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Almeida ND, Shekher R, Pepin A, Schrand TV, Goulenko V, Singh AK, Fung-Kee-Fung S. Artificial Intelligence Potential Impact on Resident Physician Education in Radiation Oncology. Adv Radiat Oncol 2024; 9:101505. [PMID: 38799112 PMCID: PMC11127091 DOI: 10.1016/j.adro.2024.101505] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Accepted: 03/16/2024] [Indexed: 05/29/2024] Open
Affiliation(s)
- Neil D. Almeida
- Department of Radiation Medicine, Roswell Park Comprehensive Cancer Center, Buffalo, New York
| | - Rohil Shekher
- Department of Radiation Medicine, Roswell Park Comprehensive Cancer Center, Buffalo, New York
| | - Abigail Pepin
- Department of Radiation Oncology, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania
| | - Tyler V. Schrand
- Department of Radiation Medicine, Roswell Park Comprehensive Cancer Center, Buffalo, New York
- Department of Chemistry, Bowling Green State University, Bowling Green, Ohio
| | - Victor Goulenko
- Department of Radiation Medicine, Roswell Park Comprehensive Cancer Center, Buffalo, New York
| | - Anurag K. Singh
- Department of Radiation Medicine, Roswell Park Comprehensive Cancer Center, Buffalo, New York
| | - Simon Fung-Kee-Fung
- Department of Radiation Medicine, Roswell Park Comprehensive Cancer Center, Buffalo, New York
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Stenhouse K, Roumeliotis M, Ciunkiewicz P, Martell K, Quirk S, Banerjee R, Doll C, Phan T, Yanushkevich S, McGeachy P. Prospective validation of a machine learning model for applicator and hybrid interstitial needle selection in high-dose-rate (HDR) cervical brachytherapy. Brachytherapy 2024; 23:368-376. [PMID: 38538415 DOI: 10.1016/j.brachy.2024.02.008] [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] [Received: 12/28/2023] [Revised: 02/23/2024] [Accepted: 02/26/2024] [Indexed: 05/18/2024]
Abstract
PURPOSE To Demonstrate the clinical validation of a machine learning (ML) model for applicator and interstitial needle prediction in gynecologic brachytherapy through a prospective clinical study in a single institution. METHODS The study included cervical cancer patients receiving high-dose-rate brachytherapy using intracavitary (IC) or hybrid interstitial (IC/IS) applicators. For each patient, the primary radiation oncologist contoured the high-risk clinical target volume on a pre-brachytherapy MRI, indicated the approximate applicator location, and made a clinical determination of the first fraction applicator. A pre-trained ML model predicted the applicator and IC/IS needle arrangement using tumor geometry. Following the first fraction, ML and radiation oncologist predictions were compared and a replanning study determined the applicator providing optimal organ-at-risk (OAR) dosimetry. The ML-predicted applicator and needle arrangement and the clinical determination were compared to this dosimetric ground truth. RESULTS Ten patients were accrued from December 2020 to October 2022. Compared to the dosimetrically optimal applicator, both the radiation oncologist and ML had an accuracy of 70%. ML demonstrated better identification of patients requiring IC/IS applicators and provided balanced IC and IC/IS predictions. The needle selection model achieved an average accuracy of 82.5%. ML-predicted needle arrangements matched or improved plan quality when compared to clinically selected arrangements. Overall, ML predictions led to an average total improvement of 2.0 Gy to OAR doses over three treatment fractions when compared to clinical predictions. CONCLUSION In the context of a single institution study, the presented ML model demonstrates valuable decision-support for the applicator and needle selection process with the potential to provide improved dosimetry. Future work will include a multi-center study to assess generalizability.
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Affiliation(s)
- Kailyn Stenhouse
- Department of Physics and Astronomy, University of Calgary, Calgary, Alberta, Canada; Department of Medical Physics, Tom Baker Cancer Centre, Calgary, Alberta, Canada.
| | - Michael Roumeliotis
- Department of Physics and Astronomy, University of Calgary, Calgary, Alberta, Canada; Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, MD.
| | - Philip Ciunkiewicz
- Department of Biomedical Engineering, University of Calgary, Calgary, Alberta, Canada
| | - Kevin Martell
- Department of Oncology, University of Calgary, Calgary, Alberta, Canada
| | - Sarah Quirk
- Department of Physics and Astronomy, University of Calgary, Calgary, Alberta, Canada; Department of Radiation Oncology, Brigham and Women's Hospital, Boston, MA
| | - Robyn Banerjee
- Department of Oncology, University of Calgary, Calgary, Alberta, Canada
| | - Corinne Doll
- Department of Oncology, University of Calgary, Calgary, Alberta, Canada
| | - Tien Phan
- Department of Oncology, University of Calgary, Calgary, Alberta, Canada
| | - Svetlana Yanushkevich
- Department of Electrical and Computer Engineering, University of Calgary, Calgary, Alberta, Canada
| | - Philip McGeachy
- Department of Physics and Astronomy, University of Calgary, Calgary, Alberta, Canada; Department of Medical Physics, Tom Baker Cancer Centre, Calgary, Alberta, Canada; Department of Oncology, University of Calgary, Calgary, Alberta, Canada
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Fechter T, Sachpazidis I, Baltas D. The use of deep learning in interventional radiotherapy (brachytherapy): A review with a focus on open source and open data. Z Med Phys 2024; 34:180-196. [PMID: 36376203 PMCID: PMC11156786 DOI: 10.1016/j.zemedi.2022.10.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Revised: 10/07/2022] [Accepted: 10/10/2022] [Indexed: 11/13/2022]
Abstract
Deep learning advanced to one of the most important technologies in almost all medical fields. Especially in areas, related to medical imaging it plays a big role. However, in interventional radiotherapy (brachytherapy) deep learning is still in an early phase. In this review, first, we investigated and scrutinised the role of deep learning in all processes of interventional radiotherapy and directly related fields. Additionally, we summarised the most recent developments. For better understanding, we provide explanations of key terms and approaches to solving common deep learning problems. To reproduce results of deep learning algorithms both source code and training data must be available. Therefore, a second focus of this work is on the analysis of the availability of open source, open data and open models. In our analysis, we were able to show that deep learning plays already a major role in some areas of interventional radiotherapy, but is still hardly present in others. Nevertheless, its impact is increasing with the years, partly self-propelled but also influenced by closely related fields. Open source, data and models are growing in number but are still scarce and unevenly distributed among different research groups. The reluctance in publishing code, data and models limits reproducibility and restricts evaluation to mono-institutional datasets. The conclusion of our analysis is that deep learning can positively change the workflow of interventional radiotherapy but there is still room for improvements when it comes to reproducible results and standardised evaluation methods.
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Affiliation(s)
- Tobias Fechter
- Division of Medical Physics, Department of Radiation Oncology, Medical Center University of Freiburg, Germany; Faculty of Medicine, University of Freiburg, Germany; German Cancer Consortium (DKTK), Partner Site Freiburg, Germany.
| | - Ilias Sachpazidis
- Division of Medical Physics, Department of Radiation Oncology, Medical Center University of Freiburg, Germany; Faculty of Medicine, University of Freiburg, Germany; German Cancer Consortium (DKTK), Partner Site Freiburg, Germany
| | - Dimos Baltas
- Division of Medical Physics, Department of Radiation Oncology, Medical Center University of Freiburg, Germany; Faculty of Medicine, University of Freiburg, Germany; German Cancer Consortium (DKTK), Partner Site Freiburg, Germany
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Grigo J, Karius A, Hanspach J, Mücke L, Laun FB, Huang Y, Strnad V, Fietkau R, Bert C, Putz F. Toward a deep learning-based magnetic resonance imaging only workflow for postimplant dosimetry in I-125 seed brachytherapy for prostate cancer. Brachytherapy 2024; 23:96-105. [PMID: 38008648 DOI: 10.1016/j.brachy.2023.09.009] [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: 09/06/2023] [Accepted: 09/07/2023] [Indexed: 11/28/2023]
Abstract
BACKGROUND AND PURPOSE The current standard imaging-technique for creating postplans in seed prostate brachytherapy is computed tomography (CT), that is associated with additional radiation exposure and poor soft tissue contrast. To establish a magnetic resonance imaging (MRI) only workflow combining improved tissue contrast and high seed detectability, a deep learning-approach for automatic seed segmentation on MRI-scans was developed. MATERIAL AND METHODS Patients treated with I-125 seed brachytherapy received a postplan-CT and a 1.5 T MRI-scan on nominal day 30 after implantation. For MRI-based seed visualization, DIXON-sequences were acquired and deep learning-based quantitative susceptibility maps (QSM) were generated from 3D-gradient-echo-sequences from 20 patients. Seed segmentations created on CT served as ground truth. For automatic seed segmentation on MRI, a 3D nnU-net model was trained using QSM and DIXON, both solely and combined. RESULTS Of the implanted seeds 94.8 ± 2.4% were detected with deep learning automatic segmentation entrained on both QSM and DIXON data. Models trained on the individual sequence data-sets performed worse with detection rates of 87.5 ± 2.6% or 88.6 ± 7.5% for QSM and DIXON respectively. The seed centers identified on CT versus QSM and DIXON were on average 1.8 ± 1.3 mm apart. Postimplant dosimetry for evaluation of positioning inaccuracies revealed only small variations of up to 0.4 ± 4.26 Gy in D90 (dose 90% of the prostate receives) between the standard CT-approach and our MRI-only workflow. CONCLUSION The proposed deep learning-based MRI-only workflow provided a promisingly accurate and robust seed localization and thus has the potential to compete with current state-of-the-art CT-based postimplant dosimetry in the future.
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Affiliation(s)
- Johanna Grigo
- Department of Radiation Oncology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany; Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany.
| | - Andre Karius
- Department of Radiation Oncology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany; Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany
| | - Jannis Hanspach
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Lion Mücke
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Frederik B Laun
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Yixing Huang
- Department of Radiation Oncology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany; Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany
| | - Vratislav Strnad
- Department of Radiation Oncology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany; Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany
| | - Rainer Fietkau
- Department of Radiation Oncology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany; Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany
| | - Christoph Bert
- Department of Radiation Oncology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany; Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany
| | - Florian Putz
- Department of Radiation Oncology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany; Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany
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Zhao JZ, Ni R, Chow R, Rink A, Weersink R, Croke J, Raman S. Artificial intelligence applications in brachytherapy: A literature review. Brachytherapy 2023; 22:429-445. [PMID: 37248158 DOI: 10.1016/j.brachy.2023.04.003] [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: 02/02/2023] [Revised: 04/02/2023] [Accepted: 04/07/2023] [Indexed: 05/31/2023]
Abstract
PURPOSE Artificial intelligence (AI) has the potential to simplify and optimize various steps of the brachytherapy workflow, and this literature review aims to provide an overview of the work done in this field. METHODS AND MATERIALS We conducted a literature search in June 2022 on PubMed, Embase, and Cochrane for papers that proposed AI applications in brachytherapy. RESULTS A total of 80 papers satisfied inclusion/exclusion criteria. These papers were categorized as follows: segmentation (24), registration and image processing (6), preplanning (13), dose prediction and treatment planning (11), applicator/catheter/needle reconstruction (16), and quality assurance (10). AI techniques ranged from classical models such as support vector machines and decision tree-based learning to newer techniques such as U-Net and deep reinforcement learning, and were applied to facilitate small steps of a process (e.g., optimizing applicator selection) or even automate the entire step of the workflow (e.g., end-to-end preplanning). Many of these algorithms demonstrated human-level performance and offer significant improvements in speed. CONCLUSIONS AI has potential to augment, automate, and/or accelerate many steps of the brachytherapy workflow. We recommend that future studies adhere to standard reporting guidelines. We also stress the importance of using larger sample sizes and reporting results using clinically interpretable measures.
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Affiliation(s)
- Jonathan Zl Zhao
- Princess Margaret Hospital Cancer Centre, Radiation Medicine Program, Toronto, Canada; Temerty Faculty of Medicine, University of Toronto, Toronto, Canada
| | - Ruiyan Ni
- Princess Margaret Hospital Cancer Centre, Radiation Medicine Program, Toronto, Canada; Department of Medical Biophysics, University of Toronto, Toronto, Canada
| | - Ronald Chow
- Princess Margaret Hospital Cancer Centre, Radiation Medicine Program, Toronto, Canada; Temerty Faculty of Medicine, University of Toronto, Toronto, Canada; Institute of Biomedical Engineering, University of Toronto, Toronto, Canada
| | - Alexandra Rink
- Princess Margaret Hospital Cancer Centre, Radiation Medicine Program, Toronto, Canada; Department of Radiation Oncology, University of Toronto, Toronto, Canada; Department of Medical Biophysics, University of Toronto, Toronto, Canada
| | - Robert Weersink
- Princess Margaret Hospital Cancer Centre, Radiation Medicine Program, Toronto, Canada; Department of Radiation Oncology, University of Toronto, Toronto, Canada; Department of Medical Biophysics, University of Toronto, Toronto, Canada; Institute of Biomedical Engineering, University of Toronto, Toronto, Canada
| | - Jennifer Croke
- Princess Margaret Hospital Cancer Centre, Radiation Medicine Program, Toronto, Canada; Department of Radiation Oncology, University of Toronto, Toronto, Canada
| | - Srinivas Raman
- Princess Margaret Hospital Cancer Centre, Radiation Medicine Program, Toronto, Canada; Department of Radiation Oncology, University of Toronto, Toronto, Canada.
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Dimov AV, Li J, Nguyen TD, Roberts AG, Spincemaille P, Straub S, Zun Z, Prince MR, Wang Y. QSM Throughout the Body. J Magn Reson Imaging 2023; 57:1621-1640. [PMID: 36748806 PMCID: PMC10192074 DOI: 10.1002/jmri.28624] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 01/19/2023] [Accepted: 01/20/2023] [Indexed: 02/08/2023] Open
Abstract
Magnetic materials in tissue, such as iron, calcium, or collagen, can be studied using quantitative susceptibility mapping (QSM). To date, QSM has been overwhelmingly applied in the brain, but is increasingly utilized outside the brain. QSM relies on the effect of tissue magnetic susceptibility sources on the MR signal phase obtained with gradient echo sequence. However, in the body, the chemical shift of fat present within the region of interest contributes to the MR signal phase as well. Therefore, correcting for the chemical shift effect by means of water-fat separation is essential for body QSM. By employing techniques to compensate for cardiac and respiratory motion artifacts, body QSM has been applied to study liver iron and fibrosis, heart chamber blood and placenta oxygenation, myocardial hemorrhage, atherosclerotic plaque, cartilage, bone, prostate, breast calcification, and kidney stone.
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Affiliation(s)
- Alexey V. Dimov
- Department of Radiology, Weill Cornell Medicine, New York, NY, United States
| | - Jiahao Li
- Department of Radiology, Weill Cornell Medicine, New York, NY, United States
| | - Thanh D. Nguyen
- Department of Radiology, Weill Cornell Medicine, New York, NY, United States
| | | | - Pascal Spincemaille
- Department of Radiology, Weill Cornell Medicine, New York, NY, United States
| | - Sina Straub
- Department of Radiology, Mayo Clinic, Jacksonville, FL, United States
| | - Zungho Zun
- Department of Radiology, Weill Cornell Medicine, New York, NY, United States
| | - Martin R. Prince
- Department of Radiology, Weill Cornell Medicine, New York, NY, United States
| | - Yi Wang
- Department of Radiology, Weill Cornell Medicine, New York, NY, United States
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8
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Shi C, Qiu Z, Liu C, Chen H, Ye Y, Zhu Y, Liu X, Zheng H, Liang D, Wang H. Rapid variable flip angle positive susceptibility contrast imaging for clinical metal seeds. JOURNAL OF MAGNETIC RESONANCE (SAN DIEGO, CALIF. : 1997) 2022; 340:107232. [PMID: 35588593 DOI: 10.1016/j.jmr.2022.107232] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Revised: 04/25/2022] [Accepted: 04/26/2022] [Indexed: 06/15/2023]
Abstract
Positive susceptibility contrast imaging (PSCI) based on susceptibility mapping exhibits excellent efficacy for visualizing magnetic resonance (MR)-compatible metallic devices because of their high magnetic susceptibility compared to that of human tissues. However, the long-acquisition time required by the two-dimensional fast spin echo (2D FSE)-based PSCI approach, impedes its practical applications in 3D imaging. In this study, a three-dimensional (3D) susceptibility-based variable flip angle (vFA) FSE sequence was proposed to accelerate data acquisition in the clinical radiotherapy applications of ex vivo and in vivo rapid 3D PSCI for the imaging of metal seeds. Here, the proposed scheme applied a 3D modulated vFA technique for refocused imaging with an extended echo-train sequence for sampling data. The scheme integrated the projection-onto-dipole fields (PDF) to remove the background field and accelerate PSCI by using a compressive sensing framework with a variable-densitysampling mask. The experiments involved some gelatin phantoms, porcine tissues and patients with scapular tumors and brachytherapy seeds. All of the experimental results showed that the proposed scheme could accelerate data acquisition of 3D PSCI at the reduction factors of 2 ∼ 5 while accurately localizing the actual positions of the brachytherapy seeds in the ex vivo and in vivo applications. The results were compared with those of the existing methods, including susceptibility gradient mapping using the original resolution (SUMO) and gradient echo acquisition for superparamagnetic particle (GRASP).
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Affiliation(s)
- Caiyun Shi
- Paul C. Lauterbur Research Centre for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China; Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, Guangdong, China
| | - Zhilang Qiu
- Paul C. Lauterbur Research Centre for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China
| | - Congcong Liu
- Paul C. Lauterbur Research Centre for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China; Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, Guangdong, China
| | - Hanwei Chen
- Department of Radiology, Panyu Central Hospital, Guangzhou, China; Medical Imaging Institute of Panyu, Guangzhou, China
| | - Yufeng Ye
- Department of Radiology, Panyu Central Hospital, Guangzhou, China; Medical Imaging Institute of Panyu, Guangzhou, China
| | - Yanjie Zhu
- Paul C. Lauterbur Research Centre for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China
| | - Xin Liu
- Paul C. Lauterbur Research Centre for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China
| | - Hairong Zheng
- Paul C. Lauterbur Research Centre for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China
| | - Dong Liang
- Paul C. Lauterbur Research Centre for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China; Research Centre for Medical AI, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China.
| | - Haifeng Wang
- Paul C. Lauterbur Research Centre for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China.
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Song WY, Robar JL, Morén B, Larsson T, Carlsson Tedgren Å, Jia X. Emerging technologies in brachytherapy. Phys Med Biol 2021; 66. [PMID: 34710856 DOI: 10.1088/1361-6560/ac344d] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2021] [Accepted: 10/28/2021] [Indexed: 01/15/2023]
Abstract
Brachytherapy is a mature treatment modality. The literature is abundant in terms of review articles and comprehensive books on the latest established as well as evolving clinical practices. The intent of this article is to part ways and look beyond the current state-of-the-art and review emerging technologies that are noteworthy and perhaps may drive the future innovations in the field. There are plenty of candidate topics that deserve a deeper look, of course, but with practical limits in this communicative platform, we explore four topics that perhaps is worthwhile to review in detail at this time. First, intensity modulated brachytherapy (IMBT) is reviewed. The IMBT takes advantage ofanisotropicradiation profile generated through intelligent high-density shielding designs incorporated onto sources and applicators such to achieve high quality plans. Second, emerging applications of 3D printing (i.e. additive manufacturing) in brachytherapy are reviewed. With the advent of 3D printing, interest in this technology in brachytherapy has been immense and translation swift due to their potential to tailor applicators and treatments customizable to each individual patient. This is followed by, in third, innovations in treatment planning concerning catheter placement and dwell times where new modelling approaches, solution algorithms, and technological advances are reviewed. And, fourth and lastly, applications of a new machine learning technique, called deep learning, which has the potential to improve and automate all aspects of brachytherapy workflow, are reviewed. We do not expect that all ideas and innovations reviewed in this article will ultimately reach clinic but, nonetheless, this review provides a decent glimpse of what is to come. It would be exciting to monitor as IMBT, 3D printing, novel optimization algorithms, and deep learning technologies evolve over time and translate into pilot testing and sensibly phased clinical trials, and ultimately make a difference for cancer patients. Today's fancy is tomorrow's reality. The future is bright for brachytherapy.
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Affiliation(s)
- William Y Song
- Department of Radiation Oncology, Virginia Commonwealth University, Richmond, Virginia, United States of America
| | - James L Robar
- Department of Radiation Oncology, Dalhousie University, Halifax, Nova Scotia, Canada
| | - Björn Morén
- Department of Mathematics, Linköping University, Linköping, Sweden
| | - Torbjörn Larsson
- Department of Mathematics, Linköping University, Linköping, Sweden
| | - Åsa Carlsson Tedgren
- Radiation Physics, Department of Medical and Health Sciences, Linköping University, Linköping, Sweden.,Medical Radiation Physics and Nuclear Medicine, Karolinska University Hospital, Stockholm, Sweden.,Department of Oncology Pathology, Karolinska Institute, Stockholm, Sweden
| | - Xun Jia
- Innovative Technology Of Radiotherapy Computations and Hardware (iTORCH) Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, United States of America
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Kan H, Tsuchiya T, Yamada M, Kunitomo H, Kasai H, Shibamoto Y. Delineation of prostatic calcification using quantitative susceptibility mapping: Spatial accuracy for magnetic resonance-only radiotherapy planning. J Appl Clin Med Phys 2021; 23:e13469. [PMID: 34726833 PMCID: PMC8833270 DOI: 10.1002/acm2.13469] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Revised: 10/07/2021] [Accepted: 10/19/2021] [Indexed: 11/16/2022] Open
Abstract
To investigate the spatial accuracy of delineating prostatic calcifications by quantitative susceptibility mapping (QSM) in comparison with computed tomography (CT), we conducted phantom and human studies. Five differently‐sized spherical hydroxyapatites mimicking prostatic calcification (pseudo‐calcification) were arranged in the order of their sizes at the center of a plastic container filled with gelatin. This calcification phantom underwent magnetic resonance (MR) imaging, including the multiple spoiled gradient‐echo sequences (SPGR) for the QSM and CT as a reference. The volume of each pseudo‐calcification and center‐to‐center distance between the pseudo‐calcifications delineated by QSM and CT were measured. In the human study, eight patients with prostate cancer who underwent radiation therapy and had some prostatic calcifications were included. The patients underwent CT and SPGR and modified DIXON sequence for MR‐only simulation. The hybrid QSM processing combined with the complex signals in the SPGR and water and fat fraction maps estimated from the modified DIXON sequence were used to reconstruct the pelvic susceptibility map in humans. The threshold of CT numbers was set at 130 HU, while the QSM images were manually segmented in the calcification phantom and human studies. In the phantom study, there was an excellent agreement in the pseudo‐calcification volumes between QSM and CT (y = 1.02x – 7.38, R2 = 0.99). The signal profiles had similar trends in CT and QSM. The center‐to‐center distances between the pseudo‐calcifications in the phantom were also identical in QSM and CT. The calcification volumes were almost identical between the QSM and CT in the human study (y = 0.95x – 9.32, R2 = 1.00). QSM can offer geometric and volumetric accuracies to delineate prostatic calcifications, similar to CT. The prostatic calcification delineated by QSM may facilitate image‐guided radiotherapy in the MR‐only simulation workflow.
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Affiliation(s)
- Hirohito Kan
- Department of Integrated Health Sciences, Graduate School of Medicine, Nagoya University, Nagoya, Japan.,Department of Radiology, Graduate School of Medical Sciences, Nagoya City University, Nagoya, Japan
| | - Takahiro Tsuchiya
- Department of Radiology, Nagoya City University Hospital, Nagoya City University, Nagoya, Japan
| | - Masato Yamada
- Department of Radiology, Nagoya City University Hospital, Nagoya City University, Nagoya, Japan
| | - Hiroshi Kunitomo
- Department of Radiology, Nagoya City University Hospital, Nagoya City University, Nagoya, Japan
| | - Harumasa Kasai
- Department of Radiology, Nagoya City University Hospital, Nagoya City University, Nagoya, Japan
| | - Yuta Shibamoto
- Department of Radiology, Graduate School of Medical Sciences, Nagoya City University, Nagoya, Japan
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Emmerich J, Bachert P, Ladd ME, Straub S. A novel phantom with dia- and paramagnetic substructure for quantitative susceptibility mapping and relaxometry. Phys Med 2021; 88:278-284. [PMID: 34332237 DOI: 10.1016/j.ejmp.2021.07.015] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/20/2020] [Revised: 06/22/2021] [Accepted: 07/12/2021] [Indexed: 12/20/2022] Open
Abstract
PURPOSE A phantom is presented in this study that allows for an experimental evaluation of QSM reconstruction algorithms. The phantom contains susceptibility producing particles with dia- and paramagnetic properties embedded in an MRI visible medium and is suitable to assess the performance of algorithms that attempt to separate isotropic dia- and paramagnetic susceptibility at the sub-voxel level. METHODS The phantom was built from calcium carbonate (diamagnetic) and tungsten carbide particles (paramagnetic) embedded in gelatin and surrounded by agarose gel. Different mass fractions and mixing ratios of both susceptibility sources were used. Gradient echo data were acquired at 1.5 T, 3 T and 7 T. Susceptibility maps were calculated using the MEDI toolbox and relaxation rates ΔR2∗ were determined using exponential fitting. RESULTS Relaxation rates as well as susceptibility values generally coincide with the theoretical values for particles fulfilling the assumptions of the the static dephasing regime with stronger deviations for relaxation rates at higher field strength and for high susceptibility values. MRI raw data are available for free academic use as supplementary material. CONCLUSIONS In this study, a susceptibility phantom is presented that might find its application in the development and quantitative validation of current and future QSM reconstruction algorithms which aim to separate the influence of isotropic dia- and paramagnetic substructure in quantitative susceptibility mapping.
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Affiliation(s)
- Julian Emmerich
- Division of Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany; Faculty of Physics and Astronomy, Heidelberg University, Heidelberg, Germany
| | - Peter Bachert
- Division of Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany; Faculty of Physics and Astronomy, Heidelberg University, Heidelberg, Germany
| | - Mark E Ladd
- Division of Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany; Faculty of Physics and Astronomy, Heidelberg University, Heidelberg, Germany; Faculty of Medicine, Heidelberg University, Heidelberg, Germany
| | - Sina Straub
- Division of Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany.
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12
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Nosrati R, Lam WW, Paudel M, Pejović-Milić A, Morton G, Stanisz GJ. Feasibility of using a single MRI acquisition for fiducial marker localization and synthetic CT generation towards MRI-only prostate radiation therapy treatment planning. Biomed Phys Eng Express 2021; 7. [PMID: 34034242 DOI: 10.1088/2057-1976/ac0501] [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: 02/12/2021] [Accepted: 05/25/2021] [Indexed: 11/12/2022]
Abstract
Purpose.To investigate the feasibility of using a single MRI acquisition for fiducial marker identification and synthetic CT (sCT) generation towards MRI-only treatment planning for prostate external beam radiation therapy (EBRT).Methods.Seven prostate cancer patients undergoing EBRT, each with three implanted gold fiducial markers, participated in this study. In addition to the planning CT scan, all patients were scanned on a 3 T MR scanner with a 3D double-echo gradient echo (GRE) sequence. Quantitative susceptibility mapping (QSM) was performed for marker localization. QSM-derived marker positions were compared to those from CT. The bulk density assignment technique for sCT generation was adopted. The magnitude GRE images were segmented into muscle, bone, fat, and air using a combination of unsupervised intensity-based classification of soft tissue and convolutional neural networks (CNN) for bone segmentation.Results.All implanted markers were visualized and accurately identified (average error: 0.7 ± 0.5 mm). QSM generated distinctive contrast for hemorrhage, calcifications, and gold fiducial markers. The estimated susceptibility/HU values on QSM/CT for gold and calcifications were 31.5 ± 2.9 ppm/1220 ± 100 HU and 14.6 ± 0.9 ppm/440 ± 100 HU, respectively. The intensity-based soft tissue classification resulted in an average Dice score of 0.97 ± 0.02; bone segmentation using CNN resulted in an average Dice score of 0.93 ± 0.03.Conclusion.This work indicates the feasibility of simultaneous fiducial marker identification and sCT generation using a single MRI acquisition. Future works includes evaluation of the proposed method in a large cohort of patients with optimized acquisition parameters as well as dosimetric evaluations.
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Affiliation(s)
- R Nosrati
- Harvard Medical School, Boston, MA, United States of America.,Boston Children's Hospital, Boston, MA, United States of America
| | - W W Lam
- Sunnybrook Health Sciences Centre, ON, Canada
| | - M Paudel
- Sunnybrook Health Sciences Centre, ON, Canada.,University of Toronto, Toronto, ON, Canada
| | | | - G Morton
- Sunnybrook Health Sciences Centre, ON, Canada.,University of Toronto, Toronto, ON, Canada
| | - G J Stanisz
- Sunnybrook Health Sciences Centre, ON, Canada.,University of Toronto, Toronto, ON, Canada
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13
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Banerjee S, Goyal S, Mishra S, Gupta D, Bisht SS, K V, Narang K, Kataria T. Artificial intelligence in brachytherapy: a summary of recent developments. Br J Radiol 2021; 94:20200842. [PMID: 33914614 DOI: 10.1259/bjr.20200842] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
Abstract
Artificial intelligence (AI) applications, in the form of machine learning and deep learning, are being incorporated into practice in various aspects of medicine, including radiation oncology. Ample evidence from recent publications explores its utility and future use in external beam radiotherapy. However, the discussion on its role in brachytherapy is sparse. This article summarizes available current literature and discusses potential uses of AI in brachytherapy, including future directions. AI has been applied for brachytherapy procedures during almost all steps, starting from decision-making till treatment completion. AI use has led to improvement in efficiency and accuracy by reducing the human errors and saving time in certain aspects. Apart from direct use in brachytherapy, AI also contributes to contemporary advancements in radiology and associated sciences that can affect brachytherapy decisions and treatment. There is a renewal of interest in brachytherapy as a technique in recent years, contributed largely by the understanding that contemporary advances such as intensity modulated radiotherapy and stereotactic external beam radiotherapy cannot match the geometric gains and conformality of brachytherapy, and the integrated efforts of international brachytherapy societies to promote brachytherapy training and awareness. Use of AI technologies may consolidate it further by reducing human effort and time. Prospective validation over larger studies and incorporation of AI technologies for a larger patient population would help improve the efficiency and acceptance of brachytherapy. The enthusiasm favoring AI needs to be balanced against the short duration and quantum of experience with AI in limited patient subsets, need for constant learning and re-learning to train the AI algorithms, and the inevitability of humans having to take responsibility for the correctness and safety of treatments.
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Affiliation(s)
- Susovan Banerjee
- Division of Radiation Oncology, Medanta- The Medicity, Gurgaon, Haryana, India
| | - Shikha Goyal
- Department of Radiotherapy, Post Graduate Institute of Medical Education and Research, Chandigarh, India
| | - Saumyaranjan Mishra
- Division of Radiation Oncology, Medanta- The Medicity, Gurgaon, Haryana, India
| | - Deepak Gupta
- Division of Radiation Oncology, Medanta- The Medicity, Gurgaon, Haryana, India
| | - Shyam Singh Bisht
- Division of Radiation Oncology, Medanta- The Medicity, Gurgaon, Haryana, India
| | - Venketesan K
- Division of Radiation Oncology, Medanta- The Medicity, Gurgaon, Haryana, India
| | - Kushal Narang
- Division of Radiation Oncology, Medanta- The Medicity, Gurgaon, Haryana, India
| | - Tejinder Kataria
- Division of Radiation Oncology, Medanta- The Medicity, Gurgaon, Haryana, India
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14
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Fortier V, Fortin MA, Pater P, Souhami L, Levesque IR. A role for magnetic susceptibility in synthetic computed tomography. Phys Med 2021; 85:137-146. [PMID: 34004446 DOI: 10.1016/j.ejmp.2021.05.001] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/07/2021] [Revised: 04/22/2021] [Accepted: 05/03/2021] [Indexed: 12/12/2022] Open
Abstract
PURPOSE Radiotherapy treatment planning based on magnetic resonance imaging (MRI) benefits from increased soft-tissue contrast and functional imaging. MRI-only planning is attractive but limited by the lack of electron density information required for dose calculation, and the difficulty to differentiate air and bone. MRI can map magnetic susceptibility to separate bone from air. A method is introduced to produce synthetic CT (sCT) through automatic voxel-wise assignment of CT numbers from an MRI dataset processed that includes magnetic susceptibility mapping. METHODS Volumetric multi-echo gradient echo datasets were acquired in the heads of five healthy volunteers and fourteen patients with cancer using a 3 T MRI system. An algorithm for CT synthesis was designed using the volunteer data, based on fuzzy c-means clustering and adaptive thresholding of the MR data (magnitude, fat, water, and magnetic susceptibility). Susceptibility mapping was performed using a modified version of the iterative phase replacement algorithm. On patient data, the algorithm was assessed by direct comparison to X-ray computed tomography (CT) scans. RESULTS The skull, spine, teeth, and major sinuses were clearly distinguished in all sCT, from healthy volunteers and patients. The mean absolute CT number error between X-ray CT and sCT in patients ranged from 78 and 134 HU. CONCLUSION Susceptibility mapping using MRI can differentiate air and bone for CT synthesis. The proposed method is automated, fast, and based on a commercially available MRI pulse sequence. The method avoids registration errors and does not rely on a priori information, making it suitable for nonstandard anatomy.
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Affiliation(s)
- Véronique Fortier
- Medical Physics Unit, McGill University, Montréal, QC, Canada; Biomedical Engineering, McGill University, Montréal, QC, Canada.
| | | | - Piotr Pater
- Medical Physics Unit, McGill University, Montréal, QC, Canada; Gerald Bronfman Department of Oncology, McGill University, Montréal, QC, Canada
| | - Luis Souhami
- Gerald Bronfman Department of Oncology, McGill University, Montréal, QC, Canada; Research Institute of the McGill University Health Centre, Montréal, QC, Canada
| | - Ives R Levesque
- Medical Physics Unit, McGill University, Montréal, QC, Canada; Biomedical Engineering, McGill University, Montréal, QC, Canada; Gerald Bronfman Department of Oncology, McGill University, Montréal, QC, Canada; Research Institute of the McGill University Health Centre, Montréal, QC, Canada
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15
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Stenhouse K, Roumeliotis M, Banerjee R, Yanushkevich S, McGeachy P. Development of a Machine Learning Model for Optimal Applicator Selection in High-Dose-Rate Cervical Brachytherapy. Front Oncol 2021; 11:611437. [PMID: 33747926 PMCID: PMC7973285 DOI: 10.3389/fonc.2021.611437] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Accepted: 01/12/2021] [Indexed: 12/24/2022] Open
Abstract
Purpose To develop and validate a preliminary machine learning (ML) model aiding in the selection of intracavitary (IC) versus hybrid interstitial (IS) applicators for high-dose-rate (HDR) cervical brachytherapy. Methods From a dataset of 233 treatments using IC or IS applicators, a set of geometric features of the structure set were extracted, including the volumes of OARs (bladder, rectum, sigmoid colon) and HR-CTV, proximity of OARs to the HR-CTV, mean and maximum lateral and vertical HR-CTV extent, and offset of the HR-CTV centre-of-mass from the applicator tandem axis. Feature selection using an ANOVA F-test and mutual information removed uninformative features from this set. Twelve classification algorithms were trained and tested over 100 iterations to determine the highest performing individual models through nested 5-fold cross-validation. Three models with the highest accuracy were combined using soft voting to form the final model. This model was trained and tested over 1,000 iterations, during which the relative importance of each feature in the applicator selection process was determined. Results Feature selection indicated that the mean and maximum lateral and vertical extent, volume, and axis offset of the HR-CTV were the most informative features and were thus provided to the ML models. Relative feature importances indicated that the HR-CTV volume and mean lateral extent were most important for applicator selection. From the comparison of the individual classification algorithms, it was found that the highest performing algorithms were tree-based ensemble methods – AdaBoost Classifier (ABC), Gradient Boosting Classifier (GBC), and Random Forest Classifier (RFC). The accuracy of the individual models was compared to the voting model for 100 iterations (ABC = 91.6 ± 3.1%, GBC = 90.4 ± 4.1%, RFC = 89.5 ± 4.0%, Voting Model = 92.2 ± 1.8%) and the voting model was found to have superior accuracy. Over the final 1,000 evaluation iterations, the final voting model demonstrated a high predictive accuracy (91.5 ± 0.9%) and F1 Score (90.6 ± 1.1%). Conclusion The presented model demonstrates high discriminative performance, highlighting the potential for utilization in informing applicator selection prospectively following further clinical validation.
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Affiliation(s)
- Kailyn Stenhouse
- Department of Physics and Astronomy, University of Calgary, Calgary, AB, Canada.,Department of Medical Physics, Tom Baker Cancer Centre, Calgary, AB, Canada
| | - Michael Roumeliotis
- Department of Physics and Astronomy, University of Calgary, Calgary, AB, Canada.,Department of Medical Physics, Tom Baker Cancer Centre, Calgary, AB, Canada.,Department of Oncology, University of Calgary, Calgary, AB, Canada
| | - Robyn Banerjee
- Department of Oncology, University of Calgary, Calgary, AB, Canada.,Department of Radiation Oncology, Tom Baker Cancer Centre, Calgary, AB, Canada
| | - Svetlana Yanushkevich
- Department of Electrical and Computer Engineering, University of Calgary, Calgary, AB, Canada
| | - Philip McGeachy
- Department of Physics and Astronomy, University of Calgary, Calgary, AB, Canada.,Department of Medical Physics, Tom Baker Cancer Centre, Calgary, AB, Canada.,Department of Oncology, University of Calgary, Calgary, AB, Canada
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Artificial Intelligence and Machine Learning in Prostate Cancer Patient Management-Current Trends and Future Perspectives. Diagnostics (Basel) 2021; 11:diagnostics11020354. [PMID: 33672608 PMCID: PMC7924061 DOI: 10.3390/diagnostics11020354] [Citation(s) in RCA: 52] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2021] [Revised: 02/16/2021] [Accepted: 02/17/2021] [Indexed: 12/24/2022] Open
Abstract
Artificial intelligence (AI) is the field of computer science that aims to build smart devices performing tasks that currently require human intelligence. Through machine learning (ML), the deep learning (DL) model is teaching computers to learn by example, something that human beings are doing naturally. AI is revolutionizing healthcare. Digital pathology is becoming highly assisted by AI to help researchers in analyzing larger data sets and providing faster and more accurate diagnoses of prostate cancer lesions. When applied to diagnostic imaging, AI has shown excellent accuracy in the detection of prostate lesions as well as in the prediction of patient outcomes in terms of survival and treatment response. The enormous quantity of data coming from the prostate tumor genome requires fast, reliable and accurate computing power provided by machine learning algorithms. Radiotherapy is an essential part of the treatment of prostate cancer and it is often difficult to predict its toxicity for the patients. Artificial intelligence could have a future potential role in predicting how a patient will react to the therapy side effects. These technologies could provide doctors with better insights on how to plan radiotherapy treatment. The extension of the capabilities of surgical robots for more autonomous tasks will allow them to use information from the surgical field, recognize issues and implement the proper actions without the need for human intervention.
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17
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Wilby S, Palmer A, Polak W, Bucchi A. A review of brachytherapy physical phantoms developed over the last 20 years: clinical purpose and future requirements. J Contemp Brachytherapy 2021; 13:101-115. [PMID: 34025743 PMCID: PMC8117707 DOI: 10.5114/jcb.2021.103593] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2020] [Accepted: 11/13/2020] [Indexed: 12/04/2022] Open
Abstract
Within the brachytherapy community, many phantoms are constructed in-house, and less commercial development is observed as compared to the field of external beam. Computational or virtual phantom design has seen considerable growth; however, physical phantoms are beneficial for brachytherapy, in which quality is dependent on physical processes, such as accuracy of source placement. Focusing on the design of physical phantoms, this review paper presents a summary of brachytherapy specific phantoms in published journal articles over the last twenty years (January 1, 2000 - December 31, 2019). The papers were analyzed and tabulated by their primary clinical purpose, which was deduced from their associated publications. A substantial body of work has been published on phantom designs from the brachytherapy community, but a standardized method of reporting technical aspects of the phantoms is lacking. In-house phantom development demonstrates an increasing interest in magnetic resonance (MR) tissue mimicking materials, which is not yet reflected in commercial phantoms available for brachytherapy. The evaluation of phantom design provides insight into the way, in which brachytherapy practice has changed over time, and demonstrates the customised and broad nature of treatments offered.
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Affiliation(s)
- Sarah Wilby
- Department of Radiotherapy Physics, Clinical Hematology, and Oncology Centre, Portsmouth Hospitals NHS Trust, Cosham, Portsmouth, United Kingdom
- Department of Mechanical Engineering, Faculty of Technology University of Portsmouth, Portsmouth, United Kingdom
| | - Antony Palmer
- Department of Radiotherapy Physics, Clinical Hematology, and Oncology Centre, Portsmouth Hospitals NHS Trust, Cosham, Portsmouth, United Kingdom
- Department of Mechanical Engineering, Faculty of Technology University of Portsmouth, Portsmouth, United Kingdom
| | - Wojciech Polak
- Department of Radiotherapy Physics, Clinical Hematology, and Oncology Centre, Portsmouth Hospitals NHS Trust, Cosham, Portsmouth, United Kingdom
- Department of Mechanical Engineering, Faculty of Technology University of Portsmouth, Portsmouth, United Kingdom
| | - Andrea Bucchi
- Department of Mechanical Engineering, Faculty of Technology University of Portsmouth, Portsmouth, United Kingdom
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Nosrati R, Wronski M, Tseng CL, Chung H, Pejović-Milić A, Morton G, Stanisz GJ. Postimplant Dosimetry of Permanent Prostate Brachytherapy: Comparison of MRI-Only and CT-MRI Fusion-Based Workflows. Int J Radiat Oncol Biol Phys 2020; 106:206-215. [DOI: 10.1016/j.ijrobp.2019.10.009] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2019] [Revised: 09/11/2019] [Accepted: 10/07/2019] [Indexed: 11/24/2022]
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19
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Nosrati R, Song WY, Wronski M, Pejović-Milić A, Morton G, Stanisz GJ. Feasibility of an MRI-only workflow for postimplant dosimetry of low-dose-rate prostate brachytherapy: Transition from phantoms to patients. Brachytherapy 2019; 18:863-874. [DOI: 10.1016/j.brachy.2019.06.004] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2019] [Revised: 06/12/2019] [Accepted: 06/20/2019] [Indexed: 12/14/2022]
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20
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Nosrati R, Paudel M, Ravi A, Pejovic-Milic A, Morton G, Stanisz GJ. Potential applications of the quantitative susceptibility mapping (QSM) in MR-guided radiation therapy. ACTA ACUST UNITED AC 2019; 64:145013. [DOI: 10.1088/1361-6560/ab2623] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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21
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Hrinivich WT, Park S, Le Y, Song DY, Lee J. Deformable registration of x ray and MRI for postimplant dosimetry in low dose rate prostate brachytherapy. Med Phys 2019; 46:3961-3973. [PMID: 31215042 DOI: 10.1002/mp.13667] [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: 02/11/2019] [Revised: 05/06/2019] [Accepted: 06/05/2019] [Indexed: 11/09/2022] Open
Abstract
PURPOSE Dosimetric assessment following permanent prostate brachytherapy (PPB) commonly involves seed localization using CT and prostate delineation using coregistered MRI. However, pelvic CT leads to additional imaging dose and requires significant resources to acquire and process both CT and MRI. In this study, we propose an automatic postimplant dosimetry approach that retains MRI for soft-tissue contouring, but eliminates the need for CT and reduces imaging dose while overcoming the inconsistent appearance of seeds on MRI with three projection x rays acquired using a mobile C-arm. METHODS Implanted seeds are reconstructed using x rays by solving a combinatorial optimization problem and deformably registered to MRI. Candidate seeds are located in MR images using local hypointensity identification. X ray-based seeds are registered to these candidate seeds in three steps: (a) rigid registration using a stochastic evolutionary optimizer, (b) affine registration using an iterative closest point optimizer, and (c) deformable registration using a local feature point search and nonrigid coherent point drift. The algorithm was evaluated using 20 PPB patients with x rays acquired immediately postimplant and T2-weighted MR images acquired the next day at 1.5 T with mean 0.8 × 0.8 × 3.0 mm 3 voxel dimensions. Target registration error (TRE) was computed based on the distance from algorithm results to manually identified seed locations using coregistered CT acquired the same day as the MRI. Dosimetric accuracy was determined by comparing prostate D90 determined using the algorithm and the ground truth CT-based seed locations. RESULTS The mean ± standard deviation TREs across 20 patients including 1774 seeds were 2.23 ± 0.52 mm (rigid), 1.99 ± 0.49 mm (rigid + affine), and 1.76 ± 0.43 mm (rigid + affine + deformable). The corresponding mean ± standard deviation D90 errors were 5.8 ± 4.8%, 3.4 ± 3.4%, and 2.3 ± 1.9%, respectively. The mean computation time of the registration algorithm was 6.1 s. CONCLUSION The registration algorithm accuracy and computation time are sufficient for clinical PPB postimplant dosimetry.
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Affiliation(s)
- William T Hrinivich
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, MD, 21287, USA
| | - Seyoun Park
- Department of Radiology, Johns Hopkins University, Baltimore, MD, 21287, USA
| | - Yi Le
- Department of Radiation Oncology, Indiana University, Indianapolis, IN, 46202, USA
| | - Daniel Y Song
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, MD, 21287, USA
| | - Junghoon Lee
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, MD, 21287, USA
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Vafay Eslahi S, Ji J. Accelerated positive contrast MRI of interventional devices using parallel compressed sensing imaging. Magn Reson Imaging 2019; 60:130-136. [PMID: 31028791 DOI: 10.1016/j.mri.2019.04.006] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2018] [Revised: 04/04/2019] [Accepted: 04/06/2019] [Indexed: 11/24/2022]
Abstract
Susceptibility-based magnetic resonance imaging (MRI) method can image small MR-compatible devices with positive contrast. However, the relatively long data acquisition time required by the method hinders its practical applications. This study presents a parallel compressive sensing technique with a modified fast spin echo to accelerate data acquisition for the susceptibility-based positive contrast MRI. The method integrates the generalized autocalibrating partially parallel acquisitions and the compressive sensing techniques in the reconstruction algorithm. MR imaging data acquired from several phantoms containing interventional devices such as biopsy needles, stent, and brachytherapy seeds, used for validating the proposed technique. The results show that it can speed up data acquisition by a factor of about five while preserving the quality of the positive contrast images.
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Affiliation(s)
- Samira Vafay Eslahi
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, USA
| | - Jim Ji
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, USA.
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