Musculoskeletal Wide-Detector CT Kinematic Evaluation: From Motion to Image

Teixeira, Pedro Augusto Gondim; Gervaise, A.; Louis, M.; Raymond, A.; Formery, Anne-Sophie; Lecocq, S.; Blum, Alain

doi:10.1055/s-0035-1569257

Cited by 19 publications

(4 citation statements)

References 14 publications

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“…In addition, Euclidean distance maps of the ground truth manual segmentations and the surface of the corresponding segmentation obtained from the atlas-based method, were used to compute the Hausdorff distance [34]. Equation (7) shows the definition of the Hausdorff distance.…”

Section: Validationmentioning

confidence: 99%

“…Dynamic computer tomography (4D-CT) enables acquisition of a series of high temporal-resolution 3D CT datasets of moving structures. Various phantom studies [6][7][8][9] demonstrated the validity and feasibility of dynamic CT for evaluating MSK diseases. Several patient studies have been conducted investigating different joint disorders of the wrist, knee, hip, shoulder and foot [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

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Automated Motion Analysis of Bony Joint Structures from Dynamic Computer Tomography Images: A Multi-Atlas Approach

et al. 2021

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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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Section: Validationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automated Motion Analysis of Bony Joint Structures from Dynamic Computer Tomography Images: A Multi-Atlas Approach

et al. 2021

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“…Currently, in-vivo analysis of articular motion can be performed by several techniques [11]: ultrasonography [12], fluoroscopy [13], computed tomography (CT) [14], and Magnetic Resonance Imaging (MRI) [6]. Ultrasonography, however, is limited to the evaluation of soft tissues around the joint.…”

Section: Introductionmentioning

confidence: 99%

In-Vivo Quantification of Knee Deep-Flexion in Physiological Loading Condition trough Dynamic MRI

Conconi

Carli²,

Berni

et al. 2023

Applied Sciences

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The in-vivo quantification of knee motion in physiological loading conditions is paramount for the understanding of the joint’s natural behavior and the comprehension of articular disorders. Dynamic MRI (DMRI) represents an emerging technology that makes it possible to investigate the functional interaction among all the joint tissues without risks for the patient. However, traditional MRI scanners normally offer a reduced space of motion, and complex apparatus are needed to load the articulation, due to the horizontal orientation of the scanning bed. In this study, we present an experimental and computational procedure that combines an open, weight-bearing MRI scanner with an original registration algorithm to reconstruct the three-dimensional kinematics of the knee from DMRI, thus allowing the investigation of knee deep-flexion under physiological loads in space. To improve the accuracy of the procedure, an MR-compatible rig has been developed to guide the knee flexion of the patient. We tested the procedure on three volunteers. The overall rotational and positional accuracy achieved are 1.8° ± 1.4 and 1.2 mm ± 0.8, respectively, and they are sufficient for the characterization of the joint behavior under load.

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“…Four-dimensional computed tomography (4DCT) is an increasingly popular imaging modality and in the last decade, its applications for musculoskeletal (MSK) investigations have become more apparent 1–3 . Within this modality, one of the main applications reported so far is the estimation of integrity of (intra-)articular ligaments and the analysis of complex motion in several joints with strong rotatory components 4 .…”

Section: Introductionmentioning

confidence: 99%

Four-dimensional CT as a valid approach to detect and quantify kinematic changes after selective ankle ligament sectioning

Buzzatti

Keelson

Apperloo

et al. 2019

Sci Rep

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The objective of the current study was to explore the potential of dynamic computed tomography to detect kinematic changes, induced by sequential sectioning of the lateral collateral ligaments of the ankle, during full motion sequence of the talocrural joint. A custom-made device was used to induce cyclic controlled ankle inversion movement in one fresh frozen cadaver leg. A 256-slice CT scanner was used to investigate four different scenarios. Scenario 1 with all ligaments intact was first investigated followed by sequential section of the anterior talo-fibular ligament (Scenario 2), the calcaneo-fibular ligament (Scenario 3) and posterior talo-fibular ligament (Scenario 4). Off-line image processing based on semi-automatic segmentation and bone rigid registration was performed. Motion parameters such as translation, rotational angles and orientation and position of the axis of rotation were calculated. Differences between scenarios were calculated. Progressive increase of cranio-caudal displacement up to 3.9 mm and flexion up to 10° compared to Scenario 1 were reported. Progressive changes in orientation (up to 20.6°) and position (up to 4.1 mm) of the axis of rotation were also shown. Estimated effective dose of 0.005 mSv (1.9 mGy CTDIvol) was reported. This study demonstrated that kinematic changes due to the absence of ligament integrity can be detected with 4DCT with minimal radiation exposure. Identifying abnormal kinematic patterns could have future application in helping clinicians to choose patients’ optimal treatment. Therefore, further studies with bigger in vitro sample sizes and consequent investigations in vivo are recommended to confirm the current findings.

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Musculoskeletal Wide-Detector CT Kinematic Evaluation: From Motion to Image

Cited by 19 publications

References 14 publications

Automated Motion Analysis of Bony Joint Structures from Dynamic Computer Tomography Images: A Multi-Atlas Approach

Automated Motion Analysis of Bony Joint Structures from Dynamic Computer Tomography Images: A Multi-Atlas Approach

In-Vivo Quantification of Knee Deep-Flexion in Physiological Loading Condition trough Dynamic MRI

Four-dimensional CT as a valid approach to detect and quantify kinematic changes after selective ankle ligament sectioning

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