Keywords: Analysis/Processing, Prostate, Quality Control, DWI, ADC, Preduiction
Motivation: ADC maps are an essential tool for early prostate cancer detection but are often uninterpretable due to imaging artifacts
Goal(s): Detect problems early in the imaging procedure using T2 images to predict the future quality of the ADC map
Approach: Constructed a multisite corpus of 486 patients imaged at both the NIH and outside. Investigated the influence of acquisition parameters on image quality and the predictive power of neural networks and simple anatomy measurements from the T2 image
Results: ADC image quality can be predicted from the T2 image using either a neural network approach or measurement of the rectal cross-section
Impact: The probability of a low quality, uninterpretable ADC maps can be inferred early in the imaging process, allowing corrective action (e.g. removal of gas by a muscle relaxant) to be employed
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