Medical image tomography: A statistically tailored neural network approach

In medical computed tomography (CT) the tomographic images are reconstructed from planar information collected 180∘ to 360∘ around the patient. In clinical applications, the reconstructions are typically produced using a filtered backprojection algorithm. Filtered backprojection methods have limitations that create a high percentage of statistical uncertainty in the reconstructed images. Many techniques have been developed which produce better reconstructions, but they tend to be computationally expensive, and thus, impractical for clinical use;Artificial neural networks (ANN) have been shown to be adept at learning and then simulating complex functional relationships. For medical tomography, a neural network can be trained to produce a reconstructed medical image given the planar data as input. Once trained an ANN can produce an accurate reconstruction very quickly;A backpropagation ANN with statistically derived activation functions has been developed to improve the trainability and generalization ability of a network to produce accurate reconstructions. The tailored activation functions are derived from the estimated probability density functions (p.d.f.s) of the ANN training data set. A set of sigmoid derivative functions are fitted to the p.d.f.s and then integrated to produce the ANN activation functions, which are also estimates of the cumulative distribution functions (c.d.f.s) of the training data. The statistically tailored activation functions and their derivatives are substituted for the logistic function and its derivative that are typically used in backpropagation ANNs;A set of geometric images was derived for training an ANN for cardiac SPECT image reconstruction. The planar projections for the geometric images were simulated using the Monte Carlo method to produce sixty-four 64-quadrant planar views taken 180 about each image. A 4096 x 629 x 4096 architecture ANN was simulated on the MasPar MP-2, a massively parallel single-instruction multiple-data (SIMD) computer. The ANN was trained on the set of geometric tomographic images. Trained on the geometric images, the ANN was able to generalize the input-to-output function of the planar data-to-tomogram and accurately reconstruct actual cardiac SPECT images

Radiation Sensing: Design and Deployment of Sensors and Detectors

Radiation detection is important in many fields, and it poses significant challenges for instrument designers. Radiation detection instruments, particularly for nuclear decommissioning and security applications, are required to operate in unknown environments and should detect and characterise radiation fields in real time. This book covers both theory and practice, and it solicits recent advances in radiation detection, with a particular focus on radiation detection instrument design, real-time data processing, radiation simulation and experimental work, robot design, control systems, task planning and radiation shielding

Improvements in Cardiac Spect/CT for the Purpose of Tracking Transplanted Cells

Regenerative therapy via stem cell transplantation has received increased attention to help treat the myocardial injury associated with heart disease. Currently, the hybridisation of SPECT with X-ray CT is expanding the utility of SPECT. This thesis compared two SPECT/CT systems for attenuation correction using slow or fast-CT attenuation maps (mu-maps). We then developed a method to localize transplanted cells in relation to compromised blood flow in the myocardium following a myocardial infarction using SPECT/CT. Finally, a method to correct for image truncation was studied for a new SPECT/CT design that incorporated small field-of-view (FOV) detectors. Computer simulations compared gated-SPECT reconstructions using slow-CT and fast-CT mu-maps with gated-CT mu-maps. Using fast-CT mu-maps improved the Root Mean Squared (RMS) error from 4.2% to 4.0%. Three canine experiments were performed comparing SPECT/CT reconstruction using the Infinia/Hawkeye-4 (slow-CT) and Symbia T6 (fast-CT). Canines were euthanized prior to imaging, and then ventilated. The results showed improvements in both RMS errors and correlation coefficients for all canines. A first-pass contrast CT imaging technique can identify regions of myocardial infarction and can be fused with SPECT. Ten canines underwent surgical ligation of the left-anterior-descending artery. Cells were labeled with 111In-tropolone and transplanted into the myocardium. SPECT/CT was performed on day of transplantation, 4, and 10 days post-transplantation. For each imaging session first-pass perfusion CT was performed and successfully delineated the infarct zone. Delayed-enhanced MRI was performed and correlated well with first-pass CT. Contrast-to-noise ratios were calculated for 111In-SPECT and suggested that cells can be followed for 11 effective half-lives. We evaluated a modified SPECT/CT acquisition and reconstruction method for truncated SPECT. Cardiac SPECT/CT scans were acquired in 14 patients. The original projections were truncated to simulate a small FOV acquisition. Data was reconstructed in three ways: non-truncated and standard reconstruction (NTOSEM), which was our gold-standard; truncated and standard reconstruction (TOSEM); and truncated and a modified reconstruction (TMOSEM). Compared with NTOSEM, small FOV imaging incurred an average cardiac count ratio error greater than 100% using TOSEM and 8.9% using TMOSEM. When we plotted NTOSEM against TOSEM and TMOSEM the correlation coefficient was 0.734 and 0.996 respectively

What scans we will read: imaging instrumentation trends in clinical oncology

Oncological diseases account for a significant portion of the burden on public healthcare systems with associated costs driven primarily by complex and long-lasting therapies. Through the visualization of patient-specific morphology and functional-molecular pathways, cancerous tissue can be detected and characterized non- invasively, so as to provide referring oncologists with essential information to support therapy management decisions. Following the onset of stand-alone anatomical and functional imaging, we witness a push towards integrating molecular image information through various methods, including anato-metabolic imaging (e.g., PET/ CT), advanced MRI, optical or ultrasound imaging. This perspective paper highlights a number of key technological and methodological advances in imaging instrumentation related to anatomical, functional, molecular medicine and hybrid imaging, that is understood as the hardware-based combination of complementary anatomical and molecular imaging. These include novel detector technologies for ionizing radiation used in CT and nuclear medicine imaging, and novel system developments in MRI and optical as well as opto-acoustic imaging. We will also highlight new data processing methods for improved non-invasive tissue characterization. Following a general introduction to the role of imaging in oncology patient management we introduce imaging methods with well-defined clinical applications and potential for clinical translation. For each modality, we report first on the status quo and point to perceived technological and methodological advances in a subsequent status go section. Considering the breadth and dynamics of these developments, this perspective ends with a critical reflection on where the authors, with the majority of them being imaging experts with a background in physics and engineering, believe imaging methods will be in a few years from now. Overall, methodological and technological medical imaging advances are geared towards increased image contrast, the derivation of reproducible quantitative parameters, an increase in volume sensitivity and a reduction in overall examination time. To ensure full translation to the clinic, this progress in technologies and instrumentation is complemented by progress in relevant acquisition and image-processing protocols and improved data analysis. To this end, we should accept diagnostic images as “data”, and – through the wider adoption of advanced analysis, including machine learning approaches and a “big data” concept – move to the next stage of non-invasive tumor phenotyping. The scans we will be reading in 10 years from now will likely be composed of highly diverse multi- dimensional data from multiple sources, which mandate the use of advanced and interactive visualization and analysis platforms powered by Artificial Intelligence (AI) for real-time data handling by cross-specialty clinical experts with a domain knowledge that will need to go beyond that of plain imaging

Radiation Sensing: Design and Deployment of Sensors and Detectors

No abstract available

Bone recognition in UTE MR images by artificial neural networks for attenuation correction of brain imaging in MR/PET scanners

Tese de mestrado em Engenharia Biomédica e Biofísica (Radiações em Diagnóstico e Terapia), apresentada à Universidade de Lisboa, através da Faculdade de Ciências, 2012Aim: Due to space and technical limitations in PET/MR scanners one of the difficulties is the generation of an attenuation correction (AC) map to correct the PET image data. Different methods have been suggested that make use of the images acquired with an ultrashort echo time (UTE) sequence. However, in most of them precise thresholds need to be defined and these may depend on the sequence parameters. In this thesis different algorithm based on artificial neural networks (ANN) are presented requiring little to any user interaction. Material and methods: An MR UTE sequence delivering two images with 0.07 ms and 2.46 ms echo times was acquired from a 3T MR-BrainPET for 9 patients. To correct for intensity inhomogeneities prior to attenuation map estimation a method based on multispetral images was developed and used to correct both images from UTE sequence. The training samples from the corrected images were feed to the proposed algorithms for learning and the methods posterior used for classification. The generated AC maps were compared to co-registered CT images based on the co-classification voxels, dice coefficients and sensitivity correction map (for the 9 patients), and relative differences (for 4 patients) in reconstructed PET images. Results: In overall the methods proposed showed high dice coefficients for air and soft tissue and lower to bone. Adittionaly, the proposed methods showed to present higher dice coefficients than remain methods. High linear correlation between the sensitivity correction maps was verified for all methods. The reconstructed PET images showed mean relative differences 5% for all methods except keereman method, where a mean of 6% was observed. Discussion: The different analysis showed slightly different results regarding the methods that perform best. Nevertheless, all the analysis showed that the methods developed work similar to better than the ones curently proposed. Conclusion: The methods aided by the template image showed to be more robust and with higher specificity than the ones without, altough loosing in sensitivity. Finally, the continuous methods developed showed to be promising as they can estimate different attenuation coefficients within a certain range for the same tissue and therefore account for different densities

Multiphase flow measurement using gamma-based techniques

The oil and gas industry need for high performing and low cost multiphase meters is ever more justified given the rapid depletion of conventional oil reserves. This has led oil companies to develop smaller/marginal fields and reservoirs in remote locations and deep offshore, thereby placing great demands for compact and more cost effective soluti8ons of on-line continuous multiphase flow measurement. The pattern recognition approach for clamp-on multiphase measurement employed in this research study provides one means for meeting this need. Cont/d

Investigation of Personalised Post-Reconstruction Positron Range Correction in 68Ga Positron Emission Tomography Imaging

Positron range limits the spatial resolution of Positron Emission Tomography, reducing image quality and accuracy. This thesis investigated factors affecting the magnitude of positron range, developed a personalised approach to range correction, and demonstrated the approach using simulated, phantom and patient data. The Geant4 Application for Emission Tomography software was utilised to model positron range when emitted by radionuclides, namely 18F and 68Ga, in water, bone and lung. The impact of range blurring in lungs was found to be ten times larger than in bone and four times larger than in water or soft tissue, regardless of the positron energy. Range effects occurring with different isotopes (18F and 68Ga) were evaluated across measurement and reconstructed spatial resolutions. It was found that range correction was not necessary when using 18F for voxel sizes larger than 4 mm. In contrast, range correction was required for images generated using 68Ga, particularly within or adjacent to the lung. An iterative, post-reconstruction range correction method was developed which relied only on the measured data. The correction method was validated in both simulation and phantom studies. Image quality and quantification accuracy of corrected images was shown to be superior when imaging with 68Ga. Importantly, the range correction suppressed and controlled image noise at high iteration numbers. Finally, in a patient study, image noise in regions of uniform uptake was significantly increased by ~2% (p<0.05), yet mean standardised uptake values remained unchanged after correction, showing the same uptake for normal radionuclide distributions. The lesion contrast and maximum uptake values were improved by 20% and 45%, respectively with statistical significance (p<0.05). Although these promising results show that the proposed method of range correction can be generalised to reconstructed images regardless of measurement system, acquisition parameters and radionuclides used, further research is warranted to improve the method, particularly with respect to removing or reducing the artefacts which were shown to impacted reader preference

Task-based Optimization of Administered Activity for Pediatric Renal SPECT Imaging

Like any real-world problem, the design of an imaging system always requires tradeoffs. For medical imaging modalities using ionization radiation, a major tradeoff is between diagnostic image quality (IQ) and risk to the patient from absorbed dose (AD). In nuclear medicine, reducing the AD requires reducing the administered activity (AA). Lower AA to the patient can reduce risk and adverse effects, but can also result in reduced diagnostic image quality. Thus, ultimately, it is desirable to use the lowest AA that gives sufficient image quality for accurate clinical diagnosis. In this dissertation, we proposed and developed tools for a general framework for optimizing RD with task-based assessment of IQ. Here, IQ is defined as an objective measure of the user performing the diagnostic task that the images were acquired to answer. To investigate IQ as a function of renal defect detectability, we have developed a projection image database modeling imaging of 99mTc-DMSA, a renal function agent. The database uses a highly-realistic population of pediatric phantoms with anatomical and body morphological variations. Using the developed projection image database, we have explored patient factors that affect IQ and are currently in the process of determining relationships between IQ and AA in terms of these found factors. Our data have shown that factors that are more local to the target organ may be more robust than weight for estimating the AA needed to provide a constant IQ across a population of patients. In the case of renal imaging, we have discovered that girth is more robust than weight (currently used in clinical practice) in predicting AA needed to provide a desired IQ. In addition to exploring the patient factors, we also did some work on improving the task simulating capability for anthropomorphic model observer. We proposed a deep learning-based anthropomorphic model observer to fully and efficiently (in terms of both training data and computational cost) model the clinical 3D detection task using multi-slice, multi-orientation image sets. The proposed model observer is important and could be readily adapted to model human observer performance on detection tasks for other imaging modalities such as PET, CT or MRI