MRI-TRUS Image Synthesis with Application to Image-Guided Prostate Intervention

• Published in: Simulation and Synthesis in Medical Imaging Vol. 9968; pp. 157 - 166
• Main Authors: Onofrey, John A., Oksuz, Ilkay, Sarkar, Saradwata, Venkataraman, Rajesh, Staib, Lawrence H., Papademetris, Xenophon
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2016; Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: 3D graphics & modelling; Appearance Model; Dictionary Learning; Image processing; Magnetic Resonance Imaging Reconstruction; Pattern recognition; Principal Component Analysis Model; Target Registration Error
• ISBN: 3319466291; 9783319466293
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-319-46630-9_16

Accurate and robust fusion of pre-procedure magnetic resonance imaging (MRI) to intra-procedure trans-rectal ultrasound (TRUS) imaging is necessary for image-guided prostate cancer biopsy procedures. The current clinical standard for image fusion relies on non-rigid surface-based registration between semi-automatically segmented prostate surfaces in both the MRI and TRUS. This surface-based registration method does not take advantage of internal anatomical prostate structures, which have the potential to provide useful information for image registration. However, non-rigid, multi-modal intensity-based MRI-TRUS registration is challenging due to highly non-linear intensities relationships between MRI and TRUS. In this paper, we present preliminary work using image synthesis to cast this problem into a mono-modal registration task by using a large database of over 100 clinical MRI-TRUS image pairs to learn a joint model of MR-TRUS appearance. Thus, given an MRI, we use this learned joint appearance model to synthesize the patient’s corresponding TRUS image appearance with which we could potentially perform mono-modal intensity-based registration. We present preliminary results of this approach.