Focal Spot, Winter 2005/2006

https://digitalcommons.wustl.edu/focal_spot_archives/1101/thumbnail.jp

Focal Spot, Fall/Winter 1998

https://digitalcommons.wustl.edu/focal_spot_archives/1080/thumbnail.jp

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

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

Development of Approaches of Tumor Trapping Enhanced BB2R-Targeted Radiopharmaceuticals for Prostate Cancer

The Gastrin-Releasing Peptide Receptor (BB2r) has been intensively investigated as a cancer target over the years. Numerous diagnostic and therapeutic BB2r-targeted agents have been developed for various solid tumors, including prostate cancers, due to the high expression level of BB2r on neoplastic relative to normal tissues. The development of those targeted agents have mainly utilized the modified c-terminal of bombesin(BBN), a peptide that has nanomolar binding affinity to human BB2r. However, a major issue that hinders the clinical translational potential of low-molecular weight, receptor-targted agents, is their short residence time at tumor tissues due to the intrinsically high diffusion and clearance rates. Detailed in this dissertation is a comparison study investigating important biological differences between two mouse models of prostate cancer and how these factors impact the delivery of BB2r-targeted agents. Specifically, we have evaluated the impact of differences in tumor vassculatur density, hypoxia burden and perfusion efficacy on BB2r-targeted agent uptake and distribution. Furthermore, herein, we proposed two different approaches to increase the tumor residualization of BB2r-targeted radiopharmaceutical agents, by developing chemical approaches to “trap” BB2r-targeted agents in prostate cancer cells through adduct formation with macromolecules. First proposed, BB2r-targted agents incorporating hydrophilic cysteine cathepsin (CC) inhibitors was developed. Due to the high concentration of CCs found in endolysosomal compartments, our agents have the ability to irreversibly bind to CCs after endocytosis. Two analogs, based on BB2r-targeted agonist and antagonist separately, demonstrated enhanced tumor retention and optimal tumor-to-non-target ratios compared to the matching controls. The second approach focused on the hypoxic nature of prostate cancer, which is due to the distorted architecture leading to the insuffucient delivery of oxygen, and was examined as a mechanism to increase retention of BB2r-targeted agents. Specifically, we explored the hypoxic-selectivity of a 2-nitroimidazole phosphoramide nitrogen mustard (2-NIPAM), a potential tumor trapping agent which is able to irreversibly bind to intracellular nucleophiles in hypoxic tissues. Overall, we seek to determine the most suitable mouse model in evaluating the BB2r-targeted agents. We are also exploring strategies to elongate the retention time of BB2r-targeted agents in the prostate tumor tissues, thereby increasing the clinical translational potential of these agents. Future works include: 1) synthesis and evaluation of 2-NIPAM incorporated BB2r-targeted peptides in vitro and in vivo; 2) synthesis and assessment of hydrophilic CC inhibitors integrated into other receptor-targeted analogs. Further work is needed to demonstrate the feasibility and wide applicability of our strategies in different receptor-targeted systems

Machine Learning/Deep Learning in Medical Image Processing

Many recent studies on medical image processing have involved the use of machine learning (ML) and deep learning (DL). This special issue, “Machine Learning/Deep Learning in Medical Image Processing”, has been launched to provide an opportunity for researchers in the area of medical image processing to highlight recent developments made in their fields with ML/DL. Seven excellent papers that cover a wide variety of medical/clinical aspects are selected in this special issue

PET-MR imaging using a tri-modality PET/CT-MR system with a dedicated shuttle in clinical routine

Tri-modality PET/CT-MRI includes the transfer of the patient on a dedicated shuttle from one system into the other. Advantages of this system include a true CT-based attenuation correction, reliable PET-quantification and higher flexibility in patient throughput on both systems. Comparative studies of PET/MRI versus PET/CT are readily accomplished without repeated PET with a different PET scanner at a different time point. Additionally, there is a higher imaging flexibility based on the availability of three imaging modalities, which can be combined for the characterization of the disease. The downside is a somewhat higher radiation dose of up to 3mSv with a low dose CT based on the CT-component, longer acquisition times and potential misalignment between the imaging components. Overall, the tri-modality PET/CT-MR system offers comparative studies using the three different imaging modalities in the same patient virtually at the same time, and may help to develop reliable attenuation algorithms at the same tim