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Visualization and Detection of Prostatic Carcinoma
Joseph Marino, Xin Zhao, Ruirui Jiang, Wei Zeng, Arie Kaufman, Xianfeng Gu
Stony Brook University, Stony Brook, NY 11794-4400
Introduction
Visualization & Detection
Registration
• Prostate cancer is the most commonly diagnosed
cancer and the second leading cause of cancerrelated 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:
• 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:
Data
• Multiple orientations and modes of MR data are
acquired for prostate cancer detection
• We use five image sequences for each patient:
T2 Axial
T2 Sagittal
MRSI
T2 Coronal
Score = MRSIA + MRSIB + T2A + T2S + T2C + T1A
Where:
MRSIA = (ratioA – threshMRSI) x percentage x 0.5
MRSIB = (ratioB – threshMRSI) x percentage x 0.5
T2A = (threshT2 – T2axial) x 0.333
T2S = (threshT2 – T2sagittal) x 0.333
T2C = (threshT2 – T2coronal) x 0.333
T1A = 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:
T1 Axial
• The position & orientation information are known
for each image sequence
• They can be aligned with respect to each other:
Cancer indicated in left & right midgland & base
Segmented
Prostate Slice
Edges &
Corners
Final Detected
Features
• Map the two prostate volumes to balls using
volumetric conformal mapping
• Align the volumes using the feature points:
MR Prostate
Histology
Unaligned
Aligned
Proposed Work
• Continue to explore better methods of detecting cancer and registering different scans
• Investigate further MR modalities which can
improve cancer detection (e.g., diffusionweighted, 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