Analysis of contrast-enhanced medical images.

Early detection of human organ diseases is of great importance for the accurate diagnosis and institution of appropriate therapies. This can potentially prevent progression to end-stage disease by detecting precursors that evaluate organ functionality. In addition, it also assists the clinicians for therapy evaluation, tracking diseases progression, and surgery operations. Advances in functional and contrast-enhanced (CE) medical images enabled accurate noninvasive evaluation of organ functionality due to their ability to provide superior anatomical and functional information about the tissue-of-interest. The main objective of this dissertation is to develop a computer-aided diagnostic (CAD) system for analyzing complex data from CE magnetic resonance imaging (MRI). The developed CAD system has been tested in three case studies: (i) early detection of acute renal transplant rejection, (ii) evaluation of myocardial perfusion in patients with ischemic heart disease after heart attack; and (iii), early detection of prostate cancer. However, developing a noninvasive CAD system for the analysis of CE medical images is subject to multiple challenges, including, but are not limited to, image noise and inhomogeneity, nonlinear signal intensity changes of the images over the time course of data acquisition, appearances and shape changes (deformations) of the organ-of-interest during data acquisition, determination of the best features (indexes) that describe the perfusion of a contrast agent (CA) into the tissue. To address these challenges, this dissertation focuses on building new mathematical models and learning techniques that facilitate accurate analysis of CAs perfusion in living organs and include: (i) accurate mathematical models for the segmentation of the object-of-interest, which integrate object shape and appearance features in terms of pixel/voxel-wise image intensities and their spatial interactions; (ii) motion correction techniques that combine both global and local models, which exploit geometric features, rather than image intensities to avoid problems associated with nonlinear intensity variations of the CE images; (iii) fusion of multiple features using the genetic algorithm. The proposed techniques have been integrated into CAD systems that have been tested in, but not limited to, three clinical studies. First, a noninvasive CAD system is proposed for the early and accurate diagnosis of acute renal transplant rejection using dynamic contrast-enhanced MRI (DCE-MRI). Acute rejection–the immunological response of the human immune system to a foreign kidney–is the most sever cause of renal dysfunction among other diagnostic possibilities, including acute tubular necrosis and immune drug toxicity. In the U.S., approximately 17,736 renal transplants are performed annually, and given the limited number of donors, transplanted kidney salvage is an important medical concern. Thus far, biopsy remains the gold standard for the assessment of renal transplant dysfunction, but only as the last resort because of its invasive nature, high cost, and potential morbidity rates. The diagnostic results of the proposed CAD system, based on the analysis of 50 independent in-vivo cases were 96% with a 95% confidence interval. These results clearly demonstrate the promise of the proposed image-based diagnostic CAD system as a supplement to the current technologies, such as nuclear imaging and ultrasonography, to determine the type of kidney dysfunction. Second, a comprehensive CAD system is developed for the characterization of myocardial perfusion and clinical status in heart failure and novel myoregeneration therapy using cardiac first-pass MRI (FP-MRI). Heart failure is considered the most important cause of morbidity and mortality in cardiovascular disease, which affects approximately 6 million U.S. patients annually. Ischemic heart disease is considered the most common underlying cause of heart failure. Therefore, the detection of the heart failure in its earliest forms is essential to prevent its relentless progression to premature death. While current medical studies focus on detecting pathological tissue and assessing contractile function of the diseased heart, this dissertation address the key issue of the effects of the myoregeneration therapy on the associated blood nutrient supply. Quantitative and qualitative assessment in a cohort of 24 perfusion data sets demonstrated the ability of the proposed framework to reveal regional perfusion improvements with therapy, and transmural perfusion differences across the myocardial wall; thus, it can aid in follow-up on treatment for patients undergoing the myoregeneration therapy. Finally, an image-based CAD system for early detection of prostate cancer using DCE-MRI is introduced. Prostate cancer is the most frequently diagnosed malignancy among men and remains the second leading cause of cancer-related death in the USA with more than 238,000 new cases and a mortality rate of about 30,000 in 2013. Therefore, early diagnosis of prostate cancer can improve the effectiveness of treatment and increase the patient’s chance of survival. Currently, needle biopsy is the gold standard for the diagnosis of prostate cancer. However, it is an invasive procedure with high costs and potential morbidity rates. Additionally, it has a higher possibility of producing false positive diagnosis due to relatively small needle biopsy samples. Application of the proposed CAD yield promising results in a cohort of 30 patients that would, in the near future, represent a supplement of the current technologies to determine prostate cancer type. The developed techniques have been compared to the state-of-the-art methods and demonstrated higher accuracy as shown in this dissertation. The proposed models (higher-order spatial interaction models, shape models, motion correction models, and perfusion analysis models) can be used in many of today’s CAD applications for early detection of a variety of diseases and medical conditions, and are expected to notably amplify the accuracy of CAD decisions based on the automated analysis of CE images

Guidelines for Cardiovascular Magnetic Resonance Imaging from the Korean Society of Cardiovascular Imaging-Part 3: Perfusion, Delayed Enhancement, and T1- and T2 Mapping

This document is the third part of the guidelines for the protocol, the interpretation and post-processing of cardiac magnetic resonance (CMR) studies. These consensus recommendations have been developed by the Consensus Committee of the Korean Society of Cardiovascular Imaging to standardize the requirements for image interpretation and post-processing of CMR. This third part of the recommendations describes tissue characterization modules, including perfusion, late gadolinium enhancement, and T1- and T2 mapping. Additionally, this document provides guidance for visual and quantitative assessment consisting of "What-to-See," "How-To," and common pitfalls for the analysis of each module. The Consensus Committee hopes that this document will contribute to the standardization of image interpretation and post-processing of CMR studies.ope

A Semi-parametric Technique for the Quantitative Analysis of Dynamic Contrast-enhanced MR Images Based on Bayesian P-splines

Dynamic Contrast-enhanced Magnetic Resonance Imaging (DCE-MRI) is an important tool for detecting subtle kinetic changes in cancerous tissue. Quantitative analysis of DCE-MRI typically involves the convolution of an arterial input function (AIF) with a nonlinear pharmacokinetic model of the contrast agent concentration. Parameters of the kinetic model are biologically meaningful, but the optimization of the non-linear model has significant computational issues. In practice, convergence of the optimization algorithm is not guaranteed and the accuracy of the model fitting may be compromised. To overcome this problems, this paper proposes a semi-parametric penalized spline smoothing approach, with which the AIF is convolved with a set of B-splines to produce a design matrix using locally adaptive smoothing parameters based on Bayesian penalized spline models (P-splines). It has been shown that kinetic parameter estimation can be obtained from the resulting deconvolved response function, which also includes the onset of contrast enhancement. Detailed validation of the method, both with simulated and in vivo data, is provided

Intraoperative Quantification of Bone Perfusion in Lower Extremity Injury Surgery

Orthopaedic surgery is one of the most common surgical categories. In particular, lower extremity injuries sustained from trauma can be complex and life-threatening injuries that are addressed through orthopaedic trauma surgery. Timely evaluation and surgical debridement following lower extremity injury is essential, because devitalized bones and tissues will result in high surgical site infection rates. However, the current clinical judgment of what constitutes “devitalized tissue” is subjective and dependent on surgeon experience, so it is necessary to develop imaging techniques for guiding surgical debridement, in order to control infection rates and to improve patient outcome. In this thesis work, computational models of fluorescence-guided debridement in lower extremity injury surgery will be developed, by quantifying bone perfusion intraoperatively using Dynamic contrast-enhanced fluorescence imaging (DCE-FI) system. Perfusion is an important factor of tissue viability, and therefore quantifying perfusion is essential for fluorescence-guided debridement. In Chapters 3-7 of this thesis, we explore the performance of DCE-FI in quantifying perfusion from benchtop to translation: We proposed a modified fluorescent microsphere quantification technique using cryomacrotome in animal model. This technique can measure bone perfusion in periosteal and endosteal separately, and therefore to validate bone perfusion measurements obtained by DCE-FI; We developed pre-clinical rodent contaminated fracture model to correlate DCE-FI with infection risk, and compare with multi-modality scanning; Furthermore in clinical studies, we investigated first-pass kinetic parameters of DCE-FI and arterial input functions for characterization of perfusion changes during lower limb amputation surgery; We conducted the first in-human use of dynamic contrast-enhanced texture analysis for orthopaedic trauma classification, suggesting that spatiotemporal features from DCE-FI can classify bone perfusion intraoperatively with high accuracy and sensitivity; We established clinical machine learning infection risk predictive model on open fracture surgery, where pixel-scaled prediction on infection risk will be accomplished. In conclusion, pharmacokinetic and spatiotemporal patterns of dynamic contrast-enhanced imaging show great potential for quantifying bone perfusion and prognosing bone infection. The thesis work will decrease surgical site infection risk and improve successful rates of lower extremity injury surgery

Automated Determination of Arterial Input Function Areas in Perfusion Analysis

Perfusion in biological system refers to capillary-level blood flow in tissues, and is a critical parameter used for detecting physiological changes. Medical imaging provides an effective way to measure tissue perfusion. Quantitative analysis of perfusion studies requires the accurate determination of the arterial input function (AIF), which describes the delivery of intravascular tracers to tissues. Automating the process of finding the AIF can save operating time, remove the inter-operator variability, and correct the errors in the presence of the dispersion of the arterial system. Even though several methods are currently developed for automatically extracting an AIF, they are specific to a single modality and particular to a certain tissue. In this thesis, we developed an algorithm to automatically determine an AIF by classifying the characteristic parameters of image pixels' dynamic evaluation curves between blood feeding areas and tissues. This automated AIF determination can be used to facilitate the generation of parametric maps for perfusion studies based on various imaging modalities and covering a variety of tissues. Automatic AIF determination was accomplished by extracting characteristic parameters such as maximum slope, maximum enhancement, time to peak, time to wash-out, and wash-out slope. Multi-dimensional data containing the characteristic parameters were converted and reduced into two-dimensional (2-D) representations, which were presented as a plurality of 2-D plots. Then physiological phases were localized within the simplified representations. Automated segmentation of non-AIF tissues and determination of AIF areas were accomplished by automatically finding peaks and valleys of each physiological phase on the plurality of 2-D plots. The algorithm was tested in CT myocardial perfusion studies, in which a pig was used as a model of myocardial ischemia and perfusion. PET gastrointestinal (GI) perfusion studies were performed using this algorithm, in which GI perfusion was evaluated when cardiac outputs were controlled with four modes. This automated AIF determination study was compared with manual selection of AIF in PET imaging and microsphere studies to assess the effectiveness of this algorithm. In the CT myocardial perfusion study, the perfusion of infarcted myocardium was significantly lower than that of non-infarcted areas and lower than that when it was normal. In the PET abdominal perfusion study, PET imaging data gives lower value of standard deviation relative to the mean than that in microsphere results. In the manual AIF selection study, a slight change in selecting the AIF region caused a big influence on the result. On the contrary, the automated AIF selection remains consistent in the entire study and reduces inter-operator variation. A conclusion was made that this technique is applicable to several imaging modalities, such as PET, CT and MRI, and is effective on many tissues. In addition, this algorithm is straightforward and provides consistent results. More importantly, this automated AIF determination technique replaces the conventional spatial classification method with the functional classification method, taking more physiological considerations and explanations involved

A non-invasive image based system for early diagnosis of prostate cancer.

Prostate cancer is the second most fatal cancer experienced by American males. The average American male has a 16.15% chance of developing prostate cancer, which is 8.38% higher than lung cancer, the second most likely cancer. The current in-vitro techniques that are based on analyzing a patients blood and urine have several limitations concerning their accuracy. In addition, the prostate Specific Antigen (PSA) blood-based test, has a high chance of false positive diagnosis, ranging from 28%-58%. Yet, biopsy remains the gold standard for the assessment of prostate cancer, but only as the last resort because of its invasive nature, high cost, and potential morbidity rates. The major limitation of the relatively small needle biopsy samples is the higher possibility of producing false positive diagnosis. Moreover, the visual inspection system (e.g., Gleason grading system) is not quantitative technique and different observers may classify a sample differently, leading to discrepancies in the diagnosis. As reported in the literature that the early detection of prostate cancer is a crucial step for decreasing prostate cancer related deaths. Thus, there is an urgent need for developing objective, non-invasive image based technology for early detection of prostate cancer. The objective of this dissertation is to develop a computer vision methodology, later translated into a clinically usable software tool, which can improve sensitivity and specificity of early prostate cancer diagnosis based on the well-known hypothesis that malignant tumors are will connected with the blood vessels than the benign tumors. Therefore, using either Diffusion Weighted Magnetic Resonance imaging (DW-MRI) or Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI), we will be able to interrelate the amount of blood in the detected prostate tumors by estimating either the Apparent Diffusion Coefficient (ADC) in the prostate with the malignancy of the prostate tumor or perfusion parameters. We intend to validate this hypothesis by demonstrating that automatic segmentation of the prostate from either DW-MRI or DCE-MRI after handling its local motion, provides discriminatory features for early prostate cancer diagnosis. The proposed CAD system consists of three majors components, the first two of which constitute new research contributions to a challenging computer vision problem. The three main components are: (1) A novel Shape-based segmentation approach to segment the prostate from either low contrast DW-MRI or DCE-MRI data; (2) A novel iso-contours-based non-rigid registration approach to ensure that we have voxel-on-voxel matches of all data which may be more difficult due to gross patient motion, transmitted respiratory effects, and intrinsic and transmitted pulsatile effects; and (3) Probabilistic models for the estimated diffusion and perfusion features for both malignant and benign tumors. Our results showed a 98% classification accuracy using Leave-One-Subject-Out (LOSO) approach based on the estimated ADC for 30 patients (12 patients diagnosed as malignant; 18 diagnosed as benign). These results show the promise of the proposed image-based diagnostic technique as a supplement to current technologies for diagnosing prostate cancer

Reference ranges ("normal values") for cardiovascular magnetic resonance (CMR) in adults and children: 2020 update

Cardiovascular magnetic resonance (CMR) enables assessment and quantification of morphological and functional parameters of the heart, including chamber size and function, diameters of the aorta and pulmonary arteries, flow and myocardial relaxation times. Knowledge of reference ranges ("normal values") for quantitative CMR is crucial to interpretation of results and to distinguish normal from disease. Compared to the previous version of this review published in 2015, we present updated and expanded reference values for morphological and functional CMR parameters of the cardiovascular system based on the peer-reviewed literature and current CMR techniques. Further, databases and references for deep learning methods are included