1
|
Izadi S, Shiri I, F Uribe C, Geramifar P, Zaidi H, Rahmim A, Hamarneh G. Enhanced direct joint attenuation and scatter correction of whole-body PET images via context-aware deep networks. Z Med Phys 2024:S0939-3889(24)00002-3. [PMID: 38302292 DOI: 10.1016/j.zemedi.2024.01.002] [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: 05/11/2023] [Revised: 12/24/2023] [Accepted: 01/10/2024] [Indexed: 02/03/2024]
Abstract
In positron emission tomography (PET), attenuation and scatter corrections are necessary steps toward accurate quantitative reconstruction of the radiopharmaceutical distribution. Inspired by recent advances in deep learning, many algorithms based on convolutional neural networks have been proposed for automatic attenuation and scatter correction, enabling applications to CT-less or MR-less PET scanners to improve performance in the presence of CT-related artifacts. A known characteristic of PET imaging is to have varying tracer uptakes for various patients and/or anatomical regions. However, existing deep learning-based algorithms utilize a fixed model across different subjects and/or anatomical regions during inference, which could result in spurious outputs. In this work, we present a novel deep learning-based framework for the direct reconstruction of attenuation and scatter-corrected PET from non-attenuation-corrected images in the absence of structural information in the inference. To deal with inter-subject and intra-subject uptake variations in PET imaging, we propose a novel model to perform subject- and region-specific filtering through modulating the convolution kernels in accordance to the contextual coherency within the neighboring slices. This way, the context-aware convolution can guide the composition of intermediate features in favor of regressing input-conditioned and/or region-specific tracer uptakes. We also utilized a large cohort of 910 whole-body studies for training and evaluation purposes, which is more than one order of magnitude larger than previous works. In our experimental studies, qualitative assessments showed that our proposed CT-free method is capable of producing corrected PET images that accurately resemble ground truth images corrected with the aid of CT scans. For quantitative assessments, we evaluated our proposed method over 112 held-out subjects and achieved an absolute relative error of 14.30±3.88% and a relative error of -2.11%±2.73% in whole-body.
Collapse
Affiliation(s)
- Saeed Izadi
- Medical Image Analysis Lab, School of Computing Science, Simon Fraser University, Canada
| | - Isaac Shiri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211 Geneva 4, Geneva, Switzerland; Department of Cardiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.
| | - Carlos F Uribe
- Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, Canada; Department of Radiology, University of British Columbia, Vancouver, Canada; Molecular Imaging and Therapy, BC Cancer, Vancouver, BC, Canada
| | - Parham Geramifar
- Research Center for Nuclear Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211 Geneva 4, Geneva, Switzerland; Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, Netherlands; Department of Nuclear Medicine, University of Southern Denmark, Odense, Denmark; University Research and Innovation Center, Óbuda University, Budapest, Hungary
| | - Arman Rahmim
- Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, Canada; Department of Radiology, University of British Columbia, Vancouver, Canada; Department of Physics and Astronomy, University of British Columbia, Vancouver, Canada
| | - Ghassan Hamarneh
- Medical Image Analysis Lab, School of Computing Science, Simon Fraser University, Canada.
| |
Collapse
|
2
|
Lindemann ME, Gratz M, Grafe H, Jannusch K, Umutlu L, Quick HH. Systematic evaluation of human soft tissue attenuation correction in whole-body PET/MR: Implications from PET/CT for optimization of MR-based AC in patients with normal lung tissue. Med Phys 2024; 51:192-208. [PMID: 38060671 DOI: 10.1002/mp.16863] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 11/10/2023] [Accepted: 11/16/2023] [Indexed: 01/10/2024] Open
Abstract
BACKGROUND Attenuation correction (AC) is an important methodical step in positron emission tomography/magnetic resonance imaging (PET/MRI) to correct for attenuated and scattered PET photons. PURPOSE The overall quality of magnetic resonance (MR)-based AC in whole-body PET/MRI was evaluated in direct comparison to computed tomography (CT)-based AC serving as reference. The quantitative impact of isolated tissue classes in the MR-AC was systematically investigated to identify potential optimization needs and strategies. METHODS Data of n = 60 whole-body PET/CT patients with normal lung tissue and without metal implants/prostheses were used to generate six different AC-models based on the CT data for each patient, simulating variations of MR-AC. The original continuous CT-AC (CT-org) is referred to as reference. A pseudo MR-AC (CT-mrac), generated from CT data, with four tissue classes and a bone atlas represents the MR-AC. Relative difference in linear attenuation coefficients (LAC) and standardized uptake values were calculated. From the results two improvements regarding soft tissue AC and lung AC were proposed and evaluated. RESULTS The overall performance of MR-AC is in good agreement compared to CT-AC. Lungs, heart, and bone tissue were identified as the regions with most deviation to the CT-AC (myocardium -15%, bone tissue -14%, and lungs ±20%). Using single-valued LACs for AC in the lung only provides limited accuracy. For improved soft tissue AC, splitting the combined soft tissue class into muscles and organs each with adapted LAC could reduce the deviations to the CT-AC to < ±1%. For improved lung AC, applying a gradient LAC in the lungs could remarkably reduce over- or undercorrections in PET signal compared to CT-AC (±5%). CONCLUSIONS The AC is important to ensure best PET image quality and accurate PET quantification for diagnostics and radiotherapy planning. The optimized segment-based AC proposed in this study, which was evaluated on PET/CT data, inherently reduces quantification bias in normal lung tissue and soft tissue compared to the CT-AC reference.
Collapse
Affiliation(s)
- Maike E Lindemann
- High-Field and Hybrid MR Imaging, University Hospital Essen, University Duisburg-Essen, Essen, Germany
| | - Marcel Gratz
- High-Field and Hybrid MR Imaging, University Hospital Essen, University Duisburg-Essen, Essen, Germany
- Erwin L. Hahn Institute for Magnetic Resonance Imaging, University Duisburg-Essen, Essen, Germany
| | - Hong Grafe
- Department of Nuclear Medicine, University Hospital Essen, University Duisburg-Essen, Essen, Germany
| | - Kai Jannusch
- Department of Diagnostic and Interventional Radiology, University Hospital Duesseldorf, University Duesseldorf, Duesseldorf, Germany
| | - Lale Umutlu
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - Harald H Quick
- High-Field and Hybrid MR Imaging, University Hospital Essen, University Duisburg-Essen, Essen, Germany
- Erwin L. Hahn Institute for Magnetic Resonance Imaging, University Duisburg-Essen, Essen, Germany
| |
Collapse
|
3
|
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.
Collapse
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
| |
Collapse
|
4
|
Ahangari S, Beck Olin A, Kinggård Federspiel M, Jakoby B, Andersen TL, Hansen AE, Fischer BM, Littrup Andersen F. A deep learning-based whole-body solution for PET/MRI attenuation correction. EJNMMI Phys 2022; 9:55. [PMID: 35978211 PMCID: PMC9385907 DOI: 10.1186/s40658-022-00486-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Accepted: 08/08/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Deep convolutional neural networks have demonstrated robust and reliable PET attenuation correction (AC) as an alternative to conventional AC methods in integrated PET/MRI systems. However, its whole-body implementation is still challenging due to anatomical variations and the limited MRI field of view. The aim of this study is to investigate a deep learning (DL) method to generate voxel-based synthetic CT (sCT) from Dixon MRI and use it as a whole-body solution for PET AC in a PET/MRI system. MATERIALS AND METHODS Fifteen patients underwent PET/CT followed by PET/MRI with whole-body coverage from skull to feet. We performed MRI truncation correction and employed co-registered MRI and CT images for training and leave-one-out cross-validation. The network was pretrained with region-specific images. The accuracy of the AC maps and reconstructed PET images were assessed by performing a voxel-wise analysis and calculating the quantification error in SUV obtained using DL-based sCT (PETsCT) and a vendor-provided atlas-based method (PETAtlas), with the CT-based reconstruction (PETCT) serving as the reference. In addition, region-specific analysis was performed to compare the performances of the methods in brain, lung, liver, spine, pelvic bone, and aorta. RESULTS Our DL-based method resulted in better estimates of AC maps with a mean absolute error of 62 HU, compared to 109 HU for the atlas-based method. We found an excellent voxel-by-voxel correlation between PETCT and PETsCT (R2 = 0.98). The absolute percentage difference in PET quantification for the entire image was 6.1% for PETsCT and 11.2% for PETAtlas. The regional analysis showed that the average errors and the variability for PETsCT were lower than PETAtlas in all regions. The largest errors were observed in the lung, while the smallest biases were observed in the brain and liver. CONCLUSIONS Experimental results demonstrated that a DL approach for whole-body PET AC in PET/MRI is feasible and allows for more accurate results compared with conventional methods. Further evaluation using a larger training cohort is required for more accurate and robust performance.
Collapse
Affiliation(s)
- Sahar Ahangari
- Department of Clinical Physiology, Nuclear Medicine, and PET, Rigshospitalet, Copenhagen, Denmark.
| | - Anders Beck Olin
- Department of Clinical Physiology, Nuclear Medicine, and PET, Rigshospitalet, Copenhagen, Denmark
| | | | | | - Thomas Lund Andersen
- Department of Clinical Physiology, Nuclear Medicine, and PET, Rigshospitalet, Copenhagen, Denmark
| | - Adam Espe Hansen
- Department of Clinical Physiology, Nuclear Medicine, and PET, Rigshospitalet, Copenhagen, Denmark.,Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark.,Department of Diagnostic Radiology, Rigshospitalet, Copenhagen, Denmark
| | - Barbara Malene Fischer
- Department of Clinical Physiology, Nuclear Medicine, and PET, Rigshospitalet, Copenhagen, Denmark.,Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Flemming Littrup Andersen
- Department of Clinical Physiology, Nuclear Medicine, and PET, Rigshospitalet, Copenhagen, Denmark.,Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| |
Collapse
|
5
|
Li A, Xie Q, Huang J, Xiao P. Evaluation of applying space-variant resolution modeling to attenuation correction in PET. Biomed Phys Eng Express 2022; 8:045009. [PMID: 35623332 DOI: 10.1088/2057-1976/ac741c] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Attenuation correction aims to recover the underestimated tracer uptake and improve the image contrast recovery in positron emission tomography (PET). However, traditional ray-tracing-based projection of attenuation maps is inaccurate as some physical effects are not considered, such as finite crystal size, inter-crystal penetration and inter-crystal scatter. In this study, we evaluated the effects of applying resolution modeling (RM) to attenuation correction by implementing space-variant RM to complement physical effects which are usually omitted in the traditional projection model. We verified this method on a brain PET scanner developed by our group, in both Monte Carlo simulation and real-world data, in comparison with space-invariant Gaussian RM, average-depth-of-interaction, and multi-ray tracing methods. The results indicate that the space-variant RM is superior in terms of artifacts reduction and contrast recovery.
Collapse
Affiliation(s)
- Ang Li
- College of life science and technology, Huazhong University of Science and Technology, 1037 Luoyu Road, Hongshan District, Wuhan City, Hubei Province, China, Wuhan, 430074, CHINA
| | - Qingguo Xie
- Biomedical Engineering Department, Huazhong University of Science and Technology, Wuhan, Hubei 430074, Wuhan, Hubei, 430074, CHINA
| | - Jing Huang
- Huazhong University of Science and Technology, 1037 Luoyu Road, Hongshan District, Wuhan City, Hubei Province, China, Wuhan, 430074, CHINA
| | - Peng Xiao
- Biomedical Engineering Department, Huazhong University of Science and Technology, Wuhan, Hubei 430074, Wuhan, Hubei, 430074, CHINA
| |
Collapse
|
6
|
Rao F, Wu Z, Han L, Yang B, Han W, Zhu W. Delayed PET imaging using image synthesis network and nonrigid registration without additional CT scan. Med Phys 2022; 49:3233-3245. [PMID: 35218053 DOI: 10.1002/mp.15574] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Revised: 02/02/2022] [Accepted: 02/15/2022] [Indexed: 11/10/2022] Open
Abstract
PURPOSE Attenuation correction is critical for positron emission tomography (PET) image reconstruction. The standard protocol for obtaining attenuation information in a clinical PET scanner is via the coregistered computed tomography (CT) images. Therefore for delayed PET imaging, the CT scan is repeated twice, which increases the radiation dose for the patient. In this paper, we propose a zero-extra-dose delayed PET imaging method which requires no additional CT scans. METHODS A deep learning based synthesis network is designed to convert the PET data into a pseudo CT image for the delayed scan. Then, nonrigid registration is performed between this pseudo CT image and the CT image of the first scan, warping the CT image of the first scan to an estimated CT images for the delayed scan. Finally, the PET image attenuation correction in the delayed scan is obtained from this estimated CT image. Experiments with clinical datasets are implemented to assess the effectiveness of the proposed method with the well-recognized GAN method. The average peak signal-to-noise ratio (PSNR) and the mean absolute percent error (MAPE) are used in comparison. We also use scoring from three experienced radiologists as subjective measurement means, based on the diagnostic consistency of the PET images reconstructed from GAN and the proposed method with respect to the ground truth images. RESULTS The experiments show that the average PSNR is 47.04 dB (the proposed method) v.s. 44.41 dB (the traditional GAN method) for the reconstructed delayed PET images in our evaluation dataset. The average MAPEs are 1.59% for the proposed method and 3.32% for the traditional GAN method across five organ Regions of Interest (ROIs). The scores for the GAN and the proposed method rated by three experienced radiologists are 8.08±0.60 and 9.02±0.52, indicating that the proposed method yields more consistent PET images with the ground truth. CONCLUSIONS This work proposes a novel method for CT-less delayed PET imaging based on image synthesis network and nonrigid image registration. The PET image reconstructed using the proposed method yields delayed PET images with high image quality without artifacts, and is quantitatively more accurate compared with the traditional GAN method. This article is protected by copyright. All rights reserved.
Collapse
Affiliation(s)
- Fan Rao
- Research Center for Healthcare Data Science, Zhejiang Lab, China
| | - Zhuoxuan Wu
- Department of Medical Oncology, Sir Run Run Shaw Hospital, College of Medicine, Zhejiang University, China
| | - Lu Han
- Research Center for Healthcare Data Science, Zhejiang Lab, China
| | - Bao Yang
- Research Center for Healthcare Data Science, Zhejiang Lab, China
| | - Weidong Han
- Department of Medical Oncology, Sir Run Run Shaw Hospital, College of Medicine, Zhejiang University, China
| | - Wentao Zhu
- Research Center for Healthcare Data Science, Zhejiang Lab, China
| |
Collapse
|
7
|
Brusaferri L, Emond EC, Bousse A, Twyman R, Whitehead AC, Atkinson D, Ourselin S, Hutton BF, Arridge S, Thielemans K. Detection Efficiency Modeling and Joint Activity and Attenuation Reconstruction in Non-TOF 3-D PET From Multiple-Energy Window Data. IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES 2022. [DOI: 10.1109/trpms.2021.3064239] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
|
8
|
Alavi A, Werner TJ, Raynor W, Høilund-Carlsen PF, Revheim ME. Critical review of PET imaging for detection and characterization of the atherosclerotic plaques with emphasis on limitations of FDG-PET compared to NaF-PET in this setting. AMERICAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING 2021; 11:337-351. [PMID: 34754605 PMCID: PMC8569336] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Accepted: 08/12/2021] [Indexed: 06/13/2023]
Abstract
Applications of various positron emission tomography (PET) tracers for assessing atherosclerosis have been evolving over the years. 18F-fluorodeoxyglucose (FDG)-PET was introduced in 2001 as a probe for this purpose. During the past decade, numerous papers have described a major role for sodium 18F-fluoride (NaF) as another tracer for assessing this vascular disease. We have reviewed the existing data about the merits of both techniques for assessing atherosclerosis. We have to emphasize that our team has been actively involved in conducting research with both tracers over many years. In this review, we have relied upon the data from the CAMONA study which has become a gold standard for defining the role of PET imaging in atherosclerosis. This study was one of the largest of any in recent years and has allowed comprehensive comparison between these two tracers in detecting and quantifying atherosclerosis. Based on what we have learned from this major undertaking, we believe the role of FDG-PET will be limited in assessing atherosclerosis in clinical work-up. This is relevant to both major and coronary arteries. In contrast to NaF-PET, the role of FDG-PET in assessing coronary artery atherosclerosis is almost non-existent. Based on the existing data in this domain, NaF-PET is an ideal imaging modality for both research and clinical assessment of atherosclerosis. The aim of this review is to describe the pros and cons of both approaches based on the existing data in the literature.
Collapse
Affiliation(s)
- Abass Alavi
- Department of Radiology, Hospital of The University of PennsylvaniaPhiladelphia 19104, PA, USA
| | - Thomas J Werner
- Department of Radiology, Hospital of The University of PennsylvaniaPhiladelphia 19104, PA, USA
| | - William Raynor
- Department of Radiology, Hospital of The University of PennsylvaniaPhiladelphia 19104, PA, USA
| | - Poul Flemming Høilund-Carlsen
- Department of Nuclear Medicine, Odense University HospitalOdense 5000, Denmark
- Department of Clinical Research, University of Southern DenmarkOdense, Denmark
| | - Mona-Elisabeth Revheim
- Division of Radiology and Nuclear Medicine, Oslo University HospitalOslo 0424, Norway
- Institute of Clinical Medicine, Faculty of Medicine, University of OsloOslo 0424, Norway
| |
Collapse
|
9
|
Lennie E, Tsoumpas C, Sourbron S. Multimodal phantoms for clinical PET/MRI. EJNMMI Phys 2021; 8:62. [PMID: 34436671 PMCID: PMC8390737 DOI: 10.1186/s40658-021-00408-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2021] [Accepted: 08/10/2021] [Indexed: 12/02/2022] Open
Abstract
Phantoms are commonly used throughout medical imaging and medical physics for a multitude of applications, the designs of which vary between modalities and clinical or research requirements. Within positron emission tomography (PET) and nuclear medicine, phantoms have a well-established role in the validation of imaging protocols so as to reduce the administration of radioisotope to volunteers. Similarly, phantoms are used within magnetic resonance imaging (MRI) to perform quality assurance on clinical scanners, and gel-based phantoms have a longstanding use within the MRI research community as tissue equivalent phantoms. In recent years, combined PET/MRI scanners for simultaneous acquisition have entered both research and clinical use. This review explores the designs and applications of phantom work within the field of simultaneous acquisition PET/MRI as published over the period of a decade. Common themes in the design, manufacture and materials used within phantoms are identified and the solutions they provided to research in PET/MRI are summarised. Finally, the challenges remaining in creating multimodal phantoms for use with simultaneous acquisition PET/MRI are discussed. No phantoms currently exist commercially that have been designed and optimised for simultaneous PET/MRI acquisition. Subsequently, commercially available PET and nuclear medicine phantoms are often utilised, with CT-based attenuation maps substituted for MR-based attenuation maps due to the lack of MR visibility in phantom housing. Tissue equivalent and anthropomorphic phantoms are often developed by research groups in-house and provide customisable alternatives to overcome barriers such as MR-based attenuation correction, or to address specific areas of study such as motion correction. Further work to characterise materials and manufacture methods used in phantom design would facilitate the ability to reproduce phantoms across sites.
Collapse
Affiliation(s)
- Eve Lennie
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
| | - Charalampos Tsoumpas
- Biomedical Imaging Science Department, University of Leeds, Leeds, UK
- Department of Nuclear Medicine and Molecular Imaging, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Steven Sourbron
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
| |
Collapse
|
10
|
Polycarpou I, Soultanidis G, Tsoumpas C. Synergistic motion compensation strategies for positron emission tomography when acquired simultaneously with magnetic resonance imaging. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2021; 379:20200207. [PMID: 34218675 PMCID: PMC8255946 DOI: 10.1098/rsta.2020.0207] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 04/15/2021] [Indexed: 05/04/2023]
Abstract
Subject motion in positron emission tomography (PET) is a key factor that degrades image resolution and quality, limiting its potential capabilities. Correcting for it is complicated due to the lack of sufficient measured PET data from each position. This poses a significant barrier in calculating the amount of motion occurring during a scan. Motion correction can be implemented at different stages of data processing either during or after image reconstruction, and once applied accurately can substantially improve image quality and information accuracy. With the development of integrated PET-MRI (magnetic resonance imaging) scanners, internal organ motion can be measured concurrently with both PET and MRI. In this review paper, we explore the synergistic use of PET and MRI data to correct for any motion that affects the PET images. Different types of motion that can occur during PET-MRI acquisitions are presented and the associated motion detection, estimation and correction methods are reviewed. Finally, some highlights from recent literature in selected human and animal imaging applications are presented and the importance of motion correction for accurate kinetic modelling in dynamic PET-MRI is emphasized. This article is part of the theme issue 'Synergistic tomographic image reconstruction: part 2'.
Collapse
Affiliation(s)
- Irene Polycarpou
- Department of Health Sciences, European University of Cyprus, Nicosia, Cyprus
| | - Georgios Soultanidis
- Biomedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Charalampos Tsoumpas
- Biomedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Biomedical Imaging Science Department, University of Leeds, West Yorkshire, UK
- Invicro, London, UK
| |
Collapse
|
11
|
Sager G, Akgun E, Abuqbeitah M, Uslu L, Asa S, Akgun MY, Beytur F, Baydili KN, Sager S. Comparison of brain F-18 FDG PET/MRI with PET/CT imaging in pediatric patients. Clin Neurol Neurosurg 2021; 206:106669. [PMID: 33984753 DOI: 10.1016/j.clineuro.2021.106669] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Revised: 03/05/2021] [Accepted: 04/17/2021] [Indexed: 11/19/2022]
Abstract
BACKGROUND Standardized uptake values (SUVs) are important indexes for evaluating the accuracy of disease diagnoses achieved via fluoro-18 deoxyglucose (F-18 FDG) positron emission tomography/computed tomography (PET/CT) and positron emission tomography/magnetic resonance imaging (PET/MRI). The purpose of this study is to describe normal cerebral FDG uptake in the pediatric population and compare SUVmax/mean results for brain images obtained from PET/CT and PET/MRI in neurologically healthy pediatric examinees. METHODS This study included 20 patients who were < 18 years of age and were without intracranial malignancy and/or brain disorders. Patients underwent either PET/CT imaging (n = 10) or PET/MRI imaging (n = 10) after 70-80 min of F-18 FDG injection. The SUVmax and SUVmean for various brain regions were calculated and compared between sides and imaging modalities using with appropriate statistical tests. RESULTS The median SUVmax/SUVmean values of the right-sided frontal, parietal, temporal, and occipital lobes were 8.63/ 6.18, 8.85 / 6.97, 6.88 / 4.99, and 11.06 / 7.02 in PET/CT, respectively, and 11.45 / 8.59, 10.16 / 8.47, 8.82 / 6.6, and 11.71 / 8.25 in PET/MRI, respectively. The median SUVmax/SUVmean values of the left-sided frontal, parietal, temporal, and occipital lobes were 9.05 / 6.86, 8.03 / 6.62, 6.49 / 4.77, and 10.6 / 7.73 in PET/CT, respectively, and 10.7 / 8.16, 11.06 / 7.88, 8.13 / 6.09, and 10.96 / 9.22 in PET/MRI, respectively. CONCLUSIONS These results showed that there was no statistically significant difference in SUVs values between the two brain imaging modalities except from SUVmax value of left-sided parietal lobe and no asymmetric radiopharmaceutical uptake between the left and right brain regions or cerebellums in each modality, suggested that in brain imaging, PET/MRI can be used reliably instead of PET/CT.
Collapse
Affiliation(s)
- Gunes Sager
- Kartal Lutfi Kirdar Training and Research Hospital, Department of Pediatric Neurology, Istanbul, Turkey
| | - Elife Akgun
- Kirikkale Yuksek Ihtisas Hospital, Department of Nuclear Medicine, Kirikkale, Turkey.
| | - Muhammed Abuqbeitah
- Istanbul University-Cerrahpasa, School of Medicine, Department of Nuclear Medicine, Istanbul, Turkey
| | - Lebriz Uslu
- Istanbul University-Cerrahpasa, School of Medicine, Department of Nuclear Medicine, Istanbul, Turkey
| | - Sertac Asa
- Istanbul University-Cerrahpasa, School of Medicine, Department of Nuclear Medicine, Istanbul, Turkey
| | - Mehmet Yigit Akgun
- Kirikkale Yuksek Ihtisas Hospital, Department of Neurosurgery, Kirikkale, Turkey
| | - Fatih Beytur
- Istanbul University-Cerrahpasa, School of Medicine, Department of Nuclear Medicine, Istanbul, Turkey
| | | | - Sait Sager
- Istanbul University-Cerrahpasa, School of Medicine, Department of Nuclear Medicine, Istanbul, Turkey
| |
Collapse
|
12
|
Abstract
Attenuation correction has been one of the main methodological challenges in the integrated positron emission tomography and magnetic resonance imaging (PET/MRI) field. As standard transmission or computed tomography approaches are not available in integrated PET/MRI scanners, MR-based attenuation correction approaches had to be developed. Aspects that have to be considered for implementing accurate methods include the need to account for attenuation in bone tissue, normal and pathological lung and the MR hardware present in the PET field-of-view, to reduce the impact of subject motion, to minimize truncation and susceptibility artifacts, and to address issues related to the data acquisition and processing both on the PET and MRI sides. The standard MR-based attenuation correction techniques implemented by the PET/MRI equipment manufacturers and their impact on clinical and research PET data interpretation and quantification are first discussed. Next, the more advanced methods, including the latest generation deep learning-based approaches that have been proposed for further minimizing the attenuation correction related bias are described. Finally, a future perspective focused on the needed developments in the field is given.
Collapse
Affiliation(s)
- Ciprian Catana
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, United States of America
| |
Collapse
|
13
|
Zhang YD, Dong Z, Wang SH, Yu X, Yao X, Zhou Q, Hu H, Li M, Jiménez-Mesa C, Ramirez J, Martinez FJ, Gorriz JM. Advances in multimodal data fusion in neuroimaging: Overview, challenges, and novel orientation. AN INTERNATIONAL JOURNAL ON INFORMATION FUSION 2020; 64:149-187. [PMID: 32834795 PMCID: PMC7366126 DOI: 10.1016/j.inffus.2020.07.006] [Citation(s) in RCA: 115] [Impact Index Per Article: 28.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Revised: 07/06/2020] [Accepted: 07/14/2020] [Indexed: 05/13/2023]
Abstract
Multimodal fusion in neuroimaging combines data from multiple imaging modalities to overcome the fundamental limitations of individual modalities. Neuroimaging fusion can achieve higher temporal and spatial resolution, enhance contrast, correct imaging distortions, and bridge physiological and cognitive information. In this study, we analyzed over 450 references from PubMed, Google Scholar, IEEE, ScienceDirect, Web of Science, and various sources published from 1978 to 2020. We provide a review that encompasses (1) an overview of current challenges in multimodal fusion (2) the current medical applications of fusion for specific neurological diseases, (3) strengths and limitations of available imaging modalities, (4) fundamental fusion rules, (5) fusion quality assessment methods, and (6) the applications of fusion for atlas-based segmentation and quantification. Overall, multimodal fusion shows significant benefits in clinical diagnosis and neuroscience research. Widespread education and further research amongst engineers, researchers and clinicians will benefit the field of multimodal neuroimaging.
Collapse
Affiliation(s)
- Yu-Dong Zhang
- School of Informatics, University of Leicester, Leicester, LE1 7RH, Leicestershire, UK
- Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Zhengchao Dong
- Department of Psychiatry, Columbia University, USA
- New York State Psychiatric Institute, New York, NY 10032, USA
| | - Shui-Hua Wang
- Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
- School of Architecture Building and Civil engineering, Loughborough University, Loughborough, LE11 3TU, UK
- School of Mathematics and Actuarial Science, University of Leicester, LE1 7RH, UK
| | - Xiang Yu
- School of Informatics, University of Leicester, Leicester, LE1 7RH, Leicestershire, UK
| | - Xujing Yao
- School of Informatics, University of Leicester, Leicester, LE1 7RH, Leicestershire, UK
| | - Qinghua Zhou
- School of Informatics, University of Leicester, Leicester, LE1 7RH, Leicestershire, UK
| | - Hua Hu
- Department of Psychiatry, Columbia University, USA
- Department of Neurology, The Second Affiliated Hospital of Soochow University, China
| | - Min Li
- Department of Psychiatry, Columbia University, USA
- School of Internet of Things, Hohai University, Changzhou, China
| | - Carmen Jiménez-Mesa
- Department of Signal Theory, Networking and Communications, University of Granada, Granada, Spain
| | - Javier Ramirez
- Department of Signal Theory, Networking and Communications, University of Granada, Granada, Spain
| | - Francisco J Martinez
- Department of Signal Theory, Networking and Communications, University of Granada, Granada, Spain
| | - Juan Manuel Gorriz
- Department of Signal Theory, Networking and Communications, University of Granada, Granada, Spain
- Department of Psychiatry, University of Cambridge, Cambridge CB21TN, UK
| |
Collapse
|
14
|
Seifert R, Weber M, Kocakavuk E, Rischpler C, Kersting D. Artificial Intelligence and Machine Learning in Nuclear Medicine: Future Perspectives. Semin Nucl Med 2020; 51:170-177. [PMID: 33509373 DOI: 10.1053/j.semnuclmed.2020.08.003] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Artificial intelligence and machine learning based approaches are increasingly finding their way into various areas of nuclear medicine imaging. With the technical development of new methods and the expansion to new fields of application, this trend is likely to become even more pronounced in future. Possible means of application range from automated image reading and classification to correlation with clinical outcomes and to technological applications in image processing and reconstruction. In the context of tumor imaging, that is, predominantly FDG or PSMA PET imaging but also bone scintigraphy, artificial intelligence approaches can be used to quantify the whole-body tumor volume, for the segmentation and classification of pathological foci or to facilitate the diagnosis of micro-metastases. More advanced applications aim at the correlation of image features that are derived by artificial intelligence with clinical endpoints, for example, whole-body tumor volume with overall survival. In nuclear medicine imaging of benign diseases, artificial intelligence methods are predominantly used for automated and/or facilitated image classification and clinical decision making. Automated feature selection, segmentation and classification of myocardial perfusion scintigraphy can help in identifying patients that would benefit from intervention and to forecast clinical prognosis. Automated reporting of neurodegenerative diseases such as Alzheimer's disease might be extended to early diagnosis-being of special interest, if targeted treatment options might become available. Technological approaches include artificial intelligence-based attenuation correction of PET images, image reconstruction or anatomical landmarking. Attenuation correction is of special interest for avoiding the need of a coregistered CT scan, in the process of image reconstruction artefacts might be reduced, or ultra low-dose PET images might be denoised. The development of accurate ultra low-dose PET imaging might broaden the method's applicability, for example, toward oncologic PET screening. Most artificial intelligence approaches in nuclear medicine imaging are still in early stages of development, further improvements are necessary for broad clinical applications. In this review, we describe the current trends in the context fields of body oncology, cardiac imaging, and neuroimaging while an additional section puts emphasis on technological trends. Our aim is not only to describe currently available methods, but also to place a special focus on the description of possible future developments.
Collapse
Affiliation(s)
- Robert Seifert
- Department of Nuclear Medicine, University Hospital Münster, Münster, Germany; Department of Nuclear Medicine, University Hospital Essen, Essen, Germany; West German Cancer Center, Germany; German Cancer Consortium (DKTK), Germany.
| | - Manuel Weber
- Department of Nuclear Medicine, University Hospital Essen, Essen, Germany; West German Cancer Center, Germany; German Cancer Consortium (DKTK), Germany
| | - Emre Kocakavuk
- Department of Nuclear Medicine, University Hospital Essen, Essen, Germany; West German Cancer Center, Germany; German Cancer Consortium (DKTK), Germany
| | - Christoph Rischpler
- Department of Nuclear Medicine, University Hospital Essen, Essen, Germany; West German Cancer Center, Germany; German Cancer Consortium (DKTK), Germany
| | - David Kersting
- Department of Nuclear Medicine, University Hospital Essen, Essen, Germany; West German Cancer Center, Germany; German Cancer Consortium (DKTK), Germany
| |
Collapse
|
15
|
Nikpanah M, Katal S, Christensen TQ, Werner TJ, Hess S, Malayeri AA, Gholamrezanezhad A, Alavi A, Saboury B. Potential Applications of PET Scans, CT Scans, and MR Imaging in Inflammatory Diseases: Part II: Cardiopulmonary and Vascular Inflammation. PET Clin 2020; 15:559-576. [PMID: 32792228 DOI: 10.1016/j.cpet.2020.06.010] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Detecting inflammation is among the most important aims of medical imaging. Inflammatory process involves immune system activity and local tissue response. The role of PET with fludeoxyglucose F 18 has been expanded. Systemic vasculitides and cardiopulmonary inflammatory disorders constitute a wide range of diseases with multisystemic manifestations. PET with fludeoxyglucose F 18 is useful in their diagnosis, assessment, and follow-up. This article provides an overview of the current status and potentials of hybrid molecular imaging in evaluating cardiopulmonary and vascular inflammatory diseases focusing on the potential for PET with fludeoxyglucose F 18/MR imaging and PET/CT scans.
Collapse
Affiliation(s)
- Moozhan Nikpanah
- Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, 9000 Rockville Pike, Bethesda, MD 20892, USA
| | - Sanaz Katal
- Department of Nuclear Medicine/PET-CT, Kowsar Hospital, Shiraz, Iran
| | - Thomas Q Christensen
- Department of Clinical Engineering, Region of Southern Denmark, Esbjerg, Denmark 5000
| | - Thomas J Werner
- Department of Radiology, Hospital of the University of Pennsylvania, 3400 Spruce St, Philadelphia, PA 19104, USA
| | - Søren Hess
- Department of Radiology and Nuclear Medicine, Hospital of South West Jutland, University Hospital of Southern Denmark, Esbjerg, Denmark 6700; Department of Regional Health Research, Faculty of Health Sciences, University of Southern Denmark, Odense, Denmark
| | - Ashkan A Malayeri
- Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, 9000 Rockville Pike, Bethesda, MD 20892, USA
| | - Ali Gholamrezanezhad
- Department of Radiology, Keck School of Medicine, University of Southern California (USC), Health Sciences Campus, 1500 San Pablo Street, Los Angeles, California 90033, USA
| | - Abass Alavi
- Department of Radiology, Hospital of the University of Pennsylvania, 3400 Spruce St, Philadelphia, PA 19104, USA
| | - Babak Saboury
- Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, 9000 Rockville Pike, Bethesda, MD 20892, USA; Department of Radiology, Hospital of the University of Pennsylvania, 3400 Spruce St, Philadelphia, PA 19104, USA.
| |
Collapse
|
16
|
Emond EC, Bousse A, Machado M, Porter J, Groves AM, Hutton BF, Thielemans K. Effect of attenuation mismatches in time of flight PET reconstruction. Phys Med Biol 2020; 65:085009. [PMID: 32101801 DOI: 10.1088/1361-6560/ab7a6f] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
While the pursuit of better time resolution in positron emission tomography (PET) is rapidly evolving, little work has been performed on time of flight (TOF) image quality at high time resolution in the presence of modelling inconsistencies. This works focuses on the effect of using the wrong attenuation map in the system model, causing perturbations in the reconstructed radioactivity image. Previous work has usually considered the effects to be local to the area where there is attenuation mismatch, and has shown that the quantification errors in this area tend to reduce with improved time resolution. This publication shows however that errors in the PET image at a distance from the mismatch increase with time resolution. The errors depend on the reconstruction algorithm used. We quantify the errors in the hypothetical case of perfect time resolution for maximum likelihood reconstructions. In addition, we perform reconstructions on simulated and patient data. In particular, for respiratory-gated reconstructions from a wrong attenuation map, increased errors are observed with improved time resolutions in areas close to the lungs (e.g. from 13.3% in non-TOF to up to 20.9% at 200 ps in the left ventricle).
Collapse
Affiliation(s)
- Elise C Emond
- Institute of Nuclear Medicine, University College London, London NW1 2BU, United Kingdom
| | | | | | | | | | | | | |
Collapse
|