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Shu L, Li M, Guo X, Chen Y, Pu X, Lin C. Isocentric fixed angle irradiation-based DRR: a novel approach to enhance x-ray and CT image registration. Phys Med Biol 2024; 69:115032. [PMID: 38684168 DOI: 10.1088/1361-6560/ad450a] [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: 10/01/2023] [Accepted: 04/29/2024] [Indexed: 05/02/2024]
Abstract
Objective.Digitally reconstructed radiography (DRR) plays an important role in the registration of intraoperative x-ray and preoperative CT images. However, existing DRR algorithms often neglect the critical isocentric fixed angle irradiation (IFAI) principle in C-arm imaging, resulting in inaccurate simulation of x-ray images. This limitation degrades registration algorithms relying on DRR image libraries or employing DRR images (DRRs) to train neural network models. To address this issue, we propose a novel IFAI-based DRR method that accurately captures the true projection transformation during x-ray imaging of the human body.Approach.By strictly adhering to the IFAI principle and utilizing known parameters from intraoperative x-ray images paired with CT scans, our method successfully simulates the real projection transformation and generates DRRs that closely resemble actual x-ray images.Main result.Experimental results validate the effectiveness of our IFAI-based DRR method by successfully registering intraoperative x-ray images with preoperative CT images from multiple patients who underwent thoracic endovascular aortic procedures.Significance. The proposed IFAI-based DRR method enhances the quality of DRR images, significantly accelerates the construction of DRR image libraries, and thereby improves the performance of x-ray and CT image registration. Additionally, the method has the generality of registering CT and x-ray images generated by large C-arm devices.
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Affiliation(s)
- Lixia Shu
- Beijing Institute of Heart, Lung and Blood Vessel Diseases, Beijing Anzhen Hospital, Capital Medical University, Beijing, People's Republic of China
| | - Meng Li
- Beijing Institute of Heart, Lung and Blood Vessel Diseases, Beijing Anzhen Hospital, Capital Medical University, Beijing, People's Republic of China
| | - Xi Guo
- The Large Vessel Center, Beijing Anzhen Hospital, Capital Medical University, Beijing, People's Republic of China
| | - Yu Chen
- The Large Vessel Center, Beijing Anzhen Hospital, Capital Medical University, Beijing, People's Republic of China
| | - Xin Pu
- The Large Vessel Center, Beijing Anzhen Hospital, Capital Medical University, Beijing, People's Republic of China
| | - Changyan Lin
- Beijing Institute of Heart, Lung and Blood Vessel Diseases, Beijing Anzhen Hospital, Capital Medical University, Beijing, People's Republic of China
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Burton W, Crespo IR, Andreassen T, Pryhoda M, Jensen A, Myers C, Shelburne K, Banks S, Rullkoetter P. Fully automatic tracking of native glenohumeral kinematics from stereo-radiography. Comput Biol Med 2023; 163:107189. [PMID: 37393783 DOI: 10.1016/j.compbiomed.2023.107189] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Revised: 06/12/2023] [Accepted: 06/19/2023] [Indexed: 07/04/2023]
Abstract
The current work introduces a system for fully automatic tracking of native glenohumeral kinematics in stereo-radiography sequences. The proposed method first applies convolutional neural networks to obtain segmentation and semantic key point predictions in biplanar radiograph frames. Preliminary bone pose estimates are computed by solving a non-convex optimization problem with semidefinite relaxations to register digitized bone landmarks to semantic key points. Initial poses are then refined by registering computed tomography-based digitally reconstructed radiographs to captured scenes, which are masked by segmentation maps to isolate the shoulder joint. A particular neural net architecture which exploits subject-specific geometry is also introduced to improve segmentation predictions and increase robustness of subsequent pose estimates. The method is evaluated by comparing predicted glenohumeral kinematics to manually tracked values from 17 trials capturing 4 dynamic activities. Median orientation differences between predicted and ground truth poses were 1.7∘ and 8.6∘ for the scapula and humerus, respectively. Joint-level kinematics differences were less than 2∘ in 65%, 13%, and 63% of frames for XYZ orientation DoFs based on Euler angle decompositions. Automation of kinematic tracking can increase scalability of tracking workflows in research, clinical, or surgical applications.
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Affiliation(s)
- William Burton
- Center for Orthopaedic Biomechanics, University of Denver, 2155 E. Wesley Ave., Denver, CO, 80210, USA.
| | - Ignacio Rivero Crespo
- Center for Orthopaedic Biomechanics, University of Denver, 2155 E. Wesley Ave., Denver, CO, 80210, USA
| | - Thor Andreassen
- Center for Orthopaedic Biomechanics, University of Denver, 2155 E. Wesley Ave., Denver, CO, 80210, USA
| | - Moira Pryhoda
- Center for Orthopaedic Biomechanics, University of Denver, 2155 E. Wesley Ave., Denver, CO, 80210, USA
| | - Andrew Jensen
- Department of Mechanical and Aerospace Engineering, University of Florida, 939 Center Dr., Gainesville, FL, 32611, USA
| | - Casey Myers
- Center for Orthopaedic Biomechanics, University of Denver, 2155 E. Wesley Ave., Denver, CO, 80210, USA
| | - Kevin Shelburne
- Center for Orthopaedic Biomechanics, University of Denver, 2155 E. Wesley Ave., Denver, CO, 80210, USA
| | - Scott Banks
- Department of Mechanical and Aerospace Engineering, University of Florida, 939 Center Dr., Gainesville, FL, 32611, USA
| | - Paul Rullkoetter
- Center for Orthopaedic Biomechanics, University of Denver, 2155 E. Wesley Ave., Denver, CO, 80210, USA
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Taleb A, Guigou C, Leclerc S, Lalande A, Bozorg Grayeli A. Image-to-Patient Registration in Computer-Assisted Surgery of Head and Neck: State-of-the-Art, Perspectives, and Challenges. J Clin Med 2023; 12:5398. [PMID: 37629441 PMCID: PMC10455300 DOI: 10.3390/jcm12165398] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Revised: 08/08/2023] [Accepted: 08/14/2023] [Indexed: 08/27/2023] Open
Abstract
Today, image-guided systems play a significant role in improving the outcome of diagnostic and therapeutic interventions. They provide crucial anatomical information during the procedure to decrease the size and the extent of the approach, to reduce intraoperative complications, and to increase accuracy, repeatability, and safety. Image-to-patient registration is the first step in image-guided procedures. It establishes a correspondence between the patient's preoperative imaging and the intraoperative data. When it comes to the head-and-neck region, the presence of many sensitive structures such as the central nervous system or the neurosensory organs requires a millimetric precision. This review allows evaluating the characteristics and the performances of different registration methods in the head-and-neck region used in the operation room from the perspectives of accuracy, invasiveness, and processing times. Our work led to the conclusion that invasive marker-based methods are still considered as the gold standard of image-to-patient registration. The surface-based methods are recommended for faster procedures and applied on the surface tissues especially around the eyes. In the near future, computer vision technology is expected to enhance these systems by reducing human errors and cognitive load in the operating room.
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Affiliation(s)
- Ali Taleb
- Team IFTIM, Institute of Molecular Chemistry of University of Burgundy (ICMUB UMR CNRS 6302), Univ. Bourgogne Franche-Comté, 21000 Dijon, France; (C.G.); (S.L.); (A.L.); (A.B.G.)
| | - Caroline Guigou
- Team IFTIM, Institute of Molecular Chemistry of University of Burgundy (ICMUB UMR CNRS 6302), Univ. Bourgogne Franche-Comté, 21000 Dijon, France; (C.G.); (S.L.); (A.L.); (A.B.G.)
- Otolaryngology Department, University Hospital of Dijon, 21000 Dijon, France
| | - Sarah Leclerc
- Team IFTIM, Institute of Molecular Chemistry of University of Burgundy (ICMUB UMR CNRS 6302), Univ. Bourgogne Franche-Comté, 21000 Dijon, France; (C.G.); (S.L.); (A.L.); (A.B.G.)
| | - Alain Lalande
- Team IFTIM, Institute of Molecular Chemistry of University of Burgundy (ICMUB UMR CNRS 6302), Univ. Bourgogne Franche-Comté, 21000 Dijon, France; (C.G.); (S.L.); (A.L.); (A.B.G.)
- Medical Imaging Department, University Hospital of Dijon, 21000 Dijon, France
| | - Alexis Bozorg Grayeli
- Team IFTIM, Institute of Molecular Chemistry of University of Burgundy (ICMUB UMR CNRS 6302), Univ. Bourgogne Franche-Comté, 21000 Dijon, France; (C.G.); (S.L.); (A.L.); (A.B.G.)
- Otolaryngology Department, University Hospital of Dijon, 21000 Dijon, France
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Xu J, Zeng B, Egger J, Wang C, Smedby Ö, Jiang X, Chen X. A review on AI-based medical image computing in head and neck surgery. Phys Med Biol 2022; 67. [DOI: 10.1088/1361-6560/ac840f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Accepted: 07/25/2022] [Indexed: 11/11/2022]
Abstract
Abstract
Head and neck surgery is a fine surgical procedure with a complex anatomical space, difficult operation and high risk. Medical image computing (MIC) that enables accurate and reliable preoperative planning is often needed to reduce the operational difficulty of surgery and to improve patient survival. At present, artificial intelligence, especially deep learning, has become an intense focus of research in MIC. In this study, the application of deep learning-based MIC in head and neck surgery is reviewed. Relevant literature was retrieved on the Web of Science database from January 2015 to May 2022, and some papers were selected for review from mainstream journals and conferences, such as IEEE Transactions on Medical Imaging, Medical Image Analysis, Physics in Medicine and Biology, Medical Physics, MICCAI, etc. Among them, 65 references are on automatic segmentation, 15 references on automatic landmark detection, and eight references on automatic registration. In the elaboration of the review, first, an overview of deep learning in MIC is presented. Then, the application of deep learning methods is systematically summarized according to the clinical needs, and generalized into segmentation, landmark detection and registration of head and neck medical images. In segmentation, it is mainly focused on the automatic segmentation of high-risk organs, head and neck tumors, skull structure and teeth, including the analysis of their advantages, differences and shortcomings. In landmark detection, the focus is mainly on the introduction of landmark detection in cephalometric and craniomaxillofacial images, and the analysis of their advantages and disadvantages. In registration, deep learning networks for multimodal image registration of the head and neck are presented. Finally, their shortcomings and future development directions are systematically discussed. The study aims to serve as a reference and guidance for researchers, engineers or doctors engaged in medical image analysis of head and neck surgery.
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