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Guerini AE, Nici S, Magrini SM, Riga S, Toraci C, Pegurri L, Facheris G, Cozzaglio C, Farina D, Liserre R, Gasparotti R, Ravanelli M, Rondi P, Spiazzi L, Buglione M. Adoption of Hybrid MRI-Linac Systems for the Treatment of Brain Tumors: A Systematic Review of the Current Literature Regarding Clinical and Technical Features. Technol Cancer Res Treat 2023; 22:15330338231199286. [PMID: 37774771 PMCID: PMC10542234 DOI: 10.1177/15330338231199286] [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: 04/28/2023] [Revised: 07/24/2023] [Accepted: 08/08/2023] [Indexed: 10/01/2023] Open
Abstract
BACKGROUND Possible advantages of magnetic resonance (MR)-guided radiation therapy (MRgRT) for the treatment of brain tumors include improved definition of treatment volumes and organs at risk (OARs) that could allow margin reductions, resulting in limited dose to the OARs and/or dose escalation to target volumes. Recently, hybrid systems integrating a linear accelerator and an magnetic resonance imaging (MRI) scan (MRI-linacs, MRL) have been introduced, that could potentially lead to a fully MRI-based treatment workflow. METHODS We performed a systematic review of the published literature regarding the adoption of MRL for the treatment of primary or secondary brain tumors (last update November 3, 2022), retrieving a total of 2487 records; after a selection based on title and abstracts, the full text of 74 articles was analyzed, finally resulting in the 52 papers included in this review. RESULTS AND DISCUSSION Several solutions have been implemented to achieve a paradigm shift from CT-based radiotherapy to MRgRT, such as the management of geometric integrity and the definition of synthetic CT models that estimate electron density. Multiple sequences have been optimized to acquire images with adequate quality with on-board MR scanner in limited times. Various sophisticated algorithms have been developed to compensate the impact of magnetic field on dose distribution and calculate daily adaptive plans in a few minutes with satisfactory dosimetric parameters for the treatment of primary brain tumors and cerebral metastases. Dosimetric studies and preliminary clinical experiences demonstrated the feasibility of treating brain lesions with MRL. CONCLUSIONS The adoption of an MRI-only workflow is feasible and could offer several advantages for the treatment of brain tumors, including superior image quality for lesions and OARs and the possibility to adapt the treatment plan on the basis of daily MRI. The growing body of clinical data will clarify the potential benefit in terms of toxicity and response to treatment.
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Affiliation(s)
- Andrea Emanuele Guerini
- Department of Radiation Oncology, University and Spedali Civili Hospital, Brescia, Italy
- Co-first authors
| | - Stefania Nici
- Medical Physics Department, ASST Spedali Civili Hospital, Brescia, Italy
- Co-first authors
| | - Stefano Maria Magrini
- Department of Radiation Oncology, University and Spedali Civili Hospital, Brescia, Italy
| | - Stefano Riga
- Medical Physics Department, ASST Spedali Civili Hospital, Brescia, Italy
| | - Cristian Toraci
- Medical Physics Department, ASST Spedali Civili Hospital, Brescia, Italy
| | - Ludovica Pegurri
- Department of Radiation Oncology, University and Spedali Civili Hospital, Brescia, Italy
| | - Giorgio Facheris
- Department of Radiation Oncology, University and Spedali Civili Hospital, Brescia, Italy
| | - Claudia Cozzaglio
- Department of Radiation Oncology, University and Spedali Civili Hospital, Brescia, Italy
- Medical Physics Department, ASST Spedali Civili Hospital, Brescia, Italy
| | - Davide Farina
- Radiology Unit, Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, Brescia, Italy
| | - Roberto Liserre
- Department of Radiology, Neuroradiology Unit, ASST Spedali Civili University Hospital, Brescia, Italy
| | - Roberto Gasparotti
- Neuroradiology Unit, Department of Medical-Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, Brescia, Italy
| | - Marco Ravanelli
- Radiology Unit, Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, Brescia, Italy
| | - Paolo Rondi
- Radiology Unit, Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, Brescia, Italy
| | - Luigi Spiazzi
- Medical Physics Department, ASST Spedali Civili Hospital, Brescia, Italy
- Co-last author
| | - Michela Buglione
- Department of Radiation Oncology, University and Spedali Civili Hospital, Brescia, Italy
- Co-last author
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Fu Y, Zhang H, Morris ED, Glide-Hurst CK, Pai S, Traverso A, Wee L, Hadzic I, Lønne PI, Shen C, Liu T, Yang X. Artificial Intelligence in Radiation Therapy. IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES 2022; 6:158-181. [PMID: 35992632 PMCID: PMC9385128 DOI: 10.1109/trpms.2021.3107454] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
Artificial intelligence (AI) has great potential to transform the clinical workflow of radiotherapy. Since the introduction of deep neural networks, many AI-based methods have been proposed to address challenges in different aspects of radiotherapy. Commercial vendors have started to release AI-based tools that can be readily integrated to the established clinical workflow. To show the recent progress in AI-aided radiotherapy, we have reviewed AI-based studies in five major aspects of radiotherapy including image reconstruction, image registration, image segmentation, image synthesis, and automatic treatment planning. In each section, we summarized and categorized the recently published methods, followed by a discussion of the challenges, concerns, and future development. Given the rapid development of AI-aided radiotherapy, the efficiency and effectiveness of radiotherapy in the future could be substantially improved through intelligent automation of various aspects of radiotherapy.
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Affiliation(s)
- Yabo Fu
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322, USA
| | - Hao Zhang
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Eric D. Morris
- Department of Radiation Oncology, University of California-Los Angeles, Los Angeles, CA 90095, USA
| | - Carri K. Glide-Hurst
- Department of Human Oncology, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI 53792, USA
| | - Suraj Pai
- Maastricht University Medical Centre, Netherlands
| | | | - Leonard Wee
- Maastricht University Medical Centre, Netherlands
| | | | - Per-Ivar Lønne
- Department of Medical Physics, Oslo University Hospital, PO Box 4953 Nydalen, 0424 Oslo, Norway
| | - Chenyang Shen
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX 75002, USA
| | - Tian Liu
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322, USA
| | - Xiaofeng Yang
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322, USA
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Yousefi Moteghaed N, Mostaar A, Azadeh P. Generating pseudo-computerized tomography (P-CT) scan images from magnetic resonance imaging (MRI) images using machine learning algorithms based on fuzzy theory for radiotherapy treatment planning. Med Phys 2021; 48:7016-7027. [PMID: 34418104 DOI: 10.1002/mp.15174] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Revised: 07/09/2021] [Accepted: 08/03/2021] [Indexed: 12/26/2022] Open
Abstract
PURPOSE The substitution of computerized tomography (CT) with magnetic resonance imaging (MRI) has been investigated for external radiotherapy treatment planning. The present study aims to use pseudo-CT (P-CT) images created by MRI images to calculate the dose distribution for facilitating the treatment planning process. METHODS In this work, following image segmentation with a fuzzy clustering algorithm, an adaptive neuro-fuzzy algorithm was utilized to design the Hounsfield unit (HU) conversion model based on the features vector of MRI images. The model was generated on the set of extracted features from the gray-level co-occurrence matrices and the gray-level run-length matrices for 14 arbitrarily selected patients with brain malady. The performance of the algorithm was investigated on blind datasets through dose-volume histogram and isodose curve evaluations, using the RayPlan treatment planning system (TPS), along with the gamma analysis and statistical indices. RESULTS In the proposed approach, the mean absolute error within the range of 45.4 HU was found among the test data. Also, the relative dose difference between the planning target volume region of the CT and the P-CT was 0.5%, and the best gamma pass rate for 3%/3 mm was 97.2%. CONCLUSION The proposed method provides a satisfactory average error rate for the generation of P-CT data in the different parts of the brain region from a collection of MRI series. Also, dosimetric parameters evaluation shows good agreement between reference CT and related P-CT images.
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Affiliation(s)
- Niloofar Yousefi Moteghaed
- Department of Medical Physics and Biomedical Engineering, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Ahmad Mostaar
- Department of Medical Physics and Biomedical Engineering, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Payam Azadeh
- Department of Radiation Oncology, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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Lerner M, Medin J, Jamtheim Gustafsson C, Alkner S, Siversson C, Olsson LE. Clinical validation of a commercially available deep learning software for synthetic CT generation for brain. Radiat Oncol 2021; 16:66. [PMID: 33827619 PMCID: PMC8025544 DOI: 10.1186/s13014-021-01794-6] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Accepted: 03/23/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Most studies on synthetic computed tomography (sCT) generation for brain rely on in-house developed methods. They often focus on performance rather than clinical feasibility. Therefore, the aim of this work was to validate sCT images generated using a commercially available software, based on a convolutional neural network (CNN) algorithm, to enable MRI-only treatment planning for the brain in a clinical setting. METHODS This prospective study included 20 patients with brain malignancies of which 14 had areas of resected skull bone due to surgery. A Dixon magnetic resonance (MR) acquisition sequence for sCT generation was added to the clinical brain MR-protocol. The corresponding sCT images were provided by the software MRI Planner (Spectronic Medical AB, Sweden). sCT images were rigidly registered and resampled to CT for each patient. Treatment plans were optimized on CT and recalculated on sCT images for evaluation of dosimetric and geometric endpoints. Further analysis was also performed for the post-surgical cases. Clinical robustness in patient setup verification was assessed by rigidly registering cone beam CT (CBCT) to sCT and CT images, respectively. RESULTS All sCT images were successfully generated. Areas of bone resection due to surgery were accurately depicted. Mean absolute error of the sCT images within the body contour for all patients was 62.2 ± 4.1 HU. Average absorbed dose differences were below 0.2% for parameters evaluated for both targets and organs at risk. Mean pass rate of global gamma (1%/1 mm) for all patients was 100.0 ± 0.0% within PTV and 99.1 ± 0.6% for the full dose distribution. No clinically relevant deviations were found in the CBCT-sCT vs CBCT-CT image registrations. In addition, mean values of voxel-wise patient specific geometric distortion in the Dixon images for sCT generation were below 0.1 mm for soft tissue, and below 0.2 mm for air and bone. CONCLUSIONS This work successfully validated a commercially available CNN-based software for sCT generation. Results were comparable for sCT and CT images in both dosimetric and geometric evaluation, for both patients with and without anatomical anomalies. Thus, MRI Planner is feasible to use for radiotherapy treatment planning of brain tumours.
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Affiliation(s)
- Minna Lerner
- Radiation Physics, Department of Hematology, Oncology, and Radiation Physics, Skåne University Hospital, Lund, Sweden.
- Department of Translational Medicine, Medical Radiation Physics, Lund University, Malmö, Sweden.
| | - Joakim Medin
- Radiation Physics, Department of Hematology, Oncology, and Radiation Physics, Skåne University Hospital, Lund, Sweden
| | - Christian Jamtheim Gustafsson
- Radiation Physics, Department of Hematology, Oncology, and Radiation Physics, Skåne University Hospital, Lund, Sweden
- Department of Translational Medicine, Medical Radiation Physics, Lund University, Malmö, Sweden
| | - Sara Alkner
- Department of Clinical Sciences Lund, Oncology and Pathology, Lund University, Lund, Sweden
- Clinic of Oncology, Department of Hematology, Oncology and Radiation Physics, Skåne University Hospital, Lund, Sweden
| | | | - Lars E Olsson
- Radiation Physics, Department of Hematology, Oncology, and Radiation Physics, Skåne University Hospital, Lund, Sweden
- Department of Translational Medicine, Medical Radiation Physics, Lund University, Malmö, Sweden
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Smith M, Bambach S, Selvaraj B, Ho ML. Zero-TE MRI: Potential Applications in the Oral Cavity and Oropharynx. Top Magn Reson Imaging 2021; 30:105-115. [PMID: 33828062 DOI: 10.1097/rmr.0000000000000279] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
ABSTRACT Zero-echo time (ZTE) magnetic resonance imaging (MRI) is the newest in a family of MRI pulse sequences that involve ultrafast sequence readouts, permitting visualization of short-T2 tissues such as cortical bone. Inherent sequence properties enable rapid, high-resolution, quiet, and artifact-resistant imaging. ZTE can be performed as part of a "one-stop-shop" MRI examination for comprehensive evaluation of head and neck pathology. As a potential alternative to computed tomography for bone imaging, this approach could help reduce patient exposure to ionizing radiation and improve radiology resource utilization. Because ZTE is not yet widely used clinically, it is important to understand the technical limitations and pitfalls for diagnosis. Imaging cases are presented to demonstrate potential applications of ZTE for imaging of oral cavity, oropharynx, and jaw anatomy and pathology in adult and pediatric patients. Emerging studies indicate promise for future clinical implementation based on synthetic computed tomography image generation, 3D printing, and interventional applications.
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Affiliation(s)
- Mark Smith
- Department of Radiology, Nationwide Children's Hospital, Columbus, OH
| | - Sven Bambach
- Abigail Wexner Research Institute at Nationwide Children's Hospital, Columbus, OH
| | - Bhavani Selvaraj
- Department of Radiology, Nationwide Children's Hospital, Columbus, OH
| | - Mai-Lan Ho
- Department of Radiology, Nationwide Children's Hospital, Columbus, OH
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Liu X, Emami H, Nejad‐Davarani SP, Morris E, Schultz L, Dong M, K. Glide‐Hurst C. Performance of deep learning synthetic CTs for MR-only brain radiation therapy. J Appl Clin Med Phys 2021; 22:308-317. [PMID: 33410568 PMCID: PMC7856502 DOI: 10.1002/acm2.13139] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2020] [Revised: 12/02/2020] [Accepted: 12/03/2020] [Indexed: 12/31/2022] Open
Abstract
PURPOSE To evaluate the dosimetric and image-guided radiation therapy (IGRT) performance of a novel generative adversarial network (GAN) generated synthetic CT (synCT) in the brain and compare its performance for clinical use including conventional brain radiotherapy, cranial stereotactic radiosurgery (SRS), planar, and volumetric IGRT. METHODS AND MATERIALS SynCT images for 12 brain cancer patients (6 SRS, 6 conventional) were generated from T1-weighted postgadolinium magnetic resonance (MR) images by applying a GAN model with a residual network (ResNet) generator and a convolutional neural network (CNN) with 5 convolutional layers as the discriminator that classified input images as real or synthetic. Following rigid registration, clinical structures and treatment plans derived from simulation CT (simCT) images were transferred to synCTs. Dose was recalculated for 15 simCT/synCT plan pairs using fixed monitor units. Two-dimensional (2D) gamma analysis (2%/2 mm, 1%/1 mm) was performed to compare dose distributions at isocenter. Dose-volume histogram (DVH) metrics (D95% , D99% , D0.2cc, and D0.035cc ) were assessed for the targets and organ at risks (OARs). IGRT performance was evaluated via volumetric registration between cone beam CT (CBCT) to synCT/simCT and planar registration between KV images to synCT/simCT digital reconstructed radiographs (DRRs). RESULTS Average gamma passing rates at 1%/1mm and 2%/2mm were 99.0 ± 1.5% and 99.9 ± 0.2%, respectively. Excellent agreement in DVH metrics was observed (mean difference ≤0.10 ± 0.04 Gy for targets, 0.13 ± 0.04 Gy for OARs). The population averaged mean difference in CBCT-synCT registrations were <0.2 mm and 0.1 degree different from simCT-based registrations. The mean difference between kV-synCT DRR and kV-simCT DRR registrations was <0.5 mm with no statistically significant differences observed (P > 0.05). An outlier with a large resection cavity exhibited the worst-case scenario. CONCLUSION Brain GAN synCTs demonstrated excellent performance for dosimetric and IGRT endpoints, offering potential use in high precision brain cancer therapy.
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Affiliation(s)
- Xiaoning Liu
- Department of Medical PhysicsMemorial Sloan Kettering Cancer CenterMiddletownNJUSA
| | - Hajar Emami
- Department of Computer ScienceWayne State UniversityDetroitMIUSA
| | | | - Eric Morris
- Department of Radiation OncologyUniversity of California—Los AngelesLos AngelesCAUSA
| | - Lonni Schultz
- Department of Public Health SciencesHenry Ford Health SystemDetroitMIUSA
| | - Ming Dong
- Department of Computer ScienceWayne State UniversityDetroitMIUSA
| | - Carri K. Glide‐Hurst
- Department of Human OncologySchool of Medicine and Public HeathUniversity of Wisconsin – MadisonMadisonWIUSA
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Kiser KJ, Smith BD, Wang J, Fuller CD. "Après Mois, Le Déluge": Preparing for the Coming Data Flood in the MRI-Guided Radiotherapy Era. Front Oncol 2019; 9:983. [PMID: 31632914 PMCID: PMC6779062 DOI: 10.3389/fonc.2019.00983] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2019] [Accepted: 09/16/2019] [Indexed: 12/17/2022] Open
Abstract
Magnetic resonance imaging provides a sea of quantitative and semi-quantitative data. While radiation oncologists already navigate a pool of clinical (semantic) and imaging data, the tide will swell with the advent of hybrid MRI/linear accelerator devices and increasing interest in MRI-guided radiotherapy (MRIgRT), including adaptive MRIgRT. The variety of MR sequences (of greater complexity than the single parameter Hounsfield unit of CT scanning routinely used in radiotherapy), the workflow of adaptive fractionation, and the sheer quantity of daily images acquired are challenges for scaling this technology. Biomedical informatics, which is the science of information in biomedicine, can provide helpful insights for this looming transition. Funneling MRIgRT data into clinically meaningful information streams requires committing to the flow of inter-institutional data accessibility and interoperability initiatives, standardizing MRIgRT dosimetry methods, streamlining MR linear accelerator workflow, and standardizing MRI acquisition and post-processing. This review will attempt to conceptually ford these topics using clinical informatics approaches as a theoretical bridge.
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Affiliation(s)
- Kendall J Kiser
- John P. and Kathrine G. McGovern Medical School, University of Texas Health Science Center, Houston, TX, United States.,School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX, United States.,Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Benjamin D Smith
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Jihong Wang
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Clifton D Fuller
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, United States
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