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Hervier E, Glessgen C, Nkoulou R, François Deux J, Vallee JP, Adamopoulos D. Hybrid PET/MR in Cardiac Imaging. Magn Reson Imaging Clin N Am 2023; 31:613-624. [PMID: 37741645 DOI: 10.1016/j.mric.2023.04.008] [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] [Indexed: 09/25/2023]
Abstract
In the last few years, technological advances in MR imaging, PET detectors, and attenuation correction algorithms have allowed the creation of truly integrated PET/MR imaging systems, for both clinical and research applications. These machines allow a comprehensive investigation of cardiovascular diseases, by offering a wide variety of detailed anatomical and functional data in combination. Despite significant pathophysiologic mechanisms being clarified by this new data, its clinical relevance and prognostic significance have not been demonstrated yet.
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Affiliation(s)
- Elsa Hervier
- Diagnostics Department, Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Gabrielle-Perret-Gentil 4 street, 1205, Geneva, Switzerland
| | - Carl Glessgen
- Diagnostics Department, Radiology, Geneva University Hospital, Gabrielle-Perret-Gentil 4 street, 1205, Geneva, Switzerland
| | - René Nkoulou
- Diagnostics Department, Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Gabrielle-Perret-Gentil 4 street, 1205, Geneva, Switzerland
| | - Jean François Deux
- Diagnostics Department, Radiology, Geneva University Hospital, Gabrielle-Perret-Gentil 4 street, 1205, Geneva, Switzerland
| | - Jean-Paul Vallee
- Diagnostics Department, Radiology, Geneva University Hospital, Gabrielle-Perret-Gentil 4 street, 1205, Geneva, Switzerland
| | - Dionysios Adamopoulos
- Department of Medical Specialties, Cardiology, Geneva University Hospital, Gabrielle-Perret-Gentil 4 street, 1205, Geneva, Switzerland.
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A Simultaneous Multiparametric 18F-FDG PET/MRI Radiomics Model for the Diagnosis of Triple Negative Breast Cancer. Cancers (Basel) 2022; 14:cancers14163944. [PMID: 36010936 PMCID: PMC9406327 DOI: 10.3390/cancers14163944] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2022] [Revised: 08/12/2022] [Accepted: 08/13/2022] [Indexed: 11/29/2022] Open
Abstract
Simple Summary In this study, we aimed to build a machine-learning predictive model for the identification of triple negative breast cancer, the most aggressive subtype, using quantitative parameters and radiomics features extracted from tumor lesions on hybrid PET/MRI. The good performance of the model supports the hypothesis that hybrid PET/MRI can provide quantitative data able to non-invasively detect tumor biological characteristics using artificial intelligence software and further encourages the conduction of additional studies for this purpose. Abstract Purpose: To investigate whether a machine learning (ML)-based radiomics model applied to 18F-FDG PET/MRI is effective in molecular subtyping of breast cancer (BC) and specifically in discriminating triple negative (TN) from other molecular subtypes of BC. Methods: Eighty-six patients with 98 BC lesions (Luminal A = 10, Luminal B = 51, HER2+ = 12, TN = 25) were included and underwent simultaneous 18F-FDG PET/MRI of the breast. A 3D segmentation of BC lesion was performed on T2w, DCE, DWI and PET images. Quantitative diffusion and metabolic parameters were calculated and radiomics features extracted. Data were selected using the LASSO regression and used by a fine gaussian support vector machine (SVM) classifier with a 5-fold cross validation for identification of TNBC lesions. Results: Eight radiomics models were built based on different combinations of quantitative parameters and/or radiomic features. The best performance (AUROC 0.887, accuracy 82.8%, sensitivity 79.7%, specificity 86%, PPV 85.3%, NPV 80.8%) was found for the model combining first order, neighborhood gray level dependence matrix and size zone matrix-based radiomics features extracted from ADC and PET images. Conclusion: A ML-based radiomics model applied to 18F-FDG PET/MRI is able to non-invasively discriminate TNBC lesions from other BC molecular subtypes with high accuracy. In a future perspective, a “virtual biopsy” might be performed with radiomics signatures.
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Olin AB, Hansen AE, Rasmussen JH, Jakoby B, Berthelsen AK, Ladefoged CN, Kjær A, Fischer BM, Andersen FL. Deep learning for Dixon MRI-based attenuation correction in PET/MRI of head and neck cancer patients. EJNMMI Phys 2022; 9:20. [PMID: 35294629 PMCID: PMC8927520 DOI: 10.1186/s40658-022-00449-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Accepted: 03/02/2022] [Indexed: 11/10/2022] Open
Abstract
Background Quantitative whole-body PET/MRI relies on accurate patient-specific MRI-based attenuation correction (AC) of PET, which is a non-trivial challenge, especially for the anatomically complex head and neck region. We used a deep learning model developed for dose planning in radiation oncology to derive MRI-based attenuation maps of head and neck cancer patients and evaluated its performance on PET AC. Methods Eleven head and neck cancer patients, referred for radiotherapy, underwent CT followed by PET/MRI with acquisition of Dixon MRI. Both scans were performed in radiotherapy position. PET AC was performed with three different patient-specific attenuation maps derived from: (1) Dixon MRI using a deep learning network (PETDeep). (2) Dixon MRI using the vendor-provided atlas-based method (PETAtlas). (3) CT, serving as reference (PETCT). We analyzed the effect of the MRI-based AC methods on PET quantification by assessing the average voxelwise error within the entire body, and the error as a function of distance to bone/air. The error in mean uptake within anatomical regions of interest and the tumor was also assessed. Results The average (± standard deviation) PET voxel error was 0.0 ± 11.4% for PETDeep and −1.3 ± 21.8% for PETAtlas. The error in mean PET uptake in bone/air was much lower for PETDeep (−4%/12%) than for PETAtlas (−15%/84%) and PETDeep also demonstrated a more rapidly decreasing error with distance to bone/air affecting only the immediate surroundings (less than 1 cm). The regions with the largest error in mean uptake were those containing bone (mandible) and air (larynx) for both methods, and the error in tumor mean uptake was −0.6 ± 2.0% for PETDeep and −3.5 ± 4.6% for PETAtlas. Conclusion The deep learning network for deriving MRI-based attenuation maps of head and neck cancer patients demonstrated accurate AC and exceeded the performance of the vendor-provided atlas-based method both overall, on a lesion-level, and in vicinity of challenging regions such as bone and air.
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Affiliation(s)
- Anders B Olin
- Department of Clinical Physiology, Nuclear Medicine and PET & Cluster for Molecular Imaging, Rigshospitalet, University of Copenhagen, Blegdamsvej 9, 2100, Copenhagen, Denmark.
| | - Adam E Hansen
- Department of Clinical Physiology, Nuclear Medicine and PET & Cluster for Molecular Imaging, Rigshospitalet, University of Copenhagen, Blegdamsvej 9, 2100, Copenhagen, Denmark.,Faculty of Health and Medical Science, University of Copenhagen, Copenhagen, Denmark.,Department of Radiology, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Jacob H Rasmussen
- Department of Otorhinolaryngology, Head and Neck Surgery and Audiology, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Björn Jakoby
- Siemens Healthcare GmbH, Erlangen, Germany.,University of Surrey, Guildford, Surrey, UK
| | - Anne K Berthelsen
- Department of Oncology, Section of Radiotherapy, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Claes N Ladefoged
- Department of Clinical Physiology, Nuclear Medicine and PET & Cluster for Molecular Imaging, Rigshospitalet, University of Copenhagen, Blegdamsvej 9, 2100, Copenhagen, Denmark
| | - Andreas Kjær
- Department of Clinical Physiology, Nuclear Medicine and PET & Cluster for Molecular Imaging, Rigshospitalet, University of Copenhagen, Blegdamsvej 9, 2100, Copenhagen, Denmark
| | - Barbara M Fischer
- Department of Clinical Physiology, Nuclear Medicine and PET & Cluster for Molecular Imaging, Rigshospitalet, University of Copenhagen, Blegdamsvej 9, 2100, Copenhagen, Denmark.,King's College London and Guy's and St Thomas' PET Centre, School of Biomedical Engineering and Imaging Sciences, King's College London, King's Health Partners, London, UK
| | - Flemming L Andersen
- Department of Clinical Physiology, Nuclear Medicine and PET & Cluster for Molecular Imaging, Rigshospitalet, University of Copenhagen, Blegdamsvej 9, 2100, Copenhagen, Denmark
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AI-enhanced simultaneous multiparametric 18F-FDG PET/MRI for accurate breast cancer diagnosis. Eur J Nucl Med Mol Imaging 2021; 49:596-608. [PMID: 34374796 PMCID: PMC8803815 DOI: 10.1007/s00259-021-05492-z] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2021] [Accepted: 07/06/2021] [Indexed: 12/17/2022]
Abstract
Purpose To assess whether a radiomics and machine learning (ML) model combining quantitative parameters and radiomics features extracted from simultaneous multiparametric 18F-FDG PET/MRI can discriminate between benign and malignant breast lesions. Methods A population of 102 patients with 120 breast lesions (101 malignant and 19 benign) detected on ultrasound and/or mammography was prospectively enrolled. All patients underwent hybrid 18F-FDG PET/MRI for diagnostic purposes. Quantitative parameters were extracted from DCE (MTT, VD, PF), DW (mean ADC of breast lesions and contralateral breast parenchyma), PET (SUVmax, SUVmean, and SUVminimum of breast lesions, as well as SUVmean of the contralateral breast parenchyma), and T2-weighted images. Radiomics features were extracted from DCE, T2-weighted, ADC, and PET images. Different diagnostic models were developed using a fine Gaussian support vector machine algorithm which explored different combinations of quantitative parameters and radiomics features to obtain the highest accuracy in discriminating between benign and malignant breast lesions using fivefold cross-validation. The performance of the best radiomics and ML model was compared with that of expert reader review using McNemar’s test. Results Eight radiomics models were developed. The integrated model combining MTT and ADC with radiomics features extracted from PET and ADC images obtained the highest accuracy for breast cancer diagnosis (AUC 0.983), although its accuracy was not significantly higher than that of expert reader review (AUC 0.868) (p = 0.508). Conclusion A radiomics and ML model combining quantitative parameters and radiomics features extracted from simultaneous multiparametric 18F-FDG PET/MRI images can accurately discriminate between benign and malignant breast lesions. Supplementary Information The online version contains supplementary material available at 10.1007/s00259-021-05492-z.
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Standard MRI-based attenuation correction for PET/MRI phantoms: a novel concept using MRI-visible polymer. EJNMMI Phys 2021; 8:18. [PMID: 33599876 PMCID: PMC7892652 DOI: 10.1186/s40658-021-00364-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Accepted: 02/08/2021] [Indexed: 12/20/2022] Open
Abstract
Background PET/MRI phantom studies are challenged by the need of phantom-specific attenuation templates to account for attenuation properties of the phantom material. We present a PET/MRI phantom built from MRI-visible material for which attenuation correction (AC) can be performed using the standard MRI-based AC. Methods A water-fillable phantom was 3D-printed with a commercially available MRI-visible polymer. The phantom had a cylindrical shape and the fillable compartment consisted of a homogeneous region and a region containing solid rods of different diameters. The phantom was filled with a solution of water and [18F]FDG. A 30 min PET/MRI acquisition including the standard Dixon-based MR-AC method was performed. In addition, a CT scan of the phantom was acquired on a PET/CT system. From the Dixon in-phase, opposed-phase and fat images, a phantom-specific AC map (Phantom MR-AC) was produced by separating the phantom material from the water compartment using a thresholding-based method and assigning fixed attenuation coefficients to the individual compartments. The PET data was reconstructed using the Phantom MR-AC, the original Dixon MR-AC, and an MR-AC just containing the water compartment (NoWall-AC) to estimate the error of ignoring the phantom walls. CT-based AC was employed as the reference standard. Average %-differences in measured activity between the CT corrected PET and the PET corrected with the other AC methods were calculated. Results The phantom housing and the liquid compartment were both visible and distinguishable from each other in the Dixon images and allowed the segmentation of a phantom-specific MR-based AC. Compared to the CT-AC PET, average differences in measured activity in the whole water compartment in the phantom of −0.3%, 9.4%, and −24.1% were found for Dixon phantom MR-AC, MR-AC, and NoWall-AC based PET, respectively. Average differences near the phantom wall in the homogeneous region were −0.3%, 6.6%, and −34.3%, respectively. Around the rods, activity differed from the CT-AC PET by 0.7%, 8.9%, and −45.5%, respectively. Conclusion The presented phantom material is visible using standard MR sequences, and thus, supports the use of standard, phantom-independent MR measurements for MR-AC in PET/MRI phantom studies.
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Zhao J, Xue Q, Chen X, You Z, Wang Z, Yuan J, Liu H, Hu L. Evaluation of SUVlean consistency in FDG and PSMA PET/MR with Dixon-, James-, and Janma-based lean body mass correction. EJNMMI Phys 2021; 8:17. [PMID: 33598849 PMCID: PMC7889776 DOI: 10.1186/s40658-021-00363-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2020] [Accepted: 02/04/2021] [Indexed: 01/22/2023] Open
Abstract
PURPOSE To systematically evaluate the consistency of various standardized uptake value (SUV) lean body mass (LBM) normalization methods in a clinical positron emission tomography/magnetic resonance imaging (PET/MR) setting. METHODS SUV of brain, liver, prostate, parotid, blood, and muscle were measured in 90 18F-FDG and 28 18F-PSMA PET/MR scans and corrected for LBM using the James, Janma (short for Janmahasatian), and Dixon approaches. The prospective study was performed from December 2018 to August 2020 at Shanghai East Hospital. Forty dual energy X-ray absorptiometry (DXA) measurements of non-fat mass were used as the reference standard. Agreement between different LBM methods was assessed by linear regression and Bland-Altman statistics. SUV's dependency on BMI was evaluated by means of linear regression and Pearson correlation. RESULTS Compared to DXA, the Dixon approach presented the least bias in LBM/weight% than James and Janma models (bias 0.4±7.3%, - 8.0±9.4%, and - 3.3±8.3% respectively). SUV normalized by body weight (SUVbw) was positively correlated with body mass index (BMI) for both FDG (e.g., liver: r = 0.45, p < 0.001) and PSMA scans (r = 0.20, p = 0.31), while SUV normalized by lean body mass (SUVlean) revealed a decreased dependency on BMI (r = 0.22, 0.08, 0.14, p = 0.04, 0.46, 0.18 for Dixon, James, and Janma models, respectively). The liver SUVbw of obese/overweight patients was significantly larger (p < 0.001) than that of normal patients, whereas the bias was mostly eliminated in SUVlean. One-way ANOVA showed significant difference (p < 0.001) between SUVlean in major organs measured using Dixon method vs James and Janma models. CONCLUSION Significant systematic variation was found using different approaches to calculate SUVlean. A consistent correction method should be applied for serial PET/MR scans. The Dixon method provides the most accurate measure of LBM, yielding the least bias of all approaches when compared to DXA.
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Affiliation(s)
- Jun Zhao
- Department of Nuclear Medicine, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China.
| | - Qiaoyi Xue
- Central Research Institute, United Imaging Healthcare Group, Shanghai, China
| | - Xing Chen
- Department of Nuclear Medicine, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China
| | - Zhiwen You
- Department of Nuclear Medicine, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China
| | - Zhe Wang
- Central Research Institute, United Imaging Healthcare Group, Shanghai, China
| | - Jianmin Yuan
- Central Research Institute, United Imaging Healthcare Group, Shanghai, China
| | - Hui Liu
- Central Research Institute, United Imaging Healthcare Group, Shanghai, China
| | - Lingzhi Hu
- Central Research Institute, United Imaging Healthcare Group, Shanghai, China
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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.
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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
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Rischka L, Gryglewski G, Berroterán-Infante N, Rausch I, James GM, Klöbl M, Sigurdardottir H, Hartenbach M, Hahn A, Wadsak W, Mitterhauser M, Beyer T, Kasper S, Prayer D, Hacker M, Lanzenberger R. Attenuation Correction Approaches for Serotonin Transporter Quantification With PET/MRI. Front Physiol 2019; 10:1422. [PMID: 31824335 PMCID: PMC6883225 DOI: 10.3389/fphys.2019.01422] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2019] [Accepted: 11/04/2019] [Indexed: 12/26/2022] Open
Abstract
Background Several MR-based attenuation correction (AC) approaches were developed to conquer the challenging AC in hybrid PET/MR imaging. These AC methods are commonly evaluated on standardized uptake values or tissue concentration. However, in neurotransmitter system studies absolute quantification is more favorable due to its accuracy. Therefore, our aim was to investigate the accuracy of segmentation- and atlas-based MR AC approaches on serotonin transporter (SERT) distribution volumes and occupancy after a drug challenge. Methods 18 healthy subjects (7 male) underwent two [11C]DASB PET/MRI measurements in a double-blinded, placebo controlled, cross-over design. After 70 min the selective serotonin reuptake inhibitor (SSRI) citalopram or a placebo was infused. The parameters total and specific volume of distribution (VT, VS = BPP) and occupancy were quantified. All subjects underwent a low-dose CT scan as reference AC method. Besides the standard AC approaches DIXON and UTE, a T1-weighted structural image was recorded to estimate a pseudo-CT based on an MR/CT database (pseudoCT). Another evaluated AC approach superimposed a bone model on AC DIXON. Lastly, an approach optimizing the segmentation of UTE images was analyzed (RESOLUTE). PET emission data were reconstructed with all 6 AC methods. The accuracy of the AC approaches was evaluated on a region of interest-basis for the parameters VT, BPP, and occupancy with respect to the results of AC CT. Results Variations for VT and BPP were found with all AC methods with bias ranging from -15 to 17%. The smallest relative errors for all regions were found with AC pseudoCT (<|5%|). Although the bias between BPP SSRI and BPP placebo varied markedly with AC DIXON (<|12%|) and AC UTE (<|9%|), a high correlation to AC CT was obtained (r 2∼1). The relative difference of the occupancy for all tested AC methods was small for SERT high binding regions (<|4%|). Conclusion The high correlation might offer a rescaling from the biased parameters VT and BPP to the true values. Overall, the pseudoCT approach yielded smallest errors and the best agreement with AC CT. For SERT occupancy, all AC methods showed little bias in high binding regions, indicating that errors may cancel out in longitudinal assessments.
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Affiliation(s)
- Lucas Rischka
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Gregor Gryglewski
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Neydher Berroterán-Infante
- Division of Nuclear Medicine, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Ivo Rausch
- QIMP Group, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - Gregory Miles James
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Manfred Klöbl
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Helen Sigurdardottir
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Markus Hartenbach
- Division of Nuclear Medicine, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Andreas Hahn
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Wolfgang Wadsak
- Division of Nuclear Medicine, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria.,CBmed, Graz, Austria
| | - Markus Mitterhauser
- Division of Nuclear Medicine, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria.,Ludwig Boltzmann Institute Applied Diagnostics, Vienna, Austria
| | - Thomas Beyer
- QIMP Group, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - Siegfried Kasper
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Daniela Prayer
- Division of Neuroradiology and Musculoskeletal Radiology, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Marcus Hacker
- Division of Nuclear Medicine, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Rupert Lanzenberger
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
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Rausch I, Zitterl A, Berroterán-Infante N, Rischka L, Prayer D, Fenchel M, Sareshgi RA, Haug AR, Hacker M, Beyer T, Traub-Weidinger T. Dynamic [18F]FET-PET/MRI using standard MRI-based attenuation correction methods. Eur Radiol 2019; 29:4276-4285. [PMID: 30635757 PMCID: PMC6610265 DOI: 10.1007/s00330-018-5942-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2018] [Revised: 11/14/2018] [Accepted: 11/30/2018] [Indexed: 11/30/2022]
Abstract
AIM To assess if tumour grading based on dynamic [18F]FET positron emission tomography/magnetic resonance imaging (PET/MRI) studies is affected by different MRI-based attenuation correction (AC) methods. METHODS Twenty-four patients with suspected brain tumours underwent dynamic [18F]FET-PET/MRI examinations and subsequent low-dose computed tomography (CT) scans of the head. The dynamic PET data was reconstructed using the following AC methods: standard Dixon-based AC and ultra-short echo time MRI-based AC (MR-AC) and a model-based AC approach. All data were reconstructed also using CT-based AC (reference). For all lesions and reconstructions, time-activity curves (TACs) and time to peak (TTP) were extracted using different region-of-interest (ROI) and volume-of-interest (VOI) definitions. According to the most common evaluation approaches, TACs were categorised into two or three distinct curve patterns. Changes in TTP and TAC patterns compared to PET using CT-based AC were reported. RESULTS In the majority of cases, TAC patterns did not change. However, TAC pattern changes as well as changes in TTP were observed in up to 8% and 17% of the cases when using different MR-AC methods and ROI/VOI definitions, respectively. However, these changes in TTP and TAC pattern were attributed to different delineations of the ROIs/VOIs in PET corrected with different AC methods. CONCLUSION PET/MRI using different MR-AC methods can be used for the assessment of TAC patterns in dynamic [18F]FET studies, as long as a meaningful delineation of the area of interest within the tumour is ensured. KEY POINTS • PET/MRI using different MR-AC methods can be used for dynamic [18F]FET studies. • A meaningful segmentation of the area of interest needs to be ensured, mandating a visual validation of the delineation by an experienced reader.
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Affiliation(s)
- Ivo Rausch
- QIMP Team, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - Andreas Zitterl
- Division of Nuclear Medicine, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria
| | - Neydher Berroterán-Infante
- QIMP Team, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - Lucas Rischka
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Daniela Prayer
- Division of Neuroradiology, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | | | - Reza A Sareshgi
- Division of Nuclear Medicine, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria.,Division of Radiology-Technique, University of Applied Science, Vienna, Austria
| | - Alexander R Haug
- Division of Nuclear Medicine, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria
| | - Marcus Hacker
- Division of Nuclear Medicine, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria
| | - Thomas Beyer
- QIMP Team, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - Tatjana Traub-Weidinger
- Division of Nuclear Medicine, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria.
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Prognostic value of pretreatment PET/CT lean body mass-corrected parameters in patients with hepatocellular carcinoma. Nucl Med Commun 2018; 39:564-571. [PMID: 29634658 DOI: 10.1097/mnm.0000000000000842] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
OBJECTIVE This study was designed to investigate whether pretreatment fluorine-18-fluorodeoxyglucose (F-FDG) PET/computed tomography (CT) lean body mass-corrected parameters could predict the overall survival (OS) better than the established predictors in patients with hepatocellular carcinoma (HCC). PATIENTS AND METHODS We retrospectively analyzed 61 patients with HCC with pretreatment F-FDG-PET/CT. Besides obtaining clinical factors, we measured both lean body mass-corrected and body weight-corrected PET/CT parameters, including metabolic tumor volume, maximal standardized uptake value of the tumor, total lesion glycolysis, tumor-to-normal liver uptake ratio, and so on. The prognostic value of those factors for OS was assessed by statistical software. RESULTS In the univariate analysis, PET/CT parameters, ascites, serum α-fetoprotein, alkaline phosphatase, aspartate transaminase (AST), tumor number, tumor size of the maximal one, vascular invasion, TNM stage, Child-Pugh class, Barcelona Clinic Liver Cancer (BCLC) staging, and Okuda staging were significant predictors of OS. In multivariate and Kaplan-Meier analyses, lean body mass-corrected maximum standardized uptake value (lbmSUVmax) more than 3.35 g/ml, AST more than 42.00 U/l, and BCLC staging B-C were significant independent predictors of poor OS. When BCLC staging variable was stratified by four categories instead of two in the multivariate analysis, it was not the statistically significant independent predictor anymore, but lbmSUVmax and AST still were. CONCLUSION Pretreatment F-FDG-PET/CT lean body mass-corrected parameters can predict the OS in patients with HCC. Moreover, lbmSUVmax and AST, as the independent predictors of OS, could supplement the prognostic value of the BCLC staging system.
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Olin A, Ladefoged CN, Langer NH, Keller SH, Löfgren J, Hansen AE, Kjær A, Langer SW, Fischer BM, Andersen FL. Reproducibility of MR-Based Attenuation Maps in PET/MRI and the Impact on PET Quantification in Lung Cancer. J Nucl Med 2017; 59:999-1004. [PMID: 29123008 DOI: 10.2967/jnumed.117.198853] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2017] [Accepted: 10/30/2017] [Indexed: 11/16/2022] Open
Abstract
Quantitative PET/MRI is dependent on reliable and reproducible MR-based attenuation correction (MR-AC). In this study, we evaluated the quality of current vendor-provided thoracic MR-AC maps and further investigated the reproducibility of their impact on 18F-FDG PET quantification in patients with non-small cell lung cancer. Methods: Eleven patients with inoperable non-small cell lung cancer underwent 2-5 thoracic PET/MRI scan-rescan examinations within 22 d. 18F-FDG PET data were acquired along with 2 Dixon MR-AC maps for each examination. Two PET images (PETA and PETB) were reconstructed using identical PET emission data but with MR-AC from these intrasubject repeated attenuation maps. In total, 90 MR-AC maps were evaluated visually for quality and the occurrence of categorized artifacts by 2 PET/MRI-experienced physicians. Each tumor was outlined by a volume of interest (40% isocontour of maximum) on PETA, which was then projected onto the corresponding PETB SUVmean and SUVmax were assessed from the PET images. Within-examination coefficients of variation and Bland-Altman analyses were conducted for the assessment of SUV variations between PETA and PETBResults: Image artifacts were observed in 86% of the MR-AC maps, and 30% of the MR-AC maps were subjectively expected to affect the tumor SUV. SUVmean and SUVmax resulted in coefficients of variation of 5.6% and 6.6%, respectively, and scan-rescan SUV variations were within ±20% in 95% of the cases. Substantial SUV variations were seen mainly for scan-rescan examinations affected by respiratory motion. Conclusion: Artifacts occur frequently in standard thoracic MR-AC maps, affecting the reproducibility of PET/MRI. These, in combination with other well-known sources of error associated with PET/MRI examinations, lead to inconsistent SUV measurements in serial studies, which may affect the reliability of therapy response assessment. A thorough visual inspection of the thoracic MR-AC map and Dixon images from which it is derived remains crucial for the detection of MR-AC artifacts that may influence the reliability of SUV.
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Affiliation(s)
- Anders Olin
- Department of Clinical Physiology, Nuclear Medicine, and PET, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark; and
| | - Claes N Ladefoged
- Department of Clinical Physiology, Nuclear Medicine, and PET, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark; and
| | - Natasha H Langer
- Department of Clinical Physiology, Nuclear Medicine, and PET, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark; and
| | - Sune H Keller
- Department of Clinical Physiology, Nuclear Medicine, and PET, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark; and
| | - Johan Löfgren
- Department of Clinical Physiology, Nuclear Medicine, and PET, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark; and
| | - Adam E Hansen
- Department of Clinical Physiology, Nuclear Medicine, and PET, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark; and
| | - Andreas Kjær
- Department of Clinical Physiology, Nuclear Medicine, and PET, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark; and
| | - Seppo W Langer
- Department of Oncology, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Barbara M Fischer
- Department of Clinical Physiology, Nuclear Medicine, and PET, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark; and
| | - Flemming L Andersen
- Department of Clinical Physiology, Nuclear Medicine, and PET, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark; and
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12
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Rausch I, Quick HH, Cal-Gonzalez J, Sattler B, Boellaard R, Beyer T. Technical and instrumentational foundations of PET/MRI. Eur J Radiol 2017; 94:A3-A13. [PMID: 28431784 DOI: 10.1016/j.ejrad.2017.04.004] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2017] [Accepted: 04/07/2017] [Indexed: 12/23/2022]
Abstract
This paper highlights the origins of combined positron emission tomography (PET) and magnetic resonance imaging (MRI) whole-body systems that were first introduced for applications in humans in 2010. This text first covers basic aspects of each imaging modality before describing the technical and methodological challenges of combining PET and MRI within a single system. After several years of development, combined and even fully-integrated PET/MRI systems have become available and made their way into the clinic. This multi-modality imaging system lends itself to the advanced exploration of diseases to support personalized medicine in a long run. To that extent, this paper provides an introduction to PET/MRI methodology and important technical solutions.
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Affiliation(s)
- Ivo Rausch
- Center for Medical Physics and Biomedical Engineering, Medical University Vienna, Vienna, Austria.
| | - Harald H Quick
- High Field and Hybrid MR Imaging, University Hospital Essen, Essen, Germany; Erwin L. Hahn Institute for MR Imaging, University of Duisburg-Essen, Essen, Germany
| | - Jacobo Cal-Gonzalez
- Center for Medical Physics and Biomedical Engineering, Medical University Vienna, Vienna, Austria
| | - Bernhard Sattler
- Department of Nuclear Medicine, University Hospital Leipzig, Leipzig, Germany
| | - Ronald Boellaard
- Department of Nuclear Medicine and Molecular Imaging, Academisch Ziekenhuis Groningen, Groningen, The Netherlands
| | - Thomas Beyer
- Center for Medical Physics and Biomedical Engineering, Medical University Vienna, Vienna, Austria
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13
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Rausch I, Rischka L, Ladefoged CN, Furtner J, Fenchel M, Hahn A, Lanzenberger R, Mayerhoefer ME, Traub-Weidinger T, Beyer T. PET/MRI for Oncologic Brain Imaging: A Comparison of Standard MR-Based Attenuation Corrections with a Model-Based Approach for the Siemens mMR PET/MR System. J Nucl Med 2017; 58:1519-1525. [DOI: 10.2967/jnumed.116.186148] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2016] [Accepted: 01/31/2017] [Indexed: 11/16/2022] Open
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14
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Büther F, Noto B, Auf der Springe K, Allkemper T, Stegger L. An artefact of PET attenuation correction caused by iron overload of the liver in clinical PET-MRI. Eur J Hybrid Imaging 2017; 1:10. [PMID: 29782579 PMCID: PMC5954667 DOI: 10.1186/s41824-017-0015-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2017] [Accepted: 10/23/2017] [Indexed: 11/10/2022] Open
Abstract
Background Attenuation correction is one of the most important steps in producing quantitative PET image data. In hybrid PET-MRI systems, this correction is far from trivial, as MRI data are not correlated to PET attenuation properties of the scanned object. Commercially available systems often employ correction schemes based on segmenting the body into different tissue classes (air, lung tissue, fat-, and water-like soft tissue), e.g. by using a dual time-point Dixon sequence. However, several pitfalls are known for this approach. Here a specific artefact of MR-based PET attenuation correction is reported, caused by misidentifying the liver as lung tissue due to iron overload. Case presentation A patient with a history of hematopoietic stem cell transplantation underwent a whole-body [18F]FDG PET-MRI scan. Markedly low liver uptake values were noted in the PET images, seemingly caused by an erroneous assignment of lung tissue attenuation values to the liver. A closer investigation demonstrated markedly low MRI intensity values of the liver, indicative of secondary hemochromatosis (iron overload) most probably due to a history of multiple blood transfusions. Manual assignment of adequate liver attenuation values resulted in more realistic PET images. Conclusions Iron overload of the liver was identified as a cause of a specific attenuation correction artefact. It remains to be seen how frequent this artefact will be encountered; however, this case highlights that attenuation maps should always be checked during PET image interpretation in hybrid PET-MRI.
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Affiliation(s)
- Florian Büther
- 1Department of Nuclear Medicine, University Hospital Münster, Albert-Schweitzer-Campus 1, 48149 Münster, Germany.,2European Institute for Molecular Imaging, University of Münster, Münster, Germany
| | - Benjamin Noto
- 1Department of Nuclear Medicine, University Hospital Münster, Albert-Schweitzer-Campus 1, 48149 Münster, Germany
| | | | - Thomas Allkemper
- 3Institute for Clinical Radiology, University Hospital Münster, Münster, Germany
| | - Lars Stegger
- 1Department of Nuclear Medicine, University Hospital Münster, Albert-Schweitzer-Campus 1, 48149 Münster, Germany
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