Quantitative investigative analysis of tumour separability in the prostate gland using ultra-high b-value computed diffusion imaging.
High b-value diffusion-weighted imaging is a promising approach for diagnosing and localizing cancer in the prostate gland. However, ultra-high b-value imaging is difficult to achieve at a high signal-to-noise ratio due to hardware limitations. An alternative approach being recently discussed is computed diffusion-weighted imaging, which allows for estimation of ultra-high b-value images from a set of diffusion-weighted acquisitions with different magnetic gradient strengths. This paper presents a quantitative investigative analysis of the improvement in tumour separability in the prostate gland from using ultra-high b-value computed diffusion-weighted imaging. The analysis computes ultra-high b-value images for six patient cases and investigates the separability of the tumour from the normal prostate gland. Based on quantitative metrics such as expected probability of classification error and the Receiver Operating Characteristic (ROC), it was found that the use of ultra-high computed diffusion-weighted imaging may significantly improve tumour separability, with a b-value around 3000 providing optimal separability.
Department of Systems Design Engineering, University of Waterloo, Ontario, Canada, N2L 3G1. jlglaist@uwaterloo.ca
Jeffrey
J
Glaister
Andrew
A
Cameron
Alexander
A
Wong
Masoom A
MA
Haider
D000368:Aged; D000369:Aged, 80 and over; D001499:Bayes Theorem; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods* / Q000706:statistics & numerical data; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D008297:Male; D008875:Middle Aged; D011467:Prostate / Q000473:pathology; D011471:Prostatic Neoplasms / Q000175:diagnosis* / Q000473:pathology; D012372:ROC Curve