MRI-TRUS image synthesis with application to image-guided prostate intervention

John A. Onofrey, Ilkay Oksuz, Saradwata Sarkar, Rajesh Venkataraman, Lawrence H. Staib, Xenophon Papademetris

Research output: Contribution to journalConference paperpeer-review

7 Citations (Scopus)
190 Downloads (Pure)

Abstract

The synthesis of medical images is an intensity transforma-tion of a given modality in a way that represents an acquisition with a different modality (in the context of MRI this represents the synthesis of images originating from different MR sequences). Most methods fol-low a patch-based approach, which is computationally inefficient during synthesis and requires some sort of 'fusion' to synthesize a whole image from patch-level results. In this paper, we present a whole image synthesis approach that relies on deep neural networks. Our architecture resem-bles those of encoder-decoder networks, which aims to synthesize a source MRI modality to an other target MRI modality. The proposed method is computationally fast, it doesn't require extensive amounts of memory, and produces comparable results to recent patch-based approaches.
Original languageEnglish
Pages (from-to)157-166
Number of pages10
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Early online date23 Sept 2016
DOIs
Publication statusPublished - 2016

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