1. Visualization and Detection of Prostatic Carcinoma
Joseph Marino, Xin Zhao, Ruirui Jiang, Wei Zeng, Arie Kaufman, Xianfeng Gu
Stony Brook University, Stony Brook, NY
Introduction
Visualization & Detection
Registration
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 0.5
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:
Cancer indicated in left & right midgland & base
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:
Segmented Prostate Slice
Edges & Corners
Final Detected Features
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:
MR Prostate
Histology
Unaligned
Aligned
Proposed Work
T2 Axial
T2 Sagittal
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
MRSI
T2 Coronal
T1 Axial