AI Article Synopsis

  • MRSI (Magnetic Resonance Spectroscopic Imaging) can help detect and characterize prostate cancer, but its effectiveness is limited due to the need for specialized training for radiologists to interpret the data.
  • An automated quality control algorithm has been developed that evaluates each voxel in MRSI datasets, classifying them as acceptable or unacceptable based on their spectral profiles to reduce the chances of interpretation errors caused by artifacts.
  • This algorithm demonstrated high accuracy in testing, achieving both a specificity and sensitivity of 0.95, which allows for quicker processing of MRSI data without heavily relying on radiologists for quality checks.

Article Abstract

MRSI of prostate cancer provides a potential clinical tool to aid in the detection and characterisation of this disease, but its clinical use is limited by the need for the specialist training of radiologists to read these datasets. An essential part of this reading is the assessment of the usability and reliability of MRSI spectra because they can be affected by artefacts such as poor signal to noise, lipid signal contamination and broad resonances that could cause errors of interpretation. We have developed an automated quality control algorithm that classifies every voxel of an MRSI dataset as either acceptable or unacceptable for further analysis, based on the spectral profile alone. The method was trained and tested based on a gold standard of agreement of four experts. It was highly accurate: testing with a novel set of data from MRSI patients produced agreement with the experts' consensus decisions with a specificity of 0.95 and sensitivity of 0.95. This method provides fast quality control of three-dimensional MRSI datasets of the prostate, removing the need for radiologists to perform this time consuming, but necessary, task prior to further analysis.

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Source
http://dx.doi.org/10.1002/nbm.2835DOI Listing

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