to the noise and vibration is not significant.
\nIf there is need for more detailed MR images with higher quality factor QF (e.g., in scanning of particular parts of the human brain, the eye, the middle and inner ear, etc.), the time duration TDUR can be much longer (more than half an hour). In such a case, the long exposition to the vibration and acoustic noise may impose great physiological and psychological stress on the patient. Therefore, these scan parameters should be chosen only in the urgent cases.
\nThe results of the performed measurements are useful for precise description of the process of the mechanical vibration excitation and the acoustic noise radiation in the scanning area and in the vicinity of the MRI device. The measurement results and comparisons with a similar low-field MRI tomograph can be used in optimization of the acoustic noise suppression in the speech recorded parallel with application of MRI scanning for 3D modeling of the human vocal tract [19].
\nThis work was funded by the Slovak Scientific Grant Agency project VEGA 2/0001/17 and the Ministry of Education, Science, Research, and Sports of the Slovak Republic VEGA 1/0905/17, and the Slovak Research and Development Agency, project no. APVV-15-0029.
\nThe authors declare no conflict of interest.
3D printing is an additive manufacturing (AM) process that enables the manufacturing of components with complex geometries in a layer-by-layer fashion. 3D printing became popular after the first machine was introduced to the market in 1986 by Hull [1]. Charles Hull created the first stereolithography (SLA) manufacturing method which he used for the rapid design and manufacturing of small prototype plastic parts. Stereolithography uses light to activate polymers within a resin (photopolymerization) to create 3D, complex shapes [2, 3]. This SLA system was commercialized in 1987 by the company 3D Systems. Since this breakthrough invention, there has been great effort in producing machines that can process a variety of plastics. Some of the machines currently in the market are fused deposition modeling (FDM) [4, 5] and direct ink write (DIW) for extrusion-based processes [6, 7]. Powder bed fusion (PBF) and laser sintering (SLS) are used for processes requiring a laser to cure or fuse polymeric materials [8]. Inkjet printers also use light to photopolymerize ink drops into complex shapes [9]. Extensive reviews on these processing and 3D printing technologies have been published elsewhere [4, 5, 10, 11, 12, 13, 14]. This chapter focuses on applications that use AM for the 3D printing of polymeric materials.
\nSince the 1980s, 3D printing has become very popular as a result of the rapid manufacturing of components with architectures designed to meet specific applications. AM allows for the manufacturing of a variety of shapes in a layer-by-layer fashion, often without the need of post-processing such as machining. As a general scheme, AM starts with the design of a virtual object using CAD (computer-aided design) software that generates a STL (stereolithography, named after Charles Hull’s SLA process) file format [15]. A slicer program interprets the STL file and converts it into g-code (e.g. Slic3r, 3DPrinterOS, MakerBot Print, and others). The computer controls the stage and dispenser of the 3D printer allowing prototypes to be manufactured. Rapid prototyping allows one to refine product ideas while saving significant time and money because it allows for iterations prior to creating a final product. Optimization via an iterative process involves touching and feeling the prototype, in real time, in order to finalize the shape and geometry, leading to a final product. Characterization methods during iterations and on the final design include optical microscopy, SEM, and mechanical tests. Others methods, such as bio-compatibility (cell-adhesion and proliferation) and electrical performance are performed depending on the application. Figure 1 demonstrates a general scheme for the AM process. Despite the many advances in AM, the technology still has many challenges that need to be addressed. These challenges are related to the speed of the processes (which in many cases is slower than injection molding processes and machining), cost of the machines, and limited feedstock. However, advantages outweigh the challenges due to the fact that AM allows for compositional flexibility, complex macro and microstructures, and easy modeling and optimization. As a result, industries including biomedical engineering, transportation, and the military have adopted AM as the main manufacturing method for the printing of prototypes and final parts [16, 17].
\nGeneral scheme for the use of additive manufacturing processes, from the choice of material to the final product. The 3D printing of parts involves the use of a computer-assisted design software that generates a STL file format that is then sliced and formatted into gcode. The computer controls the stage and dispenser to generate materials with specific architectures, e.g. faced-centered tetragonal cushion using direct ink writing (a) and diamond structure using FDM (b).
Careful attention is imperative when choosing a material to print a given part. While there are a variety of commercially available polymers, not one polymer is inclusive and will give one the properties needed for a specific application. Furthermore, a single AM technique is not capable of printing any one individual polymer available in the market. The selection of material depends on the application and the customers’ needs. Figure 2 lists the decision criteria for the selection of a material. One must take into consideration the environment at which the part will be exposed and the properties required (e.g. temperature, mechanical load, humidity, chemical exposure, radiation, UV light), the processability, 3D printing method, and availability.
\nMaterial selection chart for product design and manufacturing.
Polymers have become consumer goods, for they are used to manufacture bottles, toys, tools, bags, phones, computers, tools, cushions, electronics and transportation components [18]. Thus, it makes sense that efforts have focused on developing materials that can be 3D printed, which allows for rapid manufacturing [2, 3, 4, 17]. Table 1 lists commercially available polymers used in some of the AM processes. Polycarbonate (PC), acrylonitrile butadiene styrene (ABS), poly ether ester ketone (PEEK), polyetherimide (ULTEM) and Nylon are common polymers used in processes requiring thermoplastics, or plastics that are processed by heating to a semi-liquid state and close to the melting point. Upon extrusion, the printed layers fuse and solidify. AM techniques that use thermoplastics are Fused-Deposition Modeling (FDM), Jetting (InkJet), and Selective Laser Sintering (SLS). SLA and Direct Ink Writing (DIW) use thermosetting polymers in their liquid state, or polymers that become solids after curing. A chemical reaction occurs prior to the melting point, resulting in a solid-state material. In SLA and DIW, polymers are formulated to meet specific properties, most importantly rheological. For example, each layer should be self-supporting and should allow for the printing of multiple layers while retaining the designed geometry [14, 19, 20, 21]. Rheologically, this corresponds to a resin that has a yield stress at high oscillatory stresses, such that the resin is solid-like at rest (low stress) and liquid like during flow (high stress) [7]. One of the main challenges in the polymer 3D printing industry is the limited feedstock available for purchase. Polymers listed in Table 1 cannot be used in all applications. Particularly, polymers in the pure state lack mechanical strength for load-bearing applications. The addition of fillers, such as silica [22, 23] and carbon fibers [24, 25], is often used to generate materials with high mechanical strength. Furthermore, the incorporation of additives enhances materials properties by adding functionality to the parts that include getter [20], UV and radiation resistance [26, 27, 28], and anti-fouling properties [29, 30, 31].
\nAM technology | \nProcess | \nPhysical state of starting material | \nFeedstock | \n
---|---|---|---|
FDM | \nMelting-solidifying | \nSolid | \nPC, ABS, PLA, ULTEM, Nylon, Carbon-filled Nylon, ASA | \n
SLA | \nPhotocuring | \nLiquid | \nThermosetting- acrylates and epoxy | \n
SLS | \nMelting-solidifying | \nSolid | \nPCL, PLA | \n
Jetting | \nPhotocuring | \nSolid | \nABS, ASA, PCL, PLA, Vero | \n
Direct Writing | \nExtrusion-heat/UV curing | \nliquid | \nThermosetting- any material with adequate viscosity | \n
List of polymers used for 3D printing applications.
The biomedical market represents 11% of the total AM market share today, and will be a strong driver for AM development and growth [32]. Since the early 2000s, there has been increased interest in using 3D printing to fabricate hard tissues (bones, teeth, cartilage) and soft tissues (organs, skin, and others) [2, 3, 4, 16, 33]. The manufacturing of prostheses and scaffolds with complex geometries is especially important for regenerative medicine, where a porous scaffold is implanted into the patient to serve as a template for tissue to regenerate while the implant degrades slowly in the body. Other implants need to stay in place for the lifetime of the patient. 3D printing allows for the rapid manufacturing of customized prosthetics and implants with controlled architectures. The structure can be designed through the translation of x-ray, MRI, and CT images into STL file formats. The STL file can be processed by software and a design can be generated based on the patient’s specific needs. Metals are commonly used to generate prosthetics for bone reconstruction. ABS and PLA are the most suitable non-biodegradable polymers used for the manufacturing of scaffolds. However, materials used in medicine must enable cell adhesion, growth, and differentiation. Current feedstock for biomaterials is limited to collagen, gelatin, fibrin, and chitosan, which are similar to natural tissue, have high affinity to cells and are highly hydrated. The main challenge with these soft natural polymers is their low mechanical strength [33]. In biomedical engineering, the main focus has been on the development of biopolymeric materials for tissue and scaffold generations with improved flexibility, strength, and patient compatibility in order to prevent implant rejection and toxicity. Some polymeric mixtures include living cells isolated from the patient and grown in the laboratory. These types of polymers are often hydrogels suitable for ink jet 3D printing technologies. Table 2 shows various polymers used for biomedical applications. Some examples of biomedical devices developed using 3D printing are implants, prosthetics, dental, orthodontics, hearing aids, and drug release tissues.
\nMaterial | \n3D printing techniques | \nComments | \n
---|---|---|
PLA, PCLA, PLGA | \nFDM | \nScaffolds. Biodegradable. Can add fillers, e.g. HA, for improved cell adhesion and mechanical properties | \n
Collagen, alginate, PEG, fibrin, chitosan | \nInkjet, extrusion | \nBiodegradable scaffolds. Can add fillers and cells for improved cell adhesion and mechanical properties | \n
PCL, methacrylate copolymers | \nSLS | \nBiodegradable scaffolds. Improved mechanical properties | \n
Polymers and processes used for the additive manufacturing of biomedical devices.
Polymers used for tissue and organ fabrication need to have various functions in order to (1) allow for cell attachment and migration, (2) transfer growth factors and waste products, (3) maintain its shape while cells are growing and (4) maintain adequate mechanical properties. Wu et al. [34] reported the generation of a biopolymeric material based on chitosan dissolved in an acid mixture of acetic acid, lactic acid, and citric acid. This biomaterial was 3D printed using an ink-writing technique, then dried under vacuum and neutralized to remove any acid residue. The structure of the scaffold was characterized using confocal laser scanning microscopy and the images showed wrinkles attributed to the volume change. Tensile mechanical tests show that the printed material exhibits a strain to failure of 400% under tensile load and a 7.5 MPa ultimate strength when in its neutralized form. Furthermore, the 3D printed material allows for excellent cell adhesion, growth, and proliferation, as demonstrated using the Live-Dead staining method, fluorescence microscopy, and SEM.
\nLuo et al. [35] reported the 3D printing of a bioceramic hollow struts-packed scaffold using an extrusion typ. 3D printer and a shell/core nozzle. The ink contained Ca7Si2P2O16, alginate and Pluronic F-127. After printing, the ink was dried overnight and sintered for 3 hours at 1400°C to remove the alginate and F-127 materials. The morphology was analyzed using an optical microscope. The micropores and the microstructure of the pores were characterized using SEM. The fabricated scaffolds (16/23 shell/core size) were subjected to mechanical testing and exhibited a compressive strength of 5 MPa, comparable to cancellous bone (2–12 MPa), and a modulus of 160 MPa. The scaffold had high porosity (65–85%), adjusted with the core/shell size nozzles. The high porosity and surface area (up to 6500 mm2/g) allowed for cell adhesion and proliferation on the outer and inner surface of the scaffold, as determined by SEM. Finally, the in-vivo bone formation study in a rabbit demonstrated that the bioceramic implant allows for good cell integration and bone formation was detected with micro-CT.
\nLewis’ team at Harvard University 3D printed a tympanic membrane scaffold composed of PDMS, PLA, and PCL based materials using a DIW technique [36]. The team demonstrated that it is possible to design and fabricate materials with similar properties when compared to human specimens. The high frequency displacement and acoustics were organized by concentric rings for each 3D printed graft, and it was very dependent on the patterns and mechanical properties, characterized via digital opto-electronic holography, laser Doppler vibrometry, and dynamic mechanical analysis. In a different study, the team 3D printed cellular materials with vascular networks for flow [37]. The 3D printed structure was fabricated using an ink composed of Pluronic F-127, GelMA (gelatin methacrylate to allow for UV curing) and fibroblast cell culture. After curing, the Pluronic F-127 was removed by cooling to 4°C, yielding open channels that represent the vascular networks. Lewis’ team demonstrated that blood and other cellular liquids can flow through the channels with minimal death of cells.
\nPatients with skin burns and thick wound injuries often suffer from long term recovery and extensive and expensive treatments. The autologous split-thickness skin graft (ASSG) is the technique most often used to treat large wounds [38]. A skin tissue is place in the injured area and assists with the wound closure and healing. This technique relies on the removal of a piece of skin from a different part of the patient’s body and reapplying it on the place of injury. The drawback with ASSG is that it is limited by the size of donor sites and also creates another place of injury [38]. 3D printing of biomaterials would alleviate the problems related to ASSG. Skin cells are cultured in a laboratory and mixed with biocompatible polymers for bioprinting. In 2012, Koch Singh et al. [39] reported the 3D printing of skin using a laser-based inkjet printing method. The inks were composed of blood plasma/alginate solution and fibroblast/keratinocytes/collagen biomaterials. Collagen is the main component of the extracellular matrix (ECM) in skin. The team proved that the laser-based printing method does not harm the cells by performing proliferation of the cells in histologic sections 10 days after printing. Ki-67 staining, which includes the protein present in cells during their active cell cycle phases, shows that proliferating cells can be found in all regions, verifying vitality. In addition, a build-up of basal lamina, cell adhesion and proliferation- sign of tissue generation was observed.
\nThe dental industry is taking advantage of 3D printing technologies for restoratives, implants, and orthodontics purposes. Currently, professionals in the dental field have access to 3D printers and it is possible to print designs in a clinical environment. A CT scan is used to generate a defined shape based on the patient’s morphology and quickly fabricate and replace a missing tooth [40]. 3D printing is used for the manufacturing of aligners, braces, dental implants, and crowns [40]. Biocompatible materials are used for the fabrication of dental parts using 3D printing, e.g. polylactic acid, polycaprolactone and polyglycolide, and acrylates [3]. It is possible to fabricate dental implants with antibacterial properties by the incorporation of additives, such as quaternary ammonium salts [41, 42, 43]. At the age of 23, Amos Dudley fabricated his own orthodontic aligners while he was a student at New Jersey Institute of Technology [44]. He used equipment available at the institute to scan and print models of his teeth. A non-toxic plastic was used to mold and eventually generate 12 clear aligners. Amos had access to a Stratasys Dimension 1200 3D printer and used a mixture of alginate powder and PermaStone as the resin to print the aligners, which were tested by fitting them on his teeth. While it was not a trivial problem to solve, Amos proved the ability of 3D printing orthodontic materials for teeth alignment.
\nAM has been widely used in the biomedical industry and will continue to impact work in the future. Some challenges will persist, such as regulatory issues, limited materials, and inconsistent quality [45]. AM biomedical products require FDA approval, which can be time consuming and difficult to obtain [46]. Biocompatibility will require the development of new techniques and materials to produce high quality, high performing AM materials [47]. Furthermore, mechanical properties of AM materials need to be well assessed such that final properties can have reliable and reproducible behaviors. Further development for on-demand and patient-specific applications will be exciting work in this field. For example, designing patient-specific implants following a CT-scan will result in quick results [48]. Complex parts with specific mechanical properties and biocompatibility can be constructed on demand and with multifunctional components if needed. AM Research and development may help to improve bio-printed scaffolds and tissues for clinical applications to reduce cost for tissue engineering [49]. Manufacturing AM artificial organs, which includes multifunctionality (i.e. bionic ear [50]), will revolutionize the field of 3D printing for biomedical applications.
\nOne of the most promising fields in the future of AM is the aerospace industry. According to Wohlers’ report, this industry account for almost 20% of the total AM market today [32]. Aerospace applications typically require light weight and high strength materials. The importance of AM relies on the reduced cost, increased flexibility of design, and increase in a variety of products to meet customer needs. Additive manufacturing is an important technology that enables the design and manufacturing of complex structured products with improved mechanical strength and lower weight, at a lower cost and reduced lead-time. The aerospace industry has replaced the conventional manufacturing methods of molding and machining with 3D printing technology for small scale production. At a small production scale, AM offers effectively low-cost design and assembly [17].
\nThe aerospace industry implemented the use of AM approximately 20 years ago [51]. The main use for 3D printing has been focused on prototyping, modeling and producing jigs, fixtures and tools [17]. Furthermore, AM is used to build replacement parts on-demand when required. The ability to build on-demand spare components reduces costs for the production of parts that may never be used due to them becoming obsolete to new technology, which also saves warehouse storage space. For example, BAE Systems is currently 3D printing window breather pipes used in jetliners [52]. These pipes cost 40% less than pipes manufactured using injection molding processes and are manufactured on an as-needed basis.
\nRecently, NASA designed a rover, named Desert RATS, that can support humans in a pressurized cabin in space [53]. The rover is intended to transport humans to Mars. It contains 70 3D printed parts that include flame-retardant vents and housings, camera mounts, large pod doors, front bumpers, complex electronics, and others. The materials used for the 3D printing of the part used in the rover were ABS, PCABS and PC, and were printed using a FDM Stratasys 3D printer. Piper Aircraft manufactures tools using PC that can withstand hydroforming pressures of 3000 to 6000 psi. Aurora Flight Science additively manufactured wings that weigh one third of the fully dense metal components [54]. Some wings have integrated electronics. Lepron generated 200 different designs for use in piloted helicopters [17]. It is foreseen that aerospace companies will replace small components with 3D printed parts, thus reducing the weight of the machines. Some examples are arm rests, seat belts, food trays, and many others [17].
\nCompanies have adopted AM for fast production without making substantial changes to their products [17]. This modification is mostly due to the fast-changing market and low cost of generating such small builds. Several challenges would have to be overcome to facilitate the growth of AM. Some of these challenges include: (1) current speed of AM machines is slow for bulk production; (2) few polymeric material options; and (3) current machines do not allow for the manufacturing of large components [17, 55]. In the future, it is expected that companies will pursue a completely different business model by performing product customization for end-product while maintaining the on-demand part supply. Future work will focus on the development of multifunctional structures with complex geometries, which allows for novel solutions for complicated problems. AM techniques, such as using functionally graded materials, can be used in order to tailor the mechanical and/or thermal response of components [56]. Furthermore, on-demand manufacturing will reduce costs and eliminates potential damage caused by storage [45].
\nElectronic devices require suitable mechanical, geometrical, and optical functionalities to allow for miniaturization, low energy consumption, and smart capabilities [57]. The production of prototypes and end-products has to rapidly change due to the fast-changing technology. The conventional method for manufacturing electronic devices is using subtractive methods that involve masking and etching of sacrificial materials [58]. AM allows for the reduction of material waste, energy consumption and processing time and steps. 3D printing is being used to substitute steps for mounting and assembling electronic devices [59]. The additive process deposits material in a controlled layer-by-layer process allowing the manufacturing of complex geometries and dimensions. In addition, it enables 3D orientation of important components to improve performance. With miniaturization, AM allows for the manufacturing of small parts that would otherwise be difficult to obtain. AM has found application for thin films [60], inductors [61], solar cells [62], and others. The most common 3D printing techniques for electronics are inkjet and direct writing of conductive inks.
\nJennifer Lewis and colleagues fully 3D printed a quantum-dot (QD) light-emitting diode (LED) system, including green and orange-red light emitters embedded in a silicone matrix [63]. The printed device exhibits a performance of 10–100-fold below the best processed QD-LEP but could potentially be optimized with the addition of an electron-transport layer. A copper nanoparticle stabilized with polyvinyl pyrrolidine was mixed with 2-(2-butoxyethoxy)ethanol to prepare ink for inkjet printing [64]. The ink was printed onto a polyimide subtracted and sintered at 200°C. The prepared electronic device resulted in low electrical resistivity (≥ 3.6 μΩcm, or ≥ 2.2 times the resistivity of bulk copper). Bionic ears were printed using an inkjet printer [50]. The inks were composed of cell-cultured alginate and chondrocytes hydrogel matrix and a conductive polymer consisting of silicone and silver nanoparticles. The 3D printed ears exhibit enhanced auditory sensing for radio-frequency reception allowing the ear to listen to stereo music. This result demonstrates that bioengineering and electronics can be merged, resulting in advanced technologies. Students from Northwest Nazaren University and Caldwell High School designed the 3D printed CubeSat [65]. The CubeSat was launched aboard Delta II rocket as part of a NASA mission in 2013. It carries miniaturized electronics and sensors and is intended to collect real-time data on the effects of the harsh environments of space (oxygen, UV, radiation, temperature and collisions) on the polymeric materials- ABS, PLA, Nylon, and PEI/PC ULTEM.
\nFuture research and development in the electronics field will take advantage of low cost methods, flexibility in design, and fast speed of 3D printers for designing and prototyping new products. For example, printing circuit boards will offer superior accuracy and flexibility, with potential cost savings, environmental impacts, faster production times, and increased design versatility. Furthermore, adaptive 3D printing, which takes advantage of a closed-loop method that combines real-time feedback control and DIW of functional materials to construct devices on dynamic surfaces, is an exciting field of research [66]. This method of 3D printing may lead to new forms of smart manufacturing technologies for directly printed wearable devices. New possibilities will emerge in the wearable device industry, in biological and biomedical research, and in the study and treatment of advanced medical treatments.
\nUnsurprisingly, the amount of plastic pollution on the planet is alarming [67]. Plastics have dominated our marketplace due to their utility and versatility and make up at least 10% by mass of our waste streams. Plastics are designed to be durable and to withstand harsh environmental conditions. Therefore, the amount of plastic waste is only expected to increase in the future. Currently, 91% of plastic is not being recycled. The negative impact plastics have on our ecosystem is well recognized and researchers are using this as a business model and opportunity [68, 69]. Considerable efforts are being placed on recycling and reusing plastic waste. Prof. Sahajwalla at the University of New South Wales Sydney and her team work on turning plastic waste into usable polymers, including 3D printing polymers [70]. The company Reflow is collecting polyethylene terephthalate (PET) waste bottles and turning them into filaments suitable for 3D FDM printers [71]. A company in Belgium, Yuma, is using recycled plastics for the 3D printing of sunglasses [72]. The U.S. Army Research Laboratory and the U.S. Marine Corps are working together to repurpose plastic waste by printing items from recycled plastic useful for soldiers [73]. This process allows for a decrease in transportation costs and manufacturing of parts on demand. This large effort is expected to have a positive impact on both the environment and communities by turning polymer 3D printing into income for waste collectors and removing waste from the streams.
\nIndustries are moving toward the implementation of 3D printing as a manufacturing process because it facilitates the design of complex structures and rapid production of prototypes. AM utilizes a computer-aided design software that allows for the design of architectures with defined porosity and structures at a microscopic level. Because of the easy production of 3D printed prototypes, modeling based on a specific application can be performed to further improve the design of the end product and potentially reduce failure risks. The 3D printing of polymers and polymer composites has significantly progressed over the last 40 years and is expected to increase in the near future. Thermoplastic materials are readily commercially available for use in FDM, SLS, and inkjet processes. Materials like PC, ABS, PLA, ULTEM, and PCLA are commonly used for the manufacturing of tools, prototypes, and items used in the aerospace industry. However, these polymers are not one-size-fits-all types of polymers and are not necessary a good choice for all applications. Thus, research efforts are focused on developing materials that are capable of meeting specific applications. For examples, polymers blended with cultured cells can be used for scaffolds and implants on biological systems. Cells can be obtained from the patient and cultivated in the laboratory, thus producing a material that is less likely to be rejected by the patient. Fillers and additives can be used to generate multifunctional materials with improved mechanical properties. Fillers, such as CNTs and graphene, can be incorporated into the polymer to produce a material that is electrically conductive.
\nDespite all of the advances in the design and development of new polymeric materials for AM applications, challenges still remain. The availability of polymeric inks suitable for extreme applications, such as low temperature environments, high load pressures, and radiation resistance, is very limited. The development of new materials is necessary to increase the usefulness of polymer 3D printing technologies. Ideally, some of these composites are recyclable and/or biodegradable to reduce the negative impact plastics have on our environment.
\nWe thank the US Department of Energy’s National Nuclear Security Administration contract DE-AC-52-06NA25396 for providing financial support.
\nThe authors declare no conflict of interest.
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