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Arbabi S, Foppen W, Gielis WP, van Stralen M, Jansen M, Arbabi V, de Jong PA, Weinans H, Seevinck P. MRI-based synthetic CT in the detection of knee osteoarthritis: Comparison with CT. J Orthop Res 2023; 41:2530-2539. [PMID: 36922347 DOI: 10.1002/jor.25557] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Revised: 03/01/2023] [Accepted: 03/11/2023] [Indexed: 03/18/2023]
Abstract
Magnetic resonance Imaging is the gold standard for assessment of soft tissues; however, X-ray-based techniques are required for evaluating bone-related pathologies. This study evaluated the performance of synthetic computed tomography (sCT), a novel MRI-based bone visualization technique, compared with CT, for the scoring of knee osteoarthritis. sCT images were generated from the 3T T1-weighted gradient-echo MR images using a trained machine learning algorithm. Two readers scored the severity of osteoarthritis in tibiofemoral and patellofemoral joints according to OACT, which enables the evaluation of osteoarthritis, from its characteristics of joint space narrowing, osteophytes, cysts and sclerosis in CT (and sCT) images. Cohen's κ was used to assess the interreader agreement for each modality, and intermodality agreement of CT- and sCT-based scores for each reader. We also compared the confidence level of readers for grading CT and sCT images using confidence scores collected during grading. Inter-reader agreement for tibiofemoral and patellofemoral joints were almost-perfect for both modalities (κ = 0.83-0.88). The intermodality agreement of osteoarthritis scores between CT and sCT was substantial to almost-perfect for tibiofemoral (κ = 0.63 and 0.84 for the two readers) and patellofemoral joints (κ = 0.78 and 0.81 for the two readers). The analysis of diagnosis confidence scores showed comparable visual quality of the two modalities, where both are showing acceptable confidence levels for scoring OA. In conclusion, in this single-center study, sCT and CT were comparable for the scoring of knee OA.
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Affiliation(s)
- Saeed Arbabi
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands
- Department of Orthopedics, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Wouter Foppen
- Department of Radiology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Willem Paul Gielis
- Department of Orthopedics, University Medical Center Utrecht, Utrecht, The Netherlands
- Department of Radiology, University Medical Center Utrecht, Utrecht, The Netherlands
| | | | - Mylène Jansen
- Rheumatology & Clinical Immunology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Vahid Arbabi
- Department of Orthopedics, University Medical Center Utrecht, Utrecht, The Netherlands
- Department of Mechanical Engineering, Faculty of Engineering, Orthopaedic-Biomechanics Research Group, Birjand, Iran
| | - Pim A de Jong
- Department of Radiology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Harrie Weinans
- Department of Orthopedics, University Medical Center Utrecht, Utrecht, The Netherlands
- Department of Biomechanical Engineering, Delft University of Technology (TU Delft), Delft, The Netherlands
| | - Peter Seevinck
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands
- MRIguidance B.V., Utrecht, The Netherlands
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Florkow MC, Nguyen CH, Sakkers RJB, Weinans H, Jansen MP, Custers RJH, van Stralen M, Seevinck PR. Magnetic resonance imaging-based bone imaging of the lower limb: Strategies for generating high-resolution synthetic computed tomography. J Orthop Res 2023. [PMID: 37807082 DOI: 10.1002/jor.25707] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Revised: 09/13/2023] [Accepted: 10/05/2023] [Indexed: 10/10/2023]
Abstract
This study aims at assessing approaches for generating high-resolution magnetic resonance imaging- (MRI-) based synthetic computed tomography (sCT) images suitable for orthopedic care using a deep learning model trained on low-resolution computed tomography (CT) data. To that end, paired MRI and CT data of three anatomical regions were used: high-resolution knee and ankle data, and low-resolution hip data. Four experiments were conducted to investigate the impact of low-resolution training CT data on sCT generation and to find ways to train models on low-resolution data while providing high-resolution sCT images. Experiments included resampling of the training data or augmentation of the low-resolution data with high-resolution data. Training sCT generation models using low-resolution CT data resulted in blurry sCT images. By resampling the MRI/CT pairs before the training, models generated sharper images, presumably through an increase in the MRI/CT mutual information. Alternatively, augmenting the low-resolution with high-resolution data improved sCT in terms of mean absolute error proportionally to the amount of high-resolution data. Overall, the morphological accuracy was satisfactory as assessed by an average intermodal distance between joint centers ranging from 0.7 to 1.2 mm and by an average intermodal root-mean-squared distances between bone surfaces under 0.7 mm. Average dice scores ranged from 79.8% to 87.3% for bony structures. To conclude, this paper proposed approaches to generate high-resolution sCT suitable for orthopedic care using low-resolution data. This can generalize the use of sCT for imaging the musculoskeletal system, paving the way for an MR-only imaging with simplified logistics and no ionizing radiation.
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Affiliation(s)
- Mateusz C Florkow
- Image Sciences Institute, University Medical Centre Utrecht, Utrecht, The Netherlands
| | - Chien H Nguyen
- Department of Orthopaedics, University Medical Centre Utrecht, Utrecht, The Netherlands
- 3D Lab, University Medical Centre Utrecht, Utrecht, The Netherlands
| | - Ralph J B Sakkers
- Department of Orthopaedics, University Medical Centre Utrecht, Utrecht, The Netherlands
| | - Harrie Weinans
- Department of Orthopaedics, University Medical Centre Utrecht, Utrecht, The Netherlands
| | - Mylene P Jansen
- Department of Rheumatology & Clinical Immunology, University Medical Centre Utrecht, Utrecht, The Netherlands
| | - Roel J H Custers
- Department of Orthopaedics, University Medical Centre Utrecht, Utrecht, The Netherlands
| | | | - Peter R Seevinck
- Image Sciences Institute, University Medical Centre Utrecht, Utrecht, The Netherlands
- MRIguidance B.V., Utrecht, The Netherlands
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Jeong H, Park T, Khang S, Koo K, Shin J, Kim KW, Lee J. Non-rigid registration based on hierarchical deformation of coronary arteries in CCTA images. Biomed Eng Lett 2022; 13:65-72. [PMID: 36711162 PMCID: PMC9873886 DOI: 10.1007/s13534-022-00254-8] [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: 07/12/2022] [Revised: 11/07/2022] [Accepted: 12/02/2022] [Indexed: 12/14/2022] Open
Abstract
In this paper, we propose an accurate and rapid non-rigid registration method between blood vessels in temporal 3D cardiac computed tomography angiography images of the same patient. This method provides auxiliary information that can be utilized in the diagnosis and treatment of coronary artery diseases. The proposed method consists of the following four steps. First, global registration is conducted through rigid registration between the 3D vessel centerlines obtained from temporal 3D cardiac CT angiography images. Second, point matching between the 3D vessel centerlines in the rigid registration results is performed, and the corresponding points are defined. Third, the outliers in the matched corresponding points are removed by using various information such as thickness and gradient of the vessels. Finally, non-rigid registration is conducted for hierarchical local transformation using an energy function. The experiment results show that the average registration error of the proposed method is 0.987 mm, and the average execution time is 2.137 s, indicating that the registration is accurate and rapid. The proposed method that enables rapid and accurate registration by using the information on blood vessel characteristics in temporal CTA images of the same patient.
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Affiliation(s)
- Heeryeol Jeong
- grid.263765.30000 0004 0533 3568School of Computer Science and Engineering , Soongsil University , 369 Sangdo-Ro, Dongjak-Gu, Seoul, 06978 Korea
| | - Taeyong Park
- grid.411945.c0000 0000 9834 782XDepartment of Biomedical Informatics , Hallym University Medical Center , 22, Gwanpyeong-ro, 170 beon- gil, Dongan-gu, Anyang-si, Gyeonggi-do 14068 Korea
| | - Seungwoo Khang
- grid.263765.30000 0004 0533 3568School of Computer Science and Engineering , Soongsil University , 369 Sangdo-Ro, Dongjak-Gu, Seoul, 06978 Korea
| | - Kyoyeong Koo
- grid.263765.30000 0004 0533 3568School of Computer Science and Engineering , Soongsil University , 369 Sangdo-Ro, Dongjak-Gu, Seoul, 06978 Korea
| | - Juneseuk Shin
- grid.264381.a0000 0001 2181 989XDepartment of Systems Management Engineering , Sungkyunkwan University , 2066, Seobu-ro, Jangan-gu, Suwon-si , Gyeong gi-do 16419 Korea
| | - Kyung Won Kim
- grid.267370.70000 0004 0533 4667Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine , 88 Olympic‑ro, 43‑gil, Songpa‑gu, Seoul, 05505 Korea
| | - Jeongjin Lee
- grid.263765.30000 0004 0533 3568School of Computer Science and Engineering , Soongsil University , 369 Sangdo-Ro, Dongjak-Gu, Seoul, 06978 Korea ,iAID Inc., 7,398, Sangdo-ro, Dongjak-gu, Seoul, 07040 Korea
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Lena B, Florkow MC, Ferrer CJ, van Stralen M, Seevinck PR, Vonken EJPA, Boomsma MF, Slotman DJ, Viergever MA, Moonen CTW, Bos C, Bartels LW. Synthetic CT for the planning of MR-HIFU treatment of bone metastases in pelvic and femoral bones: a feasibility study. Eur Radiol 2022; 32:4537-4546. [PMID: 35190891 PMCID: PMC9213310 DOI: 10.1007/s00330-022-08568-y] [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: 11/18/2021] [Revised: 12/31/2021] [Accepted: 01/05/2022] [Indexed: 12/12/2022]
Abstract
Objectives Visualization of the bone distribution is an important prerequisite for MRI-guided high-intensity focused ultrasound (MRI-HIFU) treatment planning of bone metastases. In this context, we evaluated MRI-based synthetic CT (sCT) imaging for the visualization of cortical bone. Methods MR and CT images of nine patients with pelvic and femoral metastases were retrospectively analyzed in this study. The metastatic lesions were osteolytic, osteoblastic or mixed. sCT were generated from pre-treatment or treatment MR images using a UNet-like neural network. sCT was qualitatively and quantitatively compared to CT in the bone (pelvis or femur) containing the metastasis and in a region of interest placed on the metastasis itself, through mean absolute difference (MAD), mean difference (MD), Dice similarity coefficient (DSC), and root mean square surface distance (RMSD). Results The dataset consisted of 3 osteolytic, 4 osteoblastic and 2 mixed metastases. For most patients, the general morphology of the bone was well represented in the sCT images and osteolytic, osteoblastic and mixed lesions could be discriminated. Despite an average timespan between MR and CT acquisitions of 61 days, in bone, the average (± standard deviation) MAD was 116 ± 26 HU, MD − 14 ± 66 HU, DSC 0.85 ± 0.05, and RMSD 2.05 ± 0.48 mm and, in the lesion, MAD was 132 ± 62 HU, MD − 31 ± 106 HU, DSC 0.75 ± 0.2, and RMSD 2.73 ± 2.28 mm. Conclusions Synthetic CT images adequately depicted the cancellous and cortical bone distribution in the different lesion types, which shows its potential for MRI-HIFU treatment planning. Key Points • Synthetic computed tomography was able to depict bone distribution in metastatic lesions. • Synthetic computed tomography images intrinsically aligned with treatment MR images may have the potential to facilitate MR-HIFU treatment planning of bone metastases, by combining visualization of soft tissues and cancellous and cortical bone. Supplementary Information The online version contains supplementary material available at 10.1007/s00330-022-08568-y.
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Affiliation(s)
- Beatrice Lena
- Image Sciences Institute, Division of Imaging and Oncology, University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, Q.02.4.45, 3584, CX, Utrecht, The Netherlands.
| | - Mateusz C Florkow
- Image Sciences Institute, Division of Imaging and Oncology, University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, Q.02.4.45, 3584, CX, Utrecht, The Netherlands.
| | - Cyril J Ferrer
- Division of Imaging and Oncology, University Medical Center Utrecht, Utrecht University, Heidelberglaan, 100 3584, CX, Utrecht, The Netherlands
| | - Marijn van Stralen
- Image Sciences Institute, Division of Imaging and Oncology, University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, Q.02.4.45, 3584, CX, Utrecht, The Netherlands.,MRIguidance BV, Gildstraat 91-A, 3572, EL, Utrecht, The Netherlands
| | - Peter R Seevinck
- Image Sciences Institute, Division of Imaging and Oncology, University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, Q.02.4.45, 3584, CX, Utrecht, The Netherlands.,MRIguidance BV, Gildstraat 91-A, 3572, EL, Utrecht, The Netherlands
| | - Evert-Jan P A Vonken
- Division of Imaging and Oncology, Department of Radiology, University Medical Center Utrecht, Utrecht University, Heidelberglaan, 100 3584, CX, Utrecht, The Netherlands
| | - Martijn F Boomsma
- Department of Radiology, Isala Hospital, Dokter van Heesweg 2, 8025, AB, Zwolle, The Netherlands
| | - Derk J Slotman
- Department of Radiology, Isala Hospital, Dokter van Heesweg 2, 8025, AB, Zwolle, The Netherlands
| | - Max A Viergever
- Image Sciences Institute, Division of Imaging and Oncology, University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, Q.02.4.45, 3584, CX, Utrecht, The Netherlands
| | - Chrit T W Moonen
- Division of Imaging and Oncology, University Medical Center Utrecht, Utrecht University, Heidelberglaan, 100 3584, CX, Utrecht, The Netherlands
| | - Clemens Bos
- Division of Imaging and Oncology, University Medical Center Utrecht, Utrecht University, Heidelberglaan, 100 3584, CX, Utrecht, The Netherlands
| | - Lambertus W Bartels
- Image Sciences Institute, Division of Imaging and Oncology, University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, Q.02.4.45, 3584, CX, Utrecht, The Netherlands
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