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Sabeghi P, Katal S, Chen M, Taravat F, Werner TJ, Saboury B, Gholamrezanezhad A, Alavi A. Update on Positron Emission Tomography/Magnetic Resonance Imaging: Cancer and Inflammation Imaging in the Clinic. Magn Reson Imaging Clin N Am 2023; 31:517-538. [PMID: 37741639 DOI: 10.1016/j.mric.2023.07.001] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/25/2023]
Abstract
Hybrid PET/MRI is highly valuable, having made significant strides in overcoming technical challenges and offering unique advantages such as reduced radiation, precise data coregistration, and motion correction. Growing evidence highlights the value of PET/MRI in broad clinical aspects, including inflammatory and oncological imaging in adults, pregnant women, and pediatrics, potentially surpassing PET/CT. This newly integrated solution may be preferred over PET/CT in many clinical conditions. However, further technological advancements are required to facilitate its broader adoption as a routine diagnostic modality.
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Affiliation(s)
- Paniz Sabeghi
- Department of Radiology, Keck School of Medicine of University of Southern California, Health Science Campus, 1500 San Pablo Street, Los Angeles, CA 90033, USA
| | - Sanaz Katal
- Medical Imaging Department of St. Vincent's Hospital, Melbourne, Victoria, Australia
| | - Michelle Chen
- Department of Radiology, Keck School of Medicine of University of Southern California, Health Science Campus, 1500 San Pablo Street, Los Angeles, CA 90033, USA
| | - Farzaneh Taravat
- Department of Radiology, Keck School of Medicine of University of Southern California, Health Science Campus, 1500 San Pablo Street, Los Angeles, CA 90033, USA
| | - Thomas J Werner
- Department of Radiology, Hospital of the University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA 19104, USA
| | - Babak Saboury
- Department of Radiology, Hospital of the University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA 19104, USA; 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 of University of Southern California, Health Science Campus, 1500 San Pablo Street, Los Angeles, CA 90033, USA
| | - Abass Alavi
- Department of Radiology, Hospital of the University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA 19104, USA.
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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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Magnetic resonance imaging of soft tissue sarcoma: features related to prognosis. EUROPEAN JOURNAL OF ORTHOPAEDIC SURGERY AND TRAUMATOLOGY 2021; 31:1567-1575. [PMID: 34052920 DOI: 10.1007/s00590-021-03003-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Accepted: 05/11/2021] [Indexed: 01/03/2023]
Abstract
Magnetic Resonance Imaging is a fundamental tool in the evaluation of soft tissue sarcoma. Imaging features are relevant for the assessment of treatment strategies, surgical planning and also for patients' prognosis prediction. Among soft tissue sarcoma and also other malignancies, the size of the mass is usually considered the prognostic key element in diagnostic imaging. Moreover, several other features should be obtained from MRI studies with prognostic implications in all type of soft tissue sarcoma: peritumoral enhancement, signs of necrosis, deep location, ill-defined borders/signs of infiltrations. Focusing on soft tissue sarcoma subtypes, some other magnetic resonance imaging features are more specific and related to prognosis. In myxofibrosarcoma the magnetic resonance imaging "tail sign" and a "water-like" appearance on fluid-sensitive sequences, due to rich myxoid matrix content, are both associated with higher risk of local recurrence after surgical excision; nevertheless, the "tail sign" is also related to a higher risk of distant metastases at diagnosis. The "tail sign" is associated with higher risk of local recurrence after surgical excision in undifferentiated pleomorphic sarcoma as well. In patients affected by synovial sarcoma, the "triple sign" identifiable in magnetic resonance imaging (T2w sequences) is associated with decreased disease-free survival and indicates the simultaneous presence of solid cellular elements (intermediate signal intensity), hemorrhage or necrosis (high signal intensity) and fibrotic regions (low signal intensity). In addition, absence of calcifications are associated with reduced disease-free survival in patients affected by synovial sarcoma. Signal heterogeneity is associated with worst prognosis in all type of soft tissue sarcoma, particularly in myxoid liposarcoma. In recent years, several new quantitative tools applied on magnetic resonance imaging have been proved to predict patients' prognosis. Above all the new tools, radiomics seems to be one of the most promising, and, has been proved to have the capability in discriminating low-grade from high-grade soft tissue sarcomas. Therefore, magnetic resonance imaging studies in patients with soft tissue sarcoma should be accurately evaluated and their results should be taken into account for prognostic assessment.
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