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Leung IHK, Strudwick MW. A systematic review of the challenges, emerging solutions and applications, and future directions of PET/MRI in Parkinson's disease. EJNMMI REPORTS 2024; 8:3. [PMID: 38748251 PMCID: PMC10962627 DOI: 10.1186/s41824-024-00194-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Accepted: 12/26/2023] [Indexed: 05/19/2024]
Abstract
PET/MRI is a hybrid imaging modality that boasts the simultaneous acquisition of high-resolution anatomical data and metabolic information. Having these exceptional capabilities, it is often implicated in clinical research for diagnosing and grading, as well as tracking disease progression and response to interventions. Despite this, its low level of clinical widespread use is questioned. This is especially the case with Parkinson's disease (PD), the fastest progressively disabling and neurodegenerative cause of death. To optimise the clinical applicability of PET/MRI for diagnosing, differentiating, and tracking PD progression, the emerging novel uses, and current challenges must be identified. This systematic review aimed to present the specific challenges of PET/MRI use in PD. Further, this review aimed to highlight the possible resolution of these challenges, the emerging applications and future direction of PET/MRI use in PD. EBSCOHost (indexing CINAHL Plus, PsycINFO) Ovid (Medline, EMBASE) PubMed, Web of Science, and Scopus from 2006 (the year of first integrated PET/MRI hybrid system) to 30 September 2022 were used to search for relevant primary articles. A total of 933 studies were retrieved and following the screening procedure, 18 peer-reviewed articles were included in this review. This present study is of great clinical relevance and significance, as it informs the reasoning behind hindered widespread clinical use of PET/MRI for PD. Despite this, the emerging applications of image reconstruction developed by PET/MRI research data to the use of fully automated systems show promising and desirable utility. Furthermore, many of the current challenges and limitations can be resolved by using much larger-sampled and longitudinal studies. Meanwhile, the development of new fast-binding tracers that have specific affinity to PD pathological processes is warranted.
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Krokos G, MacKewn J, Dunn J, Marsden P. A review of PET attenuation correction methods for PET-MR. EJNMMI Phys 2023; 10:52. [PMID: 37695384 PMCID: PMC10495310 DOI: 10.1186/s40658-023-00569-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Accepted: 08/07/2023] [Indexed: 09/12/2023] Open
Abstract
Despite being thirteen years since the installation of the first PET-MR system, the scanners constitute a very small proportion of the total hybrid PET systems installed. This is in stark contrast to the rapid expansion of the PET-CT scanner, which quickly established its importance in patient diagnosis within a similar timeframe. One of the main hurdles is the development of an accurate, reproducible and easy-to-use method for attenuation correction. Quantitative discrepancies in PET images between the manufacturer-provided MR methods and the more established CT- or transmission-based attenuation correction methods have led the scientific community in a continuous effort to develop a robust and accurate alternative. These can be divided into four broad categories: (i) MR-based, (ii) emission-based, (iii) atlas-based and the (iv) machine learning-based attenuation correction, which is rapidly gaining momentum. The first is based on segmenting the MR images in various tissues and allocating a predefined attenuation coefficient for each tissue. Emission-based attenuation correction methods aim in utilising the PET emission data by simultaneously reconstructing the radioactivity distribution and the attenuation image. Atlas-based attenuation correction methods aim to predict a CT or transmission image given an MR image of a new patient, by using databases containing CT or transmission images from the general population. Finally, in machine learning methods, a model that could predict the required image given the acquired MR or non-attenuation-corrected PET image is developed by exploiting the underlying features of the images. Deep learning methods are the dominant approach in this category. Compared to the more traditional machine learning, which uses structured data for building a model, deep learning makes direct use of the acquired images to identify underlying features. This up-to-date review goes through the literature of attenuation correction approaches in PET-MR after categorising them. The various approaches in each category are described and discussed. After exploring each category separately, a general overview is given of the current status and potential future approaches along with a comparison of the four outlined categories.
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Affiliation(s)
- Georgios Krokos
- School of Biomedical Engineering and Imaging Sciences, The PET Centre at St Thomas' Hospital London, King's College London, 1st Floor Lambeth Wing, Westminster Bridge Road, London, SE1 7EH, UK.
| | - Jane MacKewn
- School of Biomedical Engineering and Imaging Sciences, The PET Centre at St Thomas' Hospital London, King's College London, 1st Floor Lambeth Wing, Westminster Bridge Road, London, SE1 7EH, UK
| | - Joel Dunn
- School of Biomedical Engineering and Imaging Sciences, The PET Centre at St Thomas' Hospital London, King's College London, 1st Floor Lambeth Wing, Westminster Bridge Road, London, SE1 7EH, UK
| | - Paul Marsden
- School of Biomedical Engineering and Imaging Sciences, The PET Centre at St Thomas' Hospital London, King's College London, 1st Floor Lambeth Wing, Westminster Bridge Road, London, SE1 7EH, UK
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Hwang D, Kang SK, Kim KY, Choi H, Lee JS. Comparison of deep learning-based emission-only attenuation correction methods for positron emission tomography. Eur J Nucl Med Mol Imaging 2021; 49:1833-1842. [PMID: 34882262 DOI: 10.1007/s00259-021-05637-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Accepted: 11/24/2021] [Indexed: 11/26/2022]
Abstract
PURPOSE This study aims to compare two approaches using only emission PET data and a convolution neural network (CNN) to correct the attenuation (μ) of the annihilation photons in PET. METHODS One of the approaches uses a CNN to generate μ-maps from the non-attenuation-corrected (NAC) PET images (μ-CNNNAC). In the other method, CNN is used to improve the accuracy of μ-maps generated using maximum likelihood estimation of activity and attenuation (MLAA) reconstruction (μ-CNNMLAA). We investigated the improvement in the CNN performance by combining the two methods (μ-CNNMLAA+NAC) and the suitability of μ-CNNNAC for providing the scatter distribution required for MLAA reconstruction. Image data from 18F-FDG (n = 100) or 68 Ga-DOTATOC (n = 50) PET/CT scans were used for neural network training and testing. RESULTS The error of the attenuation correction factors estimated using μ-CT and μ-CNNNAC was over 7%, but that of scatter estimates was only 2.5%, indicating the validity of the scatter estimation from μ-CNNNAC. However, CNNNAC provided less accurate bone structures in the μ-maps, while the best results in recovering the fine bone structures were obtained by applying CNNMLAA+NAC. Additionally, the μ-values in the lungs were overestimated by CNNNAC. Activity images (λ) corrected for attenuation using μ-CNNMLAA and μ-CNNMLAA+NAC were superior to those corrected using μ-CNNNAC, in terms of their similarity to λ-CT. However, the improvement in the similarity with λ-CT by combining the CNNNAC and CNNMLAA approaches was insignificant (percent error for lung cancer lesions, λ-CNNNAC = 5.45% ± 7.88%; λ-CNNMLAA = 1.21% ± 5.74%; λ-CNNMLAA+NAC = 1.91% ± 4.78%; percent error for bone cancer lesions, λ-CNNNAC = 1.37% ± 5.16%; λ-CNNMLAA = 0.23% ± 3.81%; λ-CNNMLAA+NAC = 0.05% ± 3.49%). CONCLUSION The use of CNNNAC was feasible for scatter estimation to address the chicken-egg dilemma in MLAA reconstruction, but CNNMLAA outperformed CNNNAC.
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Affiliation(s)
- Donghwi Hwang
- Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, South Korea
- Department of Nuclear Medicine, Seoul National University College of Medicine, 103 Daehak-ro, Jongno-gu, Seoul, 03080, South Korea
- Artificial Intelligence Institute, Seoul National University, Seoul, South Korea
| | - Seung Kwan Kang
- Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, South Korea
- Department of Nuclear Medicine, Seoul National University College of Medicine, 103 Daehak-ro, Jongno-gu, Seoul, 03080, South Korea
- Artificial Intelligence Institute, Seoul National University, Seoul, South Korea
- Brightonix Imaging Inc., Seoul, South Korea
| | - Kyeong Yun Kim
- Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, South Korea
- Department of Nuclear Medicine, Seoul National University College of Medicine, 103 Daehak-ro, Jongno-gu, Seoul, 03080, South Korea
- Brightonix Imaging Inc., Seoul, South Korea
| | - Hongyoon Choi
- Department of Nuclear Medicine, Seoul National University College of Medicine, 103 Daehak-ro, Jongno-gu, Seoul, 03080, South Korea
| | - Jae Sung Lee
- Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, South Korea.
- Department of Nuclear Medicine, Seoul National University College of Medicine, 103 Daehak-ro, Jongno-gu, Seoul, 03080, South Korea.
- Artificial Intelligence Institute, Seoul National University, Seoul, South Korea.
- Brightonix Imaging Inc., Seoul, South Korea.
- Institute of Radiation Medicine, Medical Research Center, Seoul National University College of Medicine, Seoul, South Korea.
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Lee JS, Kim KM, Choi Y, Kim HJ. A Brief History of Nuclear Medicine Physics, Instrumentation, and Data Sciences in Korea. Nucl Med Mol Imaging 2021; 55:265-284. [PMID: 34868376 DOI: 10.1007/s13139-021-00721-7] [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: 07/19/2021] [Revised: 10/14/2021] [Accepted: 10/18/2021] [Indexed: 10/19/2022] Open
Abstract
We review the history of nuclear medicine physics, instrumentation, and data sciences in Korea to commemorate the 60th anniversary of the Korean Society of Nuclear Medicine. In the 1970s and 1980s, the development of SPECT, nuclear stethoscope, and bone densitometry systems, as well as kidney and cardiac image analysis technology, marked the beginning of nuclear medicine physics and engineering in Korea. With the introduction of PET and cyclotron in Korea in 1994, nuclear medicine imaging research was further activated. With the support of large-scale government projects, the development of gamma camera, SPECT, and PET systems was carried out. Exploiting the use of PET scanners in conjunction with cyclotrons, extensive studies on myocardial blood flow quantification and brain image analysis were also actively pursued. In 2005, Korea's first domestic cyclotron succeeded in producing radioactive isotopes, and the cyclotron was provided to six universities and university hospitals, thereby facilitating the nationwide supply of PET radiopharmaceuticals. Since the late 2000s, research on PET/MRI has been actively conducted, and the advanced research results of Korean scientists in the fields of silicon photomultiplier PET and simultaneous PET/MRI have attracted significant attention from the academic community. Currently, Korean researchers are actively involved in endeavors to solve a variety of complex problems in nuclear medicine using artificial intelligence and deep learning technologies.
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Affiliation(s)
- Jae Sung Lee
- Department of Nuclear Medicine, Seoul National University College of Medicine, 103 Daehak-ro, Jongno-gu, Seoul, 03080 Korea
| | - Kyeong Min Kim
- Department of Isotopic Drug Development, Korea Radioisotope Center for Pharmaceuticals, Korea Institute of Radiological and Medical Sciences, Seoul, Korea
| | - Yong Choi
- Department of Electronic Engineering, Sogang University, Seoul, Korea
| | - Hee-Joung Kim
- Department of Radiological Science, Yonsei University, Wonju, Korea
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Lei Y, Wang T, Dong X, Tian S, Liu Y, Mao H, Curran WJ, Shu HK, Liu T, Yang X. MRI classification using semantic random forest with auto-context model. Quant Imaging Med Surg 2021; 11:4753-4766. [PMID: 34888187 PMCID: PMC8611460 DOI: 10.21037/qims-20-1114] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2020] [Accepted: 04/28/2021] [Indexed: 11/06/2022]
Abstract
BACKGROUND It is challenging to differentiate air and bone on MR images of conventional sequences due to their low contrast. We propose to combine semantic feature extraction under auto-context manner into random forest to improve reasonability of the MRI segmentation for MRI-based radiotherapy treatment planning or PET attention correction. METHODS We applied a semantic classification random forest (SCRF) method which consists of a training stage and a segmentation stage. In the training stage, patch-based MRI features were extracted from registered MRI-CT training images, and the most informative elements were selected via feature selection to train an initial random forest. The rest sequence of random forests was trained by a combination of MRI feature and semantic feature under an auto-context manner. During segmentation, the MRI patches were first fed into these random forests to derive patch-based segmentation. By using patch fusion, the final end-to-end segmentation was obtained. RESULTS The Dice similarity coefficient (DSC) for air, bone and soft tissue classes obtained via proposed method were 0.976±0.007, 0.819±0.050 and 0.932±0.031, compared to 0.916±0.099, 0.673±0.151 and 0.830±0.083 with random forest (RF), and 0.942±0.086, 0.791±0.046 and 0.917±0.033 with U-Net. SCRF also outperformed the competing methods in sensitivity and specificity for all three structure types. CONCLUSIONS The proposed method accurately segmented bone, air and soft tissue. It is promising in facilitating advanced MR application in diagnosis and therapy.
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Affiliation(s)
- Yang Lei
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, USA
| | - Tonghe Wang
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, USA
| | - Xue Dong
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, USA
| | - Sibo Tian
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, USA
| | - Yingzi Liu
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, USA
| | - Hui Mao
- Department of Radiology and Imaging Sciences and Winship Cancer Institute, Emory University, Atlanta, GA, USA
| | - Walter J. Curran
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, USA
| | - Hui-Kuo Shu
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, USA
| | - Tian Liu
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, USA
| | - Xiaofeng Yang
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, USA
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6
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Gao F. Integrated Positron Emission Tomography/Magnetic Resonance Imaging in clinical diagnosis of Alzheimer's disease. Eur J Radiol 2021; 145:110017. [PMID: 34826792 DOI: 10.1016/j.ejrad.2021.110017] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Revised: 08/30/2021] [Accepted: 10/31/2021] [Indexed: 12/01/2022]
Abstract
Alzheimer's disease (AD), a progressive neurodegenerative disease which seriously endangers the health of the aged, is the most common etiology of senile dementia. With the increasing progress of neuroimaging technology, more and more imaging methods have been applied to study Alzheimer's disease. The emergence of integrated PET/MRI (Positron Emission Tomography/Magnetic Resonance Imaging) is a major advance in multimodal molecular imaging with many advantages on the structure of resolution and contrast of image over computed tomography (CT), PET and MRI. PET/MRI is now used stepwise in neurodegenerative diseases, and also has broad prospect of application in the early diagnosis of AD. In this review, we emphatically introduce the imaging advances of AD including functional imaging and molecular imaging, the advantages of PET/MRI over other imaging methods and prospects of PET/MRI in AD clinical diagnosis, especially in early diagnosis, clinical assessment and prediction on AD.
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Affiliation(s)
- Feng Gao
- Key Laboratory for Experimental Teratology of the Ministry of Education and Center for Experimental Nuclear Medicine, School of Basic Medical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, 250012, China.
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7
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Accurate Transmission-Less Attenuation Correction Method for Amyloid-β Brain PET Using Deep Neural Network. ELECTRONICS 2021. [DOI: 10.3390/electronics10151836] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
The lack of physically measured attenuation maps (μ-maps) for attenuation and scatter correction is an important technical challenge in brain-dedicated stand-alone positron emission tomography (PET) scanners. The accuracy of the calculated attenuation correction is limited by the nonuniformity of tissue composition due to pathologic conditions and the complex structure of facial bones. The aim of this study is to develop an accurate transmission-less attenuation correction method for amyloid-β (Aβ) brain PET studies. We investigated the validity of a deep convolutional neural network trained to produce a CT-derived μ-map (μ-CT) from simultaneously reconstructed activity and attenuation maps using the MLAA (maximum likelihood reconstruction of activity and attenuation) algorithm for Aβ brain PET. The performance of three different structures of U-net models (2D, 2.5D, and 3D) were compared. The U-net models generated less noisy and more uniform μ-maps than MLAA μ-maps. Among the three different U-net models, the patch-based 3D U-net model reduced noise and cross-talk artifacts more effectively. The Dice similarity coefficients between the μ-map generated using 3D U-net and μ-CT in bone and air segments were 0.83 and 0.67. All three U-net models showed better voxel-wise correlation of the μ-maps compared to MLAA. The patch-based 3D U-net model was the best. While the uptake value of MLAA yielded a high percentage error of 20% or more, the uptake value of 3D U-nets yielded the lowest percentage error within 5%. The proposed deep learning approach that requires no transmission data, anatomic image, or atlas/template for PET attenuation correction remarkably enhanced the quantitative accuracy of the simultaneously estimated MLAA μ-maps from Aβ brain PET.
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8
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Hwang D, Kang SK, Kim KY, Choi H, Seo S, Lee JS. Data-driven respiratory phase-matched PET attenuation correction without CT. Phys Med Biol 2021; 66. [PMID: 33910170 DOI: 10.1088/1361-6560/abfc8f] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2020] [Accepted: 04/28/2021] [Indexed: 12/20/2022]
Abstract
We propose a deep learning-based data-driven respiratory phase-matched gated-PET attenuation correction (AC) method that does not need a gated-CT. The proposed method is a multi-step process that consists of data-driven respiratory gating, gated attenuation map estimation using maximum-likelihood reconstruction of attenuation and activity (MLAA) algorithm, and enhancement of the gated attenuation maps using convolutional neural network (CNN). The gated MLAA attenuation maps enhanced by the CNN allowed for the phase-matched AC of gated-PET images. We conducted a non-rigid registration of the gated-PET images to generate motion-free PET images. We trained the CNN by conducting a 3D patch-based learning with 80 oncologic whole-body18F-fluorodeoxyglucose (18F-FDG) PET/CT scan data and applied it to seven regional PET/CT scans that cover the lower lung and upper liver. We investigated the impact of the proposed respiratory phase-matched AC of PET without utilizing CT on tumor size and standard uptake value (SUV) assessment, and PET image quality (%STD). The attenuation corrected gated and motion-free PET images generated using the proposed method yielded sharper organ boundaries and better noise characteristics than conventional gated and ungated PET images. A banana artifact observed in a phase-mismatched CT-based AC was not observed in the proposed approach. By employing the proposed method, the size of tumor was reduced by 12.3% and SUV90%was increased by 13.3% in tumors with larger movements than 5 mm. %STD of liver uptake was reduced by 11.1%. The deep learning-based data-driven respiratory phase-matched AC method improved the PET image quality and reduced the motion artifacts.
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Affiliation(s)
- Donghwi Hwang
- Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, Republic of Korea.,Department of Nuclear Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Seung Kwan Kang
- Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, Republic of Korea.,Department of Nuclear Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Kyeong Yun Kim
- Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, Republic of Korea.,Department of Nuclear Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Hongyoon Choi
- Department of Nuclear Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Seongho Seo
- Department of Electronic Engineering, Pai Chai University, Daejeon, Republic of Korea
| | - Jae Sung Lee
- Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, Republic of Korea.,Department of Nuclear Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea.,Institute of Radiation Medicine, Medical Research Center, Seoul National University College of Medicine, Seoul, Republic of Korea
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9
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Kang SK, Lee JS. Anatomy-guided PET reconstruction using l1bowsher prior. Phys Med Biol 2021; 66. [PMID: 33780912 DOI: 10.1088/1361-6560/abf2f7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Accepted: 03/29/2021] [Indexed: 12/22/2022]
Abstract
Advances in simultaneous positron emission tomography/magnetic resonance imaging (PET/MRI) technology have led to an active investigation of the anatomy-guided regularized PET image reconstruction algorithm based on MR images. Among the various priors proposed for anatomy-guided regularized PET image reconstruction, Bowsher's method based on second-order smoothing priors sometimes suffers from over-smoothing of detailed structures. Therefore, in this study, we propose a Bowsher prior based on thel1-norm and an iteratively reweighting scheme to overcome the limitation of the original Bowsher method. In addition, we have derived a closed solution for iterative image reconstruction based on this non-smooth prior. A comparison study between the originall2and proposedl1Bowsher priors was conducted using computer simulation and real human data. In the simulation and real data application, small lesions with abnormal PET uptake were better detected by the proposedl1Bowsher prior methods than the original Bowsher prior. The originall2Bowsher leads to a decreased PET intensity in small lesions when there is no clear separation between the lesions and surrounding tissue in the anatomical prior. However, the proposedl1Bowsher prior methods showed better contrast between the tumors and surrounding tissues owing to the intrinsic edge-preserving property of the prior which is attributed to the sparseness induced byl1-norm, especially in the iterative reweighting scheme. Besides, the proposed methods demonstrated lower bias and less hyper-parameter dependency on PET intensity estimation in the regions with matched anatomical boundaries in PET and MRI. Therefore, these methods will be useful for improving the PET image quality based on the anatomical side information.
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Affiliation(s)
- Seung Kwan Kang
- Department of Nuclear Medicine, Seoul National University Hospital, Seoul 03080, Republic of Korea.,Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul 03080, Republic of Korea.,Brightonix Imaging Inc., Seoul 04793, Republic of Korea
| | - Jae Sung Lee
- Department of Nuclear Medicine, Seoul National University Hospital, Seoul 03080, Republic of Korea.,Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul 03080, Republic of Korea.,Institute of Radiation Medicine, Medical Research Center, Seoul National University College of Medicine, Seoul 03080, Republic of Korea.,Brightonix Imaging Inc., Seoul 04793, Republic of Korea
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10
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Lee JS. A Review of Deep-Learning-Based Approaches for Attenuation Correction in Positron Emission Tomography. IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES 2021. [DOI: 10.1109/trpms.2020.3009269] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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11
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Mecheter I, Alic L, Abbod M, Amira A, Ji J. MR Image-Based Attenuation Correction of Brain PET Imaging: Review of Literature on Machine Learning Approaches for Segmentation. J Digit Imaging 2020; 33:1224-1241. [PMID: 32607906 PMCID: PMC7573060 DOI: 10.1007/s10278-020-00361-x] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022] Open
Abstract
Recent emerging hybrid technology of positron emission tomography/magnetic resonance (PET/MR) imaging has generated a great need for an accurate MR image-based PET attenuation correction. MR image segmentation, as a robust and simple method for PET attenuation correction, has been clinically adopted in commercial PET/MR scanners. The general approach in this method is to segment the MR image into different tissue types, each assigned an attenuation constant as in an X-ray CT image. Machine learning techniques such as clustering, classification and deep networks are extensively used for brain MR image segmentation. However, only limited work has been reported on using deep learning in brain PET attenuation correction. In addition, there is a lack of clinical evaluation of machine learning methods in this application. The aim of this review is to study the use of machine learning methods for MR image segmentation and its application in attenuation correction for PET brain imaging. Furthermore, challenges and future opportunities in MR image-based PET attenuation correction are discussed.
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Affiliation(s)
- Imene Mecheter
- Department of Electronic and Computer Engineering, Brunel University London, Uxbridge, UK.
- Department of Electrical and Computer Engineering, Texas A & M University at Qatar, Doha, Qatar.
| | - Lejla Alic
- Magnetic Detection and Imaging Group, Faculty of Science and Technology, University of Twente, Enschede, Netherlands
| | - Maysam Abbod
- Department of Electronic and Computer Engineering, Brunel University London, Uxbridge, UK
| | - Abbes Amira
- Institute of Artificial Intelligence, De Montfort University, Leicester, UK
| | - Jim Ji
- Department of Electrical and Computer Engineering, Texas A & M University at Qatar, Doha, Qatar
- Department of Electrical and Computer Engineering, Texas A & M University, College Station, TX, USA
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12
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Han PK, Horng DE, Gong K, Petibon Y, Kim K, Li Q, Johnson KA, El Fakhri G, Ouyang J, Ma C. MR-based PET attenuation correction using a combined ultrashort echo time/multi-echo Dixon acquisition. Med Phys 2020; 47:3064-3077. [PMID: 32279317 DOI: 10.1002/mp.14180] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2019] [Revised: 03/26/2020] [Accepted: 04/02/2020] [Indexed: 12/11/2022] Open
Abstract
PURPOSE To develop a magnetic resonance (MR)-based method for estimation of continuous linear attenuation coefficients (LACs) in positron emission tomography (PET) using a physical compartmental model and ultrashort echo time (UTE)/multi-echo Dixon (mUTE) acquisitions. METHODS We propose a three-dimensional (3D) mUTE sequence to acquire signals from water, fat, and short T2 components (e.g., bones) simultaneously in a single acquisition. The proposed mUTE sequence integrates 3D UTE with multi-echo Dixon acquisitions and uses sparse radial trajectories to accelerate imaging speed. Errors in the radial k-space trajectories are measured using a special k-space trajectory mapping sequence and corrected for image reconstruction. A physical compartmental model is used to fit the measured multi-echo MR signals to obtain fractions of water, fat, and bone components for each voxel, which are then used to estimate the continuous LAC map for PET attenuation correction. RESULTS The performance of the proposed method was evaluated via phantom and in vivo human studies, using LACs from computed tomography (CT) as reference. Compared to Dixon- and atlas-based MRAC methods, the proposed method yielded PET images with higher correlation and similarity in relation to the reference. The relative absolute errors of PET activity values reconstructed by the proposed method were below 5% in all of the four lobes (frontal, temporal, parietal, and occipital), cerebellum, whole white matter, and gray matter regions across all subjects (n = 6). CONCLUSIONS The proposed mUTE method can generate subject-specific, continuous LAC map for PET attenuation correction in PET/MR.
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Affiliation(s)
- Paul Kyu Han
- Department of Radiology, Gordon Center for Medical Imaging, Massachusetts General Hospital, Boston, MA, 02114, USA.,Department of Radiology, Harvard Medical School, Boston, MA, 02115, USA
| | - Debra E Horng
- Department of Radiology, Gordon Center for Medical Imaging, Massachusetts General Hospital, Boston, MA, 02114, USA.,Department of Radiology, Harvard Medical School, Boston, MA, 02115, USA
| | - Kuang Gong
- Department of Radiology, Gordon Center for Medical Imaging, Massachusetts General Hospital, Boston, MA, 02114, USA.,Department of Radiology, Harvard Medical School, Boston, MA, 02115, USA
| | - Yoann Petibon
- Department of Radiology, Gordon Center for Medical Imaging, Massachusetts General Hospital, Boston, MA, 02114, USA.,Department of Radiology, Harvard Medical School, Boston, MA, 02115, USA
| | - Kyungsang Kim
- Department of Radiology, Gordon Center for Medical Imaging, Massachusetts General Hospital, Boston, MA, 02114, USA.,Department of Radiology, Harvard Medical School, Boston, MA, 02115, USA
| | - Quanzheng Li
- Department of Radiology, Gordon Center for Medical Imaging, Massachusetts General Hospital, Boston, MA, 02114, USA.,Department of Radiology, Harvard Medical School, Boston, MA, 02115, USA
| | - Keith A Johnson
- Department of Radiology, Gordon Center for Medical Imaging, Massachusetts General Hospital, Boston, MA, 02114, USA.,Department of Radiology, Harvard Medical School, Boston, MA, 02115, USA.,Department of Neurology, Massachusetts General Hospital, Boston, MA, 02114, USA.,Center for Alzheimer Research and Treatment, Department of Neurology, Brigham and Women's Hospital, Boston, MA, 02115, USA
| | - Georges El Fakhri
- Department of Radiology, Gordon Center for Medical Imaging, Massachusetts General Hospital, Boston, MA, 02114, USA.,Department of Radiology, Harvard Medical School, Boston, MA, 02115, USA
| | - Jinsong Ouyang
- Department of Radiology, Gordon Center for Medical Imaging, Massachusetts General Hospital, Boston, MA, 02114, USA.,Department of Radiology, Harvard Medical School, Boston, MA, 02115, USA
| | - Chao Ma
- Department of Radiology, Gordon Center for Medical Imaging, Massachusetts General Hospital, Boston, MA, 02114, USA.,Department of Radiology, Harvard Medical School, Boston, MA, 02115, USA
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13
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Krämer M, Herzau B, Reichenbach JR. Segmentation and visualization of the human cranial bone by T2* approximation using ultra-short echo time (UTE) magnetic resonance imaging. Z Med Phys 2019; 30:51-59. [PMID: 31277935 DOI: 10.1016/j.zemedi.2019.06.003] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2019] [Revised: 05/08/2019] [Accepted: 06/03/2019] [Indexed: 11/19/2022]
Abstract
Segmentation of the human cranial bone from MRI data is challenging, because compact bone is characterized by very short transverse relaxation times and typically produces no signal when using conventional magnetic resonance imaging (MRI) sequences. In this work, we propose a fully automated segmentation algorithm, which uses dual-echo, ultra-short echo-time (UTE) MRI data. The segmentation was initialized by interval thresholding of approximated T2* relaxation time maps in the range of 1ms<T2*<3ms. This parameter range was derived from a manual region-of-interest analysis of high resolution data of the cranial layers, resulting in average T2* relaxation times of 1.7±0.3ms in the lamina externa, 2.5±0.3ms in the diploe and 1.7±0.2ms in the lamina interna. Segmentations were performed based on data of 8 healthy volunteers that were acquired with different acquisition parameters and spatial resolutions to test the stability of the algorithm. Comparison with computed tomography data demonstrated close agreement with the segmented UTE MRI data. Visualization of the segmented cranial bone was performed by volumetric rendering and by using the approximated T2* values for color coding, clearly visualizing the cranial sutures as well as their intersections.
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Affiliation(s)
- Martin Krämer
- Medical Physics Group, Institute of Diagnostic and Interventional Radiology, Jena University Hospital - Friedrich Schiller University Jena, Germany.
| | - Benedikt Herzau
- Medical Physics Group, Institute of Diagnostic and Interventional Radiology, Jena University Hospital - Friedrich Schiller University Jena, Germany
| | - Jürgen R Reichenbach
- Medical Physics Group, Institute of Diagnostic and Interventional Radiology, Jena University Hospital - Friedrich Schiller University Jena, Germany
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14
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Park J, Bae S, Seo S, Park S, Bang JI, Han JH, Lee WW, Lee JS. Measurement of Glomerular Filtration Rate using Quantitative SPECT/CT and Deep-learning-based Kidney Segmentation. Sci Rep 2019; 9:4223. [PMID: 30862873 PMCID: PMC6414660 DOI: 10.1038/s41598-019-40710-7] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2018] [Accepted: 02/12/2019] [Indexed: 12/01/2022] Open
Abstract
Quantitative SPECT/CT is potentially useful for more accurate and reliable measurement of glomerular filtration rate (GFR) than conventional planar scintigraphy. However, manual drawing of a volume of interest (VOI) on renal parenchyma in CT images is a labor-intensive and time-consuming task. The aim of this study is to develop a fully automated GFR quantification method based on a deep learning approach to the 3D segmentation of kidney parenchyma in CT. We automatically segmented the kidneys in CT images using the proposed method with remarkably high Dice similarity coefficient relative to the manual segmentation (mean = 0.89). The GFR values derived using manual and automatic segmentation methods were strongly correlated (R2 = 0.96). The absolute difference between the individual GFR values using manual and automatic methods was only 2.90%. Moreover, the two segmentation methods had comparable performance in the urolithiasis patients and kidney donors. Furthermore, both segmentation modalities showed significantly decreased individual GFR in symptomatic kidneys compared with the normal or asymptomatic kidney groups. The proposed approach enables fast and accurate GFR measurement.
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Affiliation(s)
- Junyoung Park
- Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, Korea.,Department of Nuclear Medicine, Seoul National University College of Medicine, Seoul, Korea
| | - Sungwoo Bae
- Department of Nuclear Medicine, Seoul National University College of Medicine, Seoul, Korea.,Department of Nuclear Medicine, Seoul National University Bundang Hospital, Seongnam-si, Gyeonggi-do, Korea
| | - Seongho Seo
- Department of Neuroscience, College of Medicine, Gachon University, Incheon, Korea
| | - Sohyun Park
- Department of Nuclear Medicine, National Cancer Center, Goyang-si, Gyeonggi-do, Korea
| | - Ji-In Bang
- Department of Nuclear Medicine, Ewha Womans University School of Medicine, Seoul, Korea
| | - Jeong Hee Han
- Department of Nuclear Medicine, Seoul National University Bundang Hospital, Seongnam-si, Gyeonggi-do, Korea
| | - Won Woo Lee
- Department of Nuclear Medicine, Seoul National University College of Medicine, Seoul, Korea. .,Department of Nuclear Medicine, Seoul National University Bundang Hospital, Seongnam-si, Gyeonggi-do, Korea. .,Institute of Radiation Medicine, Medical Research Center, Seoul National University, Seoul, Korea.
| | - Jae Sung Lee
- Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, Korea. .,Department of Nuclear Medicine, Seoul National University College of Medicine, Seoul, Korea. .,Institute of Radiation Medicine, Medical Research Center, Seoul National University, Seoul, Korea.
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15
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Hwang D, Kang SK, Kim KY, Seo S, Paeng JC, Lee DS, Lee JS. Generation of PET Attenuation Map for Whole-Body Time-of-Flight 18F-FDG PET/MRI Using a Deep Neural Network Trained with Simultaneously Reconstructed Activity and Attenuation Maps. J Nucl Med 2019; 60:1183-1189. [PMID: 30683763 DOI: 10.2967/jnumed.118.219493] [Citation(s) in RCA: 91] [Impact Index Per Article: 18.2] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2018] [Accepted: 12/20/2018] [Indexed: 02/06/2023] Open
Abstract
We propose a new deep learning-based approach to provide more accurate whole-body PET/MRI attenuation correction than is possible with the Dixon-based 4-segment method. We use activity and attenuation maps estimated using the maximum-likelihood reconstruction of activity and attenuation (MLAA) algorithm as inputs to a convolutional neural network (CNN) to learn a CT-derived attenuation map. Methods: The whole-body 18F-FDG PET/CT scan data of 100 cancer patients (38 men and 62 women; age, 57.3 ± 14.1 y) were retrospectively used for training and testing the CNN. A modified U-net was trained to predict a CT-derived μ-map (μ-CT) from the MLAA-generated activity distribution (λ-MLAA) and μ-map (μ-MLAA). We used 1.3 million patches derived from 60 patients' data for training the CNN, data of 20 others were used as a validation set to prevent overfitting, and the data of the other 20 were used as a test set for the CNN performance analysis. The attenuation maps generated using the proposed method (μ-CNN), μ-MLAA, and 4-segment method (μ-segment) were compared with the μ-CT, a ground truth. We also compared the voxelwise correlation between the activity images reconstructed using ordered-subset expectation maximization with the μ-maps, and the SUVs of primary and metastatic bone lesions obtained by drawing regions of interest on the activity images. Results: The CNN generates less noisy attenuation maps and achieves better bone identification than MLAA. The average Dice similarity coefficient for bone regions between μ-CNN and μ-CT was 0.77, which was significantly higher than that between μ-MLAA and μ-CT (0.36). Also, the CNN result showed the best pixel-by-pixel correlation with the CT-based results and remarkably reduced differences in activity maps in comparison to CT-based attenuation correction. Conclusion: The proposed deep neural network produced a more reliable attenuation map for 511-keV photons than the 4-segment method currently used in whole-body PET/MRI studies.
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Affiliation(s)
- Donghwi Hwang
- Department of Biomedical Sciences, Seoul National University, Seoul, Korea.,Department of Nuclear Medicine, Seoul National University, Seoul, Korea
| | - Seung Kwan Kang
- Department of Biomedical Sciences, Seoul National University, Seoul, Korea.,Department of Nuclear Medicine, Seoul National University, Seoul, Korea
| | - Kyeong Yun Kim
- Department of Biomedical Sciences, Seoul National University, Seoul, Korea.,Department of Nuclear Medicine, Seoul National University, Seoul, Korea
| | - Seongho Seo
- Department of Neuroscience, College of Medicine, Gachon University, Incheon, Korea
| | - Jin Chul Paeng
- Department of Nuclear Medicine, Seoul National University, Seoul, Korea.,Institute of Radiation Medicine, Medical Research Center, Seoul National University, Seoul, Korea; and
| | - Dong Soo Lee
- Department of Nuclear Medicine, Seoul National University, Seoul, Korea .,Institute of Radiation Medicine, Medical Research Center, Seoul National University, Seoul, Korea; and.,Department of Molecular Medicine and Biopharmaceutical Sciences, Graduate School of Convergence Science and Technology, Seoul National University, Suwon, Korea
| | - Jae Sung Lee
- Department of Biomedical Sciences, Seoul National University, Seoul, Korea .,Department of Nuclear Medicine, Seoul National University, Seoul, Korea.,Institute of Radiation Medicine, Medical Research Center, Seoul National University, Seoul, Korea; and
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16
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Kang SK, Seo S, Shin SA, Byun MS, Lee DY, Kim YK, Lee DS, Lee JS. Adaptive template generation for amyloid PET using a deep learning approach. Hum Brain Mapp 2018; 39:3769-3778. [PMID: 29752765 PMCID: PMC6866631 DOI: 10.1002/hbm.24210] [Citation(s) in RCA: 39] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2017] [Revised: 04/27/2018] [Accepted: 05/01/2018] [Indexed: 12/26/2022] Open
Abstract
Accurate spatial normalization (SN) of amyloid positron emission tomography (PET) images for Alzheimer's disease assessment without coregistered anatomical magnetic resonance imaging (MRI) of the same individual is technically challenging. In this study, we applied deep neural networks to generate individually adaptive PET templates for robust and accurate SN of amyloid PET without using matched 3D MR images. Using 681 pairs of simultaneously acquired 11 C-PIB PET and T1-weighted 3D MRI scans of AD, MCI, and cognitively normal subjects, we trained and tested two deep neural networks [convolutional auto-encoder (CAE) and generative adversarial network (GAN)] that produce adaptive best PET templates. More specifically, the networks were trained using 685,100 pieces of augmented data generated by rotating 527 randomly selected datasets and validated using 154 datasets. The input to the supervised neural networks was the 3D PET volume in native space and the label was the spatially normalized 3D PET image using the transformation parameters obtained from MRI-based SN. The proposed deep learning approach significantly enhanced the quantitative accuracy of MRI-less amyloid PET assessment by reducing the SN error observed when an average amyloid PET template is used. Given an input image, the trained deep neural networks rapidly provide individually adaptive 3D PET templates without any discontinuity between the slices (in 0.02 s). As the proposed method does not require 3D MRI for the SN of PET images, it has great potential for use in routine analysis of amyloid PET images in clinical practice and research.
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Affiliation(s)
- Seung Kwan Kang
- Department of Biomedical SciencesSeoul National UniversitySeoulKorea
- Department of Nuclear MedicineSeoul National UniversitySeoulKorea
| | - Seongho Seo
- Department of Neuroscience, College of MedicineGachon UniversityIncheonKorea
| | - Seong A. Shin
- Department of Biomedical SciencesSeoul National UniversitySeoulKorea
- Department of Nuclear MedicineSeoul National University Boramae Medical CenterSeoulKorea
| | - Min Soo Byun
- Department of NeuropsychiatrySeoul National UniversitySeoulKorea
| | - Dong Young Lee
- Department of NeuropsychiatrySeoul National UniversitySeoulKorea
| | - Yu Kyeong Kim
- Department of Nuclear MedicineSeoul National University Boramae Medical CenterSeoulKorea
| | - Dong Soo Lee
- Department of Nuclear MedicineSeoul National UniversitySeoulKorea
- Department of Molecular Medicine and Biopharmaceutical Sciences, Graduate School of Convergence Science and TechnologySeoul National UniversitySuwonKorea
- Institute of Radiation MedicineMedical Research Center, Seoul National UniversitySeoulKorea
| | - Jae Sung Lee
- Department of Biomedical SciencesSeoul National UniversitySeoulKorea
- Department of Nuclear MedicineSeoul National UniversitySeoulKorea
- Institute of Radiation MedicineMedical Research Center, Seoul National UniversitySeoulKorea
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17
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Shandiz MS, Rad HS, Ghafarian P, Yaghoubi K, Ay MR. Capturing Bone Signal in MRI of Pelvis, as a Large FOV Region, Using TWIST Sequence and Generating a 5-Class Attenuation Map for Prostate PET/MRI Imaging. Mol Imaging 2018; 17:1536012118789314. [PMID: 30064303 PMCID: PMC6071149 DOI: 10.1177/1536012118789314] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
Purpose: Prostate imaging is a major application of hybrid positron emission tomography/magnetic
resonance imaging (PET/MRI). Currently, MRI-based attenuation correction (MRAC) for
whole-body PET/MRI in which the bony structures are ignored is the main obstacle to
successful implementation of the hybrid modality in the clinical work flow. Ultrashort
echo time sequence captures bone signal but needs specific hardware–software and is
challenging in large field of view (FOV) regions, such as pelvis. The main aims of the
work are (1) to capture a part of the bone signal in pelvis using short echo time (STE)
imaging based on time-resolved angiography with interleaved stochastic trajectories
(TWIST) sequence and (2) to consider the bone in pelvis attenuation map (µ-map) to MRAC
for PET/MRI systems. Procedures: Time-resolved angiography with interleaved stochastic trajectories, which is routinely
used for MR angiography with high temporal and spatial resolution, was employed for
fast/STE MR imaging. Data acquisition was performed in a TE of 0.88 milliseconds (STE)
and 4.86 milliseconds (long echo time [LTE]) in pelvis region. Region of interest
(ROI)-based analysis was used for comparing the signal-to-noise ratio (SNR) of cortical
bone in STE and LTE images. A hybrid segmentation protocol, which is comprised of image
subtraction, a Fuzzy-based segmentation, and a dedicated morphologic operation, was used
for generating a 5-class µ-map consisting of cortical bone, air cavity, fat, soft
tissue, and background (µ-mapMR-5c). A MR-based 4-class µ-map
(µ-mapMR-4c) that considered soft tissue rather than bone was generated. As
such, a bilinear (µ-mapCT-ref), 5 (µ-mapCT-5c), and 4 class µ-map
(µ-mapCT-4c) based on computed tomography (CT) images were generated.
Finally, simulated PET data were corrected using µ-mapMR-5c (PET-MRAC5c),
µ-mapMR-4c (PET-MRAC4c), µ-mapCT-5c (PET-CTAC5c), and
µ-mapCT-ref (PET-CTAC). Results: The ratio of SNRbone to SNRair cavity in LTE images was 0.8, this
factor was increased to 4.4 in STE images. The Dice, Sensitivity, and Accuracy metrics
for bone segmentation in proposed method were 72.4% ± 5.5%, 69.6% ± 7.5%, and 96.5% ±
3.5%, respectively, where the segmented CT served as reference. The mean relative error
in bone regions in the simulated PET images were −13.98% ± 15%, −35.59% ± 15.41%, and
1.81% ± 12.2%, respectively, in PET-MRAC5c, PET-MRAC4c, and PET-CTAC5c where PET-CTAC
served as the reference. Despite poor correlation in the joint histogram of
µ-mapMR-4c versus µ-mapCT-5c (R2 > 0.78) and
PET-MRAC4c versus PET-CTAC5c (R2 = 0.83), high correlations were observed in
µ-mapMR-5c versus µ-mapCT-5c (R2 > 0.94) and
PET-MRAC5c versus PET-CTAC5c (R2 > 0.96). Conclusions: According to the SNRSTE, pelvic bone, the cortical bone can be separate from
air cavity in STE imaging based on TWIST sequence. The proposed method generated an
MRI-based µ-map containing bone and air cavity that led to more accurate tracer uptake
estimation than MRAC4c. Uptake estimation in hybrid PET/MRI can be improved by employing
the proposed method.
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Affiliation(s)
- Mehdi Shirin Shandiz
- 1 Department of Medical Physics, Zahedan University of Medical Sciences, Zahedan, Iran
| | - Hamid Saligheh Rad
- 2 Department of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences, Tehran, Iran.,3 Research Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, Tehran, Iran
| | - Pardis Ghafarian
- 4 Chronic Respiratory Diseases Research Center, National Research Institute of Tuberculosis and Lung Diseases (NRITLD), Shahid Beheshti University of Medical Sciences, Tehran, Iran.,5 PET/CT and Cyclotron Center, Masih Daneshvari Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Khadijeh Yaghoubi
- 1 Department of Medical Physics, Zahedan University of Medical Sciences, Zahedan, Iran
| | - Mohammad Reza Ay
- 2 Department of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences, Tehran, Iran.,3 Research Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, Tehran, Iran
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18
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Musafargani S, Ghosh KK, Mishra S, Mahalakshmi P, Padmanabhan P, Gulyás B. PET/MRI: a frontier in era of complementary hybrid imaging. Eur J Hybrid Imaging 2018; 2:12. [PMID: 29998214 PMCID: PMC6015803 DOI: 10.1186/s41824-018-0030-6] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2017] [Accepted: 03/14/2018] [Indexed: 12/19/2022] Open
Abstract
With primitive approaches, the diagnosis and therapy were operated at the cellular, molecular, or even at the genetic level. As the diagnostic techniques are more concentrated towards molecular level, multi modal imaging becomes specifically essential. Multi-modal imaging has extensive applications in clinical as well as in pre-clinical studies. Positron Emission Tomography (PET) has flourished in the field of nuclear medicine, which has motivated it to fuse with Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) for PET/CT and PET/MRI respectively. However, the challenges in PET/CT are due to the inability of simultaneous acquisition and reduced soft tissue contrast, which has led to the development of PET/MRI. Also, MRI offers the better soft tissue contrast over CT. Hence, fusion of PET and MRI results in combining structural information with functional image from PET. Yet, it has many technical challenges due to the interference between the modalities. Also, it must be resolved with various approaches for addressing the shortcomings of each system and improvise on the image quantification system. This review elaborates on the various challenges in the present PET/MRI system and the future directions of the hybrid modality. Also, the different data acquisition and analysis techniques of PET/MRI system are discussed with enhanced details on the software tools.
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Affiliation(s)
- Sikkandhar Musafargani
- Lee Kong Chian School of Medicine, Nanyang Technological University, 59 Nanyang Drive, Singapore, 636921 Singapore
| | - Krishna Kanta Ghosh
- Lee Kong Chian School of Medicine, Nanyang Technological University, 59 Nanyang Drive, Singapore, 636921 Singapore
| | - Sachin Mishra
- Lee Kong Chian School of Medicine, Nanyang Technological University, 59 Nanyang Drive, Singapore, 636921 Singapore
| | | | - Parasuraman Padmanabhan
- Lee Kong Chian School of Medicine, Nanyang Technological University, 59 Nanyang Drive, Singapore, 636921 Singapore
| | - Balázs Gulyás
- Lee Kong Chian School of Medicine, Nanyang Technological University, 59 Nanyang Drive, Singapore, 636921 Singapore
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19
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Hwang D, Kim KY, Kang SK, Seo S, Paeng JC, Lee DS, Lee JS. Improving the Accuracy of Simultaneously Reconstructed Activity and Attenuation Maps Using Deep Learning. J Nucl Med 2018; 59:1624-1629. [DOI: 10.2967/jnumed.117.202317] [Citation(s) in RCA: 99] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2017] [Accepted: 01/25/2018] [Indexed: 12/25/2022] Open
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20
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Fathi Kazerooni A, Ay MR, Arfaie S, Khateri P, Saligheh Rad H. Single STE-MR Acquisition in MR-Based Attenuation Correction of Brain PET Imaging Employing a Fully Automated and Reproducible Level-Set Segmentation Approach. Mol Imaging Biol 2017; 19:143-152. [PMID: 27459977 DOI: 10.1007/s11307-016-0990-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
PURPOSE The aim of this study is to introduce a fully automatic and reproducible short echo-time (STE) magnetic resonance imaging (MRI) segmentation approach for MR-based attenuation correction of positron emission tomography (PET) data in head region. PROCEDURES Single STE-MR imaging was followed by generating attenuation correction maps (μ-maps) through exploiting an automated clustering-based level-set segmentation approach to classify head images into three regions of cortical bone, air, and soft tissue. Quantitative assessment was performed by comparing the STE-derived region classes with the corresponding regions extracted from X-ray computed tomography (CT) images. RESULTS The proposed segmentation method returned accuracy and specificity values of over 90 % for cortical bone, air, and soft tissue regions. The MR- and CT-derived μ-maps were compared by quantitative histogram analysis. CONCLUSIONS The results suggest that the proposed automated segmentation approach can reliably discriminate bony structures from the proximal air and soft tissue in single STE-MR images, which is suitable for generating MR-based μ-maps for attenuation correction of PET data.
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Affiliation(s)
- Anahita Fathi Kazerooni
- Quantitative MR Imaging and Spectroscopy Group (QMISG), Research Center for Molecular and Cellular Imaging (RCMCI), Tehran University of Medical Sciences, Tehran, Iran
- Department of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences, Tehran, Iran
| | - Mohammad Reza Ay
- Department of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences, Tehran, Iran
- Medical Imaging Systems Group (MISG), Research Center for Molecular and Cellular Imaging (RCMCI), Tehran University of Medical Sciences, Tehran, Iran
| | - Saman Arfaie
- College of Letters and Science, Department of Molecular and Cell Biology, University of California, Berkeley, USA
| | - Parisa Khateri
- Medical Imaging Systems Group (MISG), Research Center for Molecular and Cellular Imaging (RCMCI), Tehran University of Medical Sciences, Tehran, Iran
| | - Hamidreza Saligheh Rad
- Quantitative MR Imaging and Spectroscopy Group (QMISG), Research Center for Molecular and Cellular Imaging (RCMCI), Tehran University of Medical Sciences, Tehran, Iran.
- Department of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences, Tehran, Iran.
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Interactive segmentation in MRI for orthopedic surgery planning: bone tissue. Int J Comput Assist Radiol Surg 2017; 12:1031-1039. [PMID: 28342107 DOI: 10.1007/s11548-017-1570-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2017] [Accepted: 03/15/2017] [Indexed: 10/19/2022]
Abstract
PURPOSE Planning orthopedic surgeries is commonly performed in computed tomography (CT) images due to the higher contrast of bony structure. However, soft tissues such as muscles and ligaments that may determine the functional outcome of a procedure are not easy to identify in CT, for which fast and accurate segmentation in MRI would be desirable. To be usable in daily practice, such method should provide convenient means of interaction for modifications and corrections, e.g., during perusal by the surgeon or the planning physician for quality control. METHODS We propose an interactive segmentation framework for MR images and evaluate the outcome for segmentation of bones. We use a random forest classification and a random walker-based spatial regularization. The latter enables the incorporation of user input as well as enforcing a single connected anatomical structures, thanks to which a selective sampling strategy is proposed to substantially improve the supervised learning performance. RESULTS We evaluated our segmentation framework on 10 patient humerus MRI as well as 4 high-resolution MRI from volunteers. Interactive humerus segmentations for patients took on average 150 s with over 3.5 times time-gain compared to manual segmentations, with accuracies comparable (converging) to that of much longer interactions. For high-resolution data, a novel multi-resolution random walker strategy further reduced the run time over 20 times of the manual segmentation, allowing for a feasible interactive segmentation framework. CONCLUSIONS We present a segmentation framework that allows iterative corrections leading to substantial speed gains in bone annotation in MRI. This will allow us to pursue semi-automatic segmentations of other musculoskeletal anatomy first in a user-in-the-loop manner, where later less user interactions or perhaps only few for quality control will be necessary as our annotation suggestions improve.
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22
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Jeon SY, Seo S, Lee JS, Choi SH, Lee DH, Jung YH, Song MK, Lee KJ, Kim YC, Kwon HW, Im HJ, Lee DS, Cheon GJ, Kang DH. [11C]-(R)-PK11195 positron emission tomography in patients with complex regional pain syndrome: A pilot study. Medicine (Baltimore) 2017; 96:e5735. [PMID: 28072713 PMCID: PMC5228673 DOI: 10.1097/md.0000000000005735] [Citation(s) in RCA: 37] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/23/2023] Open
Abstract
Complex regional pain syndrome (CRPS) is characterized by severe and chronic pain, but the pathophysiology of this disease are not clearly understood. The primary aim of our case-control study was to explore neuroinflammation in patients with CRPS using positron emission tomography (PET), with an 18-kDa translocator protein specific radioligand [C]-(R)-PK11195. [C]-(R)-PK11195 PET scans were acquired for 11 patients with CRPS (30-55 years) and 12 control subjects (30-52 years). Parametric image of distribution volume ratio (DVR) for each participant was generated by applying a relative equilibrium-based graphical analysis. The DVR of [C]-(R)-PK11195 in the caudate nucleus (t(21) = -3.209, P = 0.004), putamen (t(21) = -2.492, P = 0.022), nucleus accumbens (t(21) = -2.218, P = 0.040), and thalamus (t(21) = -2.395, P = 0.026) were significantly higher in CRPS patients than in healthy controls. Those of globus pallidus (t(21) = -2.045, P = 0.054) tended to be higher in CRPS patients than in healthy controls. In patients with CRPS, there was a positive correlation between the DVR of [C]-(R)-PK11195 in the caudate nucleus and the pain score, the visual analog scale (r = 0.661, P = 0.026, R = 0.408) and affective subscales of McGill Pain Questionnaire (r = 0.604, P = 0.049, R = 0.364). We demonstrated that neuroinflammation of CRPS patients in basal ganglia. Our results suggest that microglial pathology can be an important pathophysiology of CRPS. Association between the level of caudate nucleus and pain severity indicated that neuroinflammation in this region might play a key role. These results may be essential for developing effective medical treatments.
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Affiliation(s)
- So Yeon Jeon
- Department of Neuropsychiatry, Seoul National University Hospital
| | - Seongho Seo
- Department of Nuclear Medicine, Seoul National University College of Medicine
- Department of Brain and Cognitive Science, Seoul National University College of Natural Sciences
| | - Jae Sung Lee
- Department of Nuclear Medicine, Seoul National University College of Medicine
- Department of Brain and Cognitive Science, Seoul National University College of Natural Sciences
- Institute of Radiation Medicine, Medical Research Center
| | - Soo-Hee Choi
- Department of Neuropsychiatry, Seoul National University Hospital
- Department of Psychiatry, Seoul National University College of Medicine
| | - Do-Hyeong Lee
- Department of Neuropsychiatry, Seoul National University Hospital
| | - Ye-Ha Jung
- Department of Neuropsychiatry, Seoul National University Hospital
| | - Man-Kyu Song
- Department of Neuropsychiatry, Seoul National University Hospital
| | - Kyung-Jun Lee
- Department of Neuropsychiatry, Seoul National University Hospital
| | - Yong Chul Kim
- Department of Anesthesiology and Pain Medicine, Seoul National University Hospital, Seoul
| | - Hyun Woo Kwon
- Department of Nuclear Medicine, Seoul National University College of Medicine
- Department of Molecular Medicine and Biopharmaceutical Sciences, Graduate School of Convergence Science and Technology, Seoul National University, Suwon, Republic of Korea
| | - Hyung-Jun Im
- Department of Nuclear Medicine, Seoul National University College of Medicine
- Department of Molecular Medicine and Biopharmaceutical Sciences, Graduate School of Convergence Science and Technology, Seoul National University, Suwon, Republic of Korea
| | - Dong Soo Lee
- Department of Nuclear Medicine, Seoul National University College of Medicine
- Department of Molecular Medicine and Biopharmaceutical Sciences, Graduate School of Convergence Science and Technology, Seoul National University, Suwon, Republic of Korea
| | - Gi Jeong Cheon
- Department of Nuclear Medicine, Seoul National University College of Medicine
- Institute of Radiation Medicine, Medical Research Center
| | - Do-Hyung Kang
- Department of Neuropsychiatry, Seoul National University Hospital
- Department of Psychiatry, Seoul National University College of Medicine
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Shi K, Fürst S, Sun L, Lukas M, Navab N, Förster S, Ziegler SI. Individual refinement of attenuation correction maps for hybrid PET/MR based on multi-resolution regional learning. Comput Med Imaging Graph 2016; 60:50-57. [PMID: 27914956 DOI: 10.1016/j.compmedimag.2016.11.005] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2016] [Revised: 10/09/2016] [Accepted: 11/11/2016] [Indexed: 12/01/2022]
Abstract
PET/MR is an emerging hybrid imaging modality. However, attenuation correction (AC) remains challenging for hybrid PET/MR in generating accurate PET images. Segmentation-based methods on special MR sequences are most widely recommended by vendors. However, their accuracy is usually not high. Individual refinement of available certified attenuation maps may be helpful for further clinical applications. In this study, we proposed a multi-resolution regional learning (MRRL) scheme to utilize the internal consistency of the patient data. The anatomical and AC MR sequences of the same subject were employed to guide the refinement of the provided AC maps. The developed algorithm was tested on 9 patients scanned consecutively with PET/MR and PET/CT (7 [18F]FDG and 2 [18F]FET). The preliminary results showed that MRRL can improve the accuracy of segmented attenuation maps and consequently the accuracy of PET reconstructions.
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Affiliation(s)
- Kuangyu Shi
- Dept. Nuclear Medicine, Technische Universität München, Munich, Germany.
| | - Sebastian Fürst
- Dept. Nuclear Medicine, Technische Universität München, Munich, Germany
| | - Liang Sun
- Microsoft Corporation, Seattle, WA, USA
| | - Mathias Lukas
- Dept. Nuclear Medicine, Technische Universität München, Munich, Germany
| | - Nassir Navab
- Chair of Computer-aided Medical Procedure, Dept. Computer Science, Technische Universität München, Munich, Germany
| | - Stefan Förster
- Dept. Nuclear Medicine, Technische Universität München, Munich, Germany
| | - Sibylle I Ziegler
- Dept. Nuclear Medicine, Technische Universität München, Munich, Germany
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Lee WW. Recent Advances in Nuclear Cardiology. Nucl Med Mol Imaging 2016; 50:196-206. [PMID: 27540423 DOI: 10.1007/s13139-016-0433-x] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2016] [Accepted: 06/24/2016] [Indexed: 11/24/2022] Open
Abstract
Nuclear cardiology is one of the major fields of nuclear medicine practice. Myocardial perfusion studies using single-photon emission computed tomography (SPECT) have played a crucial role in the management of coronary artery diseases. Positron emission tomography (PET) has also been considered an important tool for the assessment of myocardial viability and perfusion. However, the recent development of computed tomography (CT)/magnetic resonance imaging (MRI) technologies and growing concerns about the radiation exposure of patients remain serious challenges for nuclear cardiology. In response to these challenges, remarkable achievements and improvements are currently in progress in the field of myocardial perfusion imaging regarding the applicable software and hardware. Additionally, myocardial perfusion positron emission tomography (PET) is receiving increasing attention owing to its unique capability of absolute myocardial blood flow estimation. An F-18-labeled perfusion agent for PET is under clinical trial with promising interim results. The applications of F-18 fluorodeoxyglucose (FDG) and F-18 sodium fluoride (NaF) to cardiovascular diseases have revealed details on the basic pathophysiology of ischemic heart diseases. PET/MRI seems to be particularly promising for nuclear cardiology in the future. Restrictive diseases, such as cardiac sarcoidosis and amyloidosis, are effectively evaluated using a variety of nuclear imaging tools. Considering these advances, the current challenges of nuclear cardiology will become opportunities if more collaborative efforts are devoted to this exciting field of nuclear medicine.
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Affiliation(s)
- Won Woo Lee
- Department of Nuclear Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine, 82, Gumi-ro 173 Beon-gil, Bundang-gu, Seongnam-si, Gyeonggi-do 463-707 Korea ; Institute of Radiation Medicine, Medical Research Center, Seoul National University, Seoul, South Korea
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