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Raj J, Millardet M, Krishnamoorthy S, Karp JS, Surti S, Matej S. Recovery of the spatially-variant deformations in dual-panel PET reconstructions using deep-learning. Phys Med Biol 2024; 69:055028. [PMID: 38330448 DOI: 10.1088/1361-6560/ad278e] [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: 11/04/2023] [Accepted: 02/08/2024] [Indexed: 02/10/2024]
Abstract
Dual panel PET systems, such as Breast-PET (B-PET) scanner, exhibit strong asymmetric and anisotropic spatially-variant deformations in the reconstructed images due to the limited-angle data and strong depth of interaction effects for the oblique LORs inherent in such systems. In our previous work, we studied time-of-flight (TOF) effects and image-based spatially-variant PSF resolution models within dual-panel PET reconstruction to reduce these deformations. The application of PSF based models led to better and more uniform quantification of small lesions across the field of view (FOV). However, the ability of such a model to correct for PSF deformation is limited to small objects. On the other hand, large object deformations caused by the limited-angle reconstruction cannot be corrected with the PSF modeling alone. In this work, we investigate the ability of deep-learning (DL) networks to recover such strong spatially-variant image deformations using first simulated PSF deformations in image space of a generic dual panel PET system and then using simulated and acquired phantom reconstructions from dual panel B-PET system developed in our lab at University of Pennsylvania. For the studies using real B-PET data, the network was trained on the simulated synthetic data sets providing ground truth for objects resembling experimentally acquired phantoms on which the network deformation corrections were then tested. The synthetic and acquired limited-angle B-PET data were reconstructed using DIRECT-RAMLA reconstructions, which were then used as the network inputs. Our results demonstrate that DL approaches can significantly eliminate deformations of limited angle systems and improve their quantitative performance.
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Affiliation(s)
- Juhi Raj
- Department of Radiology, University of Pennsylvania, Philadelphia 19104, United States of America
| | - Maël Millardet
- Department of Radiology, University of Pennsylvania, Philadelphia 19104, United States of America
| | - Srilalan Krishnamoorthy
- Department of Radiology, University of Pennsylvania, Philadelphia 19104, United States of America
| | - Joel S Karp
- Department of Radiology, University of Pennsylvania, Philadelphia 19104, United States of America
| | - Suleman Surti
- Department of Radiology, University of Pennsylvania, Philadelphia 19104, United States of America
| | - Samuel Matej
- Department of Radiology, University of Pennsylvania, Philadelphia 19104, United States of America
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2
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Shi Y, Wang Y, Meng F, Zhou J, Wen B, Zhang X, Liu Y, Li L, Li J, Cao X, Kang F, Zhu S. 3D directional gradient L 0 norm minimization guided limited-view reconstruction in a dual-panel positron emission mammography. Comput Biol Med 2023; 161:107010. [PMID: 37235943 DOI: 10.1016/j.compbiomed.2023.107010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Revised: 04/13/2023] [Accepted: 05/03/2023] [Indexed: 05/28/2023]
Abstract
BACKGROUND Dual-panel PET is often used for local organ imaging, especially breast imaging, due to its simple structure, high sensitivity, good in-plane resolution, and straightforward fusion with other imaging modalities. Nevertheless, because of data loss caused by the dual-panel structure, using conventional image reconstruction methods results in limited-view artifacts and low image quality in dual-panel positron emission mammography (PEM), which may seriously affect the diagnosis. To mitigate the limited-view artifacts in the dual-panel PEM, we propose a 3D directional gradient L0 norm minimization (3D-DL0) guided reconstruction method. METHODS The detailed derivation and reasonable simplification of the 3D-DL0 algorithm are given first. Using this algorithm, we then obtain a prior image with edge recovery but contrast loss. To limit the solution space, the 3D-DL0 prior is introduced into the Maximum a Posteriori reconstruction. Meanwhile, a space-invariant point spread function is also implemented to restore image contrast and boundaries. Finally, the reconstructed images with limited-view artifact suppression are obtained. The proposed method was evaluated using the data acquired from physical phantoms and patients with breast tumors on a commercial dual-panel PET system. RESULTS The qualitative and quantitative studies for phantom data and the blind reader study for clinical data show that the proposed method is more effective in reaching a balance between artifact elimination and image contrast improvement compared with various limited-view reconstruction methods. In addition, the iteration process of the method is proved convergent numerically. CONCLUSIONS The image quality improvement confirms the potential value of the proposed reconstruction algorithm to address the limited-view problem, and thus improve diagnostic accuracy in dual-panel PEM imaging.
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Affiliation(s)
- Yu Shi
- School of Life Science and Technology, Xidian University & Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, Xi'an, Shaanxi, 710126, China; Xi'an Key Laboratory of Intelligent Sensing and Regulation of Trans-Scale Life Information & International Joint Research Center for Advanced Medical Imaging and Intelligent Diagnosis and Treatment, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, 710126, China; Innovation Center for Advanced Medical Imaging and Intelligent Medicine, Guangzhou Institute of Technology, Xidian University, Guangzhou, Guangdong, 51055, China
| | - Yirong Wang
- Department of Nuclear Medicine, Xijing Hospital, Fourth Military Medical University, Xi'an, Shaanxi, 710032, China
| | - Fanzhen Meng
- School of Life Science and Technology, Xidian University & Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, Xi'an, Shaanxi, 710126, China; School of Medical Imaging, Hebei Medical University, Shijiazhuang City, Hebei, 050017, China
| | - Jianwei Zhou
- School of Life Science and Technology, Xidian University & Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, Xi'an, Shaanxi, 710126, China; Xi'an Key Laboratory of Intelligent Sensing and Regulation of Trans-Scale Life Information & International Joint Research Center for Advanced Medical Imaging and Intelligent Diagnosis and Treatment, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, 710126, China; Innovation Center for Advanced Medical Imaging and Intelligent Medicine, Guangzhou Institute of Technology, Xidian University, Guangzhou, Guangdong, 51055, China
| | - Bo Wen
- School of Life Science and Technology, Xidian University & Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, Xi'an, Shaanxi, 710126, China; Xi'an Key Laboratory of Intelligent Sensing and Regulation of Trans-Scale Life Information & International Joint Research Center for Advanced Medical Imaging and Intelligent Diagnosis and Treatment, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, 710126, China; Innovation Center for Advanced Medical Imaging and Intelligent Medicine, Guangzhou Institute of Technology, Xidian University, Guangzhou, Guangdong, 51055, China
| | - Xuexue Zhang
- School of Life Science and Technology, Xidian University & Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, Xi'an, Shaanxi, 710126, China; Xi'an Key Laboratory of Intelligent Sensing and Regulation of Trans-Scale Life Information & International Joint Research Center for Advanced Medical Imaging and Intelligent Diagnosis and Treatment, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, 710126, China; Innovation Center for Advanced Medical Imaging and Intelligent Medicine, Guangzhou Institute of Technology, Xidian University, Guangzhou, Guangdong, 51055, China
| | - Yanyun Liu
- School of Life Science and Technology, Xidian University & Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, Xi'an, Shaanxi, 710126, China; Xi'an Key Laboratory of Intelligent Sensing and Regulation of Trans-Scale Life Information & International Joint Research Center for Advanced Medical Imaging and Intelligent Diagnosis and Treatment, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, 710126, China; Innovation Center for Advanced Medical Imaging and Intelligent Medicine, Guangzhou Institute of Technology, Xidian University, Guangzhou, Guangdong, 51055, China
| | - Lei Li
- School of Life Science and Technology, Xidian University & Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, Xi'an, Shaanxi, 710126, China; Xi'an Key Laboratory of Intelligent Sensing and Regulation of Trans-Scale Life Information & International Joint Research Center for Advanced Medical Imaging and Intelligent Diagnosis and Treatment, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, 710126, China; Innovation Center for Advanced Medical Imaging and Intelligent Medicine, Guangzhou Institute of Technology, Xidian University, Guangzhou, Guangdong, 51055, China
| | - Juntao Li
- School of Life Science and Technology, Xidian University & Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, Xi'an, Shaanxi, 710126, China; Xi'an Key Laboratory of Intelligent Sensing and Regulation of Trans-Scale Life Information & International Joint Research Center for Advanced Medical Imaging and Intelligent Diagnosis and Treatment, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, 710126, China; Innovation Center for Advanced Medical Imaging and Intelligent Medicine, Guangzhou Institute of Technology, Xidian University, Guangzhou, Guangdong, 51055, China
| | - Xu Cao
- School of Life Science and Technology, Xidian University & Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, Xi'an, Shaanxi, 710126, China; Xi'an Key Laboratory of Intelligent Sensing and Regulation of Trans-Scale Life Information & International Joint Research Center for Advanced Medical Imaging and Intelligent Diagnosis and Treatment, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, 710126, China; Innovation Center for Advanced Medical Imaging and Intelligent Medicine, Guangzhou Institute of Technology, Xidian University, Guangzhou, Guangdong, 51055, China.
| | - Fei Kang
- Department of Nuclear Medicine, Xijing Hospital, Fourth Military Medical University, Xi'an, Shaanxi, 710032, China.
| | - Shouping Zhu
- School of Life Science and Technology, Xidian University & Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, Xi'an, Shaanxi, 710126, China; Xi'an Key Laboratory of Intelligent Sensing and Regulation of Trans-Scale Life Information & International Joint Research Center for Advanced Medical Imaging and Intelligent Diagnosis and Treatment, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, 710126, China; Innovation Center for Advanced Medical Imaging and Intelligent Medicine, Guangzhou Institute of Technology, Xidian University, Guangzhou, Guangdong, 51055, China.
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Schaart DR, Schramm G, Nuyts J, Surti S. Time of Flight in Perspective: Instrumental and Computational Aspects of Time Resolution in Positron Emission Tomography. IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES 2021; 5:598-618. [PMID: 34553105 PMCID: PMC8454900 DOI: 10.1109/trpms.2021.3084539] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
The first time-of-flight positron emission tomography (TOF-PET) scanners were developed as early as in the 1980s. However, the poor light output and low detection efficiency of TOF-capable detectors available at the time limited any gain in image quality achieved with these TOF-PET scanners over the traditional non-TOF PET scanners. The discovery of LSO and other Lu-based scintillators revived interest in TOF-PET and led to the development of a second generation of scanners with high sensitivity and spatial resolution in the mid-2000s. The introduction of the silicon photomultiplier (SiPM) has recently yielded a third generation of TOF-PET systems with unprecedented imaging performance. Parallel to these instrumentation developments, much progress has been made in the development of image reconstruction algorithms that better utilize the additional information provided by TOF. Overall, the benefits range from a reduction in image variance (SNR increase), through allowing joint estimation of activity and attenuation, to better reconstructing data from limited angle systems. In this work, we review these developments, focusing on three broad areas: 1) timing theory and factors affecting the time resolution of a TOF-PET system; 2) utilization of TOF information for improved image reconstruction; and 3) quantification of the benefits of TOF compared to non-TOF PET. Finally, we offer a brief outlook on the TOF-PET developments anticipated in the short and longer term. Throughout this work, we aim to maintain a clinically driven perspective, treating TOF as one of multiple (and sometimes competitive) factors that can aid in the optimization of PET imaging performance.
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Affiliation(s)
- Dennis R Schaart
- Section Medical Physics & Technology, Radiation Science and Technology Department, Delft University of Technology, 2629 JB Delft, The Netherlands
| | - Georg Schramm
- Department of Imaging and Pathology, Division of Nuclear Medicine, KU/UZ Leuven, 3000 Leuven, Belgium
| | - Johan Nuyts
- Department of Imaging and Pathology, Division of Nuclear Medicine, KU/UZ Leuven, 3000 Leuven, Belgium
| | - Suleman Surti
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104 USA
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Vergara M, Rezaei A, Schramm G, Rodriguez-Alvarez MJ, Benlloch Baviera JM, Nuyts J. 2D feasibility study of joint reconstruction of attenuation and activity in limited angle TOF-PET. IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES 2021; 5:712-722. [PMID: 34541435 PMCID: PMC8445242 DOI: 10.1109/trpms.2021.3079462] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Several research groups are studying organ-dedicated limited angle positron emission tomography (PET) systems to optimize performance-cost ratio, sensitivity, access to the patient and/or flexibility. Often open systems are considered, typically consisting of two detector panels of various sizes. Such systems provide incomplete sampling due to limited angular coverage and/or truncation, which leads to artefacts in the reconstructed activity images. In addition, these organ-dedicated PET systems are usually stand-alone systems, and as a result, no attenuation information can be obtained from anatomical images acquired in the same imaging session. It has been shown that the use of time-of-flight information reduces incomplete data artefacts and enables the joint estimation of the activity and the attenuation factors. In this work, we explore with simple 2D simulations the performance and stability of a joint reconstruction algorithm, for imaging with a limited angle PET system. The reconstruction is based on the so-called MLACF (Maximum Likelihood Attenuation Correction Factors) algorithm and uses linear attenuation coefficients in a known-tissue-class region to obtain absolute quantification. Different panel sizes and different time-of-flight (TOF) resolutions are considered. The noise propagation is compared to that of MLEM reconstruction with exact attenuation correction (AC) for the same PET system. The results show that with good TOF resolution, images of good visual quality can be obtained. If also a good scatter correction can be implemented, quantitative PET imaging will be possible. Further research, in particular on scatter correction, is required.
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Affiliation(s)
- Marina Vergara
- Department of Imaging and Pathology, Division of Nuclear Medicine, KU Leuven, Belgium and Instituto de Instrumentación para Imagen Molecular Centro Mixto CSIC—Universitat Politècnica de València, Valencia, Spain
| | - Ahmadreza Rezaei
- Department of Imaging and Pathology, Division of Nuclear Medicine, KU Leuven, Belgium
| | - Georg Schramm
- Department of Imaging and Pathology, Division of Nuclear Medicine, KU Leuven, Belgium
| | - Maria Jose Rodriguez-Alvarez
- Instituto de Instrumentación para Imagen Molecular Centro Mixto CSIC—Universitat Politècnica de València, Valencia, Spain
| | - Jose Maria Benlloch Baviera
- Instituto de Instrumentación para Imagen Molecular Centro Mixto CSIC—Universitat Politècnica de València, Valencia, Spain
| | - Johan Nuyts
- Department of Imaging and Pathology, Division of Nuclear Medicine, KU Leuven, Belgium
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Wu W, Chen P, Wang S, Vardhanabhuti V, Liu F, Yu H. Image-domain Material Decomposition for Spectral CT using a Generalized Dictionary Learning. IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES 2021; 5:537-547. [PMID: 34222737 PMCID: PMC8248524 DOI: 10.1109/trpms.2020.2997880] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
The spectral computed tomography (CT) has huge advantages by providing accurate material information. Unfortunately, due to the instability or overdetermination of material decomposition model, the accuracy of material decomposition can be compromised in practice. Very recently, the dictionary learning based image-domain material decomposition (DLIMD) can obtain high accuracy for material decompositions from reconstructed spectral CT images. This method can explore the correlation of material components to some extent by training a unified dictionary from all material images. In addition, the dictionary learning based prior as a penalty is applied on material components independently, and many parameters would be carefully elaborated in practice. Because the concentration of contrast agent in clinical applications is low, it can result in data inconsistency for dictionary based representation during the iteration process. To avoid the aforementioned limitations and further improve the accuracy of materials, we first construct a generalized dictionary learning based image-domain material decomposition (GDLIMD) model. Then, the material tensor image is unfolded along the mode-1 to enhance the correlation of different materials. Finally, to avoid the data inconsistency of low iodine contrast, a normalization strategy is employed. Both physical phantom and tissue-synthetic phantom experiments demonstrate the proposed GDLIMD method outperforms the DLIMD and direct inversion (DI) methods.
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Affiliation(s)
- Weiwen Wu
- Department of Diagnostic Radiology, The University of Hong Kong, Hong Kong, 999077, China
| | - Peijun Chen
- Key Lab of Optoelectronic Technology and Systems, Ministry of Education, Chongqing University, Chongqing 400044, China
| | - Shaoyu Wang
- Key Lab of Optoelectronic Technology and Systems, Ministry of Education, Chongqing University, Chongqing 400044, China
| | - Varut Vardhanabhuti
- Department of Diagnostic Radiology, The University of Hong Kong, Hong Kong, 999077, China
| | - Fenglin Liu
- Key Lab of Optoelectronic Technology and Systems, Ministry of Education, Chongqing University, Chongqing 400044, China
| | - Hengyong Yu
- Department of Electrical and Computer Engineering, University of Massachusetts Lowell, Lowell, MA 01854, USA
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Efthimiou N, Kratochwil N, Gundacker S, Polesel A, Salomoni M, Auffray E, Pizzichemi M. TOF-PET Image Reconstruction With Multiple Timing Kernels Applied on Cherenkov Radiation in BGO. IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES 2020; 5:703-711. [PMID: 34541434 PMCID: PMC8445518 DOI: 10.1109/trpms.2020.3048642] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Today Time-of-Flight (TOF), in PET scanners, assumes a single, well-defined timing resolution for all events. However, recent BGO-Cherenkov detectors, combining prompt Cherenkov emission and the typical BGO scintillation, can sort events into multiple timing kernels, best described by the Gaussian mixture models. The number of Cherenkov photons detected per event impacts directly the detector time resolution and signal rise time, which can later be used to improve the coincidence timing resolution. This work presents a simulation toolkit which applies multiple timing spreads on the coincident events and an image reconstruction that incorporates this information. A full cylindrical BGO-Cherenkov PET model was compared, in terms of contrast recovery and contrast-to-noise ratio, against an LYSO model with a time resolution of 213 ps. Two reconstruction approaches for the mixture kernels were tested: 1) mixture Gaussian and 2) decomposed simple Gaussian kernels. The decomposed model used the exact mixture component applied during the simulation. Images reconstructed using mixture kernels provided similar mean value and less noise than the decomposed. However, typically, more iterations were needed. Similarly, the LYSO model, with a single TOF kernel, converged faster than the BGO-Cherenkov with multiple kernels. The results indicate that the model complexity slows down convergence. However, due to the higher sensitivity, the contrast-to-noise ratio was 26.4% better for the BGO model.
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Affiliation(s)
- Nikos Efthimiou
- Department Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104 USA
| | | | - Stefan Gundacker
- Department of Physics of Molecular Imaging Systems, Institute for Experimental Molecular Imaging, RWTH Aachen University, 52062 Aachen, Germany
| | - Andrea Polesel
- Physics Department, University of Milano-Bicocca, 20126 Milan, Italy
| | - Matteo Salomoni
- Physics Department, University of Milano-Bicocca, 20126 Milan, Italy
| | | | - Marco Pizzichemi
- Physics Department, University of Milano-Bicocca, 20126 Milan, Italy
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