Six Big Losses model proposed by Nakajima [3].
\\n\\n
These books synthesize perspectives of renowned scientists from the world’s most prestigious institutions - from Fukushima Renewable Energy Institute in Japan to Stanford University in the United States, including Columbia University (US), University of Sidney (AU), University of Miami (USA), Cardiff University (UK), and many others.
\\n\\nThis collaboration embodied the true essence of Open Access by simplifying the approach to OA publishing for Academic editors and authors who contributed their research and allowed the new research to be made available free and open to anyone anywhere in the world.
\\n\\nTo celebrate the 50 books published, we have gathered them at one location - just one click away, so that you can easily browse the subjects of your interest, download the content directly, share it or read online.
\\n\\n\\n\\n\\n"}]',published:!0,mainMedia:null},components:[{type:"htmlEditorComponent",content:'
IntechOpen and Knowledge Unlatched formed a partnership to support researchers working in engineering sciences by enabling an easier approach to publishing Open Access content. Using the Knowledge Unlatched crowdfunding model to raise the publishing costs through libraries around the world, Open Access Publishing Fee (OAPF) was not required from the authors.
\n\nInitially, the partnership supported engineering research, but it soon grew to include physical and life sciences, attracting more researchers to the advantages of Open Access publishing.
\n\n\n\nThese books synthesize perspectives of renowned scientists from the world’s most prestigious institutions - from Fukushima Renewable Energy Institute in Japan to Stanford University in the United States, including Columbia University (US), University of Sidney (AU), University of Miami (USA), Cardiff University (UK), and many others.
\n\nThis collaboration embodied the true essence of Open Access by simplifying the approach to OA publishing for Academic editors and authors who contributed their research and allowed the new research to be made available free and open to anyone anywhere in the world.
\n\nTo celebrate the 50 books published, we have gathered them at one location - just one click away, so that you can easily browse the subjects of your interest, download the content directly, share it or read online.
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"If you can not measure it, you can not improve it."(Lord Kelvin)
It is a common opinion that productivity improvement is nowadays the biggest challenge for companies in order to remain competitive in a global market [1, 2]. A well-known way of measuring the effectiveness is the Overall Equipment Efficiency (OEE) index. It has been firstly developed by the Japan Institute for Plant Maintenance (JIPM) and it is widely used in many industries. Moreover it is the backbone of methodologies for quality improvement as TQM and Lean Production.
The strength of the OEE index is in making losses more transparent and in highlighting areas of improvement. OEE is often seen as a catalyst for change and it is easy to understand as a lot of articles and discussion have been generated about this topic over the last years.
The aim of this chapter is to answer to general questions as
This chapter begins with an explanation of the difference between efficiency, effectiveness and productivity as well as with a formal definition for the components of effectiveness. Mathematical formulas for calculating OEE are provided too.
After the introduction to the fundamental of OEE, some interesting issues concerning the way to implement the index are investigated. Starting with the question that in calculating OEE you have to take into consideration machines as operating in a linked and complex environment. So we analyze almost a model for the OEE calculation that lets a wider approach to the performance of the whole factory. The second issue concerns with monitoring the factory performance through OEE. It implies that information for decision-making have to be guaranteed real-time. It is possible only through automated systems for calculating OEE and through the capability to collect a large amount of data. So we propose an examination of the main automated OEE systems from the simplest to high-level systems integrated into ERP software. Even data collection strategies are screened for rigorous measurement of OEE.
The last issue deals with how OEE has evolved into tools like TEEP, PEE, OFE, OPE and OAE in order to fit with different requirements.
At the end of the chapter, industrial examples of OEE application are presented and the results are discussed.
Overall equipment efficiency or effectiveness (OEE) is a hierarchy of metrics proposed by Seiichi Nakajima [3] to measure the performance of the equipment in a factory. OEE is a really powerful tool that can be used also to perform diagnostics as well as to compare production units in differing industries. The OEE has born as the backbone of Total Productive Maintenance (TPM) and then of other techniques employed in asset management programs, Lean manufacturing [4], Six Sigma [5], World Class Manufacturing [4].
By the end of the 1980’s, the concept of Total Production Maintenance became more widely known in the Western world [7] and along with it OEE implementation too. From then on an extensive literature [8-11] made OEE accessible and feasible for many Western companies.
Confusion exists as to whether OEE has indeed been an effectiveness or efficiency measure. The traditional vision of TMP referred to Overall Equipment Efficiency while now it is generally recognized as Overall Equipment Effectiveness. The difference between efficiency and effectiveness is that effectiveness is the actual output over the reference output and efficiency is the actual input over the reference input. The Equipment Efficiency refers thus to ability to perform well at the lowest overall cost. Equipment Efficiency is then unlinked from output and company goals. Hence the concept of Equipment Effectiveness relates to the ability of producing repeatedly what is intended producing, that is to say to produce value for the company (see Figure 1).
Productivity is defined as the actual output over the actual input (e.g. number of final products per employee), and both the effectiveness and the efficiency can influence it. Regarding to OEE, in a modern, customer-driven “lean” environment it is more useful to cope with effectiveness.
Efficiency versus Effectiveness versus Productivity.
According to the previous remark a basic definition of OEE is:
where:
Valuable Operating Time is the net time during which the equipment actually produces an acceptable product;
Loading Time is the actual number of hours that the equipment is expected to work in a specific period (year, month, week, or day).
The formula indicates how much the equipment is doing what it is supposed to do and it captures the degree of conforming to output requirements. It is clearly a measure of effectiveness.
OEE is not only a metric, but it also provides a framework to improve the process. A model for OEE calculation aims to point out each aspect of the process that can be ranked for improvement. To maximize equipment effectiveness it is necessary to bring the equipment to peak operating conditions and then keeping it there by eliminating or at least minimizing any factor that might diminish its performance. In other words a model for OEE calculation should be based on the identification of any losses that prevent equipment from achieving its maximum effectiveness.
The OEE calculation model is then designed to isolate losses that degrade the equipment effectiveness.
Losses are activities that absorb resources without creating value. Losses can be divided by their frequency of occurrence, their cause and by different types they are. The latter one has been developed by Nakajima [3] and it is the well-known Six Big Losses framework. The other ones are interesting in order to rank rightly losses.
According to Johnson et al. [12], losses can be chronic or sporadic. The chronic disturbances are usually described as
Regarding divide losses by their causes, three different ones can be found:
machine malfunctioning: an equipment or a part of this does not fulfill the demands;
process: the way the equipment is used during production;
external: cause of losses that cannot be improved by the maintenance or production team.
The external causes such as shortage of raw materials, lack of personnel or limited demand do not touch the equipment effectiveness. They are of great importance for top management and they should be examined carefully because their reduction can directly increase the revenues and profit. However they are not responsible of the production or maintenance team and so they are not taken into consideration through the OEE metric.
To improve the equipment effectiveness the losses because of external causes have to be taken out and the losses caused by machine malfunctioning and process, changeable by the daily organization, can still be divided into:
The framework in which we have divided losses in down time, speed and quality losses completely fits with the Six Big Losses model proposed by Nakajima [3] and that we summarize in the Table 1:
\n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t
DOWNTIME | \n\t\t\t- Breakdown - Set-up and adjustments | \n\t\t
SPEED | \n\t\t\t- Idling, minor stoppages - Reduced speed | \n\t\t
QUALITY | \n\t\t\t- Quality losses - Reduced yield | \n\t\t
Six Big Losses model proposed by Nakajima [3].
On the base of Six Big Losses model, it is possible to understand how the Loading Time decreases until to the Valuable Operating Time and the effectiveness is compromised. Let’s go through the next Figure 2.
Breakdown of the calendar time.
At this point we can define:
Please note that:
and
finally
So through a bottom-up approach based on the Six Big Losses model, OEE breaks the performance of equipment into three separate and measurable components: Availability, Performance and Quality.
After the various factors are taken into account, all the results are expressed as a percentage that can be viewed as a snapshot of the current equipment effectiveness.
The value of the OEE is an indication of the size of the technical losses (machine malfunctioning and process) as a whole. The gap between the value of the OEE and 100% indicates the share of technical losses compared to the Loading Time.
The compound effect of Availability, Performance and Quality provides surprising results, as visualized by e.g. Louglin [13].
Let’s go through a practical example in the Table 2.
\n\t\t\t\t | \n\t\t\t86,7% | \n\t\t
\n\t\t\t\t | \n\t\t\t93% | \n\t\t
\n\t\t\t\t | \n\t\t\t95% | \n\t\t
\n\t\t\t\t | \n\t\t\t76,6% | \n\t\t
Example of OEE calculation.
The example in Table 2 illustrates the sensitivity of the OEE measure to a low and combined performance. Consequently, it is impossible to reach 100 % OEE within an industrial context. Worldwide studies indicate that the average OEE rate in manufacturing plants is 60%. As pointed out by e.g. Bicheno [14] world class level of OEE is in the range of 85 % to 92 % for non-process industry. Clearly, there is room for improvement in most manufacturing plants! The challenge is, however, not to peak on those levels but thus to exhibit a stable OEE at world-class level [15].
By having a structured framework based on the Six Big Losses, OEE lets to track underlying issues and root causes. By knowing what the Six Big Losses are and some of the causes that contribute to them, the next step is to focus on ways to monitor and correct them. In the following let’s see what is the way:
During the last decades, both practitioners and researchers have raised the discussion about OEE in many ways. One of the most popular has led to modification and enlargement of individual original OEE tool to fit a broader perspective as supposed important for the companies [16]. With the evolution of OEE, different definitions have also come up in literature and in practice, coupled with their changed formulations. Some of these formulations (TEEP and PEE) are still at the equipment level, while the others (OAE, OFE and OPE) extended OEE to the factory level. Let’s go through the main features of each formulation.
TEEP stands for
OEE and TEEP are thus two closely related measurements. Typically the equipment is on site and thus TEEP is metric that shows how well equipment is utilized. TEEP is useful for business analysis and important to maximize before spending capital dollars for more capacity.
PEE stands for
At the level of the factory we found
OPE and OAE are extensively implemented in industry under different formulations. They involve a practical approach developed to fit the specific requirements of different industries.
As mentioned in the previous section equipment operates in a linked and complex environment. So it is necessary to pay attention beyond the performance of individual tools towards the performance of the whole factory. According to Scott and Pisa [21], the answer to this requirement is the OFE metric, which is about combining activities and relationships between different parts of the equipment, and integrating information, decisions, and actions across many independent systems and subsystems. The problem is that a specific and unique method to calculate OFE does not exist. There many methodologies and approaches, with different level of complexity, different information coming from and different lacks.
A first common-sense approach is to measure OEE at the end of the line or process. Following this approach we can see OEE as
and
Here OEE measures effectiveness in term of output that is easy to be taken out at factory level too. So OFE becomes:
It is not always ideal. The complexity of OEE measurement arises where single or multiple sub-cells are constrained by an upstream or downstream operation or bottleneck operation. The flow is always restricted or limited by a bottleneck operation, just as a chain is only as strong as its weakest link. So according to Goldratt [22] we can measure OEE in real time at the bottleneck. Any variations at the bottleneck correlate directly to upstream and downstream process performance. Huang et al. [23] proposed a manufacturing system modeling approach, which captures the equipment interconnectivity information. It identifies four unique subsystems (series, parallel, assembly and expansion) as a basis for modeling a manufacturing system, as shown in Figure 3.
Types of manufacturing subsystems.
Muthiah et al. [24] developed the approach to derive OTE metrics for these subsystems based on a “system constraint” approach that automatically takes into account equipment idle time.
Other methods are based on modeling the manufacturing systems. Some of these notable approaches are queuing analysis methods [25], Markovian methods [26], Petri net based methods [27], integrated computer-aided manufacturing definition (IDEF) method [28], and structured analysis and design technique (SADT) [29]. In addition to them there are several commercial tools that have been reviewed and categorized by Muthiah and Huang [30].
OEE provides simple and consolidated formulas to measure effectiveness of the equipment or production system. Moreover Dal et al. [31] point out that it can also be used as an indicator of process improvement activities since OEE is directly linked to the losses as well as OEE can be even used to compare performance across the factory highlighting poor line performance or to quantify improvements made [31]. Moreover improving can be pursued by:
Backtracking to determine what loss reduces effectiveness.
Identifying bottlenecks as not only the slowest machine, but as the machine both slower and less effective.
All these goals need of an approach based on the Deming Cycle [32]. It is an improvement cycle to increase the plant OEE rating until the target goals and world class manufacturing status are achieved (Figure 4)
Improvement approach to increase the plant OEE.
This approach requires a large amount of data that can be provided both in a static or dynamic way. In the first case data are picked up only at the end of a certain period and used in the Diagnosis & Analysis stage.
There is another way to use OEE and it is to know exactly what is happening in real time through a continuous monitoring to immediately identify possible problems and react in real-time using appropriate corrective actions. Information on OEE items (maintenance and operational equipment effectiveness, product data accuracy, uptimes, utilization, bottlenecks, yield and scrap metrics, etc.) is really valuable in environments where making decisions in near real-time is critical. This second approach requires then a data collection system completely automatized and moreover the Diagnosis & Analysis stage should be automatic.
In the next sections we will take into consideration different strategies to acquire data and we will illustrate main automated tool for the OEE integration.
The OEE calculations should be based on correct input parameters from the production system as reported by Ericsson [33]. Data acquisition strategies range from very manual to very automated. The manual data collection method consists of a paper template, where the operators fill in the cause and duration of a breakdown and provide comments about minor stoppages and speed losses. It is a low-tech approach. On the contrary a high-tech approach runs through an automatic OEE calculation system that is governed by sensors connected to the equipment, automatically registering the start time and duration of a stoppage and prompting the operator to provide the system with information about the downtime cause. An automatic approach usually provides opportunities to set up lists of downtime causes, scheduling the available operating time and making an automatic OEE calculation for a time period. A variety of reports of production performance and visualization of the performance results are even possible to retrieve from the system.
Two approaches have to be compared through opportunity and cost both, in a quantitative as well as in a qualitative way. Regarding cost, the main figures in case of the manual approach are derived from the hourly wage cost of operators multiplied by time spent to register data on paper templates, feed them into a computer system and for generating reports and performing OEE calculations. In case of the automatic approach cost concerns a yearly license cost for an automatic OEE calculation system together with an investment cost for hardware. The introduction of both the manual and automatic data collection methods must be preceded and then associated with training of the operators on OEE as a performance measure, and on different parameters affecting the OEE outcome. The purpose of training the operators was twofold:
The quality of the input data is likely to increase in alignment with an increase in the competence of the staff;
The involvement of the operators in identifying performance loss factors is likely to create a better engagement for providing the system with accurate information.
Another issue to overcome is the balance between the efforts of providing adequate information in relation to the level of detail needed in the improvement process. In fact if a critical success factor in an improvement project driven by OEE is the retrieval of detailed information about production losses, however not all the improvement projects require a higher and really expensive data precision.
Generally there are many companies in which manual data collection is convenient. In other companies where each operator is responsible for a number of processing machines, timely and accurate data collection can be very challenging and a key goal should be fast and efficient data collection, with data put it to use throughout the day and in real-time, a more desirable approach would be realized if each machine could indicate data by itself.
An automatic OEE data recording implies:
better accuracy;
less labor;
traceability;
integrated reporting and analysis;
immediate corrective action;
motivation for operators.
In any case the implementation of data collection for OEE has limited value if it is not integrated in a continuous work procedure, as a part of the improvement initiative. Daily meeting and sharing information both cross-functionally and bottom-up in the organization hierarchy become a prerequisite. As well as it is useful integrating OEE into an automated management system. OEE can be applied when using a total manufacturing information system providing the detailed historical information that allows thorough diagnoses and improvement plans but more importantly it gives the summary signals.
Automating OEE gives a company the ability to collect and classify data from the shop floor into meaningful information that can help managers understand the root causes of production inefficiency. Therefore giving greater visibility to make more informed decisions on process improvement. An automated OEE system addresses the three primary functions of OEE:
There is a lot of commercial software that provide automated OEE system, but it is possible even to integrate OEE into general tools as ERP ones. They usually offer a wide range of capabilities. They are able to gather and coordinate the operations of a plant and provide measurable information. The advantages are that database are completely integrated so the coordination among different functions involved is better. For example manufacturing can see the upcoming planned maintenance and maintenance can see the production schedules. Automated Management systems are naturally and inherently eligible for providing feasible decision support on plant profitability and establish a foundation for addressing other manufacturing challenges in the future.
At the end of the chapter, industrial examples of OEE application are presented to remark as different industries and different goals can be all involved through the OOE metric.
Sigma/Q [34] is a leading manufacturer of quality packaging in Northland Central America serving various markets across the globe. The company’s primary goal was to improve plant performance and reduce operational costs.
The solution was to build a foundation for continuous improvement through OEE. The first step was to automate the data collection and analysis processes and introduce a real-time strategy. But the real key success factor was operator involvement in the performance improvement process. The company identified key contributors to reward them appropriately during performance reviews.
As a result, OEE increased by 40%, variability in run speed due to frequent starts and stops in the manufacturing process, was dramatically reduced and run speed was increased by 23%. Last but not least operators aspired to achieve higher levels of operational excellence, promoting a culture of continuous improvement across the various plants.
A global pharmaceutical company [35] has shown the will to understand if OEE as a metric could be used as an ongoing tool of improvement. It has chosen an off-shore plant and as pilot a packaging line running a full 144-hour weekly cycle and handling more than 90 products because it allowed the collection of data over both shifts. The line also had counters on most unit operations that could be easily utilized for the collection of quality data by the line operators. Twelve weeks of data was collected with operator buy-in. The test has shown that many of the current metrics were too high-level to extract the causes of issues and therefore target improvements to them. Therefore the more than 90 products routed through the test line were divided into six groups based on the highest pack rates. The continuous real-time monitoring was able to account the 90% of available run time for with little impact running the line.
A company providing a broad range of services to leading original equipment manufacturers in the information technology and communications industries [36] obtained three new plants from a major contract electronics manufacturer.
Each plant had distinct ways of identifying and determining downtime, as well as their own preferred techniques and practices. The goals were then:
Find a common metric to measure productivity across plants
Standardized downtime reporting among plants
The manufacturer’s issues were complicated by the fact it makes about 30,000 different products out of 300,000 different parts, and adds an average of 2,000 new products into its manufacturing mix every month. With this number of products, frequent changeovers are necessary. It also becomes vital to have a scientific method to be able to compare all the different lines. The company was searching for a common framework in order to compare its three newest plants. The solution was the identification of factors leading to assembly line downtime. Companies utilizing this information can make comparisons across plants and assembly lines to improve effectiveness. The results were:
OEE increase of 45%
Identified 25% more downtime not found with previous methods
Reduced costs
The Whirlpool Corporation’s Findlay Division manufactures dishwashers for many brands in the world [37]. The demand for product is at an all-time high. The goal was then how to get more out of the facility and its equipment without making huge capital investments? And more specifically how can the maintenance department support the needs of manufacturing to achieve the company goals?
To make these improvements, the Division used OEE as a measure of their current equipment efficiency. As the company started tracking individual pieces of equipment’s OEE ratings, it became apparent that there was room for improvement. The combination of fundamental maintenance practices such as Root Cause Failure analysis and a preventive and predictive maintenance system, along with very strong support from Division leadership, enabled the Findlay Division to get off the ground with the Total Productive Maintenance program. Again "it was the people that made this change possible" (Jim Dray, TPM Facilitator). The Division has been able to increase production by 21%, without any significant capital costs.
The OEE measure is an excellent KPI for use on both strategic and operational levels, if it is used correctly. When an organization holds people with knowledge and experience of the typical shortages of OEE and its common implementation challenges, the probability of achieving the intended benefits of OEE will certainly increase. Based on using OEE as an improvement driver at the case study company, some success factors have been identified:
A standard definition of OEE must be clearly defined and communicated at all levels within the organization since this is the foundation for its utilization. It is especially important to determine how the ideal cycle time and planned and unplanned downtime should be interpreted.
Involving the operators in the process of defining production loss causes and configuring the templates and lists to be used for monitoring promotes operator commitment, understanding of the procedure and awareness of the frequency of sporadic and chronic disturbances.
Driving the OEE implementation as a project with a predefined organization, a structured working procedure promoting cross-functional and shop floor involvement, and practical guidance on what activities to execute and in what order, implies resource allocation that forces management attention and puts OEE on the agenda.
Viewing and communicating OEE as a driver for improvements rather than a management measure for follow-up and control of performance (although this is also the case) is one of the cornerstones for a successful OEE implementation.
Active involvement of the support functions, especially production engineering and maintenance, is required, otherwise the level of improvements to increase OEE will not be enough and the speed of change will consequently be too low.
Separating improvement actions into those directly having an impact on process stability, i.e. OEE, from those with indirect impact is necessary especially in the initial implementation phase to show quick results.
Including reporting OEE and prioritized daily actions in the routines of daily follow-up meetings (from team level to department/site level) is an excellent way to integrate OEE as a driver for improvements in the operations management system.
Results should be communicated, e.g. by graphical visualization of the OEE improvements on the boards. Visualizing OEE and process output together are illustrative and motivating.
Including production performance in the company ́s overall production strategy and managing this with a continuous follow up of OEE as a KPI on different consolidation levels is the optimal driver for efficient management. When top management attention is continuously given to the process of achieving stable production processes the possibilities of reaching good results certainly increases.
There are many challenges associated with the implementation of OEE for monitoring and managing production performance, for example:
how it is defined, interpreted and compared
how the OEE data are collected and analyzed
how it is monitored and by whom
how it aligns with the overall production strategy
how it could be utilized for sustainability purpose.
Moreover it is remarkable that setting high OEE goals in an environment with excessive capacity is of less value since it is not possible to utilize the equipment full time. OEE measure is less suitable as a target KPI, since OEE only measures the efficiency during the time the equipment is planned to be operating, while equipment and personnel drives manufacturing costs both when they are in operation and during downtime.
The purpose of measuring OEE can be questioned in the light of the financial crisis. There are some authors that have reported the need of further research work on linking OEE with financial measures. Dal et al. [31] asserts “there would appear to be a useful line of research in exploring the link between OEE and the popular business models such as balanced scorecard”. Muchiri et al. [16] suggests “Further research should explore the dynamics of translating equipment effectiveness or loss of effectiveness in terms of cost.” The authors agree with these statements, there is clearly a missing link between OEE and manufacturing cost. Jonsson et al. [39] presents a manufacturing cost model linking production performance with economic parameters. The utilization of this manufacturing cost model in developing industrially applicable productivity KPI ́s will be elaborated on in future research.
A recent survey was taken in the UK, reported 4,884 deaths from a brain tumor and about 11,633 deaths from breast cancer. The tumor detection is complicated and earlier detection leads to better chances of effective treatment, thereby increasing the survival rate. In the last decade, the concept of imaging has raised by the discovery of the X- ray radiography technique. The imaging techniques are highly meant for diagnostic applications in medical field. Different parts of a body have different range of absorption level hence the penetration of propagating light photon level varies for each and every organ, whereas this is the major concept considered for imaging. The imaging trend started with the X- ray radiography [1], it provides a one-dimensional image of the bony structures in a photographic film which could give the visualization of bony defects and the soft tissue tracks are identified only after the administration of contrast agents or dyes. The advanced version of the X- ray radiography is the Digital Radiography system which also provides a single plane image and it has the additional features such as data collection system, processing, display and storage system. Here the data obtained can be stored in a memory for future use. The limitation is even after the dye usage only the large variations in soft tissues can be identified.
In X - ray computed tomography the imaging of the organ is done in various angles and the reconstruction is demonstrated mathematically over the computer and displayed on the monitor. For the soft tissue examination, the dye fluids are pumped into the ventricles for providing the variation or contrast in the image. Here the noise increases inherently over the square root of the dose as the dose must be increased to preserve the same amount of noise. Therefore, over dosage leads to the side effects such as skin allergy, then came the existence the nuclear imaging.
Nuclear Medical Imaging (NMI) [1] systems utilize the radioisotopes for imaging. The small amount of radioactive chemicals is injected into the arm vein or inhaled through, and then the amount of radioactivity of the organ is examined using the radiation detectors. NMI includes Emission Computed Tomography which displays the single plane slice of the object with radioactivity, insisting same as.
X - ray computed tomography. In Single Positron Emission Tomography, gamma camera is used to create a three-dimensional representation of the radioisotope injected organ. Positron Emission Tomography (PET) imaging provides the cross- sectional images of positron emitting isotopes, which demonstrate the biological function and even physiological and pathological characteristics. The injected radioisotope may create allergic reactions and it takes hours to get clear from the blood and it’s a time-consuming process.
Magnetic Resonance Imaging (MRI) uses a magnetic field and high radio frequency signals to obtain anatomical information about the human body as cross- sectional images. The imaging technique needs the subject to be still while imaging, when there occurs a move and it blurs the output image. Radiations utilized here are highly ionized which causes harm and it is a tremendous time consuming and cost inefficient process for early tumor detection. The Ultrasonic imaging system is used for obtaining images of an almost entire range of internal organs in the abdomen. While it is completely reflected at boundaries with gas and there is a serious restriction in investigation of and through gas containing structures. The ultrasonic waves could not penetrate the bony structures hence imaging the brain is impossible.
Diffuse Optical Tomography (DOT) [2, 3] employs near infra-red light of range 700-1000 nm [4] which is non-invasive and non-ionizing radiation, therefore causes no harm or side effects. It has its main application of imaging the soft tissue organs such as the brain and breast for diagnosing tumor using the biological parameters [5, 6] such as oxygenation etc. The brain and breast tumor or lesion can be detected by examining the oxygenated, deoxygenated hemoglobin, water and lipids (proteins). DOT imaging [7] provides a number of advantages, such as reduced size setup in turn lead to portability, real-time imaging, low instrumental cost and less time consumption when compared to the other imaging techniques but is generally known to have a low image resolution which limits its further clinical application. Table 1: Compares Biomedical Imaging Modalities- Diffuse optical tomography evaluated with Computer Tomography (CT), Magnetic Resonance Imaging (MRI), and Positron Emission Tomography (PET). The parameters namely cost, imaging time, size, sensitivity and specificity are compared.
Parameters | DOT | CT | MRI | PET |
---|---|---|---|---|
$150,000 | $300,000 | $1,000,000 | $1,446,546 | |
15–20 mins | 45–60 mins | 45–70 mins | 75–90 mins | |
60 x 45 cm | 50 x 50 cm | 4x4m | 25x36x17cm | |
50% | 90% | 91% | 93% | |
100% | 56% | 71% | 70% |
Comparison of biomedical imaging modalities.
The main absorbers of near-infrared (NIR) light in blood-perfused tissues are Oxy-hemoglobin, deoxyhemoglobin, Lipids (Bulk proteins) and water. NIR Spectral Window absorption spectra are between 650 and 1000 NM are shown in Figure 1 is obtained from compiled absorption data for water [8] and hemoglobin [9]. Hence, light in this spectral window penetrates deeply into tissues, thus allowing for non-invasive investigations. The NIR light penetration depth into tissues is limited, by the hemoglobin absorption at shorter wavelengths and by the water absorption at longer wavelengths.
Absorption spectra of deoxy-hemoglobin (Hb), oxy-hemoglobin (HbO2), lipids and water.
Different systems in DOT are Continuous Wave (CW) imaging [6], Time Domain (TD) and Frequency Domain (FD). Continuous imaging is the study of hemodynamic and oxygenation changes in superficial tissues. It requires a source of constant intensity modulated at low frequency. Measuring the intensity of light transmitted between two points on the surface of the tissue is economical. Optimum sensitivity is achieved by a number of distinct sources and detectors. Intensity measurements are sensitive and are unable to distinguish between the absorption and scattering effects. Time Domain (TD) system uses photon counting detectors, slow but highly sensitive. The temporal distribution of photons is produced in short duration. Short pulses of light are transmitted through a highly scattering medium known as a Temporal Point Spread Function (TPSF). Frequency Domain (FD) [9] system is relatively inexpensive, easy to develop and provides fast temporal sampling up to 50HZ.The system acquires quick measurements regarding the amplitude and phase of scattering and absorption in the frequency domain at high detected intensities.
The NIR light propagates within the biological tissue in a turbid medium [5]. Light particles scatters with cell particles and the medium either absorbs or scatter the light. The positions and orientations of scatters are described by mesoscopic and macroscopic. In mesoscopic the particles in turbid media of dense concentration and light transport are modeled by Radiative Transport Equation (RTE) [3]. In macroscopic photon transport on mean free path, diffusion approximation holds good for turbid media. Therefore, the isotropic scattering effect and light transport within the tissues is described by the diffusion Equation.
Light transport in tissues derived using RTE, assumes the energy particles do not change in collisions hence refractive index is constant with the medium [8]. RTE is used to describe anisotropic field and the photon propagation in tissue, is given by
I(r,
f (
where θ is the angle between the two directions ŝ and ŝ’, and g is the anisotropy factor which is used to characterize the angular distribution of tissue scattering.
The fluence at point r modulation frequency ω and in the direction ŝ is defined by
The Monte Carlo Method is used to solve the radiative transfer Equation.
The directional flux magnitude is less compared to isotropic fluence magnitude within the tissue. The light field ‘diffuses’ means the scattering interaction dominates over absorption. The diffusion equation [10] approximation is given as
Diffusion coefficient is described as
Where the reduced scattering coefficient is
Analytical model has fast computation and the Green’s function is applied for modeling the diffusion equation or RTE analysis. The Green’s function provides a solution when the source is a spatial and temporal function. It is commonly used to solve the forward problem for image reconstruction, specifically for fast imaging techniques. Optical properties are modeled by a green’s function [3] for a slab representing the homogeneous background; with an additional perturbation term represent the spherical insertion.
Models the individual photon with Poisson error incorporated in the model. Monte Carlo method is a gold standard statistical technique in diffuse optics. The geometry of the model is defined by μa, μs, the refractive index and the photon trajectories. Light propagation in non- diffusive domains is calculated by Monte Carlo techniques. Random walk theory provides a distinct approach in which photon transport is modeled as a series of steps on the discrete cubic lattice. Random walk theory [9] is particularly suited to model time-domain measurements. The random walk extension technique has been developed for modeling media with anisotropic optical properties, maintaining the cubic lattice.
Numerical techniques have the potential for modeling complex geometries. Finite Element Method (FEM) [8] is used to represent the inhomogeneous distribution [12] of optical properties in an arbitrary geometry. Boundary Element Method (BEM) [3], Finite Difference Method (FDM) and Finite Volume Method (FVM) are applied in more specialized applications. Finite Element Method divides the reconstruction domain into finite element meshes. The optimal computational efficiency of FEM depends on the smallest number of elements to represent the internal field by a finite element mesh. Adaptively refine the mesh by placing more elements when the field changes rapidly.
The ill-condition inverse problem [5] in image reconstruction provides poor localization of imaging in localized or sparse regions. To overcome the ill posed problem in inverse model, regularization is applied in inverse model. The various forms of regularization are standard/Tikhonov regularization, exponential/spatial regularization, generalized least mean square regularization, adaptive regularization and model-based regularization [8, 11, 13, 14, 15].
Standard regularization [16] or constant regularization is of Tikhonov type. Here the regularization is based on the already available information that may be the noise characteristics [11] or structural information [17] of the data, more prior information [13] usage leads to a better outcome of reconstruction procedure or robustness to the noise in the data.
λ is a regularization parameter (i.e.) constant chosen to stabilize the solution and its value varies from 1e-6 to 10.
The ill posed problem with inverse model is solved by adding the penalty term to the objective function.
y = ln (A) is the measured experimental data here A specifies the amplitude, G (μa) modeled data and penalty term is P(μa) removes the high frequency components. Iteratively linearization minimizes “Ω” by
L is the dimensionless matrix and μ0 is prior estimate. The penalty term minimization scheme along with linearization leads to the updated equation (Gauss-Newton update equation)
‘J’ is a Jacobian ∂G (μa) / ∂ (μa) gives the rate of change with modeled data with respect to μa and I represent the Identity matrix. The diffuse optical tomography inverse problem sets a least square problem, which is solved by matching experimentally measured boundary data with modeled data iteratively.
Linearization of the as in (7) leads to an updated equation.
The
In adaptive regularization [15] the regularization parameter λ varies with respect to the projection error [18]. Projection error Φ is defined as the difference in measured data in the modeled data which is expressed as in (12).
The regularization parameter λ is denoted as
The regularization parameter λ varies in the range from 1/3 to 1/2. As in (13) ‘e’ represents the exponential function and ∆Φ representing the change in projection error. A penalty term for projection error-based regularization is expressed as
Linearization leads to an updated equation.
As in Eq. (15) ∆μ represents the change in absorption coefficient. Projection error determines the accuracy, while JJT is denoted as the Hessian matrix with diagonal elements.
Exponential regularization is otherwise called as spatially varying regularization [14] or wavelength chromophore specific regularization, which is based on the physics of the problem. This simplicity makes it widely used for solving inverse problems especially in the cases where the prior information is not available. λ(r) is spatially varying regularization parameter, where r represents the position spatially. The spatial variation is attained by an exponential function in the form
As in (16) the radius of imaging domain is R, λc, λe are the regularization parameters at the edge and center of the location. The spatially varying regularization has exponential term with low value at the center and large value near the boundary of the imaging domain in-order to neutralize the hypersensitivity near the boundary, which appears due to detectors located at the boundary. In order to determine the regularization parameter λ(r), the generalized objective function is given as,
As in (17) L is a dimensionless regularization matrix and μ0 is the prior estimate the of properties, while the penalty term for exponential regularization is represented as
Linearizing (17) leads to a Jacobian updated equation as
Exponential Regularization captures the hessian matrix diagonal as JTJ.
Model based regularization utilizes the combination of model resolution matrix [19] and data resolution matrix. The objective is to match the modeled data with the observed data. By this method of regularization, the spatial resolution of the reconstructed image is improved [18].
Expanding using Taylor series gives the equation
Jacobian matrix J = G′ (μa) and Hessian matrix H = G″ (μa).
Linearizing (21) then
using y - G (μa0) = δ and
Change in absorption coefficient (Δμa) is derived as in (18) as
In the case of λ = 0
Regularization term is linearized using the model resolution matrix, which depends on the forward model and regularization but not on data. Because of the ill posed nature of the problem as in (25) λ > 0, then
As in Eq. (24) leads to a model resolution matrix.
As in (27) M has the dimension of NN x NN and it purely depends on JTJ and the regularization term used. Linearization of as in (27) leads to an updated Jacobian matrix. λ varies from 0 to 1.
The regularization parameter of a model resolution matrix is given as
The model resolution matrix can be applied for deriving the linearization as in (26) for both constant and spatially varying regularization parameters. The matrix varies for constant and spatially varying regularization. The model resolution matrix main aim is to provide the better resolution characteristics without depending on data.
The data resolution matrix concentrates only on the data not on the image characteristics [21]. It defines that how well the estimated Δμa fits the observed data, hence it is important to consider data too in order to improve the resolution characteristics.
Data resolution matrix is derived using the Jacobian matrix (J) and the regularization technique which is used for reconstruction. It is evaluated by matching the predicted data with the obtained data [22].
The data-resolution matrix does not depend on a specific data (y) or error in it but are exclusively the properties of J and the regularization (λ) used. The closer it is to the identity matrix, the smaller are the prediction errors for δ, where δ` as in (31) representing the data misfit.
Data resolution matrix D is given as
Linearizing (31) leads to an updated equation
The regularization parameter of a data resolution matrix is given as
As in (26) and as in (34) the regularization parameter of the model-based regularization λi is given as
Penalty term for the regularization scheme is given as
Where c provides the weight for penalty term and it is a constant term.
Newton - Raphson iterative method to find the optical parameter μa, μs by solving the minimum objective function.
Φm and Φc are calculated and measured radiance at the detectors. λ is regularization parameter, μ, μ0 are current and initial estimates of optical properties at each node.
The initial values of absorption and scattering properties were estimated homogenously [23]. Update
∆μ Optical parameter update vector, Hmax maximum main diagonal element value of the matrix JJT. J is Jacobian matrix for inverse problem plots the variation in log amplitude and phase for both absorption and diffusion modification in every node.
Jacobian matrix J has the size as number of measurements NM by the number of FEM nodes NN i.e. NM x NN is calculated using ad joint method. Limit the Jacobian [23, 24] to the measured amplitude data and optical absorption. Jacobian links a change in log amplitude, at the boundary with a change in absorption coefficient μa.
The size of the Jacobian matrix is reduced by calculating the total sensitivity throughout the imaging domain and a new Jacobian
‘j’ corresponds to a node number within the domain. Reduction of Jacobian matrix improves the computational speed and efficiency of image reconstruction.
Ill posed condition of DOT problem, the solution is robust. To overcome this problem a priori information is incorporated constraint in the space of unknowns. Bayesian approach proposes an algorithm for spatial physiological prior [26]. High resolution anatomical image is segmented into sub-images. Each image is assigned a mean value with a prior probability density function of the image. ‘Confidence level’ is defined in the form of an image variance formulation to allow local variations within sub-images. MAP (Maximum a posteriori) estimates of the image [26, 27] is formed based on the formulation of the image’s probability density function.
p(y/x) is log likelihood function; p(x) is a probability density function.
Alternating minimization algorithm sequentially updates the unknown parameters, solves the optimization problem. Probability density function of the ith sub-image is defined in the spatial prior as
M is number of sub regions, Ni is number of voxels in the ith sub image, xi is the unknown sub image, Ci is chromosphere mean concentration,
The confidence level is incorporated into the statistical reconstruction procedure, the sub-image variance.
The practical setup of image acquisition as shown in Figure 2 includes optical components, electrical components, control, data acquisition and image reconstruction [28].
The practical imaging system.
The optical Multiplexer has three parts namely the motor, drive and Black box (PMT) Photon multiplier tube. Driver rotates the optical multiplexer to guide the energy to PMT, which converts light to electrical signals. The signal is amplified by an Amplifier and preprocessed electrical signals are given to Data acquisition card. Data acquisition software samples the raw data, post process and controls the hardware. The personal computer delivers commands to alter the fiber switch (source channel) sequentially. 16 X 16 input and output fibers constitutes to 256 sources – detector pairs. Image is reconstructed using inverse modeling such as Jacobian reduction with FEM.
The optical Multiplexer has three parts namely the motor, drive and Black box (PMT) Photon multiplier tube. Driver rotates the optical multiplexer to guide the energy to PMT, which converts light to electrical signals. The signal is amplified by an Amplifier and preprocessed electrical signals are given to Data acquisition card. Data acquisition software samples the raw data, post process and controls the hardware. The personal computer delivers commands to alter the fiber switch (source channel) sequentially. 16 X 16 input and output fibers constitutes to 256 sources – detector pairs. Image is reconstructed using inverse modeling such as Jacobian reduction with FEM.
Artificial Neural Network (ANN) is data structure accurately approximates a nonlinear relationship between a set of input and output parameters. It maps the input optical properties for spatial frequency domain in inverse modeling. Perform Monte Carlo simulation and fit it to ANN to output the data. Neural Network is trained to predict the tissue reflectance for strongly and weakly absorbing media.
The parallel Back propagation neural network distinguishes nonlinear relationship between spatial location of tumors and light intensity around the boundary of the tissue [29]. The neural network is trained for fast reconstruction in diffuse optical tomography. Location and spacing of optical sources and detectors are optimized using neural network. To improve the resolution of DOT images in inverse model Fixed Grid Wavelet Network [30] image segmentation is applied to extract a smooth boundary in tumor images.
Reconstruction of optical in homogeneities embedded in turbid medium using diffuse optical tomography. The optimization problem is solved by using genetic algorithm minimizing objective function [31, 32]. This approach is applied for full non- linear range of quantitative reconstruction. Crosstalk near the source detector artifacts are the major inaccuracies in diffuse optical tomography images [33]. This problem can be solved by a global optimization method namely genetic algorithm for estimating the optical parameters.
Adaptive Neuro fuzzy Inference system can be used for optical imaging, solving the non-linear ill posed problem with accurate qualitative and quantitative optical image reconstruction. The proposed method using ANFIS architecture will provide fast and accurate optical image reconstruction hence can achieve classification accuracy, volume and the layer thickness measurement of tumor.
The simulation software for modeling diffuse optical tomography is CULA, NIRFAST NETGEN and MIMICS. CULA is GPU Accelerated Linear Algebra which has a parallel computing architecture to dramatically improve the computation speed of sophisticated mathematics and also contains routines for systems solvers, singular value decompositions and Eigen problems. For reconstruction in diffuse optical tomography it facilitates singular value decomposition, matrix multiplication, matrix inversion etc.
NIRFAST is Near Infrared Fluorescence and Spectroscopy Tomography [33, 34] which is an FEM based software package designed for modeling Near Infrared Frequency domain [35] light transport in tissue.
NETGEN [36] is an automatic 3D tetrahedral mesh generator which accepts input from Constructive Solid Geometry (CSG) or Boundary Representation (BR) from the STL (Stereo Lithography) file format. It contains modules for mesh optimization and hierarchical mesh refinement and it is also open-source software available for Unix/Linux and Windows.
MIMICS is software specially developed for medical image processing. The ROI (Region of Interest) is selected in the segmentation process which is converted to a 3D surface model using an adapted marching cubes algorithm that takes the partial volume effect into account, leading to very accurate 3D model. The 3D files are represented in the STL format.
The Diffuse Optical Tomography (DOT) imaging experimental setup has three kinds of noise namely thermal noise, shot noise and relatively intensity noise. The shot noise from dark current of photodetector has Poisson statistics, solved by using Bayesian network in inverse problem. DOT has undetermined problem due less measured data in the forward model compared to the pixels reconstructed in inverse model. The forward problem solved by FEM and regularization techniques to improve the spatial resolution of DOT images. Diffuse Optical Tomography (DOT) has significant advancement since it becomes faster, more robust, less susceptible to error, and able to acquire data at a number of wavelengths with more source–detector combinations. Images reconstructed in 3D, uses more sophisticated techniques, which can be adapted by incorporating prior information and by compensating for some of the unavoidable sources of measurement error. DOT imaging is still a laboratory-based technique, yet to progress to develop a handheld for detection of tumor in morphological tissues in clinical applications. Qualitative and quantitative accuracy has to be improved in DOT, both of which are limited by poor spatial resolution. Improved image quality is achievable by adopting the optimization techniques namely Artificial Neural Networks, Genetic Algorithm and Adaptive Neuro Fuzzy Inference System. Enhancement of DOT can also achieve higher performance using multimodal imaging techniques. DOT is as a low-cost, portable imaging system to be developed at the bedside. The best modeling and reconstruction methods provide an ideal DOT instrumentation.
The research was carried out in Department of Electronics and Communication and Department of Mechanical Engineering in SRM TRP Engineering College, Trichy, Tamil Nadu, India. We express our gratitude to our management, faculty and research scholars in SRM TRP Engineering College, Trichy, Tamil Nadu, India, who provided skill and proficiency that completely helped in the development of an optimized DOT instrument for detection of sarcoma cells. We are very grateful to Radiation Oncology Centre at Trichy SRM Medical College Hospital & Research Centre, Tiruchirappalli for providing the insights on tumor detection.
Our project promotes the social awareness on early tumor detection at cellular level. Diffuse Optical Tomography (DOT) provides harmless non-invasive detection of tumor cells. Today around 70% of people are suffering from sarcoma in soft tissue. Detection at earlier stage helps the patient for early diagnosis and prevents them from clinical pathology. We are interested to promote our review based on diffuse optical tomography instrumentation without any conflicts of interest as review article.
Disclosure of potential conflicts of interests.
Conflict of Interest: The authors declare that they have no conflict of interest.
Research involving human participants and/ or animals.
Ethical approval: “This chapter does not contain any studies with human participants or animals performed by any of the authors.”
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