Magnetic Resonance-Based Electrical Property Tomography (MR- EPT) for Prostate Cancer Grade Imaging

Abstract

Determining whether a man recently diagnosed with prostate cancer has aggressive disease requiring immediately radical therapy or indolent disease requiring a more passive watchful waiting or active surveillance approach is a current clinical challenge. This technology development study is focused on developing Magnetic Resonance Electrical Property Tomography (MR-EPT) specifically for prostate imaging. MR-EPT is an imaging modality that may enable clinicians to image the electrical properties of prostate at near MR resolution. These electrical properties are hypothesized to provide sufficient contrast for distinguishing between aggressive and indolent prostate cancer. Much of the first year of this program has focused on MR sequence optimization, MR-EPT image reconstruction algorithm development and optimization, experimental imaging of both simplistic and anatomically accurate phantoms, and initial ex vivo prostate imaging. During this year we have optimized our MR-EPT sequences, demonstrated the imaging capabilities of MR-EPT through phantom studies, and have produced the first conductivity images of ex vivo prostate using MR-EPT techniques. The second year of the program will primarily focus on ex vivo and in vivo data acquisition, statistical analysis of our data, and preparation of publications and proposals for follow-on more clinically focused studies.

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Document Details

Document Type
Technical Report
Publication Date
Jul 01, 2014
Accession Number
ADA608128

Entities

People

  • Ryan Halter

Tags

Communities of Interest

  • Biomedical

DTIC Thesaurus Topics

  • Acquisition
  • Algorithms
  • Data Acquisition
  • Electrical Conductivity
  • Electrical Impedance
  • Electrical Properties
  • Geometry
  • Image Reconstruction
  • Magnetic Resonance
  • Medical Personnel
  • Neoplasms
  • Prostate Cancer
  • Statistical Analysis
  • Three Dimensional
  • Tissues
  • Tomography
  • Two Dimensional

Fields of Study

  • Medicine
  • Physics

Readers

  • Image Processing and Computer Vision.
  • Neuroscience
  • Oncology and Biomarker-Based Cancer Detection.