visualization and detection of prostatic carcinoma
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Visualization and Detection of Prostatic Carcinoma
Joseph Marino, Xin Zhao, Ruirui Jiang, Wei Zeng, Arie Kaufman, Xianfeng GuStony Brook University, Stony Brook, NY 11794-4400
Introduction• Prostate cancer is the most commonly diagnosed
cancer and the second leading cause of cancer- related mortality among U.S. males
• Detection is inexact, relying on PSA blood tests, digital rectal examinations, and multiple biopsies that are generally stabbing in the dark
• MRI has been suggested as an imaging modality to help locate prostate cancer
• Computer analysis techniques can make this task
easier and more exact
• Segmentation of only one dataset is needed
• Rendering performed via volumetric ray casting
• At each sample point within the peripheral zone, a score is calculated using the six modes:
Score = MRSIA + MRSIB + T2A + T2S + T2C + T1A
Where:MRSIA = (ratioA – threshMRSI) x percentage x 0.5MRSIB = (ratioB – threshMRSI) x percentage x 0.5T2A = (threshT2 – T2axial) x 0.333T2S = (threshT2 – T2sagittal) x 0.333T2C = (threshT2 – T2coronal) x 0.333T1A = threshT1 – T1axial
This scoring corresponds to the following: A higher MRSI ratio indicates cancer Lower intensity T2 areas indicate cancer Higher intensity T1 areas indicate not cancer
• Positive score indicates likelihood for cancer.
• Scoring is integrated into the visualization of the prostatic volume
• Areas of high likelihood for cancer are mapped to
red, and low likelihood areas are mapped to blue:
Cancer indicated in left & right midgland & base
Visualization & Detection• Registration for scans acquired at different times or patient positions (not naturally registered)
• Feature points are needed to align the datasets and can be found using corner detection:
• Map the two prostate volumes to balls using volumetric conformal mapping
• Align the volumes using the feature points:
Registration
Data• Multiple orientations and modes of MR data are acquired for prostate cancer detection
• We use five image sequences for each patient:
• The position & orientation information are known for each image sequence
• They can be aligned with respect to each other:
Proposed Work• Continue to explore better methods of detect- ing cancer and registering different scans
• Investigate further MR modalities which can improve cancer detection (e.g., diffusion- weighted, perfusion, dynamic contrast enhanced)
• As more modalities are introduced into our framework, the data becomes greater and we strive to handle it all in an interactive manner
• Interventional visualization and detection as patients are in the scanner in order to localize treatment delivery
• Clinical studies to determine optimal analysis parameters and user friendliness
Segmented Prostate Slice
Edges & Corners
Final Detected Features
MR Prostate Histology Unaligned Aligned
T2 AxialT2 Axial T2 SagittalT2 Sagittal
T2 CoronalT2 Coronal T1 AxialT1 AxialMRSIMRSI
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