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Simkó A, Bylund M, Jönsson G, Löfstedt T, Garpebring A, Nyholm T, Jonsson J. Towards MR contrast independent synthetic CT generation. Z Med Phys 2024; 34:270-277. [PMID: 37537099 PMCID: PMC11156784 DOI: 10.1016/j.zemedi.2023.07.001] [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: 12/01/2022] [Revised: 07/04/2023] [Accepted: 07/04/2023] [Indexed: 08/05/2023]
Abstract
The use of synthetic CT (sCT) in the radiotherapy workflow would reduce costs and scan time while removing the uncertainties around working with both MR and CT modalities. The performance of deep learning (DL) solutions for sCT generation is steadily increasing, however most proposed methods were trained and validated on private datasets of a single contrast from a single scanner. Such solutions might not perform equally well on other datasets, limiting their general usability and therefore value. Additionally, functional evaluations of sCTs such as dosimetric comparisons with CT-based dose calculations better show the impact of the methods, but the evaluations are more labor intensive than pixel-wise metrics. To improve the generalization of an sCT model, we propose to incorporate a pre-trained DL model to pre-process the input MR images by generating artificial proton density, T1 and T2 maps (i.e. contrast-independent quantitative maps), which are then used for sCT generation. Using a dataset of only T2w MR images, the robustness towards input MR contrasts of this approach is compared to a model that was trained using the MR images directly. We evaluate the generated sCTs using pixel-wise metrics and calculating mean radiological depths, as an approximation of the mean delivered dose. On T2w images acquired with the same settings as the training dataset, there was no significant difference between the performance of the models. However, when evaluated on T1w images, and a wide range of other contrasts and scanners from both public and private datasets, our approach outperforms the baseline model. Using a dataset of T2w MR images, our proposed model implements synthetic quantitative maps to generate sCT images, improving the generalization towards other contrasts. Our code and trained models are publicly available.
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Affiliation(s)
- Attila Simkó
- Department of Radiation Sciences, Umeå University, Umeå, Sweden.
| | - Mikael Bylund
- Department of Radiation Sciences, Umeå University, Umeå, Sweden.
| | - Gustav Jönsson
- Department of Radiation Sciences, Umeå University, Umeå, Sweden
| | - Tommy Löfstedt
- Department of Computing Science, Umeå University, Umeå, Sweden
| | | | - Tufve Nyholm
- Department of Radiation Sciences, Umeå University, Umeå, Sweden
| | - Joakim Jonsson
- Department of Radiation Sciences, Umeå University, Umeå, Sweden
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2
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Yuan S, Liu Y, Wei R, Zhu J, Men K, Dai J. A novel loss function to reproduce texture features for deep learning-based MRI-to-CT synthesis. Med Phys 2024; 51:2695-2706. [PMID: 38043105 DOI: 10.1002/mp.16850] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Revised: 10/03/2023] [Accepted: 10/31/2023] [Indexed: 12/05/2023] Open
Abstract
BACKGROUND Studies on computed tomography (CT) synthesis based on magnetic resonance imaging (MRI) have mainly focused on pixel-wise consistency, but the texture features of regions of interest (ROIs) have not received appropriate attention. PURPOSE This study aimed to propose a novel loss function to reproduce texture features of ROIs and pixel-wise consistency for deep learning-based MRI-to-CT synthesis. The method was expected to assist the multi-modality studies for radiomics. METHODS The study retrospectively enrolled 127 patients with nasopharyngeal carcinoma. CT and MRI images were collected for each patient, and then rigidly registered as pre-procession. We proposed a gray-level co-occurrence matrix (GLCM)-based loss function to improve the reproducibility of texture features. This novel loss function could be embedded into the present deep learning-based framework for image synthesis. In this study, a typical image synthesis model was selected as the baseline, which contained a Unet trained mean square error (MSE) loss function. We embedded the proposed loss function and designed experiments to supervise different ROIs to prove its effectiveness. The concordance correlation coefficient (CCC) of the GLCM feature was employed to evaluate the reproducibility of GLCM features, which are typical texture features. Besides, we used a publicly available dataset of brain tumors to verify our loss function. RESULTS Compared with the baseline, the proposed method improved the pixel-wise image quality metrics (MAE: 107.5 to 106.8 HU; SSIM: 0.9728 to 0.9730). CCC values of the GLCM features in GTVnx were significantly improved from 0.78 ± 0.12 to 0.82 ± 0.11 (p < 0.05 for paired t-test). Generally, > 90% (22/24) of the GLCM-based features were improved compared with the baseline, where the Informational Measure of Correlation feature was improved the most (CCC: 0.74 to 0.83). For the public dataset, the loss function also shows its effectiveness. With our proposed loss function added, the ability to reproduce texture features was improved in the ROIs. CONCLUSIONS The proposed method reproduced texture features for MRI-to-CT synthesis, which would benefit radiomics studies based on image multi-modality synthesis.
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Affiliation(s)
- Siqi Yuan
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yuxiang Liu
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Ran Wei
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Ji Zhu
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Kuo Men
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jianrong Dai
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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3
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Cao G, Li Y, Wu S, Li W, Long J, Xie Y, Xia J. Clinical feasibility of MRI-based synthetic CT imaging in the diagnosis of lumbar disc herniation: a comparative study. Acta Radiol 2024; 65:41-48. [PMID: 37071506 PMCID: PMC10798008 DOI: 10.1177/02841851231169173] [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: 07/29/2022] [Accepted: 12/05/2022] [Indexed: 04/19/2023]
Abstract
BACKGROUND Computed tomography (CT) and magnetic resonance imaging (MRI) are indicated for use in preoperative planning and may complicate diagnosis and place a burden on patients with lumbar disc herniation. PURPOSE To investigate the diagnostic potential of MRI-based synthetic CT with conventional CT in the diagnosis of lumbar disc herniation. MATERIAL AND METHODS After obtaining prior institutional review board approval, 19 patients who underwent conventional and synthetic CT imaging were enrolled in this prospective study. Synthetic CT images were generated from the MRI data using U-net. The two sets of images were compared and analyzed qualitatively by two musculoskeletal radiologists. The images were rated on a 4-point scale to determine their subjective quality. The agreement between the conventional and synthetic images for a diagnosis of lumbar disc herniation was determined independently using the kappa statistic. The diagnostic performances of conventional and synthetic CT images were evaluated for sensitivity, specificity, and accuracy, and the consensual results based on T2-weighted imaging were employed as the reference standard. RESULTS The inter-reader and intra-reader agreement were almost moderate for all evaluated modalities (κ = 0.57-0.79 and 0.47-0.75, respectively). The sensitivity, specificity, and accuracy for detecting lumbar disc herniation were similar for synthetic and conventional CT images (synthetic vs. conventional, reader 1: sensitivity = 91% vs. 81%, specificity = 83% vs. 100%, accuracy = 87% vs. 91%; P < 0.001; reader 2: sensitivity = 84% vs. 81%, specificity = 85% vs. 98%, accuracy = 84% vs. 90%; P < 0.001). CONCLUSION Synthetic CT images can be used in the diagnostics of lumbar disc herniation.
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Affiliation(s)
- Gan Cao
- Department of Radiology, Longgang Central Hospital of Shenzhen, Shenzhen, PR China
- Department of Radiology, The First Affiliated Hospital of Shenzhen University, Health Science Center, Shenzhen Second People's Hospital, Shenzhen, PR China
| | - Yafen Li
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, PR China
| | - Shibin Wu
- PingAn Technology, Shenzhen, Guangdong, PR China
| | - Wen Li
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, PR China
| | - Jia Long
- Department of Radiology, The First Affiliated Hospital of Shenzhen University, Health Science Center, Shenzhen Second People's Hospital, Shenzhen, PR China
| | - Yaoqin Xie
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, PR China
| | - Jun Xia
- Department of Radiology, The First Affiliated Hospital of Shenzhen University, Health Science Center, Shenzhen Second People's Hospital, Shenzhen, PR China
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4
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McPhee S, Kershaw LE, Daniel CR, Peña Fernández M, Cillán-García E, Taylor SE, Wolfram U. QCT-based computational bone strength assessment updated with MRI-derived 'hidden' microporosity. J Mech Behav Biomed Mater 2023; 147:106094. [PMID: 37741181 DOI: 10.1016/j.jmbbm.2023.106094] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Revised: 08/10/2023] [Accepted: 08/26/2023] [Indexed: 09/25/2023]
Abstract
Microdamage accumulated through sustained periods of cyclic loading or single overloading events contributes to bone fragility through a reduction in stiffness and strength. Monitoring microdamage in vivo remains unattainable by clinical imaging modalities. As such, there are no established computational methods for clinical fracture risk assessment that account for microdamage that exists in vivo at any specific timepoint. We propose a method that combines multiple clinical imaging modalities to identify an indicative surrogate, which we term 'hidden porosity', that incorporates pre-existing bone microdamage in vivo. To do so, we use the third metacarpal bone of the equine athlete as an exemplary model for fatigue induced microdamage, which coalesces in the subchondral bone. N = 10 metacarpals were scanned by clinical quantitative computed tomography (QCT) and magnetic resonance imaging (MRI). We used a patch-based similarity method to quantify the signal intensity of a fluid sensitive MRI sequence in bone regions where microdamage coalesces. The method generated MRI-derived pseudoCT images which were then used to determine a pre-existing damage (Dpex) variable to quantify the proposed surrogate and which we incorporate into a nonlinear constitutive model for bone tissue. The minimum, median, and maximum detected Dpex of 0.059, 0.209, and 0.353 reduced material stiffness by 5.9%, 20.9%, and 35.3% as well as yield stress by 5.9%, 20.3%, and 35.3%. Limb-specific voxel-based finite element meshes were equipped with the updated material model. Lateral and medial condyles of each metacarpal were loaded to simulate physiological joint loading during gallop. The degree of detected Dpex correlated with a relative reduction in both condylar stiffness (p = 0.001, R2 > 0.74) and strength (p < 0.001, R2 > 0.80). Our results illustrate the complementary value of looking beyond clinical CT, which neglects the inclusion of microdamage due to partial volume effects. As we use clinically available imaging techniques, our results may aid research beyond the equine model on fracture risk assessment in human diseases such as osteoarthritis, bone cancer, or osteoporosis.
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Affiliation(s)
- Samuel McPhee
- School of Engineering and Physical Sciences, Institute of Mechanical, Process and Energy Engineering, Heriot-Watt University, Edinburgh, UK
| | - Lucy E Kershaw
- Centre for Cardiovascular Sciences and Edinburgh Imaging, The University of Edinburgh, Edinburgh, UK
| | - Carola R Daniel
- Royal (Dick) School of Veterinary Studies and the Roslin Institute, The University of Edinburgh, Edinburgh, UK
| | - Marta Peña Fernández
- School of Engineering and Physical Sciences, Institute of Mechanical, Process and Energy Engineering, Heriot-Watt University, Edinburgh, UK
| | | | - Sarah E Taylor
- Royal (Dick) School of Veterinary Studies and the Roslin Institute, The University of Edinburgh, Edinburgh, UK
| | - Uwe Wolfram
- School of Engineering and Physical Sciences, Institute of Mechanical, Process and Energy Engineering, Heriot-Watt University, Edinburgh, UK.
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Achar S, Hwang D, Finkenstaedt T, Malis V, Bae WC. Deep-Learning-Aided Evaluation of Spondylolysis Imaged with Ultrashort Echo Time Magnetic Resonance Imaging. SENSORS (BASEL, SWITZERLAND) 2023; 23:8001. [PMID: 37766055 PMCID: PMC10538057 DOI: 10.3390/s23188001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Revised: 08/31/2023] [Accepted: 09/15/2023] [Indexed: 09/29/2023]
Abstract
Isthmic spondylolysis results in fracture of pars interarticularis of the lumbar spine, found in as many as half of adolescent athletes with persistent low back pain. While computed tomography (CT) is the gold standard for the diagnosis of spondylolysis, the use of ionizing radiation near reproductive organs in young subjects is undesirable. While magnetic resonance imaging (MRI) is preferable, it has lowered sensitivity for detecting the condition. Recently, it has been shown that ultrashort echo time (UTE) MRI can provide markedly improved bone contrast compared to conventional MRI. To take UTE MRI further, we developed supervised deep learning tools to generate (1) CT-like images and (2) saliency maps of fracture probability from UTE MRI, using ex vivo preparation of cadaveric spines. We further compared quantitative metrics of the contrast-to-noise ratio (CNR), mean squared error (MSE), peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM) between UTE MRI (inverted to make the appearance similar to CT) and CT and between CT-like images and CT. Qualitative results demonstrated the feasibility of successfully generating CT-like images from UTE MRI to provide easier interpretability for bone fractures thanks to improved image contrast and CNR. Quantitatively, the mean CNR of bone against defect-filled tissue was 35, 97, and 146 for UTE MRI, CT-like, and CT images, respectively, being significantly higher for CT-like than UTE MRI images. For the image similarity metrics using the CT image as the reference, CT-like images provided a significantly lower mean MSE (0.038 vs. 0.0528), higher mean PSNR (28.6 vs. 16.5), and higher SSIM (0.73 vs. 0.68) compared to UTE MRI images. Additionally, the saliency maps enabled quick detection of the location with probable pars fracture by providing visual cues to the reader. This proof-of-concept study is limited to the data from ex vivo samples, and additional work in human subjects with spondylolysis would be necessary to refine the models for clinical use. Nonetheless, this study shows that the utilization of UTE MRI and deep learning tools could be highly useful for the evaluation of isthmic spondylolysis.
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Affiliation(s)
- Suraj Achar
- Department of Family Medicine, University of California-San Diego, La Jolla, CA 92093, USA
| | - Dosik Hwang
- Department of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Republic of Korea
- Center for Healthcare Robotics, Korea Institute of Science and Technology, Seoul 02792, Republic of Korea
- Department of Radiology, Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul 03722, Republic of Korea
- Department of Oral and Maxillofacial Radiology, Yonsei University College of Dentistry, Seoul 03722, Republic of Korea
| | - Tim Finkenstaedt
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, University Zurich, 8091 Zurich, Switzerland
| | - Vadim Malis
- Department of Radiology, University of California-San Diego, La Jolla, CA 92093, USA
| | - Won C. Bae
- Department of Radiology, University of California-San Diego, La Jolla, CA 92093, USA
- Department of Radiology, VA San Diego Healthcare System, San Diego, CA 92161, USA
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6
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Nousiainen K, Santurio GV, Lundahl N, Cronholm R, Siversson C, Edmund JM. Evaluation of MRI-only based online adaptive radiotherapy of abdominal region on MR-linac. J Appl Clin Med Phys 2023; 24:e13838. [PMID: 36347050 PMCID: PMC10018672 DOI: 10.1002/acm2.13838] [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: 08/04/2021] [Revised: 09/30/2022] [Accepted: 10/18/2022] [Indexed: 11/09/2022] Open
Abstract
PURPOSE A hybrid magnetic resonance linear accelerator (MRL) can perform magnetic resonance imaging (MRI) with high soft-tissue contrast to be used for online adaptive radiotherapy (oART). To obtain electron densities needed for the oART dose calculation, a computed tomography (CT) is often deformably registered to MRI. Our aim was to evaluate an MRI-only based synthetic CT (sCT) generation as an alternative to the deformed CT (dCT)-based oART in the abdominal region. METHODS The study data consisted of 57 patients who were treated on a 0.35 T MRL system mainly for abdominal tumors. Simulation MRI-CT pairs of 43 patients were used for training and validation of a prototype convolutional neural network sCT-generation algorithm, based on HighRes3DNet, for the abdominal region. For remaining test patients, sCT images were produced from simulation MRIs and daily MRIs. The dCT-based plans were re-calculated on sCT with identical calculation parameters. The sCT and dCT were compared in terms of geometric agreement and calculated dose. RESULTS The mean and one standard deviation of the geometric agreement metrics over dCT-sCT-pairs were: mean error of 8 ± 10 HU, mean absolute error of 49 ± 10 HU, and Dice similarity coefficient of 55 ± 12%, 60 ± 5%, and 82 ± 15% for bone, fat, and lung tissues, respectively. The dose differences between the sCT and dCT-based dose for planning target volumes were 0.5 ± 0.9%, 0.6 ± 0.8%, and 0.5 ± 0.8% at D2% , D50% , and D98% in physical dose and 0.8 ± 1.4%, 0.8 ± 1.2%, and 0.6 ± 1.1% in biologically effective dose (BED). For organs-at-risk, the dose differences of all evaluated dose-volume histogram points were within [-4.5%, 7.8%] and [-1.1 Gy, 3.5 Gy] in both physical dose and BED. CONCLUSIONS The geometric agreement metrics were within typically reported values and most average relative dose differences were within 1%. Thus, an MRI-only sCT-based approach is a promising alternative to the current clinical practice of the abdominal oART on MRL.
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Affiliation(s)
- Katri Nousiainen
- Department of Physics, University of Helsinki, Helsinki, Finland.,HUS Cancer Center, Helsinki University Hospital and University of Helsinki, Helsinki, Finland.,HUS Medical Imaging Center, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
| | - Grichar Valdes Santurio
- Department of Oncology, Radiotherapy Research Unit, Herlev and Gentofte Hospital, Copenhagen University, Herlev, Denmark
| | | | | | | | - Jens M Edmund
- Department of Oncology, Radiotherapy Research Unit, Herlev and Gentofte Hospital, Copenhagen University, Herlev, Denmark.,Nils Bohr Institute, Copenhagen University, Copenhagen, Denmark
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7
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Bambach S, Ho ML. Deep Learning for Synthetic CT from Bone MRI in the Head and Neck. AJNR Am J Neuroradiol 2022; 43:1172-1179. [PMID: 36920777 PMCID: PMC9575432 DOI: 10.3174/ajnr.a7588] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Accepted: 06/13/2022] [Indexed: 11/07/2022]
Abstract
BACKGROUND AND PURPOSE Bone MR imaging techniques enable visualization of cortical bone without the need for ionizing radiation. Automated conversion of bone MR imaging to synthetic CT is highly desirable for downstream image processing and eventual clinical adoption. Given the complex anatomy and pathology of the head and neck, deep learning models are ideally suited for learning such mapping. MATERIALS AND METHODS This was a retrospective study of 39 pediatric and adult patients with bone MR imaging and CT examinations of the head and neck. For each patient, MR imaging and CT data sets were spatially coregistered using multiple-point affine transformation. Paired MR imaging and CT slices were generated for model training, using 4-fold cross-validation. We trained 3 different encoder-decoder models: Light_U-Net (2 million parameters) and VGG-16 U-Net (29 million parameters) without and with transfer learning. Loss functions included mean absolute error, mean squared error, and a weighted average. Performance metrics included Pearson R, mean absolute error, mean squared error, bone precision, and bone recall. We investigated model generalizability by training and validating across different conditions. RESULTS The Light_U-Net architecture quantitatively outperformed VGG-16 models. Mean absolute error loss resulted in higher bone precision, while mean squared error yielded higher bone recall. Performance metrics decreased when using training data captured only in a different environment but increased when local training data were augmented with those from different hospitals, vendors, or MR imaging techniques. CONCLUSIONS We have optimized a robust deep learning model for conversion of bone MR imaging to synthetic CT, which shows good performance and generalizability when trained on different hospitals, vendors, and MR imaging techniques. This approach shows promise for facilitating downstream image processing and adoption into clinical practice.
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Affiliation(s)
- S Bambach
- From the Abigail Wexner Research Institute at Nationwide Children's Hospital (S.B.), Columbus, Ohio
| | - M-L Ho
- Department of Radiology (M.-L.H.), Nationwide Children's Hospital, Columbus, Ohio.
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8
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Wiesinger F, Ho ML. Zero-TE MRI: principles and applications in the head and neck. Br J Radiol 2022; 95:20220059. [PMID: 35616709 PMCID: PMC10162052 DOI: 10.1259/bjr.20220059] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
Zero echo-time (ZTE) MRI is a novel imaging technique that utilizes ultrafast readouts to capture signal from short-T2 tissues. Additional sequence advantages include rapid imaging times, silent scanning, and artifact resistance. A robust application of this technology is imaging of cortical bone without the use of ionizing radiation, thus representing a viable alternative to CT for both rapid screening and "one-stop-shop" MRI. Although ZTE is increasingly used in musculoskeletal and body imaging, neuroimaging applications have historically been limited by complex anatomy and pathology. In this article, we review the imaging physics of ZTE including pulse sequence options, practical limitations, and image reconstruction. We then discuss optimization of settings for ZTE bone neuroimaging including acquisition, processing, segmentation, synthetic CT generation, and artifacts. Finally, we examine clinical utility of ZTE in the head and neck with imaging examples including malformations, trauma, tumors, and interventional procedures.
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Affiliation(s)
- Florian Wiesinger
- Department for Neuroimaging, Institute of Psychiatry & Neuroscience, King's College London, London, UK.,Principal Scientist at GE Healthcare, Munich, Germany
| | - Mai-Lan Ho
- Nationwide Children's Hospital and The Ohio State University, Columbus, USA
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9
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Clinical application of deep learning-based synthetic CT from real MRI to improve dose planning accuracy in Gamma Knife radiosurgery: a proof of concept study. Biomed Eng Lett 2022; 12:359-367. [DOI: 10.1007/s13534-022-00227-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2021] [Revised: 03/21/2022] [Accepted: 04/21/2022] [Indexed: 10/18/2022] Open
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10
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Jin H, Lee SY, An HJ, Choi CH, Chie EK, Wu HG, Park JM, Park S, Kim JI. Development of an anthropomorphic multimodality pelvic phantom for quantitative evaluation of a deep-learning-based synthetic computed tomography generation technique. J Appl Clin Med Phys 2022; 23:e13644. [PMID: 35579090 PMCID: PMC9359037 DOI: 10.1002/acm2.13644] [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: 10/18/2021] [Revised: 04/06/2022] [Accepted: 04/28/2022] [Indexed: 11/11/2022] Open
Abstract
PURPOSE The objective of this study was to fabricate an anthropomorphic multimodality pelvic phantom to evaluate a deep-learning-based synthetic computed tomography (CT) algorithm for magnetic resonance (MR)-only radiotherapy. METHODS Polyurethane-based and silicone-based materials with various silicone oil concentrations were scanned using 0.35 T MR and CT scanner to determine the tissue surrogate. Five tissue surrogates were determined by comparing the organ intensity with patient CT and MR images. Patient-specific organ modeling for three-dimensional printing was performed by manually delineating the structures of interest. The phantom was finally fabricated by casting materials for each structure. For the quantitative evaluation, the mean and standard deviations were measured within the regions of interest on the MR, simulation CT (CTsim ), and synthetic CT (CTsyn ) images. Intensity-modulated radiation therapy plans were generated to assess the impact of different electron density assignments on plan quality using CTsim and CTsyn . The dose calculation accuracy was investigated in terms of gamma analysis and dose-volume histogram parameters. RESULTS For the prostate site, the mean MR intensities for the patient and phantom were 78.1 ± 13.8 and 86.5 ± 19.3, respectively. The mean intensity of the synthetic image was 30.9 Hounsfield unit (HU), which was comparable to that of the real CT phantom image. The original and synthetic CT intensities of the fat tissue in the phantom were -105.8 ± 4.9 HU and -107.8 ± 7.8 HU, respectively. For the target volume, the difference in D95% was 0.32 Gy using CTsyn with respect to CTsim values. The V65Gy values for the bladder in the plans using CTsim and CTsyn were 0.31% and 0.15%, respectively. CONCLUSION This work demonstrated that the anthropomorphic phantom was physiologically and geometrically similar to the patient organs and was employed to quantitatively evaluate the deep-learning-based synthetic CT algorithm.
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Affiliation(s)
- Hyeongmin Jin
- Department of Radiation Oncology, Seoul National University Hospital, Seoul, Republic of Korea
| | - Sung Young Lee
- Department of Radiation Oncology, Seoul National University Hospital, Seoul, Republic of Korea
| | - Hyun Joon An
- Department of Radiation Oncology, Seoul National University Hospital, Seoul, Republic of Korea.,Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea.,Biomedical Research Institute, Seoul National University Hospital, Seoul, Republic of Korea
| | - Chang Heon Choi
- Department of Radiation Oncology, Seoul National University Hospital, Seoul, Republic of Korea.,Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea.,Biomedical Research Institute, Seoul National University Hospital, Seoul, Republic of Korea
| | - Eui Kyu Chie
- Department of Radiation Oncology, Seoul National University Hospital, Seoul, Republic of Korea.,Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea.,Biomedical Research Institute, Seoul National University Hospital, Seoul, Republic of Korea.,Department of Radiation Oncology, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Hong-Gyun Wu
- Department of Radiation Oncology, Seoul National University Hospital, Seoul, Republic of Korea.,Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea.,Biomedical Research Institute, Seoul National University Hospital, Seoul, Republic of Korea.,Department of Radiation Oncology, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Jong Min Park
- Department of Radiation Oncology, Seoul National University Hospital, Seoul, Republic of Korea.,Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea.,Biomedical Research Institute, Seoul National University Hospital, Seoul, Republic of Korea.,Department of Radiation Oncology, Seoul National University College of Medicine, Seoul, Republic of Korea.,Robotics Research Laboratory for Extreme Environments, Advanced Institute of Convergence Technology, Suwon, Republic of Korea
| | - Sukwon Park
- Department of Radiation Oncology, Myongji Hospital, Goyang-si, Gyeonggi-do, Republic of Korea
| | - Jung-In Kim
- Department of Radiation Oncology, Seoul National University Hospital, Seoul, Republic of Korea.,Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea.,Biomedical Research Institute, Seoul National University Hospital, Seoul, Republic of Korea
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11
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Abu-Qasmieh IF, Masad IS, Al-Quran HH, Alawneh KZ. Generation of Synthetic-Pseudo MR Images from Real CT Images. Tomography 2022; 8:1244-1259. [PMID: 35645389 PMCID: PMC9149978 DOI: 10.3390/tomography8030103] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2022] [Revised: 04/24/2022] [Accepted: 04/25/2022] [Indexed: 11/18/2022] Open
Abstract
This study aimed to generate synthetic MR images from real CT images. CT# mean and standard deviation of a moving window across every pixel in the reconstructed CT images were mapped to their corresponding tissue-mimicking types. Identification of the tissue enabled remapping it to its corresponding intrinsic parameters: T1, T2, and proton density (ρ). Lastly, synthetic weighted MR images of a selected slice were generated by simulating a spin-echo sequence using the intrinsic parameters and proper contrast parameters (TE and TR). Experiments were performed on a 3D multimodality abdominal phantom and on human knees at different TE and TR parameters to confirm the clinical effectiveness of the approach. Results demonstrated the validity of the approach of generating synthetic MR images at different weightings using only CT images and the three predefined mapping functions. The slope of the fitting line and percentage root-mean-square difference (PRD) between real and synthetic image vector representations were (0.73, 10%), (0.9, 18%), and (0.2, 8.7%) for T1-, T2-, and ρ-weighted images of the phantom, respectively. The slope and PRD for human knee images, on average, were 0.89% and 18.8%, respectively. The generated MR images provide valuable guidance for physicians with regard to deciding whether acquiring real MR images is crucial.
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Affiliation(s)
- Isam F. Abu-Qasmieh
- Department of Biomedical Systems and Informatics Engineering, Yarmouk University, Irbid 21163, Jordan; (I.F.A.-Q.); (H.H.A.-Q.)
| | - Ihssan S. Masad
- Department of Biomedical Systems and Informatics Engineering, Yarmouk University, Irbid 21163, Jordan; (I.F.A.-Q.); (H.H.A.-Q.)
- Correspondence:
| | - Hiam H. Al-Quran
- Department of Biomedical Systems and Informatics Engineering, Yarmouk University, Irbid 21163, Jordan; (I.F.A.-Q.); (H.H.A.-Q.)
- Department of Biomedical Engineering, Jordan University of Science and Technology, Irbid 22110, Jordan
| | - Khaled Z. Alawneh
- Faculty of Medicine, Jordan University of Science and Technology, King Abdullah University Hospital, Irbid 22110, Jordan;
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Sreeja S, Muhammad Noorul Mubarak D. Pseudo computed tomography image generation from brain magnetic resonance image using integration of PCA & DCNN-UNET: A comparative analysis. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022. [DOI: 10.3233/jifs-213367] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
MRI-Only Radiation (RT) now avoids some of the issues associated with employing Computed Tomography(CT) in RT chains, such as MRI registration to a separate CT, excess dosage administration, and the cost of recurrent imaging. The fact that MRI signal intensities are unrelated to the biological tissue’s attenuation coefficient poses a problem. This raises workloads, creates uncertainty as a result of the required inter-modality image registrations, and exposes patients to needless radiation. While using only MRI would be preferable, a method for estimating a pseudo-CT (pCT)or synthetic-CT(sCT) for producing electron density maps and patient positioning reference images is required. As Deep Learning(DL) is revolutionized in so many fields these days, an effective and accurate model is required for generating pCT from MRI. So, this paper depicts an efficient DL model in which the following are the stages: a) Data Acquisition where CT and MRI images are collected b) preprocessing these to avoid the anomalies and noises using techniques like outlier elimination, data smoothening and data normalizing c) feature extraction and selection using Principal Component Analysis (PCA) & regression method d) generating pCT from MRI using Deep Convolutional Neural Network and UNET (DCNN-UNET). We here compare both feature extraction (PCA) and classification model (DCNN-UNET) with other methods such as Discrete Wavelet Tranform(DWT), Independent Component Analysis(ICA), Fourier Transform and VGG16, ResNet, AlexNet, DenseNet, CNN (Convolutional Neural Network)respectively. The performance measures used to evaluate these models are Dice Coefficient(DC), Structured Similarity Index Measure(SSIM), Mean Absolute Error(MAE), Mean Squared Error(MSE), Accuracy, Computation Time in which our proposed system outperforms better with 0.94±0.02 over other state-of-art models.
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Affiliation(s)
- S Sreeja
- Department of Computer Science, University of Kerala, Karyavattom Campus, Trivandrum, Kerala, India
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13
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Omari EA, Zhang Y, Ahunbay E, Paulson E, Amjad A, Chen X, Liang Y, Li XA. Multi parametric magnetic resonance imaging for radiation treatment planning. Med Phys 2022; 49:2836-2845. [PMID: 35170769 DOI: 10.1002/mp.15534] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Revised: 10/05/2021] [Accepted: 01/03/2022] [Indexed: 11/09/2022] Open
Abstract
In recent years, multi-parametric magnetic resonance imaging (MpMRI) has played a major role in radiation therapy treatment planning. The superior soft tissue contrast, functional or physiological imaging capabilities and the flexibility of site-specific image sequence development has placed MpMRI at the forefront. In this article, the present status of MpMRI for external beam radiation therapy planning is reviewed. Common MpMRI sequences, preprocessing and QA strategies are briefly discussed, and various image registration techniques and strategies are addressed. Image segmentation methods including automatic segmentation and deep learning techniques for organs at risk and target delineation are reviewed. Due to the advancement in MRI guided online adaptive radiotherapy, treatment planning considerations addressing MRI only planning are also discussed. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Eenas A Omari
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI, 53226, USA
| | - Ying Zhang
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI, 53226, USA
| | - Ergun Ahunbay
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI, 53226, USA
| | - Eric Paulson
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI, 53226, USA
| | - Asma Amjad
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI, 53226, USA
| | - Xinfeng Chen
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI, 53226, USA
| | - Ying Liang
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI, 53226, USA
| | - X Allen Li
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI, 53226, USA
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Yan Y, Yang J, Li Y, Ding Y, Kadbi M, Wang J. Impact of geometric distortion on dose deviation for photon and proton treatment plans. J Appl Clin Med Phys 2022; 23:e13517. [PMID: 35106908 PMCID: PMC8906217 DOI: 10.1002/acm2.13517] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Revised: 10/18/2021] [Accepted: 12/19/2021] [Indexed: 01/14/2023] Open
Abstract
We investigated the dose deviation related to geometric distortion and dose gradient on magnetic resonance‐only treatment planning for intensity‐modulated radiation therapy and proton therapy. The residual geometric distortion of two different magnetic resonance imaging (MRI) sequences (A) and (B) was applied in the computed tomography image and the structure set of each patient through a polynomial MRI geometric distortion model to simulate MRI‐based treatment planning. A 3D histogram was generated to specify the relationship of dose deviation to geometric distortion and dose gradient. When the dose gradient (Gd) approached zero, the maximum dose deviation reached 1.64% and 2.71% for photon plans of sequences A and B, respectively. For proton plans, the maximum dose deviation reached 3.15% and 4.89% for sequences A and B, respectively. When the geometric distortion (d) was close to zero, the maximum dose deviation was less than 0.8% for photon and proton plans of both sequences. Under extreme conditions (d = 2 mm and Gd = 4.5%/mm), the median value of dose deviation reached 3% and 3.49% for photon and proton plans, respectively for sequence A, and 2.93% and 4.55% for photon and proton plans, respectively, for sequence B. We demonstrate that the dose deviation is specific to MRI hardware parameters. Compared to the photon plan, the proton plan is more sensitive to the changes in geometric distortion. For typical clinical MRI geometric distortion (d ≤2 mm), the median dose deviation is expected to be within 3% and 5% for photon and proton plans, respectively.
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Affiliation(s)
- Yue Yan
- Department of Radiation Oncology, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
| | - Jinzhong Yang
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Yuting Li
- Department of Radiation Oncology, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
| | - Yao Ding
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Mo Kadbi
- MR Therapy, Philips Healthcare, Houston, Texas, USA
| | - Jihong Wang
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
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Kobayashi N, Bambach S, Ho ML. Ultrashort Echo-Time MR Imaging of the Pediatric Head and Neck. Magn Reson Imaging Clin N Am 2021; 29:583-593. [PMID: 34717846 DOI: 10.1016/j.mric.2021.06.008] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
Bone MR imaging techniques use extremely rapid echo times to maximize detection of short-T2 tissues with low water concentrations. The major approaches used in clinical practice are ultrashort echo-time and zero echo-time. Synthetic CT generation is feasible using atlas-based, voxel-based, and deep learning approaches. Major clinical applications in the pediatric head and neck include evaluation for craniosynostosis, sinonasal and jaw imaging, trauma, interventional planning, and postoperative follow-up. In this article, we review the technical background and practical usefulness of bone MR imaging with key imaging examples.
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Affiliation(s)
- Naoharu Kobayashi
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, 2021 6th Street SE, Minneapolis, MN 55455, USA
| | - Sven Bambach
- Abigail Wexner Research Institute at Nationwide Children's Hospital, 575 Children's Crossroad, Columbus, OH 43215, USA
| | - Mai-Lan Ho
- Department of Radiology, Nationwide Children's Hospital, 700 Children's Dr - ED4, Columbus, OH 43205, USA.
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Boukellouz W, Moussaoui A. Magnetic resonance-driven pseudo CT image using patch-based multi-modal feature extraction and ensemble learning with stacked generalisation. JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES 2021. [DOI: 10.1016/j.jksuci.2019.06.002] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
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17
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Ma X, Chen X, Li J, Wang Y, Men K, Dai J. MRI-Only Radiotherapy Planning for Nasopharyngeal Carcinoma Using Deep Learning. Front Oncol 2021; 11:713617. [PMID: 34568044 PMCID: PMC8457879 DOI: 10.3389/fonc.2021.713617] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2021] [Accepted: 08/25/2021] [Indexed: 11/13/2022] Open
Abstract
Background Radical radiotherapy is the main treatment modality for early and locally advanced nasopharyngeal carcinoma (NPC). Magnetic resonance imaging (MRI) has the advantages of no ionizing radiation and high soft-tissue resolution compared to computed tomography (CT), but it does not provide electron density (ED) information for radiotherapy planning. Therefore, in this study, we developed a pseudo-CT (pCT) generation method to provide necessary ED information for MRI-only planning in NPC radiotherapy. Methods Twenty patients with early-stage NPC who received radiotherapy in our hospital were investigated. First, 1433 sets of paired T1 weighted magnetic resonance (MR) simulation images and CT simulation images were rigidly registered and preprocessed. A 16-layer U-Net was used to train the pCT generative model and a "pix2pix" generative adversarial network (GAN) was also trained to compare with the pure U-Net regrading pCT quality. Second, the contours of all target volumes and organs at risk in the original CT were transferred to the pCT for planning, and the beams were copied back to the original CT for reference dose calculation. Finally, the dose distribution calculated on the pCT was compared with the reference dose distribution through gamma analysis and dose-volume indices. Results The average time for pCT generation for each patient was 7.90 ± 0.47 seconds. The average mean (absolute) error was -9.3 ± 16.9 HU (102.6 ± 11.4 HU), and the mean-root-square error was 209.8 ± 22.6 HU. There was no significant difference between the pCT quality of pix2pix GAN and that of pure U-Net (p > 0.05). The dose distribution on the pCT was highly consistent with that on the original CT. The mean gamma pass rate (2 mm/3%, 10% low dose threshold) was 99.1% ± 0.3%, and the mean absolute difference of nasopharyngeal PGTV D99% and PTV V95% were 0.4% ± 0.2% and 0.1% ± 0.1%. Conclusion The proposed deep learning model can accurately predict CT from MRI, and the generated pCT can be employed in precise dose calculations. It is of great significance to realize MRI-only planning in NPC radiotherapy, which can improve structure delineation and considerably reduce additional imaging dose, especially when an MR-guided linear accelerator is adopted for treatment.
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Affiliation(s)
- Xiangyu Ma
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xinyuan Chen
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jingwen Li
- Cloud Computing and Big Date Research Institute, China Academy of Information and Communications Technology, Beijing, China
| | - Yu Wang
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Kuo Men
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jianrong Dai
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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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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Chen Y, Ying C, Binkley MM, Juttukonda MR, Flores S, Laforest R, Benzinger TL, An H. Deep learning-based T1-enhanced selection of linear attenuation coefficients (DL-TESLA) for PET/MR attenuation correction in dementia neuroimaging. Magn Reson Med 2021; 86:499-513. [PMID: 33559218 PMCID: PMC8091494 DOI: 10.1002/mrm.28689] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2020] [Revised: 12/23/2020] [Accepted: 12/29/2020] [Indexed: 12/31/2022]
Abstract
PURPOSE The accuracy of existing PET/MR attenuation correction (AC) has been limited by a lack of correlation between MR signal and tissue electron density. Based on our finding that longitudinal relaxation rate, or R1 , is associated with CT Hounsfield unit in bone and soft tissues in the brain, we propose a deep learning T1 -enhanced selection of linear attenuation coefficients (DL-TESLA) method to incorporate quantitative R1 for PET/MR AC and evaluate its accuracy and longitudinal test-retest repeatability in brain PET/MR imaging. METHODS DL-TESLA uses a 3D residual UNet (ResUNet) for pseudo-CT (pCT) estimation. With a total of 174 participants, we compared PET AC accuracy of DL-TESLA to 3 other methods adopting similar 3D ResUNet structures but using UTE R 2 ∗ , or Dixon, or T1 -MPRAGE as input. With images from 23 additional participants repeatedly scanned, the test-retest differences and within-subject coefficient of variation of standardized uptake value ratios (SUVR) were compared between PET images reconstructed using either DL-TESLA or CT for AC. RESULTS DL-TESLA had (1) significantly lower mean absolute error in pCT, (2) the highest Dice coefficients in both bone and air, (3) significantly lower PET relative absolute error in whole brain and various brain regions, (4) the highest percentage of voxels with a PET relative error within both ±3% and ±5%, (5) similar to CT test-retest differences in SUVRs from the cerebrum and mean cortical (MC) region, and (6) similar to CT within-subject coefficient of variation in cerebrum and MC. CONCLUSION DL-TESLA demonstrates excellent PET/MR AC accuracy and test-retest repeatability.
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Affiliation(s)
- Yasheng Chen
- Dept. of Neurology, Washington University in St. Louis, St. Louis, Missouri 63110, USA
| | - Chunwei Ying
- Dept. of Biomedical Engineering, Washington University in St. Louis, St. Louis, Missouri 63110, USA
| | - Michael M. Binkley
- Dept. of Neurology, Washington University in St. Louis, St. Louis, Missouri 63110, USA
| | - Meher R. Juttukonda
- Athinoula A. Martinos Center for Biomedical Imaging, Dept. of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts 02129, USA
- Dept. of Radiology, Harvard Medical School, Boston, Massachusetts 02115, USA
| | - Shaney Flores
- Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, Missouri 63110, USA
| | - Richard Laforest
- Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, Missouri 63110, USA
| | - Tammie L.S. Benzinger
- Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, Missouri 63110, USA
| | - Hongyu An
- Dept. of Neurology, Washington University in St. Louis, St. Louis, Missouri 63110, USA
- Dept. of Biomedical Engineering, Washington University in St. Louis, St. Louis, Missouri 63110, USA
- Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, Missouri 63110, USA
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Irmak S, Zimmermann L, Georg D, Kuess P, Lechner W. Cone beam CT based validation of neural network generated synthetic CTs for radiotherapy in the head region. Med Phys 2021; 48:4560-4571. [PMID: 34028053 DOI: 10.1002/mp.14987] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2020] [Revised: 05/06/2021] [Accepted: 05/09/2021] [Indexed: 11/10/2022] Open
Abstract
PURPOSE In the past years, many different neural network-based conversion techniques for synthesizing computed tomographys (sCTs) from MR images have been published. While the model's performance can be checked during the training against the test set, test datasets can never represent the whole population. Conversion errors can still occur for special cases, for example, for unusual anatomical situations. Therefore, the performance of sCT conversion needs to be verified on a patient specific level, especially in the absence of a planning CT (pCT). In this study, the capability of cone-beam CTs (CBCTs) for the validation of sCTs generated by a neural network was investigated. METHODS 41 patients with tumors in the head region were selected. 20 of them were used for model training and 10 for validation. Different implementations of CycleGAN (with/without identity and feature loss) were used to generate sCTs. The pixel (MAE, RMSE, PSNR) and geometric error (DICE, Sensitivity, Specificity) values were reported to identify the best model. VMAT plans were created for the remaining 11 patients on the pCTs. These plans were re-calculated on sCTs and CBCTs. An automatic density overriding method ( C B C T RS ) and a population-based dose calculation method ( C B C T Pop ) were employed for CBCT-based dose calculation. The dose distributions were analysed using 3D global gamma analysis, applying a threshold of 10% with respect to the prescribed dose. Differences in DVH metrics for the PTV and the organs-at-risk were compared among the dose distributions based on pCTs, sCTs, and CBCTs. RESULTS The best model was the CycleGAN without identity and feature matching loss. Including the identity loss led to a metric decrease of 10% for DICE and a metric increase of 20-60 HU for MAE. Using the 2%/2 mm gamma criterion and pCT as reference, the mean gamma pass rates were 99.0 ± 0.4% for sCTs. Mean gamma pass rate values comparing pCT and CBCT were 99.0 ± 0.8% and 99.1 ± 0.8% for the C B C T RS and C B C T Pop , respectively. The mean gamma pass rates comparing sCT and CBCT resulted in 98.4 ± 1.6% and 99.2 ± 0.6% for C B C T RS and C B C T Pop , respectively. The differences between the gamma-pass-rates of the sCT and two CBCT-based methods were not significant. The majority of deviations of the investigated DVH metrices between sCTs and CBCTs were within 2%. CONCLUSION The dosimetric results demonstrate good agreement between sCT, CBCT, and pCT based calculations. A properly applied CBCT conversion method can serve as a tool for quality assurance procedures in an MR only radiotherapy workflow for head patients. Dosimetric deviations of DVH metrics between sCT and CBCTs of larger than 2% should be followed up. A systematic shift of approximately 1% should be taken into account when using the C B C T RS approach in an MR only workflow.
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Affiliation(s)
- Sinan Irmak
- Department of Radiation Oncology, Medical University of Vienna, Vienna, Austria
| | - Lukas Zimmermann
- Department of Radiation Oncology, Medical University of Vienna, Vienna, Austria.,Faculty of Engineering, University of Applied Sciences, Wiener Neustadt, Austria.,Competence Center for Preclinical Imaging and Biomedical Engineering, University of Applied Sciences, Wiener Neustadt, Austria
| | - Dietmar Georg
- Department of Radiation Oncology, Medical University of Vienna, Vienna, Austria
| | - Peter Kuess
- Department of Radiation Oncology, Medical University of Vienna, Vienna, Austria
| | - Wolfgang Lechner
- Department of Radiation Oncology, Medical University of Vienna, Vienna, Austria
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21
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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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22
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Edmund JM, Andreasen D, Van Leemput K. Cone beam computed tomography based image guidance and quality assessment of prostate cancer for magnetic resonance imaging-only radiotherapy in the pelvis. PHYSICS & IMAGING IN RADIATION ONCOLOGY 2021; 18:55-60. [PMID: 34258409 PMCID: PMC8254192 DOI: 10.1016/j.phro.2021.05.001] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/16/2021] [Revised: 04/23/2021] [Accepted: 05/04/2021] [Indexed: 12/22/2022]
Abstract
MRI-only IGRT accuracy is ≤2 mm as compared to CT but significant differences were observed. MRI-only CBCT-based IGRT seems feasible but caution is advised. The median absolute error (MeAE) for independent verification on the sCT quality is proposed. A MeAE around 0.1 in mass density could call for sCT quality inspection.
Background and purpose Radiotherapy (RT) based on magentic resonance imaging (MRI) only is currently used clinically in the pelvis. A synthetic computed tomography (sCT) is needed for dose planning. Here, we investigate the accuracy of cone beam CT (CBCT) based MRI-only image guided RT (IGRT) and sCT image quality. Materials and methods CT, MRI and CBCT scans of ten prostate cancer patients were included. The MRI was converted to a sCT using a multi-atlas approach. The sCT, CT and MR images were auto-matched with the CBCT on the bony anatomy. Paired sCT-CT and sCT-CBCT data were created. CT numbers were converted to relative electron (RED) and mass densities (DES) using a standard calibration curve for the CT and sCT. For the CBCT RED/DES conversion, a phantom and paired CT-CBCT population based calibration curve was used. For the latter, the CBCT numbers were averaged in 100 HU bins and the known RED/DES of the CT were assigned. The paired sCT-CT and sCT-CBCT data were averaged in bins of 10 HU or 0.01 RED/DES. The median absolute error (MeAE) between the sCT-CT and sCT-CBCT bins was calculated. Wilcoxon rank-sum tests were carried out for the IGRT and MeAE study. Results The mean sCT or MR IGRT difference from CT was ≤ 2 mm but significant differences were observed. A CBCT HU or phantom-based RED/DES MeAE did not estimate the sCT quality similar to a CT based MeAE but the CBCT population-based RED/DES MeAE did. Conclusions MRI-only CBCT-based IGRT seems feasible but caution is advised. A MeAE around 0.1 DES could call for sCT quality inspection.
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Affiliation(s)
- Jens M Edmund
- Radiotherapy Research Unit, Department of Oncology, Gentofte and Herlev Hospital, University of Copenhagen, 2730 Herlev, Denmark.,Niels Bohr Institute, University of Copenhagen, 2100 Copenhagen, Denmark
| | - Daniel Andreasen
- Department of Health Technology, Technical University of Denmark, 2800 Lyngby, Denmark
| | - Koen Van Leemput
- Department of Health Technology, Technical University of Denmark, 2800 Lyngby, Denmark.,Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
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23
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Abstract
Magnetic resonance (MR) imaging is a crucial tool for evaluation of the skull base, enabling characterization of complex anatomy by utilizing multiple image contrasts. Recent technical MR advances have greatly enhanced radiologists' capability to diagnose skull base pathology and help direct management. In this paper, we will summarize cutting-edge clinical and emerging research MR techniques for the skull base, including high-resolution, phase-contrast, diffusion, perfusion, vascular, zero echo-time, elastography, spectroscopy, chemical exchange saturation transfer, PET/MR, ultra-high-field, and 3D visualization. For each imaging technique, we provide a high-level summary of underlying technical principles accompanied by relevant literature review and clinical imaging examples.
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Affiliation(s)
- Claudia F Kirsch
- Division Chief, Neuroradiology, Professor of Neuroradiology and Otolaryngology, Department of Radiology, Northwell Health, Zucker Hofstra School of Medicine at Northwell, North Shore University Hospital, Manhasset, NY
| | - Mai-Lan Ho
- Associate Professor of Radiology, Director of Research, Department of Radiology, Director, Advanced Neuroimaging Core, Chair, Asian Pacific American Network, Secretary, Association for Staff and Faculty Women, Nationwide Children's Hospital and The Ohio State University, Columbus, OH; Division Chief, Neuroradiology, Professor of Neuroradiology and Otolaryngology, Department of Radiology, Northwell Health, Zucker Hofstra School of Medicine at Northwell, North Shore University Hospital, Manhasset, NY.
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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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25
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Tang B, Wu F, Fu Y, Wang X, Wang P, Orlandini LC, Li J, Hou Q. Dosimetric evaluation of synthetic CT image generated using a neural network for MR-only brain radiotherapy. J Appl Clin Med Phys 2021; 22:55-62. [PMID: 33527712 PMCID: PMC7984468 DOI: 10.1002/acm2.13176] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2020] [Revised: 11/15/2020] [Accepted: 12/01/2020] [Indexed: 02/05/2023] Open
Abstract
PURPOSE AND BACKGROUND The magnetic resonance (MR)-only radiotherapy workflow is urged by the increasing use of MR image for the identification and delineation of tumors, while a fast generation of synthetic computer tomography (sCT) image from MR image for dose calculation remains one of the key challenges to the workflow. This study aimed to develop a neural network to generate the sCT in brain site and evaluate the dosimetry accuracy. MATERIALS AND METHODS A generative adversarial network (GAN) was developed to translate T1-weighted MRI to sCT. First, the "U-net" shaped encoder-decoder network with some image translation-specific modifications was trained to generate sCT, then the discriminator network was adversarially trained to distinguish between synthetic and real CT images. We enrolled 37 brain cancer patients acquiring both CT and MRI for treatment position simulation. Twenty-seven pairs of 2D T1-weighted MR images and rigidly registered CT image were used to train the GAN model, and the remaining 10 pairs were used to evaluate the model performance through the metric of mean absolute error. Furthermore, the clinical Volume Modulated Arc Therapy plan was calculated on both sCT and real CT, followed by gamma analysis and comparison of dose-volume histogram. RESULTS On average, only 15 s were needed to generate one sCT from one T1-weighted MRI. The mean absolute error between synthetic and real CT was 60.52 ± 13.32 Housefield Unit over 5-fold cross validation. For dose distribution on sCT and CT, the average pass rates of gamma analysis using the 3%/3 mm and 2%/2 mm criteria were 99.76% and 97.25% over testing patients, respectively. For parameters of dose-volume histogram for both target and organs at risk, no significant differences were found between both plans. CONCLUSION The GAN model can generate synthetic CT from one single MRI sequence within seconds, and a state-of-art accuracy of CT number and dosimetry was achieved.
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Affiliation(s)
- Bin Tang
- Key Laboratory of Radiation Physics and Technology of the Ministry of EducationInstitute of Nuclear Science and TechnologySichuan UniversityChengduSichuanChina
- Department of Radiation OncologyRadiation Oncology Key Laboratory Of Sichuan ProvinceSichuan Cancer Hospital & InstituteChengduSichuanChina
| | - Fan Wu
- Department of Radiation OncologyRadiation Oncology Key Laboratory Of Sichuan ProvinceSichuan Cancer Hospital & InstituteChengduSichuanChina
| | - Yuchuan Fu
- Department of RadiotherapyWest China Hospital of Sichuan UniversityChengduSichuanChina
| | - Xianliang Wang
- Department of Radiation OncologyRadiation Oncology Key Laboratory Of Sichuan ProvinceSichuan Cancer Hospital & InstituteChengduSichuanChina
| | - Pei Wang
- Department of Radiation OncologyRadiation Oncology Key Laboratory Of Sichuan ProvinceSichuan Cancer Hospital & InstituteChengduSichuanChina
| | - Lucia Clara Orlandini
- Department of Radiation OncologyRadiation Oncology Key Laboratory Of Sichuan ProvinceSichuan Cancer Hospital & InstituteChengduSichuanChina
| | - Jie Li
- Department of Radiation OncologyRadiation Oncology Key Laboratory Of Sichuan ProvinceSichuan Cancer Hospital & InstituteChengduSichuanChina
| | - Qing Hou
- Key Laboratory of Radiation Physics and Technology of the Ministry of EducationInstitute of Nuclear Science and TechnologySichuan UniversityChengduSichuanChina
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26
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Ding S, Liu H, Li Y, Wang B, Li R, Liu B, Ouyang Y, Wu D, Huang X. Assessment of dose accuracy for online MR-guided radiotherapy for cervical carcinoma. JOURNAL OF RADIATION RESEARCH AND APPLIED SCIENCES 2021. [DOI: 10.1080/16878507.2021.1888243] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Affiliation(s)
- Shouliang Ding
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
- Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Hongdong Liu
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Yongbao Li
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Bin Wang
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Rui Li
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Biaoshui Liu
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Yi Ouyang
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Dehua Wu
- Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Xiaoyan Huang
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
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27
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Li W, Kazemifar S, Bai T, Nguyen D, Weng Y, Li Y, Xia J, Xiong J, Xie Y, Owrangi AM, Jiang SB. Synthesizing CT images from MR images with deep learning: model generalization for different datasets through transfer learning. Biomed Phys Eng Express 2021; 7. [PMID: 33545707 DOI: 10.1088/2057-1976/abe3a7] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2020] [Accepted: 02/05/2021] [Indexed: 11/12/2022]
Abstract
PURPOSE Synthetic CT generation is the focus of many studies, however, only models on data with the same dataset were tested. Therefore, how well the trained model will work for data from different hospitals and MR protocols is still unknown. In this study, we addressed the model generalization problem for the MR-to-CT conversion task. METHODS Brain T2 MR and corresponding CT images were collected from one hospital and brain T1-FLAIR, T1-POST MR, and corresponding CT images were collected from another hospital. To investigate the model's generalizability ability, four potential solutions were proposed: source model, target model, combined model, and adapted model. All models were trained using the CycleGAN network. The source model was trained with a source domain dataset from scratch and tested with a target domain dataset. The target model was trained with a target domain dataset and tested with a target domain dataset. The combined model was trained with both source domain and target domain datasets, and tested with the target domain dataset. The adapted model used a transfer learning strategy to train a CycleGAN model with a source domain dataset and retrain the pre-trained model with a target domain dataset. MAE, RMSE, PSNR, and SSIM were used to quantitatively evaluate model performance on a target domain dataset. RESULTS The adapted model achieved best quantitative results of 74.56±8.61, 193.18±17.98, 28.30±0.83, and 0.84±0.01 for MAE, RMSE, PSNR, and SSIM using the T1-FLAIR dataset and 74.89±15.64, 195.73±31.29, 27.72±1.43, and 0.83±0.04 for MAE, RMSE, PSNR, and SSIM using the T1-POST dataset. The source model had the poorest performance. CONCLUSIONS This work indicates high generalization ability to generate synthetic CT images from small training datasets of MR images using pre-trained CycleGAN. The quantitative results of the test data, including different scanning protocols and different acquisition centers, indicated the proof of this concept.
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Affiliation(s)
- Wen Li
- Department of Radiation Oncology, Chinese Academy of Sciences, 2280 Inwood Rd., Beijing, 75235, CHINA
| | - Samaneh Kazemifar
- UT Southwestern Department of Radiation Oncology, 2280 Inwood Rd., Dallas, Texas, 75235, UNITED STATES
| | - Ti Bai
- Radiation Oncology, UT Southwestern Department of Radiation Oncology, 2280 Inwood Rd., Dallas, Texas, 75235, UNITED STATES
| | - Dan Nguyen
- Department of Radiation Oncology, UT Southwestern Department of Radiation Oncology, 2280 Inwood Rd., Dallas, Texas, 75235, UNITED STATES
| | - Yaochung Weng
- Radiation Oncology, UT Southwestern Medical, 2280 Inwood Rd., Dallas, Texas, 75235, UNITED STATES
| | - Yafen Li
- Chinese Academy of Sciences, 2280 Inwood Rd., Beijing, 75235, CHINA
| | - Jun Xia
- Shenzhen Second People's Hospital, 2280 Inwood Rd., Shenzhen, 75235, CHINA
| | - Jing Xiong
- Chinese Academy of Sciences, 2280 Inwood Rd., Beijing, 75235, CHINA
| | - Yaoqin Xie
- Chinese Academy of Sciences, 2280 Inwood Rd., Beijing, 100864, CHINA
| | - Amir M Owrangi
- Department of Radiation Oncology, UT Southwestern Department of Radiation Oncology, 2280 Inwood Rd., Dallas, Texas, 75235, UNITED STATES
| | - Steve B Jiang
- Department of Radiation Oncology, UT Southwestern Department of Radiation Oncology, 2280 Inwood Rd., Dallas, Texas, 75235, UNITED STATES
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Yuan J, Fredman E, Jin JY, Choi S, Mansur D, Sloan A, Machtay M, Zheng Y. Monte Carlo Dose Calculation Using MRI Based Synthetic CT Generated by Fully Convolutional Neural Network for Gamma Knife Radiosurgery. Technol Cancer Res Treat 2021; 20:15330338211046433. [PMID: 34632872 PMCID: PMC8504229 DOI: 10.1177/15330338211046433] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Accepted: 08/27/2021] [Indexed: 11/24/2022] Open
Abstract
The aim of this work is to study the dosimetric effect from generated synthetic computed tomography (sCT) from magnetic resonance (MR) images using a deep learning algorithm for Gamma Knife (GK) stereotactic radiosurgery (SRS). The Monte Carlo (MC) method is used for dose calculations. Thirty patients were retrospectively selected with our institution IRB's approval. All patients were treated with GK SRS based on T1-weighted MR images and also underwent conventional external beam treatment with a CT scan. Image datasets were preprocessed with registration and were normalized to obtain similar intensity for the pairs of MR and CT images. A deep convolutional neural network arranged in an encoder-decoder fashion was used to learn the direct mapping from MR to the corresponding CT. A number of metrics including the voxel-wise mean error (ME) and mean absolute error (MAE) were used for evaluating the difference between generated sCT and the true CT. To study the dosimetric accuracy, MC simulations were performed based on the true CT and sCT using the same treatment parameters. The method produced an MAE of 86.6 ± 34.1 Hundsfield units (HU) and a mean squared error (MSE) of 160.9 ± 32.8. The mean Dice similarity coefficient was 0.82 ± 0.05 for HU > 200. The difference for dose-volume parameter D95 between the ground true dose and the dose calculated with sCT was 1.1% if a synthetic CT-to-density table was used, and 4.9% compared with the calculations based on the water-brain phantom.
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Affiliation(s)
- Jiankui Yuan
- University Hospitals Cleveland Medical Center, Cleveland, USA
| | - Elisha Fredman
- University Hospitals Cleveland Medical Center, Cleveland, USA
| | - Jian-Yue Jin
- University Hospitals Cleveland Medical Center, Cleveland, USA
| | - Serah Choi
- University Hospitals Cleveland Medical Center, Cleveland, USA
| | - David Mansur
- University Hospitals Cleveland Medical Center, Cleveland, USA
| | - Andrew Sloan
- University Hospitals Cleveland Medical Center, Cleveland, USA
| | | | - Yiran Zheng
- University Hospitals Cleveland Medical Center, Cleveland, USA
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Abstract
Attenuation correction has been one of the main methodological challenges in the integrated positron emission tomography and magnetic resonance imaging (PET/MRI) field. As standard transmission or computed tomography approaches are not available in integrated PET/MRI scanners, MR-based attenuation correction approaches had to be developed. Aspects that have to be considered for implementing accurate methods include the need to account for attenuation in bone tissue, normal and pathological lung and the MR hardware present in the PET field-of-view, to reduce the impact of subject motion, to minimize truncation and susceptibility artifacts, and to address issues related to the data acquisition and processing both on the PET and MRI sides. The standard MR-based attenuation correction techniques implemented by the PET/MRI equipment manufacturers and their impact on clinical and research PET data interpretation and quantification are first discussed. Next, the more advanced methods, including the latest generation deep learning-based approaches that have been proposed for further minimizing the attenuation correction related bias are described. Finally, a future perspective focused on the needed developments in the field is given.
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Affiliation(s)
- Ciprian Catana
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, United States of America
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30
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Song X, Qian P, Zheng J, Jiang Y, Xia K, Traughber B, Wu D, Muzic RF. mDixon-based synthetic CT generation via transfer and patch learning. Pattern Recognit Lett 2020. [DOI: 10.1016/j.patrec.2020.06.017] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
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31
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Peng Y, Chen S, Qin A, Chen M, Gao X, Liu Y, Miao J, Gu H, Zhao C, Deng X, Qi Z. Magnetic resonance-based synthetic computed tomography images generated using generative adversarial networks for nasopharyngeal carcinoma radiotherapy treatment planning. Radiother Oncol 2020; 150:217-224. [DOI: 10.1016/j.radonc.2020.06.049] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2019] [Revised: 06/23/2020] [Accepted: 06/25/2020] [Indexed: 12/27/2022]
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Yousefi Moteghaed N, Mostaar A, Maghooli K, Houshyari M, Ameri A. Estimation and evaluation of pseudo-CT images using linear regression models and texture feature extraction from MRI images in the brain region to design external radiotherapy planning. Rep Pract Oncol Radiother 2020; 25:738-745. [PMID: 32684863 DOI: 10.1016/j.rpor.2020.05.005] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2020] [Revised: 05/05/2020] [Accepted: 05/25/2020] [Indexed: 10/23/2022] Open
Abstract
Aim The aim of this study is to construct and evaluate Pseudo-CT images (P-CTs) for electron density calculation to facilitate external radiotherapy treatment planning. Background Despite numerous benefits, computed tomography (CT) scan does not provide accurate information on soft tissue contrast, which often makes it difficult to precisely differentiate target tissues from the organs at risk and determine the tumor volume. Therefore, MRI imaging can reduce the variability of results when registering with a CT scan. Materials and methods In this research, a fuzzy clustering algorithm was used to segment images into different tissues, also linear regression methods were used to design the regression model based on the feature extraction method and the brightness intensity values. The results of the proposed algorithm for dose-volume histogram (DVH), Isodose curves, and gamma analysis were investigated using the RayPlan treatment planning system, and VeriSoft software. Furthermore, various statistical indices such as Mean Absolute Error (MAE), Mean Error (ME), and Structural Similarity Index (SSIM) were calculated. Results The MAE of a range of 45-55 was found from the proposed methods. The relative difference error between the PTV region of the CT and the Pseudo-CT was 0.5, and the best gamma rate was 95.4% based on the polar coordinate feature and proposed polynomial regression model. Conclusion The proposed method could support the generation of P-CT data for different parts of the brain region from a collection of MRI series with an acceptable average error rate by different evaluation criteria.
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Affiliation(s)
- Niloofar Yousefi Moteghaed
- Department of Biomedical Engineering and Medical Physics, Faculty of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Ahmad Mostaar
- Department of Biomedical Engineering and Medical Physics, Faculty of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran.,Radiation Biology Research Center, Iran University of Medical Sciences, Tehran, Iran
| | - Keivan Maghooli
- Department of Biomedical Engineering, Islamic Azad University, Science and Research Branch, Tehran, Iran
| | - Mohammad Houshyari
- Department of Radiation Oncology, Shohada-e-Tajrish Hospital, Faculty of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Ahmad Ameri
- Department of Radiation Oncology, Imam Hossein Hospital, Faculty of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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Generation of Pseudo-CT using High-Degree Polynomial Regression on Dual-Contrast Pelvic MRI Data. Sci Rep 2020; 10:8118. [PMID: 32415138 PMCID: PMC7229007 DOI: 10.1038/s41598-020-64842-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2019] [Accepted: 04/20/2020] [Indexed: 01/06/2023] Open
Abstract
Increasing interests in using magnetic resonance imaging only in radiation therapy require methods for predicting the computed tomography numbers from MRI data. Here we propose a simple voxel method to generate the pseudo-CT (pCT) image using dual-contrast pelvic MRI data. The method is first trained with the CT data and dual-contrast MRI data (two sets of MRI with different sequences) of multiple patients, where the anatomical structures in the images after deformable image registration are segmented into several regions, and after MRI intensity normalizations a regression analysis is used to determine a two-variable polynomial function for each region to relate a voxel’s two MRI intensity values to its CT number. We first evaluate the accuracy via the Hounsfield unit (HU) difference between the pseudo-CT and reference-CT (rCT) images and obtain the average mean absolute error as 40.3 ± 2.9 HU from leave-one-out-cross-validation (LOOCV) across all six patients, which is better than most previous results and comparable to another study using the more complicated atlas-based method. We also perform a dosimetric evaluation of the treatment plans based on pCT and rCT images and find the average passing rate within 2% dose difference to be 95.4% in point-to-point dose comparisons. Therefore, our method shows encouraging results in predicting the CT numbers. This polynomial method needs less computer storage than the interpolation method and can be readily extended to the case of more than two MRI sequences.
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Ranta I, Teuho J, Linden J, Klén R, Teräs M, Kapanen M, Keyriläinen J. Assessment of MRI-Based Attenuation Correction for MRI-Only Radiotherapy Treatment Planning of the Brain. Diagnostics (Basel) 2020; 10:E299. [PMID: 32422950 PMCID: PMC7278007 DOI: 10.3390/diagnostics10050299] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2020] [Revised: 04/27/2020] [Accepted: 05/11/2020] [Indexed: 12/17/2022] Open
Abstract
Magnetic resonance imaging-only radiotherapy treatment planning (MRI-only RTP) and positron emission tomography (PET)-MRI imaging require generation of synthetic computed tomography (sCT) images from MRI images. In this study, initial dosimetric evaluation was performed for a previously developed MRI-based attenuation correction (MRAC) method for use in MRI-only RTP of the brain. MRAC-based sCT images were retrospectively generated from Dixon MR images of 20 patients who had previously received external beam radiation therapy (EBRT). Bone segmentation performance and Dice similarity coefficient of the sCT conversion method were evaluated for bone volumes on CT images. Dose calculation accuracy was assessed by recalculating the CT-based EBRT plans using the sCT images as the base attenuation data. Dose comparison was done for the sCT- and CT-based EBRT plans in planning target volume (PTV) and organs at risk (OAR). Parametric dose comparison showed mean relative differences of <0.4% for PTV and <1.0% for OARs. Mean gamma index pass rates of 95.7% with the 2%/2 mm agreement criterion and 96.5% with the 1%/1 mm agreement criterion were determined for glioma and metastasis patients, respectively. Based on the results, MRI-only RTP using sCT images generated from MRAC images can be a feasible alternative for radiotherapy of the brain.
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Affiliation(s)
- Iiro Ranta
- Department of Physics and Astronomy, University of Turku, Vesilinnantie 5, FI-20014 Turku, Finland;
- Department of Medical Physics, Turku University Hospital, Hämeentie 11, FI-20521 Turku, Finland; (J.T.); (M.T.)
- Department of Oncology and Radiotherapy, Turku University Hospital, Hämeentie 11, FI-20521 Turku, Finland
| | - Jarmo Teuho
- Department of Medical Physics, Turku University Hospital, Hämeentie 11, FI-20521 Turku, Finland; (J.T.); (M.T.)
- Turku PET Centre, University of Turku and Turku University Hospital, Kiinamyllynkatu 4-8, FI-20521 Turku, Finland; (J.L.); (R.K.)
| | - Jani Linden
- Turku PET Centre, University of Turku and Turku University Hospital, Kiinamyllynkatu 4-8, FI-20521 Turku, Finland; (J.L.); (R.K.)
- Department of Mathematics and Statistics, University of Turku, Vesilinnantie 5, FI-20014 Turku, Finland
| | - Riku Klén
- Turku PET Centre, University of Turku and Turku University Hospital, Kiinamyllynkatu 4-8, FI-20521 Turku, Finland; (J.L.); (R.K.)
| | - Mika Teräs
- Department of Medical Physics, Turku University Hospital, Hämeentie 11, FI-20521 Turku, Finland; (J.T.); (M.T.)
- Institute of Biomedicine, University of Turku, Kiinamyllynkatu 10, FI-20014 Turku, Finland
| | - Mika Kapanen
- Department of Medical Physics, Medical Imaging Center, Tampere University Hospital, Teiskontie 35, FI-33521 Tampere, Finland;
- Department of Oncology, Unit of Radiotherapy, Tampere University Hospital, Teiskontie 35, FI-33521 Tampere, Finland
| | - Jani Keyriläinen
- Department of Physics and Astronomy, University of Turku, Vesilinnantie 5, FI-20014 Turku, Finland;
- Department of Medical Physics, Turku University Hospital, Hämeentie 11, FI-20521 Turku, Finland; (J.T.); (M.T.)
- Department of Oncology and Radiotherapy, Turku University Hospital, Hämeentie 11, FI-20521 Turku, Finland
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Hu Y, Zhang L. MRI-only Radiation Therapy: Pseudo-CT Based on Cubic-Feature Extraction and Alternative Regression Forest. INT J PATTERN RECOGN 2020. [DOI: 10.1142/s0218001420540336] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Despite the extensive attention attracted by magnetic resonance imaging (MRI) in the radiation therapy, computed tomography was reintroduced by the researchers. During the calculation process of the 3D dose distribution of tissues, there were some arguments about the electron density information obtained from the CT scan. However, the CT-provided bones are accurate for constructing a radiograph. Recently, the advantages boosted by the soft tissue contrast relying on MRI and as well as the advantages boosted by CT imaging have been combined by the using of MRI/CT. Unfortunately, disadvantages still exist in the MRI/CT workflow because the voxel-intensities are unbalanced in the MRI and the CT scan. Here, based on the mapping method of CT and MRI, the potential of pseudo-CT (PCT) instead of CT planning was studied. The estimated PCT only from the corresponding MRI was obtained by using the patch-based random forest regression. The CT voxel target was trained by 3D Gabor feature in the MRI cube and the Local Binary Pattern (LBP). Besides, the regression task was solved by the alternative regression forest. According to the experiment, the method performs better than the current dictionary learning-based (DLB) method or atlas-based (AB) method.
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Affiliation(s)
- Yongsheng Hu
- School of Electrical and Information Engineering, Tianjin University, Tianjin, P. R. China
- School of Information Engineering, Binzhou University, Shandong, P. R. China
| | - Liyi Zhang
- School of Electrical and Information Engineering, Tianjin University, Tianjin, P. R. China
- School of Information Engineering, Tianjin University of Commerce, Tianjin, P. R. China
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Han PK, Horng DE, Gong K, Petibon Y, Kim K, Li Q, Johnson KA, El Fakhri G, Ouyang J, Ma C. MR-based PET attenuation correction using a combined ultrashort echo time/multi-echo Dixon acquisition. Med Phys 2020; 47:3064-3077. [PMID: 32279317 DOI: 10.1002/mp.14180] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2019] [Revised: 03/26/2020] [Accepted: 04/02/2020] [Indexed: 12/11/2022] Open
Abstract
PURPOSE To develop a magnetic resonance (MR)-based method for estimation of continuous linear attenuation coefficients (LACs) in positron emission tomography (PET) using a physical compartmental model and ultrashort echo time (UTE)/multi-echo Dixon (mUTE) acquisitions. METHODS We propose a three-dimensional (3D) mUTE sequence to acquire signals from water, fat, and short T2 components (e.g., bones) simultaneously in a single acquisition. The proposed mUTE sequence integrates 3D UTE with multi-echo Dixon acquisitions and uses sparse radial trajectories to accelerate imaging speed. Errors in the radial k-space trajectories are measured using a special k-space trajectory mapping sequence and corrected for image reconstruction. A physical compartmental model is used to fit the measured multi-echo MR signals to obtain fractions of water, fat, and bone components for each voxel, which are then used to estimate the continuous LAC map for PET attenuation correction. RESULTS The performance of the proposed method was evaluated via phantom and in vivo human studies, using LACs from computed tomography (CT) as reference. Compared to Dixon- and atlas-based MRAC methods, the proposed method yielded PET images with higher correlation and similarity in relation to the reference. The relative absolute errors of PET activity values reconstructed by the proposed method were below 5% in all of the four lobes (frontal, temporal, parietal, and occipital), cerebellum, whole white matter, and gray matter regions across all subjects (n = 6). CONCLUSIONS The proposed mUTE method can generate subject-specific, continuous LAC map for PET attenuation correction in PET/MR.
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Affiliation(s)
- Paul Kyu Han
- Department of Radiology, Gordon Center for Medical Imaging, Massachusetts General Hospital, Boston, MA, 02114, USA.,Department of Radiology, Harvard Medical School, Boston, MA, 02115, USA
| | - Debra E Horng
- Department of Radiology, Gordon Center for Medical Imaging, Massachusetts General Hospital, Boston, MA, 02114, USA.,Department of Radiology, Harvard Medical School, Boston, MA, 02115, USA
| | - Kuang Gong
- Department of Radiology, Gordon Center for Medical Imaging, Massachusetts General Hospital, Boston, MA, 02114, USA.,Department of Radiology, Harvard Medical School, Boston, MA, 02115, USA
| | - Yoann Petibon
- Department of Radiology, Gordon Center for Medical Imaging, Massachusetts General Hospital, Boston, MA, 02114, USA.,Department of Radiology, Harvard Medical School, Boston, MA, 02115, USA
| | - Kyungsang Kim
- Department of Radiology, Gordon Center for Medical Imaging, Massachusetts General Hospital, Boston, MA, 02114, USA.,Department of Radiology, Harvard Medical School, Boston, MA, 02115, USA
| | - Quanzheng Li
- Department of Radiology, Gordon Center for Medical Imaging, Massachusetts General Hospital, Boston, MA, 02114, USA.,Department of Radiology, Harvard Medical School, Boston, MA, 02115, USA
| | - Keith A Johnson
- Department of Radiology, Gordon Center for Medical Imaging, Massachusetts General Hospital, Boston, MA, 02114, USA.,Department of Radiology, Harvard Medical School, Boston, MA, 02115, USA.,Department of Neurology, Massachusetts General Hospital, Boston, MA, 02114, USA.,Center for Alzheimer Research and Treatment, Department of Neurology, Brigham and Women's Hospital, Boston, MA, 02115, USA
| | - Georges El Fakhri
- Department of Radiology, Gordon Center for Medical Imaging, Massachusetts General Hospital, Boston, MA, 02114, USA.,Department of Radiology, Harvard Medical School, Boston, MA, 02115, USA
| | - Jinsong Ouyang
- Department of Radiology, Gordon Center for Medical Imaging, Massachusetts General Hospital, Boston, MA, 02114, USA.,Department of Radiology, Harvard Medical School, Boston, MA, 02115, USA
| | - Chao Ma
- Department of Radiology, Gordon Center for Medical Imaging, Massachusetts General Hospital, Boston, MA, 02114, USA.,Department of Radiology, Harvard Medical School, Boston, MA, 02115, USA
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Qian P, Chen Y, Kuo JW, Zhang YD, Jiang Y, Zhao K, Al Helo R, Friel H, Baydoun A, Zhou F, Heo JU, Avril N, Herrmann K, Ellis R, Traughber B, Jones RS, Wang S, Su KH, Muzic RF. mDixon-Based Synthetic CT Generation for PET Attenuation Correction on Abdomen and Pelvis Jointly Using Transfer Fuzzy Clustering and Active Learning-Based Classification. IEEE TRANSACTIONS ON MEDICAL IMAGING 2020; 39:819-832. [PMID: 31425065 PMCID: PMC7284852 DOI: 10.1109/tmi.2019.2935916] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/28/2023]
Abstract
We propose a new method for generating synthetic CT images from modified Dixon (mDixon) MR data. The synthetic CT is used for attenuation correction (AC) when reconstructing PET data on abdomen and pelvis. While MR does not intrinsically contain any information about photon attenuation, AC is needed in PET/MR systems in order to be quantitatively accurate and to meet qualification standards required for use in many multi-center trials. Existing MR-based synthetic CT generation methods either use advanced MR sequences that have long acquisition time and limited clinical availability or use matching of the MR images from a newly scanned subject to images in a library of MR-CT pairs which has difficulty in accounting for the diversity of human anatomy especially in patients that have pathologies. To address these deficiencies, we present a five-phase interlinked method that uses mDixon MR acquisition and advanced machine learning methods for synthetic CT generation. Both transfer fuzzy clustering and active learning-based classification (TFC-ALC) are used. The significance of our efforts is fourfold: 1) TFC-ALC is capable of better synthetic CT generation than methods currently in use on the challenging abdomen using only common Dixon-based scanning. 2) TFC partitions MR voxels initially into the four groups regarding fat, bone, air, and soft tissue via transfer learning; ALC can learn insightful classifiers, using as few but informative labeled examples as possible to precisely distinguish bone, air, and soft tissue. Combining them, the TFC-ALC method successfully overcomes the inherent imperfection and potential uncertainty regarding the co-registration between CT and MR images. 3) Compared with existing methods, TFC-ALC features not only preferable synthetic CT generation but also improved parameter robustness, which facilitates its clinical practicability. Applying the proposed approach on mDixon-MR data from ten subjects, the average score of the mean absolute prediction deviation (MAPD) was 89.78±8.76 which is significantly better than the 133.17±9.67 obtained using the all-water (AW) method (p=4.11E-9) and the 104.97±10.03 obtained using the four-cluster-partitioning (FCP, i.e., external-air, internal-air, fat, and soft tissue) method (p=0.002). 4) Experiments in the PET SUV errors of these approaches show that TFC-ALC achieves the highest SUV accuracy and can generally reduce the SUV errors to 5% or less. These experimental results distinctively demonstrate the effectiveness of our proposed TFCALC method for the synthetic CT generation on abdomen and pelvis using only the commonly-available Dixon pulse sequence.
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Hsu SH, DuPre P, Peng Q, Tomé WA. A technique to generate synthetic CT from MRI for abdominal radiotherapy. J Appl Clin Med Phys 2020; 21:136-143. [PMID: 32043812 PMCID: PMC7020981 DOI: 10.1002/acm2.12816] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2019] [Revised: 11/20/2019] [Accepted: 12/12/2019] [Indexed: 12/22/2022] Open
Abstract
Purpose To investigate a method to classify tissues types for synthetic CT generation using MRI for treatment planning in abdominal radiotherapy. Methods An institutional review board approved volunteer study was performed on a 3T MRI scanner. In‐phase, fat and water images were acquired for five volunteers with breath‐hold using an mDixon pulse sequence. A method to classify different tissue types for synthetic CT generation in the abdomen was developed. Three tissue clusters (fat, high‐density tissue, and spine/air/lungs) were generated using a fuzzy‐c means clustering algorithm. The third cluster was further segmented into three sub‐clusters that represented spine, air, and lungs. Therefore, five segments were automatically generated. To evaluate segmentation accuracy using the method, the five segments were manually contoured on MRI images as the ground truth, and the volume ratio, Dice coefficient, and Hausdorff distance metric were calculated. The dosimetric effect of segmentation accuracy was evaluated on simulated targets close to air, lungs, and spine using a two‐arc volumetric modulated arc therapy (VMAT) technique. Results The volume ratio of auto‐segmentation to manual segmentation was 0.88–2.1 for the air segment and 0.72–1.13 for the remaining segments. The range of the Dice coefficient was 0.24–0.83, 0.84–0.93, 0.94–0.98, 0.93–0.96, and 0.76–0.79 for air, fat, lungs, high‐density tissue, and spine, respectively. The range of the mean Hausdorff distance was 3–29.1 mm, 0.5–1.3 mm, 0.4–1 mm, 0.7–1.6 mm, and 1.2–1.4 mm for air, fat, lungs, high‐density tissue, and spine, respectively. Despite worse segmentation accuracy in air and spine, the dosimetric effect was 0.2% ± 0.2%, with a maximum difference of 0.8% for all target locations. Conclusion A method to generate synthetic CT in the abdomen was developed, and segmentation accuracy and its dosimetric effect were evaluated. Our results demonstrate the potential of using MRI alone for treatment planning in the abdomen.
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Affiliation(s)
- Shu-Hui Hsu
- Department of Radiation Oncology, Montefiore Medical Center, Bronx, NY, USA.,Institute for Onco-Physics, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Pamela DuPre
- Department of Radiation Oncology, Montefiore Medical Center, Bronx, NY, USA
| | - Qi Peng
- Gruss Magnetic Resonance Research Center, Albert Einstein College of Medicine, Bronx, NY, USA.,Department of Radiology, Montefiore Medical Center, Bronx, NY, USA
| | - Wolfgang A Tomé
- Department of Radiation Oncology, Montefiore Medical Center, Bronx, NY, USA.,Institute for Onco-Physics, Albert Einstein College of Medicine, Bronx, NY, USA
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Koike Y, Akino Y, Sumida I, Shiomi H, Mizuno H, Yagi M, Isohashi F, Seo Y, Suzuki O, Ogawa K. Feasibility of synthetic computed tomography generated with an adversarial network for multi-sequence magnetic resonance-based brain radiotherapy. JOURNAL OF RADIATION RESEARCH 2020; 61:92-103. [PMID: 31822894 PMCID: PMC6976735 DOI: 10.1093/jrr/rrz063] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/30/2019] [Revised: 07/15/2019] [Indexed: 06/10/2023]
Abstract
The aim of this work is to generate synthetic computed tomography (sCT) images from multi-sequence magnetic resonance (MR) images using an adversarial network and to assess the feasibility of sCT-based treatment planning for brain radiotherapy. Datasets for 15 patients with glioblastoma were selected and 580 pairs of CT and MR images were used. T1-weighted, T2-weighted and fluid-attenuated inversion recovery MR sequences were combined to create a three-channel image as input data. A conditional generative adversarial network (cGAN) was trained using image patches. The image quality was evaluated using voxel-wise mean absolute errors (MAEs) of the CT number. For the dosimetric evaluation, 3D conformal radiotherapy (3D-CRT) and volumetric modulated arc therapy (VMAT) plans were generated using the original CT set and recalculated using the sCT images. The isocenter dose and dose-volume parameters were compared for 3D-CRT and VMAT plans, respectively. The equivalent path length was also compared. The mean MAEs for the whole body, soft tissue and bone region were 108.1 ± 24.0, 38.9 ± 10.7 and 366.2 ± 62.0 hounsfield unit, respectively. The dosimetric evaluation revealed no significant difference in the isocenter dose for 3D-CRT plans. The differences in the dose received by 2% of the volume (D2%), D50% and D98% relative to the prescribed dose were <1.0%. The overall equivalent path length was shorter than that for real CT by 0.6 ± 1.9 mm. A treatment planning study using generated sCT detected only small, clinically negligible differences. These findings demonstrated the feasibility of generating sCT images for MR-only radiotherapy from multi-sequence MR images using cGAN.
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Affiliation(s)
- Yuhei Koike
- Department of Radiation Oncology, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Yuichi Akino
- Oncology Center, Osaka University Hospital, Osaka, Japan
| | - Iori Sumida
- Department of Radiation Oncology, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Hiroya Shiomi
- Department of Radiation Oncology, Osaka University Graduate School of Medicine, Osaka, Japan
- Miyakojima IGRT Clinic, Osaka, Japan
| | - Hirokazu Mizuno
- Department of Medical Physics and Engineering, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Masashi Yagi
- Department of Carbon Ion Radiotherapy, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Fumiaki Isohashi
- Department of Radiation Oncology, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Yuji Seo
- Department of Radiation Oncology, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Osamu Suzuki
- Department of Carbon Ion Radiotherapy, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Kazuhiko Ogawa
- Department of Radiation Oncology, Osaka University Graduate School of Medicine, Osaka, Japan
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Thapa R, Ahunbay E, Nasief H, Chen X, Allen Li X. Automated air region delineation on MRI for synthetic CT creation. Phys Med Biol 2020; 65:025009. [PMID: 31775128 DOI: 10.1088/1361-6560/ab5c5b] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Automatically and accurately separating air from other low signal regions (especially bone, liver, etc) in an MRI is difficult because these tissues produce similar MR intensities, resulting in errors in synthetic CT generation for MRI-based radiation therapy planning. This work aims to develop a technique to accurately and automatically determine air-regions for MR-guided adaptive radiation therapy. CT and MRI scans (T2-weighted) of phantoms with fabricated air-cavities and abdominal cancer patients were used to establish an MR intensity threshold for air delineation. From the phantom data, air/tissue boundaries in MRI were identified by CT-MRI registration. A formula relating the MRI intensities of air and surrounding materials was established to auto-threshold air-regions. The air-regions were further refined by using quantitative image texture features. A naive Bayesian classifier was trained using the extracted features with a leave-one-out cross validation technique to differentiate air from non-air voxels. The multi-step air auto-segmentation method was tested against the manually segmented air-regions. The dosimetry impacts of the air-segmentation methods were studied. Air-regions in the abdomen can be segmented on MRI within 1 mm accuracy using a multi-step auto-segmentation method as compared to manually delineated contours. The air delineation based on the MR threshold formula was improved using the MRI texture differences between air and tissues, as judged by the area under the receiver operating characteristic curve of 81% when two texture features (autocorrelation and contrast) were used. The performance increased to 82% with using three features (autocorrelation, sum-variance, and contrast). Dosimetric analysis showed no significant difference between the auto-segmentation and manual MR air delineation on commonly used dose volume parameters. The proposed techniques consisting of intensity-based auto-thresholding and image texture-based voxel classification can automatically and accurately segment air-regions on MRI, allowing synthetic CT to be generated quickly and precisely for MR-guided adaptive radiation therapy.
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Affiliation(s)
- Ranjeeta Thapa
- Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI, United States of America
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Nielsen JS, Edmund JM, Van Leemput K. Magnetic resonance-based computed tomography metal artifact reduction using Bayesian modelling. Phys Med Biol 2019; 64:245012. [PMID: 31766033 DOI: 10.1088/1361-6560/ab5b70] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Metal artifact reduction (MAR) algorithms reduce the errors caused by metal implants in x-ray computed tomography (CT) images and are an important part of error management in radiotherapy. A promising MAR approach is to leverage the information in magnetic resonance (MR) images that can be acquired for organ or tumor delineation. This is however complicated by the ambiguous relationship between CT values and conventional-sequence MR intensities as well as potential co-registration issues. In order to address these issues, this paper proposes a self-tuning Bayesian model for MR-based MAR that combines knowledge of the MR image intensities in local spatial neighborhoods with the information in an initial, corrupted CT reconstructed using filtered back projection. We demonstrate the potential of the resulting model in three widely-used MAR scenarios: image inpainting, sinogram inpainting and model-based iterative reconstruction. Compared to conventional alternatives in a retrospective study on nine head-and-neck patients with CT and T1-weighted MR scans, we find improvements in terms of image quality and quantitative CT value accuracy within each scenario. We conclude that the proposed model provides a versatile way to use the anatomical information in a co-acquired MR scan to boost the performance of MAR algorithms.
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Affiliation(s)
- Jonathan Scharff Nielsen
- Department of Health Technology, Technical University of Denmark, 2820 Lyngby, Denmark. Radiotherapy Research Unit, Department of Oncology, Gentofte and Herlev Hospital, University of Copenhagen, 2730 Herlev, Denmark
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Largent A, Barateau A, Nunes JC, Mylona E, Castelli J, Lafond C, Greer PB, Dowling JA, Baxter J, Saint-Jalmes H, Acosta O, de Crevoisier R. Comparison of Deep Learning-Based and Patch-Based Methods for Pseudo-CT Generation in MRI-Based Prostate Dose Planning. Int J Radiat Oncol Biol Phys 2019; 105:1137-1150. [DOI: 10.1016/j.ijrobp.2019.08.049] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2019] [Revised: 08/16/2019] [Accepted: 08/22/2019] [Indexed: 12/25/2022]
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Lei Y, Shu HK, Tian S, Wang T, Liu T, Mao H, Shim H, Curran WJ, Yang X. Pseudo CT Estimation using Patch-based Joint Dictionary Learning. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2019; 2018:5150-5153. [PMID: 30441499 DOI: 10.1109/embc.2018.8513475] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Magnetic resonance (MR) simulators have recently gained popularity; it avoids the unnecessary radiation exposure associated with Computed Tomography (CT) when used for radiation therapy planning. We propose a method for pseudo CT estimation from MR images based on joint dictionary learning. Patient-specific anatomical features were extracted from the aligned training images and adopted as signatures for each voxel. The most relevant and informative features were identified to train the joint dictionary learning-based model. The well-trained dictionary was used to predict the pseudo CT of a new patient. This prediction technique was validated with a clinical study of 12 patients with MR and CT images of the brain. The mean absolute error (MAE), peak signal-to-noise ratio (PSNR), normalized cross correlation (NCC) indexes were used to quantify the prediction accuracy. We compared our proposed method with a state-of-the-art dictionary learning method. Overall our proposed method significantly improves the prediction accuracy over the state-of-the-art dictionary learning method. We have investigated a novel joint dictionary Iearning- based approach to predict CT images from routine MRIs and demonstrated its reliability. This CT prediction technique could be a useful tool for MRI-based radiation treatment planning or attenuation correction for quantifying PET images for PET/MR imaging.
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Shafai-Erfani G, Lei Y, Liu Y, Wang Y, Wang T, Zhong J, Liu T, McDonald M, Curran WJ, Zhou J, Shu HK, Yang X. MRI-Based Proton Treatment Planning for Base of Skull Tumors. Int J Part Ther 2019; 6:12-25. [PMID: 31998817 DOI: 10.14338/ijpt-19-00062.1] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2019] [Accepted: 08/15/2019] [Indexed: 01/22/2023] Open
Abstract
Purpose To introduce a novel, deep-learning method to generate synthetic computed tomography (SCT) scans for proton treatment planning and evaluate its efficacy. Materials and Methods 50 Patients with base of skull tumors were divided into 2 nonoverlapping training and study cohorts. Computed tomography and magnetic resonance imaging pairs for patients in the training cohort were used for training our novel 3-dimensional generative adversarial network (cycleGAN) algorithm. Upon completion of the training phase, SCT scans for patients in the study cohort were predicted based on their magnetic resonance images only. The SCT scans obtained were compared against the corresponding original planning computed tomography scans as the ground truth, and mean absolute errors (in Hounsfield units [HU]) and normalized cross-correlations were calculated. Proton plans of 45 Gy in 25 fractions with 2 beams per plan were generated for the patients based on their planning computed tomographies and recalculated on SCT scans. Dose-volume histogram endpoints were compared. A γ-index analysis along 3 cardinal planes intercepting at the isocenter was performed. Proton distal range along each beam was calculated. Results Image quality metrics show agreement between the generated SCT scans and the ground truth with mean absolute error values ranging from 38.65 to 65.12 HU and an average of 54.55 ± 6.81 HU and a normalized cross-correlation average of 0.96 ± 0.01. The dosimetric evaluation showed no statistically significant differences (p > 0.05) within planning target volumes for dose-volume histogram endpoints and other metrics studied, with the exception of the dose covering 95% of the target volume, with a relative difference of 0.47%. The γ-index analysis showed an average passing rate of 98% with a 10% threshold and 2% and 2-mm criteria. Proton ranges of 48 of 50 beams (96%) in this study were within clinical tolerance adopted by 4 institutions. Conclusions This study shows our method is capable of generating SCT scans with acceptable image quality, dose distribution agreement, and proton distal range compared with the ground truth. Our results set a promising approach for magnetic resonance imaging-based proton treatment planning.
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Affiliation(s)
- Ghazal Shafai-Erfani
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322, USA
| | - Yang Lei
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322, USA
| | - Yingzi Liu
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322, USA
| | - Yinan Wang
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322, USA
| | - Tonghe Wang
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322, USA
| | - Jim Zhong
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322, USA
| | - Tian Liu
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322, USA
| | - Mark McDonald
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322, USA
| | - Walter J Curran
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322, USA
| | - Jun Zhou
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322, USA
| | - Hui-Kuo Shu
- 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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Yu H, Oliver M, Leszczynski K, Lee Y, Karam I, Sahgal A. Tissue segmentation-based electron density mapping for MR-only radiotherapy treatment planning of brain using conventional T1-weighted MR images. J Appl Clin Med Phys 2019; 20:11-20. [PMID: 31257709 PMCID: PMC6698944 DOI: 10.1002/acm2.12654] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2019] [Revised: 04/12/2019] [Accepted: 05/13/2019] [Indexed: 12/11/2022] Open
Abstract
PURPOSE Magnetic resonance imaging (MRI) is the primary modality for targeting brain tumors in radiotherapy treatment planning (RTP). MRI is not directly used for dose calculation since image voxel intensities of MRI are not associated with EDs of tissues as those of computed tomography (CT). The purpose of the present study is to develop and evaluate a tissue segmentation-based method to generate a synthetic-CT (sCT) by mapping EDs to corresponding tissues using only T1-weighted MR images for MR-only RTP. METHODS Air regions were contoured in several slices. Then, air, bone, brain, cerebrospinal fluid (CSF), and other soft tissues were automatically segmented with an in-house algorithm based on edge detection and anatomical information and relative intensity distribution. The intensities of voxels in each segmented tissue were mapped into their CT number range to generate a sCT. Twenty-five stereotactic radiosurgery and stereotactic ablative radiotherapy patients' T1-weighted MRI and coregistered CT images from two centers were retrospectively evaluated. The CT was used as ground truth. Distances between bone contours of the external skull of sCT and CT were measured. The mean error (ME) and mean absolute error (MAE) of electron density represented by standardized CT number was calculated in HU. RESULTS The average distance between the contour of the external skull in sCT and the contour in coregistered CT is 1.0 ± 0.2 mm (mean ± 1SD). The ME and MAE differences for air, soft tissue and whole body voxels within external body contours are -4 HU/24 HU, 2 HU/26 HU, and -2 HU/125 HU, respectively. CONCLUSIONS A MR-sCT generation technique was developed based on tissue segmentation and voxel-based tissue ED mapping. The generated sCT is comparable to real CT in terms of anatomical position of tissues and similarity to the ED assignment. This method provides a feasible method to generate sCT for MR-only radiotherapy treatment planning.
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Affiliation(s)
- Huan Yu
- Department of Medical Physics, Northeast Cancer Centre, Health Sciences North, Medical Sciences Division, Northern Ontario School of Medicine, Faculty of MedicineLaurentian University, Lakehead UniversitySudburyONCanada
| | - Michael Oliver
- Department of Medical Physics, Northeast Cancer Centre, Health Sciences North, Medical Sciences Division, Northern Ontario School of Medicine, Faculty of MedicineLaurentian University, Lakehead UniversitySudburyONCanada
| | - Konrad Leszczynski
- Department of Medical Physics, Northeast Cancer Centre, Health Sciences North, Medical Sciences Division, Northern Ontario School of Medicine, Faculty of MedicineLaurentian University, Lakehead UniversitySudburyONCanada
| | - Young Lee
- Department of Medical Physics, Odette Cancer CentreSunnybrook Health Science CenterTorontoONCanada
- Department of Radiation OncologyUniversity of TorontoTorontoONCanada
| | - Irene Karam
- Department of Radiation OncologyUniversity of TorontoTorontoONCanada
- Department of Radiation Oncology, Odette Cancer CentreSunnybrook Health Science CenterTorontoONCanada
| | - Arjun Sahgal
- Department of Radiation OncologyUniversity of TorontoTorontoONCanada
- Department of Radiation Oncology, Odette Cancer CentreSunnybrook Health Science CenterTorontoONCanada
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Zhong L, Chen Y, Zhang X, Liu S, Wu Y, Liu Y, Lin L, Feng Q, Chen W, Yang W. Flexible Prediction of CT Images From MRI Data Through Improved Neighborhood Anchored Regression for PET Attenuation Correction. IEEE J Biomed Health Inform 2019; 24:1114-1124. [PMID: 31295129 DOI: 10.1109/jbhi.2019.2927368] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Given the complicated relationship between the magnetic resonance imaging (MRI) signals and the attenuation values, the attenuation correction in hybrid positron emission tomography (PET)/MRI systems remains a challenging task. Currently, existing methods are either time-consuming or require sufficient samples to train the models. In this paper, an efficient approach for predicting pseudo computed tomography (CT) images from T1- and T2-weighted MRI data with limited data is proposed. The proposed approach uses improved neighborhood anchored regression (INAR) as a baseline method to pre-calculate projected matrices to flexibly predict the pseudo CT patches. Techniques, including the augmentation of the MR/CT dataset, learning of the nonlinear descriptors of MR images, hierarchical search for nearest neighbors, data-driven optimization, and multi-regressor ensemble, are adopted to improve the effectiveness of the proposed approach. In total, 22 healthy subjects were enrolled in the study. The pseudo CT images obtained using INAR with multi-regressor ensemble yielded mean absolute error (MAE) of 92.73 ± 14.86 HU, peak signal-to-noise ratio of 29.77 ± 1.63 dB, Pearson linear correlation coefficient of 0.82 ± 0.05, dice similarity coefficient of 0.81 ± 0.03, and the relative mean absolute error (rMAE) in PET attenuation correction of 1.30 ± 0.20% compared with true CT images. Moreover, our proposed INAR method, without any refinement strategies, can achieve considerable results with only seven subjects (MAE 106.89 ± 14.43 HU, rMAE 1.51 ± 0.21%). The experiments prove the superior performance of the proposed method over the six innovative methods. Moreover, the proposed method can rapidly generate the pseudo CT images that are suitable for PET attenuation correction.
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Liu F, Yadav P, Baschnagel AM, McMillan AB. MR-based treatment planning in radiation therapy using a deep learning approach. J Appl Clin Med Phys 2019; 20:105-114. [PMID: 30861275 PMCID: PMC6414148 DOI: 10.1002/acm2.12554] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2018] [Revised: 01/21/2019] [Accepted: 02/04/2019] [Indexed: 01/03/2023] Open
Abstract
Purpose To develop and evaluate the feasibility of deep learning approaches for MR‐based treatment planning (deepMTP) in brain tumor radiation therapy. Methods and materials A treatment planning pipeline was constructed using a deep learning approach to generate continuously valued pseudo CT images from MR images. A deep convolutional neural network was designed to identify tissue features in volumetric head MR images training with co‐registered kVCT images. A set of 40 retrospective 3D T1‐weighted head images was utilized to train the model, and evaluated in 10 clinical cases with brain metastases by comparing treatment plans using deep learning generated pseudo CT and using an acquired planning kVCT. Paired‐sample Wilcoxon signed rank sum tests were used for statistical analysis to compare dosimetric parameters of plans made with pseudo CT images generated from deepMTP to those made with kVCT‐based clinical treatment plan (CTTP). Results deepMTP provides an accurate pseudo CT with Dice coefficients for air: 0.95 ± 0.01, soft tissue: 0.94 ± 0.02, and bone: 0.85 ± 0.02 and a mean absolute error of 75 ± 23 HU compared with acquired kVCTs. The absolute percentage differences of dosimetric parameters between deepMTP and CTTP was 0.24% ± 0.46% for planning target volume (PTV) volume, 1.39% ± 1.31% for maximum dose and 0.27% ± 0.79% for the PTV receiving 95% of the prescribed dose (V95). Furthermore, no significant difference was found for PTV volume (P = 0.50), the maximum dose (P = 0.83) and V95 (P = 0.19) between deepMTP and CTTP. Conclusions We have developed an automated approach (deepMTP) that allows generation of a continuously valued pseudo CT from a single high‐resolution 3D MR image and evaluated it in partial brain tumor treatment planning. The deepMTP provided dose distribution with no significant difference relative to a kVCT‐based standard volumetric modulated arc therapy plans.
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Affiliation(s)
- Fang Liu
- Department of Radiology, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
| | - Poonam Yadav
- Department of Human Oncology, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
| | - Andrew M Baschnagel
- Department of Human Oncology, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
| | - Alan B McMillan
- Department of Radiology, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
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[Radiotherapy treatment planning of prostate cancer using magnetic resonance imaging]. Cancer Radiother 2019; 23:281-289. [PMID: 31151816 DOI: 10.1016/j.canrad.2018.09.005] [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: 05/22/2018] [Revised: 09/10/2018] [Accepted: 09/27/2018] [Indexed: 11/21/2022]
Abstract
PURPOSE Magnetic resonance imaging (MRI) plays an increasing role in radiotherapy dose planning. Indeed, MRI offers superior soft tissue contrast compared to computerized tomography (CT) and therefore could provide a better delineation of target volumes and organs at risk than CT for radiotherapy. Furthermore, an MRI-only radiotherapy workflow would suppress registration errors inherent to the registration of MRI with CT. However, the estimation of the electronic density of tissues using MRI images is still a challenging issue. The purpose of this work was to design and evaluate a pseudo-CT generation method for prostate cancer treatments. MATERIALS AND METHODS A pseudo-CT was generated for ten prostate cancer patients using an elastic deformation based method. For each patient, dose delivered to the patient was calculated using both the planning CT and the pseudo-CT. Dose differences between CT and pseudo-CT were investigated. RESULTS Mean dose relative difference in the planning target volume is 0.9% on average and ranges from 0.1% to 1.7%. In organs at risks, this value is 1.8%, 0.8%, 0.8% and 1% on average in the rectum, the right and left femoral heads, and the bladder respectively. CONCLUSION The dose calculated using the pseudo-CT is very close to the dose calculated using the CT for both organs at risk and PTV. These results confirm that pseudo-CT images generated using the proposed method could be used to calculate radiotherapy treatment doses on MRI images.
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Pandey A, Yoganathan SA, Guo B, Zhang R. Feasibility of generating synthetic CT from T1-weighted MRI using a linear mixed-effects regression model. Biomed Phys Eng Express 2019; 5. [DOI: 10.1088/2057-1976/ab27a6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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Lei Y, Harms J, Wang T, Liu Y, Shu HK, Jani AB, Curran WJ, Mao H, Liu T, Yang X. MRI-only based synthetic CT generation using dense cycle consistent generative adversarial networks. Med Phys 2019; 46:3565-3581. [PMID: 31112304 DOI: 10.1002/mp.13617] [Citation(s) in RCA: 150] [Impact Index Per Article: 30.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2019] [Revised: 05/14/2019] [Accepted: 05/14/2019] [Indexed: 02/07/2023] Open
Abstract
PURPOSE Automated synthetic computed tomography (sCT) generation based on magnetic resonance imaging (MRI) images would allow for MRI-only based treatment planning in radiation therapy, eliminating the need for CT simulation and simplifying the patient treatment workflow. In this work, the authors propose a novel method for generation of sCT based on dense cycle-consistent generative adversarial networks (cycle GAN), a deep-learning based model that trains two transformation mappings (MRI to CT and CT to MRI) simultaneously. METHODS AND MATERIALS The cycle GAN-based model was developed to generate sCT images in a patch-based framework. Cycle GAN was applied to this problem because it includes an inverse transformation from CT to MRI, which helps constrain the model to learn a one-to-one mapping. Dense block-based networks were used to construct generator of cycle GAN. The network weights and variables were optimized via a gradient difference (GD) loss and a novel distance loss metric between sCT and original CT. RESULTS Leave-one-out cross-validation was performed to validate the proposed model. The mean absolute error (MAE), peak signal-to-noise ratio (PSNR), and normalized cross correlation (NCC) indexes were used to quantify the differences between the sCT and original planning CT images. For the proposed method, the mean MAE between sCT and CT were 55.7 Hounsfield units (HU) for 24 brain cancer patients and 50.8 HU for 20 prostate cancer patients. The mean PSNR and NCC were 26.6 dB and 0.963 in the brain cases, and 24.5 dB and 0.929 in the pelvis. CONCLUSION We developed and validated a novel learning-based approach to generate CT images from routine MRIs based on dense cycle GAN model to effectively capture the relationship between the CT and MRIs. The proposed method can generate robust, high-quality sCT in minutes. The proposed method offers strong potential for supporting near real-time MRI-only treatment planning in the brain and pelvis.
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Affiliation(s)
- Yang Lei
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, 30322, USA
| | - Joseph Harms
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, 30322, USA
| | - Tonghe Wang
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, 30322, USA
| | - Yingzi Liu
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, 30322, USA
| | - Hui-Kuo Shu
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, 30322, USA
| | - Ashesh B Jani
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, 30322, USA
| | - Walter J Curran
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, 30322, USA
| | - Hui Mao
- Department of Radiology and Imaging Sciences and Winship Cancer Institute, Emory University, Atlanta, GA, 30322, 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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