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Related Concept Videos

Magnetic Resonance Imaging01:24

Magnetic Resonance Imaging

Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...
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Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging
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Computed diffusion-weighted MR imaging may improve tumor detection.

Matthew D Blackledge1, Martin O Leach, David J Collins

  • 1Cancer Research UK and Engineering and Physical Sciences Research Council Cancer Imaging Centre, Institute of Cancer Research and Royal Marsden Hospital, Downs Rd, Sutton, Surrey SM2 5PT, England.

Radiology
|August 20, 2011
PubMed
Summary
This summary is machine-generated.

Computed diffusion weighted (DW) magnetic resonance (MR) imaging synthesizes high-b-value images from lower b-values, enhancing lesion detection in oncologic patients. This technique shows improved sensitivity and specificity for cancer diagnosis.

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Area of Science:

  • Medical Imaging
  • Radiology
  • Oncology

Background:

  • Diffusion-weighted (DW) magnetic resonance (MR) imaging is crucial for oncologic imaging.
  • Acquiring high-b-value DW MR images can improve lesion conspicuity.
  • Synthesizing high-b-value images from lower b-values offers a potential solution.

Purpose of the Study:

  • To describe computed diffusion weighted (DW) magnetic resonance (MR) imaging for obtaining high-b-value images from lower b-values.
  • To investigate the feasibility of computed DW MR imaging for improving lesion detection in oncologic cases.

Main Methods:

  • DW MR imaging was performed on a phantom and in 10 oncologic patients.
  • Computed DW MR images were synthesized at higher b-values (1500 and 2000 sec/mm(2)) from lower acquired b-values (0-900 sec/mm(2)).
  • Signal-to-noise ratio (SNR), image quality, background suppression, and diagnostic performance were evaluated.

Main Results:

  • Computed DW MR imaging showed close agreement with theoretical SNR predictions.
  • Higher SNR was achieved with computed DW MR imaging compared to acquired images, especially at b > 840 sec/mm(2).
  • Computed b=2000 sec/mm(2) images demonstrated good quality, high background suppression, and significantly improved diagnostic sensitivity (96.0%) and specificity (96.6%) compared to acquired b=900 sec/mm(2) images.

Conclusions:

  • Computed DW MR imaging enables high-b-value image acquisition with good SNR in the body.
  • The technique is feasible for clinical use and shows potential for enhanced disease detection in oncologic patients.