Noninvasive Localization of Prostate Cancer via Diffusion Sensitive MRI

Abstract

Diffusion tensor magnetic resonance imaging (DTI) measurements of prostate cancer (PCa) were performed in vivo, in patients undergoing radical prostatectomy, and ex vivo, in the same patients' prostatectomy specimens. For the first time, the imaging data were co-registered to histological sections of the prostatectomy specimens, thereby enabling unambiguous characterization of diffusion parameters in cancerous and benign tissues. Through image co-registration and histological analysis, we have shown that increased cellularity, and thence decreased luminal spaces, in peripheral zone PCa leads to about 40 % and 50 % apparent diffusion coefficient (ADC) decrease compared to benign peripheral zone tissues in vivo and ex vivo, respectively. In contrast, no significant diffusion anisotropy differences between the cancerous and non-cancerous peripheral zone tissues were observed. The bundled fibromuscular tissues in prostate, such as stromal tissues in benign prostatic hyperplasia (BPH), exhibited high diffusion anisotropy facilitating the differentiation of PCa from BPH in central gland. A tissue classification method, combining DTI and T2w images, was proposed to provide more specific PCa detection. An ADC threshold for PCa was also established to provide unsupervised PCa localization. The PCa identified using this method correlate well with histologically identified PCa foci.

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 2008
Accession Number
ADA482920

Entities

People

  • Junqian Xu

Organizations

  • Washington University in St. Louis

Tags

DTIC Thesaurus Topics

  • Army
  • Coefficients
  • Detection
  • Diffusion
  • Diffusion Coefficient
  • High Resolution
  • Histological Techniques
  • Magnetic Resonance
  • Magnetic Resonance Imaging
  • Measurement
  • Medical Personnel
  • Neoplasms
  • Prostate Cancer
  • Three Dimensional
  • Tissues
  • Two Dimensional
  • United States

Fields of Study

  • Medicine
  • Physics

Readers

  • Medical Imaging.
  • Oncology (Cancer Research).

Technology Areas

  • Space