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Al-Thani A, Sharif A, El Borgi S, Abdulla S, Ahmed Saleh MR, Al-Khal R, Velasquez C, Aboumarzouk O, Dakua SP. Development of a flexible liver phantom for hepatocellular carcinoma treatment planning: a useful tool for training & education. 3D Print Med 2024; 10:24. [PMID: 39037479 PMCID: PMC11265145 DOI: 10.1186/s41205-024-00228-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2024] [Accepted: 07/11/2024] [Indexed: 07/23/2024] Open
Abstract
PURPOSE Hepatocellular carcinoma (HCC) is one of the most common types of liver cancer that could potentially be surrounded by healthy arteries or veins that a surgeon would have to avoid during treatment. A realistic 3D liver model is an unmet need for HCC preoperative planning. METHODS This paper presents a method to create a soft phantom model of the human liver with the help of a 3D-printed mold, silicone, ballistic gel, and a blender. RESULTS For silicone, the elastic modulus of seven different ratios of base silicone and silicone hardener are tested; while for ballistic gel, a model using 20% gelatin and 10% gelatin is created for the tumor and the rest of the liver, respectively. It is found that the silicone modulus of elasticity matches with the real liver modulus of elasticity. It is also found that the 10% gelatin part of the ballistic gel model is an excellent emulation of a healthy human liver. CONCLUSION The 3D flexible liver phantom made from a 10% gelatin-to-water mixture demonstrates decent fidelity to real liver tissue in terms of texture and elasticity. It holds significant potential for improving medical training, preoperative planning, and surgical research. We believe that continued development and validation of such models could further enhance their utility and impact in the field of hepatobiliary treatment planning and education.
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Affiliation(s)
- Abdulla Al-Thani
- Department of Mechanical Engineering, Texas A&M University at Qatar, Doha, 23874, Qatar
| | - Abdulrahman Sharif
- Department of Mechanical Engineering, Texas A&M University at Qatar, Doha, 23874, Qatar
| | - Sami El Borgi
- Department of Mechanical Engineering, Texas A&M University at Qatar, Doha, 23874, Qatar
| | - Shameel Abdulla
- Department of Mechanical Engineering, Texas A&M University at Qatar, Doha, 23874, Qatar
| | | | - Reem Al-Khal
- Department of Surgery, Hamad Medical Corporation, Doha, 3050, Qatar
| | - Carlos Velasquez
- Department of Surgery, Hamad Medical Corporation, Doha, 3050, Qatar
| | - Omar Aboumarzouk
- Department of Surgery, Hamad Medical Corporation, Doha, 3050, Qatar
- College of Health and Medical Sciences, Qatar University, Doha, 2713, Qatar
| | - Sarada Prasad Dakua
- Department of Surgery, Hamad Medical Corporation, Doha, 3050, Qatar.
- College of Health and Medical Sciences, Qatar University, Doha, 2713, Qatar.
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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 2024; 42:843-854. [PMID: 37807082 DOI: 10.1002/jor.25707] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [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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McNaughton J, Fernandez J, Holdsworth S, Chong B, Shim V, Wang A. Machine Learning for Medical Image Translation: A Systematic Review. Bioengineering (Basel) 2023; 10:1078. [PMID: 37760180 PMCID: PMC10525905 DOI: 10.3390/bioengineering10091078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Revised: 07/30/2023] [Accepted: 09/07/2023] [Indexed: 09/29/2023] Open
Abstract
BACKGROUND CT scans are often the first and only form of brain imaging that is performed to inform treatment plans for neurological patients due to its time- and cost-effective nature. However, MR images give a more detailed picture of tissue structure and characteristics and are more likely to pick up abnormalities and lesions. The purpose of this paper is to review studies which use deep learning methods to generate synthetic medical images of modalities such as MRI and CT. METHODS A literature search was performed in March 2023, and relevant articles were selected and analyzed. The year of publication, dataset size, input modality, synthesized modality, deep learning architecture, motivations, and evaluation methods were analyzed. RESULTS A total of 103 studies were included in this review, all of which were published since 2017. Of these, 74% of studies investigated MRI to CT synthesis, and the remaining studies investigated CT to MRI, Cross MRI, PET to CT, and MRI to PET. Additionally, 58% of studies were motivated by synthesizing CT scans from MRI to perform MRI-only radiation therapy. Other motivations included synthesizing scans to aid diagnosis and completing datasets by synthesizing missing scans. CONCLUSIONS Considerably more research has been carried out on MRI to CT synthesis, despite CT to MRI synthesis yielding specific benefits. A limitation on medical image synthesis is that medical datasets, especially paired datasets of different modalities, are lacking in size and availability; it is therefore recommended that a global consortium be developed to obtain and make available more datasets for use. Finally, it is recommended that work be carried out to establish all uses of the synthesis of medical scans in clinical practice and discover which evaluation methods are suitable for assessing the synthesized images for these needs.
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Affiliation(s)
- Jake McNaughton
- Auckland Bioengineering Institute, University of Auckland, 6/70 Symonds Street, Auckland 1010, New Zealand; (J.M.)
| | - Justin Fernandez
- Auckland Bioengineering Institute, University of Auckland, 6/70 Symonds Street, Auckland 1010, New Zealand; (J.M.)
- Department of Engineering Science and Biomedical Engineering, University of Auckland, 3/70 Symonds Street, Auckland 1010, New Zealand
| | - Samantha Holdsworth
- Faculty of Medical and Health Sciences, University of Auckland, 85 Park Road, Auckland 1023, New Zealand
- Centre for Brain Research, University of Auckland, 85 Park Road, Auckland 1023, New Zealand
- Mātai Medical Research Institute, 400 Childers Road, Tairāwhiti Gisborne 4010, New Zealand
| | - Benjamin Chong
- Auckland Bioengineering Institute, University of Auckland, 6/70 Symonds Street, Auckland 1010, New Zealand; (J.M.)
- Faculty of Medical and Health Sciences, University of Auckland, 85 Park Road, Auckland 1023, New Zealand
- Centre for Brain Research, University of Auckland, 85 Park Road, Auckland 1023, New Zealand
| | - Vickie Shim
- Auckland Bioengineering Institute, University of Auckland, 6/70 Symonds Street, Auckland 1010, New Zealand; (J.M.)
- Mātai Medical Research Institute, 400 Childers Road, Tairāwhiti Gisborne 4010, New Zealand
| | - Alan Wang
- Auckland Bioengineering Institute, University of Auckland, 6/70 Symonds Street, Auckland 1010, New Zealand; (J.M.)
- Faculty of Medical and Health Sciences, University of Auckland, 85 Park Road, Auckland 1023, New Zealand
- Centre for Brain Research, University of Auckland, 85 Park Road, Auckland 1023, New Zealand
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Advanced Image Segmentation and Modeling - A Review of the 2021-2022 Thematic Series. 3D Print Med 2023; 9:1. [PMID: 36692662 PMCID: PMC9872408 DOI: 10.1186/s41205-022-00163-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Accepted: 11/14/2022] [Indexed: 01/25/2023] Open
Abstract
Medical 3D printing is a form of manufacturing that benefits patient care, particularly when the 3D printed part is patient-specific and either enables or facilitates an intervention for a specific condition. Most of the patient-specific medical 3D printing begins with volume based medical images of the patient. Several digital manipulations are typically performed to prescribe a final anatomic representation that is then 3D printed. Among these are image segmentation where a volume of interest such as an organ or a set of tissues is digitally extracted from the volumetric imaging data. Image segmentation requires medical expertise, training, software, and effort. The theme of image segmentation has a broad intersection with medical 3D printing. The purpose of this editorial is to highlight different points of that intersection in a recent thematic series within 3D Printing in Medicine.
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Lombardi AF, Ma YJ, Jang H, Jerban S, Du J, Chang EY, Chung CB. Synthetic CT in Musculoskeletal Disorders: A Systematic Review. Invest Radiol 2023; 58:43-59. [PMID: 36070535 PMCID: PMC9742139 DOI: 10.1097/rli.0000000000000916] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
ABSTRACT Repeated computed tomography (CT) examinations increase patients' ionizing radiation exposure and health costs, making an alternative method desirable. Cortical and trabecular bone, however, have short T2 relaxation times, causing low signal intensity on conventional magnetic resonance (MR) sequences. Different techniques are available to create a "CT-like" contrast of bone, such as ultrashort echo time, zero echo time, gradient-echo, and susceptibility-weighted image MR sequences, and artificial intelligence. This systematic review summarizes the essential technical background and developments of ultrashort echo time, zero echo time, gradient-echo, susceptibility-weighted image MR imaging sequences and artificial intelligence; presents studies on research and clinical applications of "CT-like" MR imaging; and describes their main advantages and limitations. We also discuss future opportunities in research, which patients would benefit the most, the most appropriate situations for using the technique, and the potential to replace CT in the clinical workflow.
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Affiliation(s)
- Alecio F Lombardi
- From the Department of Radiology, University of California San Diego, La Jolla, and the Research Service, Veterans Affairs San Diego Healthcare System, California
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Pankratov AS, Lartsev YV, Rubtsov AA, Ogurtsov DA, Kim YD, Shmel'kov AV, Knyazev NA. Application of 3D modeling in a personalized approach to bone osteosynthesis (A literature review). BULLETIN OF THE MEDICAL INSTITUTE "REAVIZ" (REHABILITATION, DOCTOR AND HEALTH) 2022. [DOI: 10.20340/vmi-rvz.2023.1.ictm.3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/10/2023]
Abstract
Three-dimensional printing opens up many opportunities for use in traumatology and orthopedics, because it takes into account personal characteristics of the patients. Modern methods of high-resolution medical imaging can process data to create threedimensional images for printing physical objects. Today, three-dimensional printers are able to create a model of any complexity of shape and geometry. The article provides a review of the literature about three-dimensional digital modeling in shaping implants for osteosynthesis. Data search was carried out on the Scopus, Web of Scince, Pubmed, RSCI databases for the period 2012–2022. The effectiveness of three-dimensional printing for preoperative modeling of bone plates has been confirmed: implants perfectly corresponds with the unique anatomy of the patient, since the template for it is based on the materials of computed tomography. Individual templates can be useful when the geometry of patients' bones goes beyond the standard, and when improved results of surgery are expected due to better matching of implants to the anatomical needs of patients.
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Meyer-Szary J, Luis MS, Mikulski S, Patel A, Schulz F, Tretiakow D, Fercho J, Jaguszewska K, Frankiewicz M, Pawłowska E, Targoński R, Szarpak Ł, Dądela K, Sabiniewicz R, Kwiatkowska J. The Role of 3D Printing in Planning Complex Medical Procedures and Training of Medical Professionals-Cross-Sectional Multispecialty Review. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:3331. [PMID: 35329016 PMCID: PMC8953417 DOI: 10.3390/ijerph19063331] [Citation(s) in RCA: 43] [Impact Index Per Article: 21.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Revised: 02/18/2022] [Accepted: 03/05/2022] [Indexed: 12/19/2022]
Abstract
Medicine is a rapidly-evolving discipline, with progress picking up pace with each passing decade. This constant evolution results in the introduction of new tools and methods, which in turn occasionally leads to paradigm shifts across the affected medical fields. The following review attempts to showcase how 3D printing has begun to reshape and improve processes across various medical specialties and where it has the potential to make a significant impact. The current state-of-the-art, as well as real-life clinical applications of 3D printing, are reflected in the perspectives of specialists practicing in the selected disciplines, with a focus on pre-procedural planning, simulation (rehearsal) of non-routine procedures, and on medical education and training. A review of the latest multidisciplinary literature on the subject offers a general summary of the advances enabled by 3D printing. Numerous advantages and applications were found, such as gaining better insight into patient-specific anatomy, better pre-operative planning, mock simulated surgeries, simulation-based training and education, development of surgical guides and other tools, patient-specific implants, bioprinted organs or structures, and counseling of patients. It was evident that pre-procedural planning and rehearsing of unusual or difficult procedures and training of medical professionals in these procedures are extremely useful and transformative.
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Affiliation(s)
- Jarosław Meyer-Szary
- Department of Pediatric Cardiology and Congenital Heart Defects, Faculty of Medicine, Medical University of Gdańsk, 80-210 Gdańsk, Poland
| | - Marlon Souza Luis
- Department of Pediatric Cardiology and Congenital Heart Defects, Faculty of Medicine, Medical University of Gdańsk, 80-210 Gdańsk, Poland
- First Doctoral School, Medical University of Gdańsk, 80-211 Gdańsk, Poland
| | - Szymon Mikulski
- Department of Head and Neck Surgery, Singapore General Hospital, Singapore 169608, Singapore
| | - Agastya Patel
- First Doctoral School, Medical University of Gdańsk, 80-211 Gdańsk, Poland
- Department of General, Endocrine and Transplant Surgery, Faculty of Medicine, Medical University of Gdańsk, 80-214 Gdańsk, Poland
| | - Finn Schulz
- University Clinical Centre in Gdańsk, 80-952 Gdańsk, Poland
| | - Dmitry Tretiakow
- Department of Otolaryngology, Faculty of Medicine, Medical University of Gdańsk, 80-214 Gdańsk, Poland
| | - Justyna Fercho
- Neurosurgery Department, Faculty of Medicine, Medical University of Gdańsk, 80-210 Gdańsk, Poland
| | - Kinga Jaguszewska
- Department of Gynecology, Obstetrics and Neonatology, Division of Gynecology and Obstetrics, Faculty of Medicine, Medical University of Gdańsk, 80-210 Gdańsk, Poland
| | - Mikołaj Frankiewicz
- Department of Urology, Faculty of Medicine, Medical University of Gdańsk, 80-210 Gdańsk, Poland
| | - Ewa Pawłowska
- Department of Oncology and Radiotherapy, Faculty of Medicine, Medical University of Gdańsk, 80-210 Gdańsk, Poland
| | - Radosław Targoński
- 1st Department of Cardiology, Faculty of Medicine, Medical University of Gdańsk, 80-210 Gdańsk, Poland
| | - Łukasz Szarpak
- Institute of Outcomes Research, Maria Sklodowska-Curie Medical Academy, 03-411 Warsaw, Poland
- Research Unit, Maria Sklodowska-Curie Bialystok Oncology Center, 15-027 Bialystok, Poland
- Henry JN Taub Department of Emergency Medicine, Baylor College of Medicine, Houston, TX 77030, USA
| | - Katarzyna Dądela
- Department of Pediatric Cardiology, University Children's Hospital, Faculty of Medicine, Jagiellonian University Medical College, 30-663 Krakow, Poland
| | - Robert Sabiniewicz
- Department of Pediatric Cardiology and Congenital Heart Defects, Faculty of Medicine, Medical University of Gdańsk, 80-210 Gdańsk, Poland
| | - Joanna Kwiatkowska
- Department of Pediatric Cardiology and Congenital Heart Defects, Faculty of Medicine, Medical University of Gdańsk, 80-210 Gdańsk, Poland
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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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Florkow MC, Willemsen K, Mascarenhas VV, Oei EHG, van Stralen M, Seevinck PR. Magnetic Resonance Imaging Versus Computed Tomography for Three-Dimensional Bone Imaging of Musculoskeletal Pathologies: A Review. J Magn Reson Imaging 2022; 56:11-34. [PMID: 35044717 PMCID: PMC9305220 DOI: 10.1002/jmri.28067] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Revised: 12/31/2021] [Accepted: 01/05/2022] [Indexed: 12/18/2022] Open
Abstract
Magnetic resonance imaging (MRI) is increasingly utilized as a radiation‐free alternative to computed tomography (CT) for the diagnosis and treatment planning of musculoskeletal pathologies. MR imaging of hard tissues such as cortical bone remains challenging due to their low proton density and short transverse relaxation times, rendering bone tissues as nonspecific low signal structures on MR images obtained from most sequences. Developments in MR image acquisition and post‐processing have opened the path for enhanced MR‐based bone visualization aiming to provide a CT‐like contrast and, as such, ease clinical interpretation. The purpose of this review is to provide an overview of studies comparing MR and CT imaging for diagnostic and treatment planning purposes in orthopedic care, with a special focus on selective bone visualization, bone segmentation, and three‐dimensional (3D) modeling. This review discusses conventional gradient‐echo derived techniques as well as dedicated short echo time acquisition techniques and post‐processing techniques, including the generation of synthetic CT, in the context of 3D and specific bone visualization. Based on the reviewed literature, it may be concluded that the recent developments in MRI‐based bone visualization are promising. MRI alone provides valuable information on both bone and soft tissues for a broad range of applications including diagnostics, 3D modeling, and treatment planning in multiple anatomical regions, including the skull, spine, shoulder, pelvis, and long bones.
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Affiliation(s)
- Mateusz C Florkow
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Koen Willemsen
- Department of Orthopedics, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Vasco V Mascarenhas
- Musculoskeletal Imaging Unit, Imaging Center, Hospital da Luz, Lisbon, Portugal
| | - Edwin H G Oei
- Department of Radiology and Nuclear Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Marijn van Stralen
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands.,MRIguidance BV, Utrecht, The Netherlands
| | - Peter R Seevinck
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands.,MRIguidance BV, Utrecht, The Netherlands
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Correction to Acknowledgement. 3D Print Med 2021; 7:37. [PMID: 34787757 PMCID: PMC8597234 DOI: 10.1186/s41205-021-00126-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
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