Abstract
Our prostate tissue-typing studies seek to develop imaging methods that have high sensitivity and specificity for visualizing cancerous regions of the prostate to improve the effectiveness of biopsy guidance, therapy targeting, and treatment monitoring. Spectral parameters obtained from radio-frequency (RF) ultrasonic (US) echo signals acquired from biopsy regions, along with the PSA are used to train a neural-network classifier. This method produced an ROC curve area of 0.84 compared to 0.64 obtained from B-mode, image-based classification. We are investigating the integration of ultrasonic and magnetic resonance (MR) methods to further improve our prostate tissue-typing methods. To take advantage of the independent information provided by US and MR, data acquired from the two imaging modalities need to be co-registered spatially. Therefore, we are developing effective means of 3D spatial co-registration of US and MR data along with histological data used for validation
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