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Khodatars D, Gupta A, Welck M, Saifuddin A. An update on imaging of tarsal tunnel syndrome. Skeletal Radiol 2022; 51:2075-2095. [PMID: 35562562 DOI: 10.1007/s00256-022-04072-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/13/2022] [Revised: 05/02/2022] [Accepted: 05/07/2022] [Indexed: 02/02/2023]
Abstract
Tarsal tunnel syndrome (TTS) is an entrapment neuropathy of the tibial nerve (TN) within the tarsal tunnel (TT) at the level of the tibio-talar and/or talo-calcaneal joints. Making a diagnosis of TTS can be challenging, especially when symptoms overlap with other conditions and electrophysiological studies lack specificity. Imaging, in particular MRI, can help identify causative factors in individuals with suspected TTS and help aid surgical management. In this article, we review the anatomy of the TT, the diagnosis of TTS, aetiological factors implicated in TTS and imaging findings, with an emphasis on MRI.
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Affiliation(s)
- Davoud Khodatars
- Radiology Department, Royal National Orthopaedic Hospital, Stanmore, UK.
| | - Ankur Gupta
- Foot and Ankle Orthopaedic Surgery Department, Royal National Orthopaedic Hospital, Stanmore, UK
| | - Matthew Welck
- Foot and Ankle Orthopaedic Surgery Department, Royal National Orthopaedic Hospital, Stanmore, UK
| | - Asif Saifuddin
- Radiology Department, Royal National Orthopaedic Hospital, Stanmore, UK
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Nguyen V, Alves Pereira LF, Liang Z, Mielke F, Van Houtte J, Sijbers J, De Beenhouwer J. Automatic landmark detection and mapping for 2D/3D registration with BoneNet. Front Vet Sci 2022; 9:923449. [PMID: 36061115 PMCID: PMC9434378 DOI: 10.3389/fvets.2022.923449] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Accepted: 07/27/2022] [Indexed: 11/13/2022] Open
Abstract
The 3D musculoskeletal motion of animals is of interest for various biological studies and can be derived from X-ray fluoroscopy acquisitions by means of image matching or manual landmark annotation and mapping. While the image matching method requires a robust similarity measure (intensity-based) or an expensive computation (tomographic reconstruction-based), the manual annotation method depends on the experience of operators. In this paper, we tackle these challenges by a strategic approach that consists of two building blocks: an automated 3D landmark extraction technique and a deep neural network for 2D landmarks detection. For 3D landmark extraction, we propose a technique based on the shortest voxel coordinate variance to extract the 3D landmarks from the 3D tomographic reconstruction of an object. For 2D landmark detection, we propose a customized ResNet18-based neural network, BoneNet, to automatically detect geometrical landmarks on X-ray fluoroscopy images. With a deeper network architecture in comparison to the original ResNet18 model, BoneNet can extract and propagate feature vectors for accurate 2D landmark inference. The 3D poses of the animal are then reconstructed by aligning the extracted 2D landmarks from X-ray radiographs and the corresponding 3D landmarks in a 3D object reference model. Our proposed method is validated on X-ray images, simulated from a real piglet hindlimb 3D computed tomography scan and does not require manual annotation of landmark positions. The simulation results show that BoneNet is able to accurately detect the 2D landmarks in simulated, noisy 2D X-ray images, resulting in promising rigid and articulated parameter estimations.
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Affiliation(s)
- Van Nguyen
- Imec—Vision Lab, Department of Physics, University of Antwerp, Antwerp, Belgium
- *Correspondence: Van Nguyen
| | - Luis F. Alves Pereira
- Imec—Vision Lab, Department of Physics, University of Antwerp, Antwerp, Belgium
- Departamento de Ciência da Computação, Universidade Federal do Agreste de Pernambuco, Garanhuns, Brazil
| | - Zhihua Liang
- Imec—Vision Lab, Department of Physics, University of Antwerp, Antwerp, Belgium
| | - Falk Mielke
- Imec—Vision Lab, Department of Physics, University of Antwerp, Antwerp, Belgium
- Department of Biology, University of Antwerp, Antwerp, Belgium
| | - Jeroen Van Houtte
- Imec—Vision Lab, Department of Physics, University of Antwerp, Antwerp, Belgium
| | - Jan Sijbers
- Imec—Vision Lab, Department of Physics, University of Antwerp, Antwerp, Belgium
| | - Jan De Beenhouwer
- Imec—Vision Lab, Department of Physics, University of Antwerp, Antwerp, Belgium
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