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Ceranka J, Wuts J, Chiabai O, Lecouvet F, Vandemeulebroucke J. Computer-aided diagnosis of skeletal metastases in multi-parametric whole-body MRI. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 242:107811. [PMID: 37742486 DOI: 10.1016/j.cmpb.2023.107811] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Revised: 09/07/2023] [Accepted: 09/11/2023] [Indexed: 09/26/2023]
Abstract
The confident detection of metastatic bone disease is essential to improve patients' comfort and increase life expectancy. Multi-parametric magnetic resonance imaging (MRI) has been successfully used for monitoring of metastatic bone disease, allowing for comprehensive and holistic evaluation of the total tumour volume and treatment response assessment. The major challenges of radiological reading of whole-body MRI come from the amount of data to be reviewed and the scattered distribution of metastases, often of complex shapes. This makes bone lesion detection and quantification demanding for a radiologist and prone to error. Additionally, whole-body MRI are often corrupted with multiple spatial and intensity distortions, which further degrade the performance of image reading and image processing algorithms. In this work we propose a fully automated computer-aided diagnosis system for the detection and segmentation of metastatic bone disease using whole-body multi-parametric MRI. The system consists of an extensive image preprocessing pipeline aiming at enhancing the image quality, followed by a deep learning framework for detection and segmentation of metastatic bone disease. The system outperformed state-of-the-art methodologies, achieving a detection sensitivity of 63% with a mean of 6.44 false positives per image, and an average lesion Dice coefficient of 0.53. A provided ablation study performed to investigate the relative importance of image preprocessing shows that introduction of region of interest mask and spatial registration have a significant impact on detection and segmentation performance in whole-body MRI. The proposed computer-aided diagnosis system allows for automatic quantification of disease infiltration and could provide a valuable tool during radiological examination of whole-body MRI.
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Affiliation(s)
- Jakub Ceranka
- Vrije Universiteit Brussel, Department of Electronics and Informatics, Pleinlaan 2, Brussels, 1050, Belgium; imec, Kapeldreef 75, Leuven, B-3001, Belgium.
| | - Joris Wuts
- Vrije Universiteit Brussel, Department of Electronics and Informatics, Pleinlaan 2, Brussels, 1050, Belgium; imec, Kapeldreef 75, Leuven, B-3001, Belgium; Cliniques universitaires Saint Luc, Institut de Recherche Expérimentale et Clinique (IREC), Université catholique de Louvain (UCLouvain), Avenue Hippocrate 10, Brussels, 1200, Belgium.
| | - Ophélye Chiabai
- Cliniques universitaires Saint Luc, Institut de Recherche Expérimentale et Clinique (IREC), Université catholique de Louvain (UCLouvain), Avenue Hippocrate 10, Brussels, 1200, Belgium
| | - Frédéric Lecouvet
- Cliniques universitaires Saint Luc, Institut de Recherche Expérimentale et Clinique (IREC), Université catholique de Louvain (UCLouvain), Avenue Hippocrate 10, Brussels, 1200, Belgium
| | - Jef Vandemeulebroucke
- Vrije Universiteit Brussel, Department of Electronics and Informatics, Pleinlaan 2, Brussels, 1050, Belgium; imec, Kapeldreef 75, Leuven, B-3001, Belgium; Universitair Ziekenhuis Brussel, Department of Radiology, Laarbeeklaan 101, Brussels, 1090, Belgium
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Keelson B, Buzzatti L, Ceranka J, Gutiérrez A, Battista S, Scheerlinck T, Van Gompel G, De Mey J, Cattrysse E, Buls N, Vandemeulebroucke J. Automated Motion Analysis of Bony Joint Structures from Dynamic Computer Tomography Images: A Multi-Atlas Approach. Diagnostics (Basel) 2021; 11:diagnostics11112062. [PMID: 34829409 PMCID: PMC8621122 DOI: 10.3390/diagnostics11112062] [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: 08/18/2021] [Revised: 10/27/2021] [Accepted: 11/02/2021] [Indexed: 11/16/2022] Open
Abstract
Dynamic computer tomography (CT) is an emerging modality to analyze in-vivo joint kinematics at the bone level, but it requires manual bone segmentation and, in some instances, landmark identification. The objective of this study is to present an automated workflow for the assessment of three-dimensional in vivo joint kinematics from dynamic musculoskeletal CT images. The proposed method relies on a multi-atlas, multi-label segmentation and landmark propagation framework to extract bony structures and detect anatomical landmarks on the CT dataset. The segmented structures serve as regions of interest for the subsequent motion estimation across the dynamic sequence. The landmarks are propagated across the dynamic sequence for the construction of bone embedded reference frames from which kinematic parameters are estimated. We applied our workflow on dynamic CT images obtained from 15 healthy subjects on two different joints: thumb base (n = 5) and knee (n = 10). The proposed method resulted in segmentation accuracies of 0.90 ± 0.01 for the thumb dataset and 0.94 ± 0.02 for the knee as measured by the Dice score coefficient. In terms of motion estimation, mean differences in cardan angles between the automated algorithm and manual segmentation, and landmark identification performed by an expert were below 1°. Intraclass correlation (ICC) between cardan angles from the algorithm and results from expert manual landmarks ranged from 0.72 to 0.99 for all joints across all axes. The proposed automated method resulted in reproducible and reliable measurements, enabling the assessment of joint kinematics using 4DCT in clinical routine.
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Affiliation(s)
- Benyameen Keelson
- Department of Radiology, Vrije Universiteit Brussel (VUB), Universitair Ziekenhuis Brussel (UZ Brussel), 1090 Brussels, Belgium; (A.G.); (G.V.G.); (J.D.M.); (N.B.); (J.V.)
- Department of Electronics and Informatics (ETRO), Vrije Universiteit Brussel (VUB), 1050 Brussels, Belgium;
- IMEC, Kapeldreef 75, B-3002 Leuven, Belgium
- Correspondence:
| | - Luca Buzzatti
- Department of Physiotherapy, Human Physiology and Anatomy (KIMA), Vrije Universiteit Brussel (VUB), Vrije Universiteit, 1090 Brussel, Belgium; (L.B.); (E.C.)
| | - Jakub Ceranka
- Department of Electronics and Informatics (ETRO), Vrije Universiteit Brussel (VUB), 1050 Brussels, Belgium;
- IMEC, Kapeldreef 75, B-3002 Leuven, Belgium
| | - Adrián Gutiérrez
- Department of Radiology, Vrije Universiteit Brussel (VUB), Universitair Ziekenhuis Brussel (UZ Brussel), 1090 Brussels, Belgium; (A.G.); (G.V.G.); (J.D.M.); (N.B.); (J.V.)
| | - Simone Battista
- Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health, Campus of Savona, University of Genova, 17100 Savona, Italy;
| | - Thierry Scheerlinck
- Department of Orthopaedic Surgery and Traumatology, Vrije Universiteit Brussel (VUB), Universitair Ziekenhuis Brussel (UZ Brussel), 1090 Brussels, Belgium;
| | - Gert Van Gompel
- Department of Radiology, Vrije Universiteit Brussel (VUB), Universitair Ziekenhuis Brussel (UZ Brussel), 1090 Brussels, Belgium; (A.G.); (G.V.G.); (J.D.M.); (N.B.); (J.V.)
| | - Johan De Mey
- Department of Radiology, Vrije Universiteit Brussel (VUB), Universitair Ziekenhuis Brussel (UZ Brussel), 1090 Brussels, Belgium; (A.G.); (G.V.G.); (J.D.M.); (N.B.); (J.V.)
| | - Erik Cattrysse
- Department of Physiotherapy, Human Physiology and Anatomy (KIMA), Vrije Universiteit Brussel (VUB), Vrije Universiteit, 1090 Brussel, Belgium; (L.B.); (E.C.)
| | - Nico Buls
- Department of Radiology, Vrije Universiteit Brussel (VUB), Universitair Ziekenhuis Brussel (UZ Brussel), 1090 Brussels, Belgium; (A.G.); (G.V.G.); (J.D.M.); (N.B.); (J.V.)
| | - Jef Vandemeulebroucke
- Department of Radiology, Vrije Universiteit Brussel (VUB), Universitair Ziekenhuis Brussel (UZ Brussel), 1090 Brussels, Belgium; (A.G.); (G.V.G.); (J.D.M.); (N.B.); (J.V.)
- Department of Electronics and Informatics (ETRO), Vrije Universiteit Brussel (VUB), 1050 Brussels, Belgium;
- IMEC, Kapeldreef 75, B-3002 Leuven, Belgium
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Wu J, Zhu Y, Zhang X, Wang X, Zhang J. An automatic framework for evaluating the vascular permeability of bone metastases from prostate cancer. Phys Med Biol 2021; 66. [PMID: 34010811 DOI: 10.1088/1361-6560/ac02d3] [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: 04/29/2021] [Accepted: 05/19/2021] [Indexed: 11/11/2022]
Abstract
Objectives.Vascular permeability can reflect tumorigenesis and metastasis. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can assess microvascular permeability by pharmacokinetic parameter estimation. Most estimation methods require manually selected arterial input function (AIF) or reference regions. However, the result will be unstable due to the annotation, which relies on personal experience. In this study, we propose an automatic framework for evaluating vascular permeability of bone metastases from prostate cancer without selecting AIF.Materials and methods.This retrospective study comprised of 15 prostate cancer patients with bone metastases. Based on clinical consensus for three typical DCE-MRI curve patterns, three characteristic curves as regularization constraints were introduced to the extended Tofts model (ETM) using clustering strategy, and the clustering-based blind identification of multichannel (CBM) framework was then proposed for pharmacokinetic parameter estimation. With automatic segmentation of the whole bone area, we obtained the estimation of the pharmacokinetic parameters in the bone area and quantified for bone metastases. Two experienced radiologists compared the CBM estimations with the diagnostic results and we compared the estimations with those of the ETM in bone metastasis regions to evaluate the feasibility of the CBM framework.Results.The higher signal regions ofKtransandKepindicated the metastasis of prostate cancer, which is consistent with the cancer area marked by the radiologists. In addition, theKtransandKepin bone metastasis regions were significantly higher than in normal bone regions (P < 0.001,P < 0.001). The consistency of estimation by using the CBM framework and conventional ETM method was confirmed by Bland-Altman analysis.Conclusion.The proposed CBM framework can provide a fully automatic and reliable quantitative estimation of vascular permeability for bone metastases in prostate cancer patients.
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Affiliation(s)
- Junjie Wu
- College of Engineering, Peking University, Beijing, People's Republic of China
| | - Yi Zhu
- Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, People's Republic of China
| | - Xiaodong Zhang
- Department of Radiology, Peking University First Hospital, Beijing, People's Republic of China
| | - Xiaoying Wang
- Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, People's Republic of China.,Department of Radiology, Peking University First Hospital, Beijing, People's Republic of China
| | - Jue Zhang
- College of Engineering, Peking University, Beijing, People's Republic of China.,Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, People's Republic of China
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Summers P, Saia G, Colombo A, Pricolo P, Zugni F, Alessi S, Marvaso G, Jereczek-Fossa BA, Bellomi M, Petralia G. Whole-body magnetic resonance imaging: technique, guidelines and key applications. Ecancermedicalscience 2021; 15:1164. [PMID: 33680078 PMCID: PMC7929776 DOI: 10.3332/ecancer.2021.1164] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2020] [Indexed: 12/15/2022] Open
Abstract
Whole-body magnetic resonance imaging (WB-MRI) is an imaging method without ionising radiation that can provide WB coverage with a core protocol of essential imaging contrasts in less than 40 minutes, and it can be complemented with sequences to evaluate specific body regions as needed. In many cases, WB-MRI surpasses bone scintigraphy and computed tomography in detecting and characterising lesions, evaluating their response to therapy and in screening of high-risk patients. Consequently, international guidelines now recommend the use of WB-MRI in the management of patients with multiple myeloma, prostate cancer, melanoma and individuals with certain cancer predisposition syndromes. The use of WB-MRI is also growing for metastatic breast cancer, ovarian cancer and lymphoma as well as for cancer screening amongst the general population. In light of the increasing interest from clinicians and patients in WB-MRI as a radiation-free technique for guiding the management of cancer and for cancer screening, we review its technical basis, current international guidelines for its use and key applications.
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Affiliation(s)
- Paul Summers
- Division of Radiology, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Giulia Saia
- Division of Radiology, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy.,Advanced Screening Centers, ASC Italia, 24060 Castelli Calepio, Bergamo, Italy
| | - Alberto Colombo
- Division of Radiology, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Paola Pricolo
- Division of Radiology, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Fabio Zugni
- Division of Radiology, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Sarah Alessi
- Division of Radiology, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Giulia Marvaso
- Division of Radiotherapy, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy.,Department of Oncology and Hemato-Oncology, University of Milan, 20122 Milan, Italy
| | - Barbara Alicja Jereczek-Fossa
- Division of Radiotherapy, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy.,Department of Oncology and Hemato-Oncology, University of Milan, 20122 Milan, Italy
| | - Massimo Bellomi
- Division of Radiology, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy.,Department of Oncology and Hemato-Oncology, University of Milan, 20122 Milan, Italy
| | - Giuseppe Petralia
- Department of Oncology and Hemato-Oncology, University of Milan, 20122 Milan, Italy.,Precision Imaging and Research Unit, Department of Medical Imaging and Radiation Sciences, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
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Finite Element Analysis of the Stress Field in Peri-Implant Bone: A Parametric Study of Influencing Parameters and Their Interactions for Multi-Objective Optimization. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10175973] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
The present work proposes a parametric finite element model of the general case of a single loaded dental implant. The objective is to estimate and quantify the main effects of several parameters on stress distribution and load transfer between a loaded dental implant and its surrounding bone. The interactions between them are particularly investigated. Seven parameters (implant design and material) were considered as input variables to build the parametric finite element model: the implant diameter, length, taper and angle of inclination, Young’s modulus, the thickness of the cortical bone and Young’s modulus of the cancellous bone. All parameter combinations were tested with a full factorial design for a total of 512 models. Two biomechanical responses were identified to highlight the main effects of the full factorial design and first-order interaction between parameters: peri-implant bone stress and load transfer between bones and implants. The description of the two responses using the identified coefficients then makes it possible to optimize the implant configuration in a case study with type IV. The influence of the seven considered parameters was quantified, and objective information was given to support surgeon choices for implant design and placement. The implant diameter and Young’s modulus and the cortical thickness were the most influential parameters on the two responses. The importance of a low Young’s modulus alloy was highlighted to reduce the stress shielding between implants and the surrounding bone. This method allows obtaining optimized configurations for several case studies with a custom-made design implant.
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Wu S, He P, Yu S, Zhou S, Xia J, Xie Y. To Align Multimodal Lumbar Spine Images via Bending Energy Constrained Normalized Mutual Information. BIOMED RESEARCH INTERNATIONAL 2020; 2020:5615371. [PMID: 32733945 PMCID: PMC7369670 DOI: 10.1155/2020/5615371] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/09/2020] [Accepted: 06/15/2020] [Indexed: 12/03/2022]
Abstract
To align multimodal images is important for information fusion, clinical diagnosis, treatment planning, and delivery, while few methods have been dedicated to matching computerized tomography (CT) and magnetic resonance (MR) images of lumbar spine. This study proposes a coarse-to-fine registration framework to address this issue. Firstly, a pair of CT-MR images are rigidly aligned for global positioning. Then, a bending energy term is penalized into the normalized mutual information for the local deformation of soft tissues. In the end, the framework is validated on 40 pairs of CT-MR images from our in-house collection and 15 image pairs from the SpineWeb database. Experimental results show high overlapping ratio (in-house collection, vertebrae 0.97 ± 0.02, blood vessel 0.88 ± 0.07; SpineWeb, vertebrae 0.95 ± 0.03, blood vessel 0.93 ± 0.10) and low target registration error (in-house collection, ≤2.00 ± 0.62 mm; SpineWeb, ≤2.37 ± 0.76 mm) are achieved. The proposed framework concerns both the incompressibility of bone structures and the nonrigid deformation of soft tissues. It enables accurate CT-MR registration of lumbar spine images and facilitates image fusion, spine disease diagnosis, and interventional treatment delivery.
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Affiliation(s)
- Shibin Wu
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Pin He
- Department of Radiology, Shenzhen Second People's Hospital, The First Affiliated Hospital of Shenzhen University, Shenzhen 518035, China
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen 518116, China
| | - Shaode Yu
- Department of Radiation Oncology, University of Texas, Southwestern Medical Center, Dallas, TX 75390, USA
| | - Shoujun Zhou
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Jun Xia
- Department of Radiology, Shenzhen Second People's Hospital, The First Affiliated Hospital of Shenzhen University, Shenzhen 518035, China
| | - Yaoqin Xie
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
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