R01EB035908
Project Grant
Overview
Grant Description
Ultra-high spatial resolution photon-counting CT with multiple focal spots - Project summary / abstract
The emergence of clinical photon-counting CT shows much promise for new clinical diagnostics owing to its noise advantages, improved material discrimination, and small detector pixels.
The latter element has the potential to have particular impact on the overall spatial resolution of the system.
Recently available high-resolution systems have demonstrated particular advantage in cardiothoracic imaging and pulmonary imaging in particular, where good visualization of the fine structure of the lung can have significant impact on diagnostics.
These X-ray detector developments have, in some ways, outpaced X-ray source developments; and, with these new systems the X-ray now tends to be the limiting factor for high-resolution capability.
The fundamental issue is that smaller detector voxels require a smaller X-ray focal spot – which tends to limit fluence and increase overall noise.
This compounds with the general need for more fluence when image voxels are made smaller (higher resolution) to keep from increasing noise.
In X-ray tube design there is an inherent trade-off where smaller focal spots have lower capacity for generating fluence.
Thus, much of the capability of high-resolution CT with photon-counting detectors is unrealized.
We proposed a novel data acquisition strategy that uses multiple focal spots.
That is multiple, structured spots that change the balance of focal spot size and fluence – e.g. performing a data acquisition with both a large and small focal spot to get both high-resolution information but sufficient fluence to reduce noise.
Combined with state-of-the-art model-based and deep learning approaches, we will develop a new paradigm for ultra-high-spatial resolution CT (UHR-CT).
We seek to accomplish that development through the following specific aims:
Aim 1: Develop system models and reconstruction approaches for data acquisition with multiple focal spots, in which high-fidelity model form a framework for both simulation and joint data processing of the multiresolution data associated with the multiple focal spot technique.
Aim 2: Investigate and evaluate different multiple focal spot strategies for UHR-CT, wherein we study a range of system designs from current technology to more complex focal spot designs, and evaluate optimized strategies in simulated and physical systems.
Aim 3: Assessment of UHR-CT in clinical PCCT on lifelike phantoms and an in-vivo animal model, where we translate the multiple focal spot method to a clinical PCCT for immediate impact.
Successful completion of these aims will demonstrate the underlying technology, validate the methods, and characterize the potential for high-resolution performance improvements using both current and emerging X-ray source technologies.
The availability of the ultra-high-resolution capability opens the doors to a wide range of potential clinical diagnostic as smaller and smaller features can be resolved.
The emergence of clinical photon-counting CT shows much promise for new clinical diagnostics owing to its noise advantages, improved material discrimination, and small detector pixels.
The latter element has the potential to have particular impact on the overall spatial resolution of the system.
Recently available high-resolution systems have demonstrated particular advantage in cardiothoracic imaging and pulmonary imaging in particular, where good visualization of the fine structure of the lung can have significant impact on diagnostics.
These X-ray detector developments have, in some ways, outpaced X-ray source developments; and, with these new systems the X-ray now tends to be the limiting factor for high-resolution capability.
The fundamental issue is that smaller detector voxels require a smaller X-ray focal spot – which tends to limit fluence and increase overall noise.
This compounds with the general need for more fluence when image voxels are made smaller (higher resolution) to keep from increasing noise.
In X-ray tube design there is an inherent trade-off where smaller focal spots have lower capacity for generating fluence.
Thus, much of the capability of high-resolution CT with photon-counting detectors is unrealized.
We proposed a novel data acquisition strategy that uses multiple focal spots.
That is multiple, structured spots that change the balance of focal spot size and fluence – e.g. performing a data acquisition with both a large and small focal spot to get both high-resolution information but sufficient fluence to reduce noise.
Combined with state-of-the-art model-based and deep learning approaches, we will develop a new paradigm for ultra-high-spatial resolution CT (UHR-CT).
We seek to accomplish that development through the following specific aims:
Aim 1: Develop system models and reconstruction approaches for data acquisition with multiple focal spots, in which high-fidelity model form a framework for both simulation and joint data processing of the multiresolution data associated with the multiple focal spot technique.
Aim 2: Investigate and evaluate different multiple focal spot strategies for UHR-CT, wherein we study a range of system designs from current technology to more complex focal spot designs, and evaluate optimized strategies in simulated and physical systems.
Aim 3: Assessment of UHR-CT in clinical PCCT on lifelike phantoms and an in-vivo animal model, where we translate the multiple focal spot method to a clinical PCCT for immediate impact.
Successful completion of these aims will demonstrate the underlying technology, validate the methods, and characterize the potential for high-resolution performance improvements using both current and emerging X-ray source technologies.
The availability of the ultra-high-resolution capability opens the doors to a wide range of potential clinical diagnostic as smaller and smaller features can be resolved.
Awardee
Funding Goals
TO SUPPORT HYPOTHESIS-, DESIGN-, TECHNOLOGY-, OR DEVICE-DRIVEN RESEARCH RELATED TO THE DISCOVERY, DESIGN, DEVELOPMENT, VALIDATION, AND APPLICATION OF TECHNOLOGIES FOR BIOMEDICAL IMAGING AND BIOENGINEERING. THE PROGRAM INCLUDES BIOMATERIALS (BIOMIMETICS, BIOPROCESSING, ORGANOGENESIS, REHABILITATION, TISSUE ENGINEERING, IMPLANT SCIENCE, MATERIAL SCIENCE, INTERFACE SCIENCE, PHYSICS AND STRESS ENGINEERING, TECHNOLOGY ASSESSMENT OF MATERIALS/DEVICES), BIOSENSORS/BIOTRANSDUCERS (TECHNOLOGY DEVELOPMENT, TECHNOLOGY ASSESSMENT, DEVELOPMENT OF ALGORITHMS, TELEMETRY), NANOTECHNOLOGY (NANOSCIENCE, BIOMIMETICS, DRUG DELIVERY SYSTEMS, DRUG BIOAVAILABILITY, MICROARRAY/COMBINATORIAL TECHNOLOGY, GENETIC ENGINEERING, COMPUTER SCIENCE, TECHNOLOGY ASSESSMENT), BIOINFORMATICS (COMPUTER SCIENCE, INFORMATION SCIENCE, MATHEMATICS, BIOMECHANICS, COMPUTATIONAL MODELING AND SIMULATION, REMOTE DIAGNOSIS AND THERAPY), IMAGING DEVICE DEVELOPMENT, BIOMEDICAL IMAGING TECHNOLOGY DEVELOPMENT, IMAGE EXPLOITATION, CONTRAST AGENTS, INFORMATICS AND COMPUTER SCIENCES RELATED TO IMAGING, MOLECULAR AND CELLULAR IMAGING, BIOELECTRICS/BIOMAGNETICS, ORGAN AND WHOLE BODY IMAGING, SCREENING FOR DISEASES AND DISORDERS, AND IMAGING TECHNOLOGY ASSESSMENT AND SURGERY (TECHNIQUE DEVELOPMENT AND TECHNOLOGY DEVELOPMENT).
Grant Program (CFDA)
Awarding / Funding Agency
Place of Performance
Maryland
United States
Geographic Scope
State-Wide
Related Opportunity
The Johns Hopkins University was awarded
Project Grant R01EB035908
worth $536,954
from the National Institute of Biomedical Imaging and Bioengineering in April 2025 with work to be completed primarily in Maryland United States.
The grant
has a duration of 3 years 8 months and
was awarded through assistance program 93.286 Discovery and Applied Research for Technological Innovations to Improve Human Health.
The Project Grant was awarded through grant opportunity NIH Research Project Grant (Parent R01 Clinical Trial Not Allowed).
Status
(Ongoing)
Last Modified 4/4/25
Period of Performance
4/1/25
Start Date
12/31/28
End Date
Funding Split
$537.0K
Federal Obligation
$0.0
Non-Federal Obligation
$537.0K
Total Obligated
Activity Timeline
Additional Detail
Award ID FAIN
R01EB035908
SAI Number
R01EB035908-3327291766
Award ID URI
SAI UNAVAILABLE
Awardee Classifications
Private Institution Of Higher Education
Awarding Office
75N800 NIH National Institute of Biomedical Imaging and Bioengineering
Funding Office
75N800 NIH National Institute of Biomedical Imaging and Bioengineering
Awardee UEI
FTMTDMBR29C7
Awardee CAGE
5L406
Performance District
MD-90
Senators
Benjamin Cardin
Chris Van Hollen
Chris Van Hollen
Modified: 4/4/25