# Content-based MR-TRUS Fusion without Tracking

> **NIH NIH R21** · RENSSELAER POLYTECHNIC INSTITUTE · 2021 · $192,513

## Abstract

There are about 3 million American men living with prostate cancer, the second leading cause of cancer death
for men in the United States. If the prostate cancer is caught early before it spreads to other parts of the body,
by active monitoring or treatment, most men will not die from it. Nevertheless, 22% to 47% of the patients with
negative biopsies but elevated prostate-specific antigen levels may still harbor malignant tumors, which can be
life threatening and could have been missed by the commonly used ultrasound guided random biopsy. By
contrast, fusion of magnetic resonance (MR) imaging and transrectal ultrasound (TRUS) for guiding targeted
biopsies has shown to significantly improve the cancer detection rate. However, MR-TRUS fusion itself is very
challenging due to the difficulties in directly registering images of these two very different modalities in different
dimensions. To bypass the difficult registration problems, the existing fusion techniques require the use of
specialized expensive and cumbersome hardware tracking devices, which increases cost and elongates
procedures. More importantly, due to a number of factors such as patient movement, respiratory motion and
ultrasound transducer pressure change, prostate motion can happen during a procedure and cause the images
to be misaligned. Timely noticing and correcting such motion require great skill and knowledge of radiological
imaging, where studies show a steep learning curve for mastering fusion systems. Failing in image registration
and motion compensation renders the fusion guided biopsy performing no differently than random biopsy. To
address the fundamental cause of the problems, the goal of this project is to create enabling technology of MR-
TRUS image fusion solely based on internal image content without using external tracking devices. The
proposed research is foundational for developing next generation of MR-TRUS fusion guidance systems for
prostate biopsy to achieve robust performance with lower costs. Recent advancement in machine learning,
especially deep learning, has provided us new tools and new angles to tackle this challenging problem. This
project aims for directly fusing 2D TRUS frames with 3D MR volume by developing novel deep learning methods
for image reconstruction and registration. The proposed methods are designed to exploit both population and
patient specific imaging information to accurately align images. As all learning-based image registration methods
try to better use population knowledge to improve the registration performance, few of them have been able to
efficiently use patient specific information, which can be essential to obtain robust and accurate performance.
Upon successful completion, the innovation created from the project will disrupt the common perception that
hardware tracking has to be used for multimodal image fusion-guided interventions and alleviate the demand on
physicians’ experience and skill in image analysis and fusion to he...

## Key facts

- **NIH application ID:** 10140348
- **Project number:** 5R21EB028001-03
- **Recipient organization:** RENSSELAER POLYTECHNIC INSTITUTE
- **Principal Investigator:** PINGKUN YAN
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $192,513
- **Award type:** 5
- **Project period:** 2019-07-01 → 2023-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10140348

## Citation

> US National Institutes of Health, RePORTER application 10140348, Content-based MR-TRUS Fusion without Tracking (5R21EB028001-03). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10140348. Licensed CC0.

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