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.
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.
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