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

Diffusionâ weighted imaging outside the brain: Consensus statement from an ISMRMâ sponsored workshop

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134160/1/jmri25196_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/134160/2/jmri25196.pd

MR Imaging Texture Analysis in the Abdomen and Pelvis

Texture analysis (TA) is a form of radiomics and refers to quantitative measurements of the histogram, distribution and/or relationship of pixel intensities or gray scales within a region of interest on an image. TA can be applied to MRI of the abdomen and pelvis, with the main strength being quantitative analysis of pixel intensities and heterogeneity rather than subjective/qualitative analysis. There are multiple limitations of MR texture analysis (MRTA) including a dependency on image acquisition and reconstruction parameters, non-standardized approaches without or with image filtration, diverse software methods and applications, and statistical challenges relating numerous texture analysis results to clinical outcomes in retrospective pilot studies with small sample sizes. Despite these limitations, there is a growing body of literature supporting MRTA. In this review, the application of MRTA to the abdomen and pelvis will be discussed, including tissue or tumor characterization and response evaluation or prediction of outcomes in various tumors

Development and validation of novel and quantitative MRI methods for cancer evaluation

Quantitative imaging biomarkers (QIB) offer the opportunity to further the evaluation of cancer at presentation as well as predict response to anti-cancer therapies before and early during treatment with the ultimate goal of truly personalised medical care and the mitigation of futile, often detrimental, therapy. Few QIBs are successfully translated into clinical practice and there is increasing recognition that rigorous methodologies and standardisation of research pipelines and techniques are required to move a theoretically useful biomarker into the clinic. To this end, I have aimed to give an overview of what I believe to be some of key elements within the research field beginning with the concept of imaging biomarkers, introducing concepts in development and validation, before providing a summary of the current and future utility of a range of quantitative MR imaging biomarkers techniques within the oncological imaging field. The original, prospective, research moves from the technical and analytical validation of a novel QIB use (T1 mapping in cancer), first in vivo qualification of this biomarker in cancer patient response assessment and prediction (sarcoma and breast cancer as well as prostate cancer separately), and then moving on to application of more established QIBs in cancer evaluation (R2*/BOLD imaging in head and neck cancer) as well as how existing MR data can be post-processed to improved cancer evaluation (further metrics derived from diffusion weighted imaging in head and neck cancer and textural analysis of existing clinical MR images utility in prostate cancer detection)

Developing multiparametric and novel magnetic resonance imaging biomarkers for prostate cancer

Whilst biomarker research is gaining momentum within the cancer sciences, disappointingly few biomarkers are successfully translated into clinical practice, which is partly due to lack of rigorous methodology. In this thesis, I aim to systematically study several quantitative magnetic resonance imaging (MRI) biomarkers (QIBs), at various stages of biomarker development for use as tools in the assessment of local and metastatic prostate cancer according to clinical need. I initially focus on QIBs derived from conventional multiparametric (mp) prostate MRI sequences, namely T2 weighted (T2W), apparent diffusion coefficient (ADC) and dynamic contrast enhanced (DCE). Firstly, by optimising analytical methods used throughout the thesis, deciding which approach is more reliable between single-slice region-of-interest vs. contouring the whole tumour volume using two different software packages. I then consider whether metric reproducibility can be improved by normalisation to different anatomical structures, and assess whether it is preferable to use statistics derived from imaging histograms rather than the current convention of using mean values. I combine multiple QIBs in a logistic regression model to predict a Gleason 4 component in known prostate cancer, which represents an unmet clinical need, as noninvasive tools to distinguish these more aggressive tumours do not currently exist. I subsequently ‘technically validate’ a novel microstructural diffusion-weighted MRI technique called VERDICT (Vascular, Extracellular and Restricted Diffusion for Cytometry in Tumours) to detect aggressive prostate cancer as part of a prospective cohort study. I assess the image quality, contrast-to-noise ratio, repeatability and performance of quantitative parametric VERDICT maps to discriminate between Gleason grades vs. the current best performing, but still imperfect tool of ADC. In the final two results chapters, motivated by the limited diagnostic accuracy of the prostate cancer staging modalities in current clinical use, I investigate the ability of mp whole-body (WB) MRI to stage aggressive cancer outside the prostate in patients with a high risk of metastases at primary diagnosis, and in biochemical failure following prostatectomy

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

The diagnostic accuracy and cost-effectiveness of magnetic resonance spectroscopy and enhanced magnetic resonance imaging techniques in aiding the localisation of prostate abnormalities for biopsy : a systematic review and economic evaluation

Peer reviewedPublisher PD

"Textural analysis of multiparametric MRI detects transition zone prostate cancer"

OBJECTIVES: To evaluate multiparametric-MRI (mpMRI) derived histogram textural-analysis parameters for detection of transition zone (TZ) prostatic tumour. METHODS: Sixty-seven consecutive men with suspected prostate cancer underwent 1.5T mpMRI prior to template-mapping-biopsy (TPM). Twenty-six men had 'significant' TZ tumour. Two radiologists in consensus matched TPM to the single axial slice best depicting tumour, or largest TZ diameter for those with benign histology, to define single-slice whole TZ-regions-of-interest (ROIs). Textural-parameter differences between single-slice whole TZ-ROI containing significant tumour versus benign/insignificant tumour were analysed using Mann Whitney U test. Diagnostic accuracy was assessed by receiver operating characteristic area under curve (ROC-AUC) analysis cross-validated with leave-one-out (LOO) analysis. RESULTS: ADC kurtosis was significantly lower (p < 0.001) in TZ containing significant tumour with ROC-AUC 0.80 (LOO-AUC 0.78); the difference became non-significant following exclusion of significant tumour from single-slice whole TZ-ROI (p = 0.23). T1-entropy was significantly lower (p = 0.004) in TZ containing significant tumour with ROC-AUC 0.70 (LOO-AUC 0.66) and was unaffected by excluding significant tumour from TZ-ROI (p = 0.004). Combining these parameters yielded ROC-AUC 0.86 (LOO-AUC 0.83). CONCLUSION: Textural features of the whole prostate TZ can discriminate significant prostatic cancer through reduced kurtosis of the ADC-histogram where significant tumour is included in TZ-ROI and reduced T1 entropy independent of tumour inclusion. KEY POINTS: MR textural features of prostate transition zone may discriminate significant prostatic cancer; Transition zone (TZ) containing significant tumour demonstrates a less peaked ADC histogram; TZ containing significant tumour reveals higher post-contrast T1-weighted homogeneity; The utility of MR texture analysis in prostate cancer merits further investigation

Development of Novel Protein-Based MRI Contrast Agents for the Molecular Imaging of Cancer Biomarkers

Temporal and spatial molecular imaging of disease biomarkers using non-invasive MRI with high resolution is largely limited by lack of MRI contrast agents with high sensitivity, high specificity, optimized biodistribution and pharmacokinetics. In this dissertation, I report my Ph. D. work on the development of protein-based MRI contrast agents (ProCAs) specifically targeting different cancer biomarkers, such as grastrin-releasing peptide receptor (GRPR), prostate specific membrane antigen (PSMA), and vascular endothelial growth factor receptor-2 (VEGFR-2). Similar to non-targeted ProCAs, these biomarker-targeted ProCAs exhibit 5 - 10 times higher r1 and r2 relaxivites than that of clinical MRI contrast agents. In addition, these biomarker-targeted ProCAs have high Gd3+ binding affinities and metal selectivities. The highest binding affinity of the three GRPR-targeted contrast reagents obtained by grafting a GRPR ligand binding moiety into ProCA32 for GRPR is 2.7 x 10-9 M. We further demonstrate that GRPR-targeted ProCAs were able to semi-quantitatively evaluate GRPR expression levels in xenograft mice model by MRI. In addition, we have also created a PSMA-targeted ProCA which has a binding affinity to PSMA biomarker of 5.2 x 10-7 M. Further, we developed VEGFR-targeted contrast agent which is able to image VEGFR2 in mice models using T1-weighted and T2-weighted sequences. Moreover, the relaxivities and coordination water numbers of ProCAs can be tuned by protein design of ProCA4. Since disease biomarkers are expressed in various tumors and diseases, our results may have strong preclinical and clinical implications for the diagnosis and therapeutics of cancer and other type of diseases