Our overall goal is to develop a novel computing solution for automatic and accurate registration (spatial alignment) of 3-dimensional (3D) medical images of any modality and any anatomy (rigid or deformable) in 1 minute or less. Such capability currently does not exist. Existing image registration solutions have limited accuracy and/or limited applicability, preventing wide and routine clinical use. Building on significant prior academic research, we demonstrated the feasibility of creating the proposed technology in Phase I of this project. We now propose to create a fully functional turnkey prototype-a compact, relatively low-cost (manufacturing cost: ~$20,000) PC board-of hardware- accelerated image registration, with commercialization as the ultimate goal. Image registration is a fundamental need in modern medicine-a need that remains unmet. It is the necessary first step before images with complementary information can be fused or images taken at different times can be subtracted to quantify anatomic/physiologic changes. It is also essential when creating a population- based atlas from images of many subjects. Image registration has numerous other applications, including the registration of pre- and intra-operative images in a host of emerging minimally invasive image-guided interventions, especially those to treat cancer. Our 4 specific aims for Phase II are to: (1) create a fully integrated prototype of 1-min image registration;(2) develop software for convenient third-party integration and technology demonstration;(3) perform PACS (picture archival and communication system) integration and analyze enterprise-wide utilization;and (4) develop high- impact model applications.
These aims will continue the progress made in Phase I and help create a clinically tested and viable multipurpose image registration computing technology. The proposed Phase II work will also put us in a strong position to secure private capital and licensing agreements and pursue commercialization. Our proposed low-cost, ultrafast, easy-to-use, and accurate computing solution promises to unlock the full potential of medical image registration in virtually all clinical disciplines, including radiology, oncology, neurology, and cardiology.

Public Health Relevance

Combining 2 or more medical images of different types gives more precise information on a patient's condition. Comparing images taken at different times helps monitor how a disease is responding to treatment. In either case, image registration (alignment) is the crucial first step. Current image registration methods are slow, complex, and tedious, with limited practical applicability. We propose developing automatic, high-speed, 3-dimensional registration capabilities that are applicable to most organs and image types. We demonstrated the feasibility of creating such a technology in Phase I. We propose its full development and extensive clinical demonstration in Phase II.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Small Business Technology Transfer (STTR) Grants - Phase II (R42)
Project #
Application #
Study Section
Special Emphasis Panel (ZRG1-SBMI-T (10))
Program Officer
Narayanan, Deepa
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
Igi Technologies, Inc.
United States
Zip Code
Park, Seyoun; Plishker, William; Quon, Harry et al. (2017) Deformable registration of CT and cone-beam CT with local intensity matching. Phys Med Biol 62:927-947
Park, Seyoun; McNutt, Todd; Plishker, William et al. (2016) Technical Note: scuda: A software platform for cumulative dose assessment. Med Phys 43:5339
Robertson, Matthew S; Liu, Xinyang; Plishker, William et al. (2016) Software-based PET-MR image coregistration: combined PET-MRI for the rest of us! Pediatr Radiol 46:1552-61
Kato, Takahisa; Okumura, Ichiro; Song, Sang-Eun et al. (2015) Tendon-Driven Continuum Robot for Endoscopic Surgery: Preclinical Development and Validation of a Tension Propagation Model. IEEE ASME Trans Mechatron 20:2252-2263
Park, S; Plishker, W; Robinson, A et al. (2015) TU-AB-303-08: GPU-Based Software Platform for Efficient Image-Guided Adaptive Radiation Therapy. Med Phys 42:3591
Tauscher, Sebastian; Tokuda, Junichi; Schreiber, G√ľnter et al. (2015) OpenIGTLink interface for state control and visualisation of a robot for image-guided therapy systems. Int J Comput Assist Radiol Surg 10:285-92
Park, S; Robinson, A; Plishker, W et al. (2015) TU-G-BRA-05: Predicting Volume Change of the Tumor and Critical Structures Throughout Radiation Therapy by CT-CBCT Registration with Local Intensity Correction. Med Phys 42:3631
Tokuda, Junichi; Plishker, William; Torabi, Meysam et al. (2015) Graphics Processing Unit-Accelerated Nonrigid Registration of MR Images to CT Images During CT-Guided Percutaneous Liver Tumor Ablations. Acad Radiol 22:722-33
Kato, Takahisa; Okumura, Ichiro; Song, Sang-Eun et al. (2013) Multi-section continuum robot for endoscopic surgical clipping of intracranial aneurysms. Med Image Comput Comput Assist Interv 16:364-71