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Overbeck N, Ahangari S, Conti M, Panin V, Azam A, Kurbegovic S, Kjær A, Højgaard L, Korsholm K, Fischer BM, Andersen FL, Andersen TL. Improved Positron Emission Tomography Quantification: Evaluation of a Maximum-Likelihood Scatter Scaling Algorithm. Diagnostics (Basel) 2024; 14:1075. [PMID: 38893602 PMCID: PMC11172368 DOI: 10.3390/diagnostics14111075] [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: 04/23/2024] [Revised: 05/17/2024] [Accepted: 05/20/2024] [Indexed: 06/21/2024] Open
Abstract
Incorrect scatter scaling of positron emission tomography (PET) images can lead to halo artifacts, quantitative bias, or reconstruction failure. Tail-fitted scatter scaling (TFSS) possesses performance limitations in multiple cases. This study aims to investigate a novel method for scatter scaling: maximum-likelihood scatter scaling (MLSS) in scenarios where TFSS tends to induce artifacts or are observed to cause reconstruction abortion. [68Ga]Ga-RGD PET scans of nine patients were included in cohort 1 in the scope of investigating the reduction of halo artifacts relative to the scatter estimation method. PET scans of 30 patients administrated with [68Ga]Ga-uPAR were included in cohort 2, used for an evaluation of the robustness of MLSS in cases where TFSS-integrated reconstructions are observed to fail. A visual inspection of MLSS-corrected images scored higher than TFSS-corrected reconstructions of cohort 1. The quantitative investigation near the bladder showed a relative difference in tracer uptake of up to 94.7%. A reconstruction of scans included in cohort 2 resulted in failure in 23 cases when TFSS was used. The lesion uptake values of cohort 2 showed no significant difference. MLSS is suggested as an alternative scatter-scaling method relative to TFSS with the aim of reducing halo artifacts and a robust reconstruction process.
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Affiliation(s)
- Nanna Overbeck
- Department of Clinical Physiology and Nuclear Medicine, Copenhagen University Hospital—Rigshospitalet, 2100 Copenhagen, Denmark; (N.O.); (A.K.); (L.H.); (K.K.); (B.M.F.); (F.L.A.)
| | - Sahar Ahangari
- Department of Clinical Physiology and Nuclear Medicine, Copenhagen University Hospital—Rigshospitalet, 2100 Copenhagen, Denmark; (N.O.); (A.K.); (L.H.); (K.K.); (B.M.F.); (F.L.A.)
| | - Maurizio Conti
- Siemens Medical Solutions Inc., Knoxville, TN 37932, USA; (M.C.); (V.P.)
| | - Vladimir Panin
- Siemens Medical Solutions Inc., Knoxville, TN 37932, USA; (M.C.); (V.P.)
| | - Aleena Azam
- Cluster for Molecular Imaging, Copenhagen University Hospital—Rigshospitalet, 2100 Copenhagen, Denmark; (A.A.); (S.K.)
- Department of Biomedical Sciences, University of Copenhagen, 2100 Copenhagen, Denmark
- Department of Neurosurgery, Neuroscience Center, Copenhagen University Hospital—Rigshospitalet, 2100 Copenhagen, Denmark
| | - Sorel Kurbegovic
- Cluster for Molecular Imaging, Copenhagen University Hospital—Rigshospitalet, 2100 Copenhagen, Denmark; (A.A.); (S.K.)
- Department of Biomedical Sciences, University of Copenhagen, 2100 Copenhagen, Denmark
| | - Andreas Kjær
- Department of Clinical Physiology and Nuclear Medicine, Copenhagen University Hospital—Rigshospitalet, 2100 Copenhagen, Denmark; (N.O.); (A.K.); (L.H.); (K.K.); (B.M.F.); (F.L.A.)
- Cluster for Molecular Imaging, Copenhagen University Hospital—Rigshospitalet, 2100 Copenhagen, Denmark; (A.A.); (S.K.)
- Department of Biomedical Sciences, University of Copenhagen, 2100 Copenhagen, Denmark
| | - Liselotte Højgaard
- Department of Clinical Physiology and Nuclear Medicine, Copenhagen University Hospital—Rigshospitalet, 2100 Copenhagen, Denmark; (N.O.); (A.K.); (L.H.); (K.K.); (B.M.F.); (F.L.A.)
| | - Kirsten Korsholm
- Department of Clinical Physiology and Nuclear Medicine, Copenhagen University Hospital—Rigshospitalet, 2100 Copenhagen, Denmark; (N.O.); (A.K.); (L.H.); (K.K.); (B.M.F.); (F.L.A.)
| | - Barbara Malene Fischer
- Department of Clinical Physiology and Nuclear Medicine, Copenhagen University Hospital—Rigshospitalet, 2100 Copenhagen, Denmark; (N.O.); (A.K.); (L.H.); (K.K.); (B.M.F.); (F.L.A.)
| | - Flemming Littrup Andersen
- Department of Clinical Physiology and Nuclear Medicine, Copenhagen University Hospital—Rigshospitalet, 2100 Copenhagen, Denmark; (N.O.); (A.K.); (L.H.); (K.K.); (B.M.F.); (F.L.A.)
| | - Thomas Lund Andersen
- Department of Clinical Physiology and Nuclear Medicine, Copenhagen University Hospital—Rigshospitalet, 2100 Copenhagen, Denmark; (N.O.); (A.K.); (L.H.); (K.K.); (B.M.F.); (F.L.A.)
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Sherwani MK, Gopalakrishnan S. A systematic literature review: deep learning techniques for synthetic medical image generation and their applications in radiotherapy. FRONTIERS IN RADIOLOGY 2024; 4:1385742. [PMID: 38601888 PMCID: PMC11004271 DOI: 10.3389/fradi.2024.1385742] [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: 02/13/2024] [Accepted: 03/11/2024] [Indexed: 04/12/2024]
Abstract
The aim of this systematic review is to determine whether Deep Learning (DL) algorithms can provide a clinically feasible alternative to classic algorithms for synthetic Computer Tomography (sCT). The following categories are presented in this study: ∙ MR-based treatment planning and synthetic CT generation techniques. ∙ Generation of synthetic CT images based on Cone Beam CT images. ∙ Low-dose CT to High-dose CT generation. ∙ Attenuation correction for PET images. To perform appropriate database searches, we reviewed journal articles published between January 2018 and June 2023. Current methodology, study strategies, and results with relevant clinical applications were analyzed as we outlined the state-of-the-art of deep learning based approaches to inter-modality and intra-modality image synthesis. This was accomplished by contrasting the provided methodologies with traditional research approaches. The key contributions of each category were highlighted, specific challenges were identified, and accomplishments were summarized. As a final step, the statistics of all the cited works from various aspects were analyzed, which revealed that DL-based sCTs have achieved considerable popularity, while also showing the potential of this technology. In order to assess the clinical readiness of the presented methods, we examined the current status of DL-based sCT generation.
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Affiliation(s)
- Moiz Khan Sherwani
- Section for Evolutionary Hologenomics, Globe Institute, University of Copenhagen, Copenhagen, Denmark
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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.
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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.
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Montgomery ME, Andersen FL, d’Este SH, Overbeck N, Cramon PK, Law I, Fischer BM, Ladefoged CN. Attenuation Correction of Long Axial Field-of-View Positron Emission Tomography Using Synthetic Computed Tomography Derived from the Emission Data: Application to Low-Count Studies and Multiple Tracers. Diagnostics (Basel) 2023; 13:3661. [PMID: 38132245 PMCID: PMC10742516 DOI: 10.3390/diagnostics13243661] [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: 10/28/2023] [Revised: 12/10/2023] [Accepted: 12/11/2023] [Indexed: 12/23/2023] Open
Abstract
Recent advancements in PET/CT, including the emergence of long axial field-of-view (LAFOV) PET/CT scanners, have increased PET sensitivity substantially. Consequently, there has been a significant reduction in the required tracer activity, shifting the primary source of patient radiation dose exposure to the attenuation correction (AC) CT scan during PET imaging. This study proposes a parameter-transferred conditional generative adversarial network (PT-cGAN) architecture to generate synthetic CT (sCT) images from non-attenuation corrected (NAC) PET images, with separate networks for [18F]FDG and [15O]H2O tracers. The study includes a total of 1018 subjects (n = 972 [18F]FDG, n = 46 [15O]H2O). Testing was performed on the LAFOV scanner for both datasets. Qualitative analysis found no differences in image quality in 30 out of 36 cases in FDG patients, with minor insignificant differences in the remaining 6 cases. Reduced artifacts due to motion between NAC PET and CT were found. For the selected organs, a mean average error of 0.45% was found for the FDG cohort, and that of 3.12% was found for the H2O cohort. Simulated low-count images were included in testing, which demonstrated good performance down to 45 s scans. These findings show that the AC of total-body PET is feasible across tracers and in low-count studies and might reduce the artifacts due to motion and metal implants.
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Affiliation(s)
- Maria Elkjær Montgomery
- Department of Clinical Physiology and Nuclear Medicine, Rigshospitalet, Copenhagen University Hospital, 2100 København, Denmark; (M.E.M.); (N.O.); (P.K.C.); (I.L.); (B.M.F.); (C.N.L.)
| | - Flemming Littrup Andersen
- Department of Clinical Physiology and Nuclear Medicine, Rigshospitalet, Copenhagen University Hospital, 2100 København, Denmark; (M.E.M.); (N.O.); (P.K.C.); (I.L.); (B.M.F.); (C.N.L.)
- Department of Clinical Medicine, Copenhagen University, 2200 København, Denmark
| | - Sabrina Honoré d’Este
- Department of Clinical Physiology and Nuclear Medicine, Rigshospitalet, Copenhagen University Hospital, 2100 København, Denmark; (M.E.M.); (N.O.); (P.K.C.); (I.L.); (B.M.F.); (C.N.L.)
| | - Nanna Overbeck
- Department of Clinical Physiology and Nuclear Medicine, Rigshospitalet, Copenhagen University Hospital, 2100 København, Denmark; (M.E.M.); (N.O.); (P.K.C.); (I.L.); (B.M.F.); (C.N.L.)
| | - Per Karkov Cramon
- Department of Clinical Physiology and Nuclear Medicine, Rigshospitalet, Copenhagen University Hospital, 2100 København, Denmark; (M.E.M.); (N.O.); (P.K.C.); (I.L.); (B.M.F.); (C.N.L.)
| | - Ian Law
- Department of Clinical Physiology and Nuclear Medicine, Rigshospitalet, Copenhagen University Hospital, 2100 København, Denmark; (M.E.M.); (N.O.); (P.K.C.); (I.L.); (B.M.F.); (C.N.L.)
- Department of Clinical Medicine, Copenhagen University, 2200 København, Denmark
| | - Barbara Malene Fischer
- Department of Clinical Physiology and Nuclear Medicine, Rigshospitalet, Copenhagen University Hospital, 2100 København, Denmark; (M.E.M.); (N.O.); (P.K.C.); (I.L.); (B.M.F.); (C.N.L.)
- Department of Clinical Medicine, Copenhagen University, 2200 København, Denmark
| | - Claes Nøhr Ladefoged
- Department of Clinical Physiology and Nuclear Medicine, Rigshospitalet, Copenhagen University Hospital, 2100 København, Denmark; (M.E.M.); (N.O.); (P.K.C.); (I.L.); (B.M.F.); (C.N.L.)
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, 2800 Lyngby, Denmark
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Yang J. AI applications for quantitative and qualitative PET in PET/MR-where do we stand? Eur Radiol 2023; 33:7530-7531. [PMID: 37272965 DOI: 10.1007/s00330-023-09778-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Revised: 05/18/2023] [Accepted: 05/23/2023] [Indexed: 06/06/2023]
Affiliation(s)
- Jeawon Yang
- Department of Radiology, University of Texas Southwestern (UTSW), Texas, Dallas, USA.
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McNaughton J, Fernandez J, Holdsworth S, Chong B, Shim V, Wang A. Machine Learning for Medical Image Translation: A Systematic Review. Bioengineering (Basel) 2023; 10:1078. [PMID: 37760180 PMCID: PMC10525905 DOI: 10.3390/bioengineering10091078] [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: 06/19/2023] [Revised: 07/30/2023] [Accepted: 09/07/2023] [Indexed: 09/29/2023] Open
Abstract
BACKGROUND CT scans are often the first and only form of brain imaging that is performed to inform treatment plans for neurological patients due to its time- and cost-effective nature. However, MR images give a more detailed picture of tissue structure and characteristics and are more likely to pick up abnormalities and lesions. The purpose of this paper is to review studies which use deep learning methods to generate synthetic medical images of modalities such as MRI and CT. METHODS A literature search was performed in March 2023, and relevant articles were selected and analyzed. The year of publication, dataset size, input modality, synthesized modality, deep learning architecture, motivations, and evaluation methods were analyzed. RESULTS A total of 103 studies were included in this review, all of which were published since 2017. Of these, 74% of studies investigated MRI to CT synthesis, and the remaining studies investigated CT to MRI, Cross MRI, PET to CT, and MRI to PET. Additionally, 58% of studies were motivated by synthesizing CT scans from MRI to perform MRI-only radiation therapy. Other motivations included synthesizing scans to aid diagnosis and completing datasets by synthesizing missing scans. CONCLUSIONS Considerably more research has been carried out on MRI to CT synthesis, despite CT to MRI synthesis yielding specific benefits. A limitation on medical image synthesis is that medical datasets, especially paired datasets of different modalities, are lacking in size and availability; it is therefore recommended that a global consortium be developed to obtain and make available more datasets for use. Finally, it is recommended that work be carried out to establish all uses of the synthesis of medical scans in clinical practice and discover which evaluation methods are suitable for assessing the synthesized images for these needs.
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Affiliation(s)
- Jake McNaughton
- Auckland Bioengineering Institute, University of Auckland, 6/70 Symonds Street, Auckland 1010, New Zealand; (J.M.)
| | - Justin Fernandez
- Auckland Bioengineering Institute, University of Auckland, 6/70 Symonds Street, Auckland 1010, New Zealand; (J.M.)
- Department of Engineering Science and Biomedical Engineering, University of Auckland, 3/70 Symonds Street, Auckland 1010, New Zealand
| | - Samantha Holdsworth
- Faculty of Medical and Health Sciences, University of Auckland, 85 Park Road, Auckland 1023, New Zealand
- Centre for Brain Research, University of Auckland, 85 Park Road, Auckland 1023, New Zealand
- Mātai Medical Research Institute, 400 Childers Road, Tairāwhiti Gisborne 4010, New Zealand
| | - Benjamin Chong
- Auckland Bioengineering Institute, University of Auckland, 6/70 Symonds Street, Auckland 1010, New Zealand; (J.M.)
- Faculty of Medical and Health Sciences, University of Auckland, 85 Park Road, Auckland 1023, New Zealand
- Centre for Brain Research, University of Auckland, 85 Park Road, Auckland 1023, New Zealand
| | - Vickie Shim
- Auckland Bioengineering Institute, University of Auckland, 6/70 Symonds Street, Auckland 1010, New Zealand; (J.M.)
- Mātai Medical Research Institute, 400 Childers Road, Tairāwhiti Gisborne 4010, New Zealand
| | - Alan Wang
- Auckland Bioengineering Institute, University of Auckland, 6/70 Symonds Street, Auckland 1010, New Zealand; (J.M.)
- Faculty of Medical and Health Sciences, University of Auckland, 85 Park Road, Auckland 1023, New Zealand
- Centre for Brain Research, University of Auckland, 85 Park Road, Auckland 1023, New Zealand
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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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