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Keppel C, Weisenberger A, Atanasijevic T, Wang S, Zubal G, Buchsbaum J, Brechbiel M, Capala J, Escorcia F, Obcemea C, Boehnlein A, Heyes G, Bourne P, Cherry S, Colby E, El Fakhri G, Gillo J, Gropler R, Gueye P, Tourassi G, Peggs S, Woody C. The United States Department of Energy and National Institutes of Health Collaboration: Medical Care Advances via Discovery in Physical Sciences. Med Phys 2023; 50:e53-e61. [PMID: 36705550 PMCID: PMC10033422 DOI: 10.1002/mp.16252] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Revised: 12/21/2022] [Accepted: 01/24/2023] [Indexed: 01/28/2023] Open
Abstract
Over several months, representatives from the U.S. Department of Energy (DOE) Office of Science and National Institutes of Health (NIH) had a number of meetings that lead to the conclusion that innovations in the Nation's health care could be realized by more directed interactions between NIH and DOE. It became clear that the expertise amassed and instrumentation advances developed at the DOE physical science laboratories to enable cutting-edge research in particle physics could also feed innovation in medical healthcare. To meet their scientific mission, the DOE laboratories created advances in such technologies as particle beam generation, radioisotope production, high-energy particle detection and imaging, superconducting particle accelerators, superconducting magnets, cryogenics, high-speed electronics, artificial intelligence, and big data. To move forward, NIH and DOE initiated the process of convening a joint workshop which occurred on July 12th and 13th, 2021. This Special Report presents a summary of the findings of the collaborative workshop and introduces the goals of the next one.
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Affiliation(s)
- Cynthia Keppel
- Experimental Nuclear Physics, Thomas Jefferson National Accelerator Facility, Virginia, USA
| | - Andrew Weisenberger
- Experimental Nuclear Physics, Thomas Jefferson National Accelerator Facility, Virginia, USA
| | | | - Shumin Wang
- National Institute of Biomedical Imaging and Bioengineering, Maryland, USA
| | - George Zubal
- National Institute of Biomedical Imaging and Bioengineering, Maryland, USA
| | | | | | | | | | | | - Amber Boehnlein
- Computational Sciences & Technology, Thomas Jefferson National Accelerator Facility, Virginia, USA
| | - Graham Heyes
- Computational Sciences & Technology, Thomas Jefferson National Accelerator Facility, Virginia, USA
| | - Philip Bourne
- School of Data Science, University of Virginia, Virginia, USA
| | - Simon Cherry
- Biomedical Engineering/Radiology, University of California, Davis, California, USA
| | - Eric Colby
- Office of High Energy Physics, Department of Energy, Washington, DC, USA
| | - Georges El Fakhri
- Gordon Center for Medical Imaging, Massachusetts General Hospital, Massachusetts, USA
| | - Jehanne Gillo
- Office of Isotope R&D and Production, Department of Energy, Washington, DC, USA
| | - Robert Gropler
- Mallinckrodt Institute of Radiology, Washington University, USA
| | - Paul Gueye
- Facility for Rare Isotope Beams, Michigan State University, Michigan, USA
| | - Georgia Tourassi
- Director of the National Center for Computational Sciences and the Oak Ridge Leadership Computing Facility at Oak Ridge National Laboratory, Tennessee, USA
| | - Steve Peggs
- Collider Accelerator Department, Brookhaven National Laboratory, New York, USA
| | - Craig Woody
- Physics Department, Brookhaven National Laboratory, New York, USA
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Guo J, Schmidtlein CR, Krol A, Li S, Lin Y, Ahn S, Stearns C, Xu Y. A Fast Convergent Ordered-Subsets Algorithm With Subiteration-Dependent Preconditioners for PET Image Reconstruction. IEEE TRANSACTIONS ON MEDICAL IMAGING 2022; 41:3289-3300. [PMID: 35679379 PMCID: PMC9810102 DOI: 10.1109/tmi.2022.3181813] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
We investigated the imaging performance of a fast convergent ordered-subsets algorithm with subiteration-dependent preconditioners (SDPs) for positron emission tomography (PET) image reconstruction. In particular, we considered the use of SDP with the block sequential regularized expectation maximization (BSREM) approach with the relative difference prior (RDP) regularizer due to its prior clinical adaptation by vendors. Because the RDP regularization promotes smoothness in the reconstructed image, the directions of the gradients in smooth areas more accurately point toward the objective function's minimizer than those in variable areas. Motivated by this observation, two SDPs have been designed to increase iteration step-sizes in the smooth areas and reduce iteration step-sizes in the variable areas relative to a conventional expectation maximization preconditioner. The momentum technique used for convergence acceleration can be viewed as a special case of SDP. We have proved the global convergence of SDP-BSREM algorithms by assuming certain characteristics of the preconditioner. By means of numerical experiments using both simulated and clinical PET data, we have shown that the SDP-BSREM algorithms substantially improve the convergence rate, as compared to conventional BSREM and a vendor's implementation as Q.Clear. Specifically, SDP-BSREM algorithms converge 35%-50% faster in reaching the same objective function value than conventional BSREM and commercial Q.Clear algorithms. Moreover, we showed in phantoms with hot, cold and background regions that the SDP-BSREM algorithms approached the values of a highly converged reference image faster than conventional BSREM and commercial Q.Clear algorithms.
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Ehrhardt MJ, Markiewicz P, Schönlieb CB. Faster PET reconstruction with non-smooth priors by randomization and preconditioning. Phys Med Biol 2019; 64:225019. [PMID: 31430733 DOI: 10.1088/1361-6560/ab3d07] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
Uncompressed clinical data from modern positron emission tomography (PET) scanners are very large, exceeding 350 million data points (projection bins). The last decades have seen tremendous advancements in mathematical imaging tools many of which lead to non-smooth (i.e. non-differentiable) optimization problems which are much harder to solve than smooth optimization problems. Most of these tools have not been translated to clinical PET data, as the state-of-the-art algorithms for non-smooth problems do not scale well to large data. In this work, inspired by big data machine learning applications, we use advanced randomized optimization algorithms to solve the PET reconstruction problem for a very large class of non-smooth priors which includes for example total variation, total generalized variation, directional total variation and various different physical constraints. The proposed algorithm randomly uses subsets of the data and only updates the variables associated with these. While this idea often leads to divergent algorithms, we show that the proposed algorithm does indeed converge for any proper subset selection. Numerically, we show on real PET data (FDG and florbetapir) from a Siemens Biograph mMR that about ten projections and backprojections are sufficient to solve the MAP optimisation problem related to many popular non-smooth priors; thus showing that the proposed algorithm is fast enough to bring these models into routine clinical practice.
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Affiliation(s)
- Matthias J Ehrhardt
- Institute for Mathematical Innovation, University of Bath, Bath BA2 7JU, United Kingdom
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Lin Y, Schmidtlein CR, Li Q, Li S, Xu Y. A Krasnoselskii-Mann Algorithm With an Improved EM Preconditioner for PET Image Reconstruction. IEEE TRANSACTIONS ON MEDICAL IMAGING 2019; 38:2114-2126. [PMID: 30794510 PMCID: PMC7528397 DOI: 10.1109/tmi.2019.2898271] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
This paper presents a preconditioned Krasnoselskii-Mann (KM) algorithm with an improved EM preconditioner (IEM-PKMA) for higher-order total variation (HOTV) regularized positron emission tomography (PET) image reconstruction. The PET reconstruction problem can be formulated as a three-term convex optimization model consisting of the Kullback-Leibler (KL) fidelity term, a nonsmooth penalty term, and a nonnegative constraint term which is also nonsmooth. We develop an efficient KM algorithm for solving this optimization problem based on a fixed-point characterization of its solution, with a preconditioner and a momentum technique for accelerating convergence. By combining the EM precondtioner, a thresholding, and a good inexpensive estimate of the solution, we propose an improved EM preconditioner that can not only accelerate convergence but also avoid the reconstructed image being "stuck at zero." Numerical results in this paper show that the proposed IEM-PKMA outperforms existing state-of-the-art algorithms including, the optimization transfer descent algorithm and the preconditioned L-BFGS-B algorithm for the differentiable smoothed anisotropic total variation regularized model, the preconditioned alternating projection algorithm, and the alternating direction method of multipliers for the nondifferentiable HOTV regularized model. Encouraging initial experiments using clinical data are presented.
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Häggström I, Schmidtlein CR, Campanella G, Fuchs TJ. DeepPET: A deep encoder-decoder network for directly solving the PET image reconstruction inverse problem. Med Image Anal 2019; 54:253-262. [PMID: 30954852 DOI: 10.1016/j.media.2019.03.013] [Citation(s) in RCA: 130] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2018] [Revised: 03/29/2019] [Accepted: 03/30/2019] [Indexed: 01/01/2023]
Abstract
The purpose of this research was to implement a deep learning network to overcome two of the major bottlenecks in improved image reconstruction for clinical positron emission tomography (PET). These are the lack of an automated means for the optimization of advanced image reconstruction algorithms, and the computational expense associated with these state-of-the art methods. We thus present a novel end-to-end PET image reconstruction technique, called DeepPET, based on a deep convolutional encoder-decoder network, which takes PET sinogram data as input and directly and quickly outputs high quality, quantitative PET images. Using simulated data derived from a whole-body digital phantom, we randomly sampled the configurable parameters to generate realistic images, which were each augmented to a total of more than 291,000 reference images. Realistic PET acquisitions of these images were simulated, resulting in noisy sinogram data, used for training, validation, and testing the DeepPET network. We demonstrated that DeepPET generates higher quality images compared to conventional techniques, in terms of relative root mean squared error (11%/53% lower than ordered subset expectation maximization (OSEM)/filtered back-projection (FBP), structural similarity index (1%/11% higher than OSEM/FBP), and peak signal-to-noise ratio (1.1/3.8 dB higher than OSEM/FBP). In addition, we show that DeepPET reconstructs images 108 and 3 times faster than OSEM and FBP, respectively. Finally, DeepPET was successfully applied to real clinical data. This study shows that an end-to-end encoder-decoder network can produce high quality PET images at a fraction of the time compared to conventional methods.
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Affiliation(s)
- Ida Häggström
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, United States.
| | - C Ross Schmidtlein
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, United States
| | - Gabriele Campanella
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, United States; Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY 10065, United States
| | - Thomas J Fuchs
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, United States; Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, United States; Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY 10065, United States
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Zhang J, Li S, Krol A, Schmidtlein CR, Lipson E, Feiglin D, Xu Y. Infimal convolution‐based regularization for
SPECT
reconstruction. Med Phys 2018; 45:5397-5410. [DOI: 10.1002/mp.13226] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2018] [Revised: 09/21/2018] [Accepted: 09/21/2018] [Indexed: 01/19/2023] Open
Affiliation(s)
- Jiahan Zhang
- Department of Radiation Oncology Duke University Medical Center Durham NC 27713 USA
| | - Si Li
- School of Computer Science and Technology Guangdong University of Technology Guangzhou 510006 China
| | - Andrzej Krol
- Department of Radiology Department of Pharmacology SUNY Upstate Medical University Syracuse NY 13210 USA
| | - C. Ross Schmidtlein
- Department of Medical Physics Memorial Sloan Kettering Cancer Center New York NY 10065 USA
| | - Edward Lipson
- Department of Physics Syracuse University Syracuse NY 13244 USA
| | - David Feiglin
- Department of Radiology Department of Pharmacology SUNY Upstate Medical University Syracuse NY 13210 USA
| | - Yuesheng Xu
- Department of Mathematics and Statistics Old Dominion University Norfolk VA 23529 USA
- School of Data and Computer Science Guangdong Province Key Lab of Computational Science Sun Yat‐sen University Guangzhou 510275 China
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Shojaeilangari S, Schmidtlein CR, Rahmim A, Ay MR. Recovery of missing data in partial geometry PET scanners: Compensation in projection space vs image space. Med Phys 2018; 45:5437-5449. [PMID: 30288762 DOI: 10.1002/mp.13225] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2018] [Revised: 07/30/2018] [Accepted: 08/27/2018] [Indexed: 11/09/2022] Open
Abstract
PURPOSE Robust and reliable reconstruction of images from noisy and incomplete projection data holds significant potential for proliferation of cost-effective medical imaging technologies. Since conventional reconstruction techniques can generate severe artifacts in the recovered images, a notable line of research constitutes development of appropriate algorithms to compensate for missing data and to reduce noise. In the present work, we investigate the effectiveness of state-of-the-art methodologies developed for image inpainting and noise reduction to preserve the quality of reconstructed images from undersampled PET data. We aimed to assess and ascertain whether missing data recovery is best performed in the projection space prior to reconstruction or adjoined with the reconstruction step in image space. METHODS Different strategies for data recovery were investigated using realistic patient derived phantoms (brain and abdomen) in PET scanners with partial geometry (small and large gap structures). Specifically, gap filling strategies in projection space were compared with reconstruction based compensation in image space. The methods used for filling the gap structure in sinogram PET data include partial differential equation based techniques (PDE), total variation (TV) regularization, discrete cosine transform(DCT)-based penalized regression, and dictionary learning based inpainting (DLI). For compensation in image space, compressed sensing based image reconstruction methods were applied. These include the preconditioned alternating projection (PAPA) algorithm with first and higher order total variation (HOTV) regularization as well as dictionary learning based compressed sensing (DLCS). We additionally investigated the performance of the methods for recovery of missing data in the presence of simulated lesion. The impact of different noise levels in the undersampled sinograms on performance of the approaches were also evaluated. RESULTS In our first study (brain imaging), DLI was shown to outperform other methods for small gap structure in terms of root mean square error (RMSE) and structural similarity (SSIM), though having relatively high computational cost. For large gap structure, HOTV-PAPA produces better results. In the second study (abdomen imaging), again the best performance belonged to DLI for small gap, and HOTV-PAPA for large gap. In our experiments for lesion simulation on patient brain phantom data, the best performance in term of contrast recovery coefficient (CRC) for small gap simulation belonged to DLI, while in the case of large gap simulation, HOTV-PAPA outperformed others. Our evaluation of the impact of noise on performance of approaches indicated that in case of low and medium noise levels, DLI still produces favorable results among inpainting approaches. However, for high noise levels, the performance of PDE4 (variant of PDE) and DLI are very competitive. CONCLUSIONS Our results showed that estimation of missing data in projection space as a preprocessing step before reconstruction can improve the quality of recovered images especially for small gap structures. However, when large portions of data are missing, compressed sensing techniques adjoined with the reconstruction step in image space were the best strategy.
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Affiliation(s)
- Seyedehsamaneh Shojaeilangari
- Research Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, Tehran, Iran.,School of Cognitive Science, Institute for Research in Fundamental Sciences (IPM), P.O. Box 193955746, Tehran, Iran
| | - C Ross Schmidtlein
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Arman Rahmim
- Department of Radiology, Johns Hopkins University, Baltimore, MD, 21287, USA.,Departments of Radiology and Physics & Astronomy, University of British Columbia, Columbia, BC, V5Z 1M9, Canada
| | - Mohammad Reza Ay
- Research Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, Tehran, Iran.,Department of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences, 1417613151, Tehran, Iran
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