Saradwata Sarkar

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Imaging is playing an increasingly important role in the detection of prostate cancer (PCa). This review summarizes the key imaging modalities-multiparametric ultrasound (US), multiparametric magnetic resonance imaging (MRI), MRI-US fusion imaging, and positron emission tomography (PET) imaging-used in the diagnosis and localization of PCa. Emphasis is laid(More)
This paper presents and validates a low-dimensional nonrigid registration method for fusing magnetic resonance imaging (MRI) and trans-rectal ultrasound (TRUS) in image-guided prostate biopsy. Prostate cancer is one of the most prevalent forms of cancer and the second leading cause of cancer-related death in men in the United States. Conventional clinical(More)
The purpose of this study was to compare high b-value (b = 2000 s/mm2) acquired diffusion-weighted imaging (aDWI) with computed DWI (cDWI) obtained using four diffusion models—mono-exponential (ME), intra-voxel incoherent motion (IVIM), stretched exponential (SE), and diffusional kurtosis (DK)—with respect to lesion visibility, conspicuity, contrast, and(More)
Accurate and robust non-rigid registration of pre-procedure magnetic resonance (MR) imaging to intra-procedure trans-rectal ultrasound (TRUS) is critical for image-guided biopsies of prostate cancer. Prostate cancer is one of the most prevalent forms of cancer and the second leading cause of cancer-related death in men in the United States. TRUS-guided(More)
Prostate segmentation on CT images is a challenging task. In this paper, we explore the population and patient-specific characteristics for the segmentation of the prostate on CT images. Because population learning does not consider the inter-patient variations and because patient-specific learning may not perform well for different patients, we are(More)
This paper proposes a new semi-automatic segmentation method for the prostate on 3D transrectal ultrasound images (TRUS) by combining the region and classification information. We use a random walk algorithm to express the region information efficiently and flexibly because it can avoid segmentation leakage and shrinking bias. We further use the decision(More)
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