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

Evaluation of T1 relaxation time in prostate cancer and benign prostate tissue using a Modified Look-Locker inversion recovery sequence

Purpose of this study was to evaluate the diagnostic performance of T1 relaxation time (T1) for differentiating prostate cancer (PCa) from benign tissue as well as high- from low-grade PCa. Twenty-three patients with suspicion for PCa were included in this prospective study. 3 T MRI including a Modified Look-Locker inversion recovery sequence was acquired. Subsequent targeted and systematic prostate biopsy served as a reference standard. T1 and apparent diffusion coefficient (ADC) value in PCa and reference regions without malignancy as well as high- and low-grade PCa were compared using the Mann-Whitney U test. The performance of T1, ADC value, and a combination of both to differentiate PCa and reference regions was assessed by receiver operating characteristic (ROC) analysis. T1 and ADC value were lower in PCa compared to reference regions in the peripheral and transition zone (p < 0.001). ROC analysis revealed high AUCs for T1 (0.92; 95%-CI, 0.87-0.98) and ADC value (0.97; 95%-CI, 0.94 to 1.0) when differentiating PCa and reference regions. A combination of T1 and ADC value yielded an even higher AUC. The difference was statistically significant comparing it to the AUC for ADC value alone (p = 0.02). No significant differences were found between high- and low-grade PCa for T1 (p = 0.31) and ADC value (p = 0.8). T1 relaxation time differs significantly between PCa and benign prostate tissue with lower T1 in PCa. It could represent an imaging biomarker for PCa

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

Computer-Assisted Characterization of Prostate Cancer on Magnetic Resonance Imaging

Prostate cancer (PCa) is one of the most prevalent cancers among men. Early diagnosis can improve survival and reduce treatment costs. Current inter-radiologist variability for detection of PCa is high. The use of multi-parametric magnetic resonance imaging (mpMRI) with machine learning algorithms has been investigated both for improving PCa detection and for PCa diagnosis. Widespread clinical implementation of computer-assisted PCa lesion characterization remains elusive; critically needed is a model that is validated against a histologic reference standard that is densely sampled in an unbiased fashion. We address this using our technique for highly accurate fusion of mpMRI with whole-mount digitized histology of the surgical specimen. In this thesis, we present models for characterization of malignant, benign and confounding tissue and aggressiveness of PCa. Further validation on a larger dataset could enable improved characterization performance, improving survival rates and enabling a more personalized treatment plan

Reference tissue normalization of prostate MRI with automatic multi-organ deep learning pelvis segmentation

Tese de mestrado integrado, Engenharia Biomédica e Biofísica (Engenharia Clínica e Instrumentação Médica) Universidade de Lisboa, Faculdade de Ciências, 2018Prostate cancer is the most common cancer among male patients and second leading cause of death from cancer in men (excluding non-melanoma skin cancer). Magnetic Resonance Imaging (MRI) is currently becoming the modality of choice for clinical staging of localized prostate cancer. However, MRI lacks intensity quantification which hinders its diagnostic ability. The overall aim of this dissertation is to automate a novel normalization method that can potentially quantify general MR intensities, thus improving the diagnostic ability of MRI. Two Prostate multi-parametric MRI cohorts, of 2012 and 2016, were used in this retrospective study. To improve the diagnostic ability of T2-Weighted MRI, a novel multi-reference tissue normalization method was tested and automated. This method consists of computing the average intensity of the reference tissues and the corresponding normalized reference values to define a look-up-table through interpolation. Since the method requires delineation of multiple reference tissues, an MRI-specific Deep Learning model, Aniso-3DUNET, was trained on manual segmentations and tested to automate this segmentation step. The output of the Deep Learning model, that consisted of automatic segmentations, was validated and used in an automatic normalization approach. The effect of the manual and automatic normalization approaches on diagnostic accuracy of T2-weighted intensities was determined with Receiver Operating Characteristic (ROC) analyses. The Areas Under the Curve (AUC) were compared. The automatic segmentation of multiple reference-tissues was validated with an average DICE score higher than 0.8 in the test phase. Thereafter, the method developed demonstrated that the normalized intensities lead to an improved diagnostic accuracy over raw intensities using the manual approach, with an AUC going from 0.54 (raw) to 0.68 (normalized), and automatic approach, with an AUC going from 0.68 to 0.73. This study demonstrates that multi-reference tissue normalization improves quantification of T2-weighted images and diagnostic accuracy, possibly leading to a decrease in radiologist’s interpretation variability. It is also possible to conclude that this novel T2-weighted MRI normalization method can be automatized, becoming clinically applicable

Characterization of Human Prostate Cancer Using Sodium Magnetic Resonance Imaging

Overtreatment of prostate cancer is a significant problem in the health care of men. Development of non-invasive imaging tools for improved characterization of prostate lesions has the potential to reduce overtreatment. In this thesis work, we will evaluate the ability of tissue sodium concentration obtained from sodium magnetic resonance imaging (sodium-MRI) to characterize in vivo prostate lesions. Imaging data, including multi-parametric magnetic resonance imaging (mpMRI) and sodium-MRI, were obtained from a cohort of men with biopsy-proven prostate cancer and compared to digitized whole-mount histopathology after prostatectomy. Histopathology was independently graded for Gleason score to be used as the ground truth of tumour aggression. These imaging data were all accurately co-registered, allowing for direct comparison of imaging contrast to Gleason score. The results of this thesis work suggest that tissue sodium concentration assessed by sodium-MRI has utility as a part of a “non-invasive imaging-assay” to accurately characterize prostate cancer lesions. Sodium-MRI can provide clinically useful, complementary information to mpMRI; ultimately leading to better characterization of prostate lesions throughout the whole prostate. This has potential to improve patient outcomes of men with low-risk disease who do opt for active surveillance instead of treatment

Computer-Aided Detection and diagnosis for prostate cancer based on mono and multi-parametric MRI: A review

International audienceProstate cancer is the second most diagnosed cancer of men all over the world. In the last decades, new imaging techniques based on Magnetic Resonance Imaging (MRI) have been developed improving diagnosis.In practise, diagnosis can be affected by multiple factors such as observer variability and visibility and complexity of the lesions. In this regard, computer-aided detection and computer-aided diagnosis systemshave been designed to help radiologists in their clinical practice. Research on computer-aided systems specifically focused for prostate cancer is a young technology and has been part of a dynamic field ofresearch for the last ten years. This survey aims to provide a comprehensive review of the state of the art in this lapse of time, focusing on the different stages composing the work-flow of a computer-aidedsystem. We also provide a comparison between studies and a discussion about the potential avenues for future research. In addition, this paper presents a new public online dataset which is made available to theresearch community with the aim of providing a common evaluation framework to overcome some of the current limitations identified in this survey

Role of multiparametric MRI in detection of prostatic lesions; of evaluation contrast enhanced MRI, diffusion weighted imaging and MR spectroscopy in malignant and benign prostatic lesions

Background: Prostate cancer is the most commonly diagnosed cancer in males and one of the leading causes of cancer-related death in men. Pretreatment assessment of prostate cancer is divided into detection, localization, and staging; accurate assessment is a prerequisite for optimal clinical management and therapy selection. The purpose of the study is to determine the diagnostic accuracy of multiparametric MRI for prostatic cancer detection using T2 weighted MR imaging, diffusion weighted imaging (DWI) and contrast enhanced MRI. To determine the use of MR spectroscopy in prostatic lesions.Methods: It is a prospective single institutional study done on 29 patients with prostate lesions and elevated PSA level. Axial, coronal and sagittal images were obtained using T1WI, T2WI and STIR sequences. Advanced sequences like Diffusion weighted images, Spectroscopy and post gadolinium T1WI were taken after the basic MRI images.Results: Study was done in 29 patients, age was ranging between 51years to 90 years, mean age is 70.7 years. On multiparametric MRI findings 45% were detected malignant lesions and 55% patients detected benign lesions. On biopsy correlation 42% of these cases turned out to be malignant and 58% as benign lesions. Detection of malignancy by T2WI imaging alone given sensitivity of 80.1% and specificity of 85.4%.By DWI alone sensitivity was 85.7% and specificity was 89.4%,on MRS sensitivity is 90.6% and specificity was 91.1%. Combined (MRI+DWI+MRS) gave sensitivity of 92.3% and specificity of 94.4% for detection of malignant prostatic lesion. Positive predictive value is 90% and negative predictive value was 88%.Conclusions: The best characterization of prostatic cancer in individual patients will most likely result from a multiparametric exam. Recent advances include additional functional and physiologic MR imaging techniques (diffusion weighted imaging, MR spectroscopy, and perfusion imaging), which allow extension of the obtainable information beyond anatomic assessment. Multiparametric MR imaging provides the highest accuracy in diagnosis and staging of prostate cancer