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