Considerations for Mean Upper Cervical Cord Area Implementation in a Longitudinal MRI Setting: Methods, Interrater Reliability, and MRI Quality Control

Chien, Claudia; Juenger, Valentin; Scheel, Michael; Brandt, Alexander U.; Paul, Friedemann

doi:10.3174/ajnr.a6394

Cited by 8 publications

(7 citation statements)

References 35 publications

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“…In addition to the biological-derived normalization strategy, previous studies have reported a variability in CSA measures associated with the MRI acquisition parameters, and segmentation method (Kearney et al, 2014 ; Papinutto and Henry, 2019 ; Chien et al, 2020 ; Cohen-Adad et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Automatic measure and normalization of spinal cord cross-sectional area using the pontomedullary junction

Bédard

Cohen‐Adad²

2022

Front. Neuroimaging

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Spinal cord cross-sectional area (CSA) is a relevant biomarker to assess spinal cord atrophy in neurodegenerative diseases. However, the considerable inter-subject variability among healthy participants currently limits its usage. Previous studies explored factors contributing to the variability, yet the normalization models required manual intervention and used vertebral levels as a reference, which is an imprecise prediction of the spinal levels. In this study we implemented a method to measure CSA automatically from a spatial reference based on the central nervous system (the pontomedullary junction, PMJ), we investigated factors to explain variability, and developed normalization strategies on a large cohort (N = 804). Following automatic spinal cord segmentation, vertebral labeling and PMJ labeling, the spinal cord CSA was computed on T1w MRI scans from the UK Biobank database. The CSA was computed using two methods. For the first method, the CSA was computed at the level of the C2–C3 intervertebral disc. For the second method, the CSA was computed at 64 mm caudally from the PMJ, this distance corresponding to the average distance between the PMJ and the C2–C3 disc across all participants. The effect of various demographic and anatomical factors was explored, and a stepwise regression found significant predictors; the coefficients of the best fit model were used to normalize CSA. CSA measured at C2–C3 disc and using the PMJ differed significantly (paired t-test, p-value = 0.0002). The best normalization model included thalamus, brain volume, sex and the interaction between brain volume and sex. The coefficient of variation went down for PMJ CSA from 10.09 (without normalization) to 8.59%, a reduction of 14.85%. For CSA at C2–C3, it went down from 9.96 to 8.42%, a reduction of 15.13 %. This study introduces an end-to-end automatic pipeline to measure and normalize cord CSA from a neurological reference. This approach requires further validation to assess atrophy in longitudinal studies. The inter-subject variability of CSA can be partly accounted for by demographics and anatomical factors.

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Section: Introductionmentioning

confidence: 99%

Automatic measure and normalization of spinal cord cross-sectional area using the pontomedullary junction

Bédard

Cohen‐Adad²

2022

Front. Neuroimaging

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“…Beyond IMA, the need for QC has been recognized for other technical procedures. In the context of MS research, magnetic resonance imaging and optical coherence tomography serve as examples for which recommendations have been made regarding standardized protocols, QC, and harmonious reporting thereof [ 38 - 42 ]. Therefore, we propose standardized reporting of IMA results to include information regarding the following: (1) number of recording failures during data acquisition; (2) type and amount of applied postprocessing, both technical and manual; (3) fraction of recordings undergoing QC; (4) fraction of recordings ultimately excluded from analysis (mention of respective causes would be highly valuable for future users)…”

Section: Discussionmentioning

confidence: 99%

Proposal for Post Hoc Quality Control in Instrumented Motion Analysis Using Markerless Motion Capture: Development and Usability Study

Röhling¹,

Althoff²,

Arsenova³

et al. 2022

JMIR Hum Factors

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Background Instrumented assessment of motor symptoms has emerged as a promising extension to the clinical assessment of several movement disorders. The use of mobile and inexpensive technologies such as some markerless motion capture technologies is especially promising for large-scale application but has not transitioned into clinical routine to date. A crucial step on this path is to implement standardized, clinically applicable tools that identify and control for quality concerns. Objective The main goal of this study comprises the development of a systematic quality control (QC) procedure for data collected with markerless motion capture technology and its experimental implementation to identify specific quality concerns and thereby rate the usability of recordings. Methods We developed a post hoc QC pipeline that was evaluated using a large set of short motor task recordings of healthy controls (2010 recordings from 162 subjects) and people with multiple sclerosis (2682 recordings from 187 subjects). For each of these recordings, 2 raters independently applied the pipeline. They provided overall usability decisions and identified technical and performance-related quality concerns, which yielded respective proportions of their occurrence as a main result. Results The approach developed here has proven user-friendly and applicable on a large scale. Raters’ decisions on recording usability were concordant in 71.5%-92.3% of cases, depending on the motor task. Furthermore, 39.6%-85.1% of recordings were concordantly rated as being of satisfactory quality whereas in 5.0%-26.3%, both raters agreed to discard the recording. Conclusions We present a QC pipeline that seems feasible and useful for instant quality screening in the clinical setting. Results confirm the need of QC despite using standard test setups, testing protocols, and operator training for the employed system and by extension, for other task-based motor assessment technologies. Results of the QC process can be used to clean existing data sets, optimize quality assurance measures, as well as foster the development of automated QC approaches and therefore improve the overall reliability of kinematic data sets.

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“…Beyond IMA, the need for QC has been recognized for other technical procedures. In the context of MS research, magnetic resonance imaging and optical coherence tomography serve as examples for which recommendations have been made regarding standardized protocols, QC, and harmonious reporting thereof [38][39][40][41][42]. Therefore, we propose standardized reporting of IMA results to include information regarding the following: (1) number of recording failures during data acquisition; (2) type and amount of applied postprocessing, both technical and manual; (3) fraction of recordings undergoing QC; (4) fraction of recordings ultimately excluded from analysis (mention of respective causes would be highly valuable for future users)…”

Section: Discussionmentioning

confidence: 99%

Proposal for Post Hoc Quality Control in Instrumented Motion Analysis Using Markerless Motion Capture: Development and Usability Study (Preprint)

Röhling¹,

Althoff²,

Arsenova³

et al. 2020

Preprint

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BACKGROUND Instrumented assessment of motor symptoms has emerged as a promising extension to the clinical assessment of several movement disorders. The use of mobile and inexpensive technologies such as some markerless motion capture technologies is especially promising for large-scale application but has not transitioned into clinical routine to date. A crucial step on this path is to implement standardized, clinically applicable tools that identify and control for quality concerns. OBJECTIVE The main goal of this study comprises the development of a systematic quality control (QC) procedure for data collected with markerless motion capture technology and its experimental implementation to identify specific quality concerns and thereby rate the usability of recordings. METHODS We developed a post hoc QC pipeline that was evaluated using a large set of short motor task recordings of healthy controls (2010 recordings from 162 subjects) and people with multiple sclerosis (2682 recordings from 187 subjects). For each of these recordings, 2 raters independently applied the pipeline. They provided overall usability decisions and identified technical and performance-related quality concerns, which yielded respective proportions of their occurrence as a main result. RESULTS The approach developed here has proven user-friendly and applicable on a large scale. Raters’ decisions on recording usability were concordant in 71.5%-92.3% of cases, depending on the motor task. Furthermore, 39.6%-85.1% of recordings were concordantly rated as being of satisfactory quality whereas in 5.0%-26.3%, both raters agreed to discard the recording. CONCLUSIONS We present a QC pipeline that seems feasible and useful for instant quality screening in the clinical setting. Results confirm the need of QC despite using standard test setups, testing protocols, and operator training for the employed system and by extension, for other task-based motor assessment technologies. Results of the QC process can be used to clean existing data sets, optimize quality assurance measures, as well as foster the development of automated QC approaches and therefore improve the overall reliability of kinematic data sets.

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Considerations for Mean Upper Cervical Cord Area Implementation in a Longitudinal MRI Setting: Methods, Interrater Reliability, and MRI Quality Control

Cited by 8 publications

References 35 publications

Automatic measure and normalization of spinal cord cross-sectional area using the pontomedullary junction

Automatic measure and normalization of spinal cord cross-sectional area using the pontomedullary junction

Proposal for Post Hoc Quality Control in Instrumented Motion Analysis Using Markerless Motion Capture: Development and Usability Study

Proposal for Post Hoc Quality Control in Instrumented Motion Analysis Using Markerless Motion Capture: Development and Usability Study (Preprint)

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