Unsupervised deep photon-counting computed tomography reconstruction for human extremity imaging
Active Project Grant Awarded Last action
- Award ID
- R01EB034737
- Recipient
-
University Of Massachusetts Lowell UEI LTNVSTJ3R6D5
- Awarding agency
-
National Institute of Biomedical Imaging and Bioenginering
National Institutes of Health, Department of Health and Human Services
- Obligated
- $4,396,603
Award facts
- Assistance listing
- 93.286 Discovery and Applied Research for Technological Innovations to Improve Human Health
- Completion date 86% complete
- Place of performance
- Lowell, MA 01854, USA
Award details come from USAspending.gov.
Description
- Abstract
- The state-of-the-art x-RAY photon-counting CT (pcct) generates images in multi-energy bins simultaneously with high spatial resolution and low radiation dose for tissue characterization and material decomposition. FDA has approved the techniques in 2021. Both clinical pcct and micro-pcct scanners are now commercially available. This opens a new door to opportunities for functional, cellular, and molecular x-RAY imaging with novel contrast agents such as bismuth and gold nanoparticles. However, x-RAY photon-counting detectors are not perfect, and IT remains challenging to reconstruct high-quality pcct images for various clinical applications. Over the past several years, deep learning-based tomographic imaging has become a new frontier of image reconstruction. Different from compressive sensing (CS) methods, which totally rely on the prior information in terms of an accurate mathematical constraint, the emerging deep learning-based approach is empowered by big data with which a deep network can be trained for superior tomographic reconstruction. However, a recent study published in pnas revealed three types of instabilities of deep tomographic reconstruction networks, which are believed to be fundamental due to lack of kernel awareness and "nontrivial to overcome", but CS-based reconstruction was reported in that study to be stable because of its kernel awareness. Meanwhile, IT is hard to collect large amounts of data with ground-truths for supervised network training up to the clinical image quality. To overcome the aforementioned challenges in the context of a clinical trial with pcct using medipix detectors, our overall goal is to develop an unsupervised deep learning approach (udla) for few-view and low-dose image reconstruction based on our analytic compressive iterative deep (acid) architecture but specific to pcct data, with much higher spatial resolution and computational efficiency, and without the requirement of ground- truth for training. Acid combines the data-driven power of deep learning, the kernel-awareness of CS, and iterative refinement to deliver image reconstruction results accurately and stably. To achieve our goal, three specific aims are defined as follows. Aim 1: udla will be designed, developed, optimized, and integrated into an open-source platform, including a deep end-to-end reconstruction network and an advanced CS module with a multi-constraint model; aim 2: udla will be tested for stability and generalizability, and accelerated via software optimization on a high-performance computing platform; and aim 3: udla will be evaluated and validated in simulation, experiments, and retrospective use of clinical extremity imaging pcct data. Upon the completion of this project, the udla software should have been characterized for clinical extremity imaging using medpix-based pcct to outperform contemporary iterative algorithms, without the vulnerabilities of existing deep reconstruction networks and the requirements of ground-truth for network training. In a broader perspective, our approach represents a paradigm shift towards the integration of model-based and data-driven reconstruction methods, and may have a lasting impact on pcct and other tomographic imaging modalities.
Transactions
| Action | Obligated | Date |
|---|---|---|
| Amendment | $72,748 | |
| Amendment | $12,229 | |
| Amendment | $12,229 | |
| Amendment | $464,056 | |
| Amendment | $464,056 | |
| Amendment | $464,056 | |
| Amendment | $561,703 | |
| Amendment | $564,495 | |
| Amendment | $564,495 | |
| Amendment | $608,268 | |
| Original award | $608,268 |
Show all 11
Subawards
| Subrecipient | Amount | Awarded |
|---|---|---|
| Rensselaer Polytechnic Institute | $173,970 | |
| University Of Otago | $32,391 | |
| Mars Bioimaging LIMITED | $72,144 | |
| Mars Bioimaging LIMITED | $142,845 |
Quick answers
Who received award R01EB034737?
R01EB034737 was awarded to University Of Massachusetts Lowell by National Institute of Biomedical Imaging and Bioenginering (National Institutes of Health) on July 1, 2023.
How much is award R01EB034737 worth?
$4,396,603 has been obligated on R01EB034737.
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