A database of the healthy human spinal cord morphometry in the PAM50 template space

ABSTRACT: Measures of spinal cord morphometry computed from magnetic resonance images serve as relevant prognostic biomarkers for a range of spinal cord pathologies, including traumatic and non-traumatic spinal cord injury and neurodegenerative diseases. However, interpreting these imaging biomarkers is difficult due to considerable intra- and inter-subject variability. Yet, there is no clear consensus on a normalization method that would help reduce this variability and more insights into the distribution of these morphometrics are needed. In this study, we computed a database of normative values for six commonly used measures of spinal cord morphometry: cross-sectional area, anteroposterior diameter, transverse diameter, compression ratio, eccentricity, and solidity. Normative values were computed from a large open-access dataset of healthy adult volunteers (N = 203) and were brought to the common space of the PAM50 spinal cord template using a newly proposed normalization method based on linear interpolation. Compared to traditional image-based registration, the proposed normalization approach does not involve image transformations and, therefore, does not introduce distortions of spinal cord anatomy. This is a crucial consideration in preserving the integrity of the spinal cord anatomy in conditions such as spinal cord injury. This new morphometric database allows researchers to normalize based on sex and age, thereby minimizing inter-subject variability associated with demographic and biological factors. The proposed methodology is open-source and accessible through the Spinal Cord Toolbox (SCT) v6.0 and higher

Diffusion magnetic resonance imaging reveals tract‐specific microstructural correlates of electrophysiological impairments in non‐myelopathic and myelopathic spinal cord compression

ABSTRACT: Background and purpose: Non- myelopathic degenerative cervical spinal cord compres-sion (NMDC) frequently occurs throughout aging and may progress to potentially irre-versible degenerative cervical myelopathy (DCM). Whereas standard clinical magnetic resonance imaging (MRI) and electrophysiological measures assess compression sever-ity and neurological dysfunction, respectively, underlying microstructural deficits still have to be established in NMDC and DCM patients. The study aims to establish tract- specific diffusion MRI markers of electrophysiological deficits to predict the progression of asymptomatic NMDC to symptomatic DCM. Methods: High-resolution 3 T diffusion MRI was acquired for 103 NMDC and 21 DCM patients compared to 60 healthy controls to reveal diffusion alterations and relation-ships between tract-specific diffusion metrics and corresponding electrophysiological measures and compression severity. Relationship between the degree of DCM disability, assessed by the modified Japanese Orthopaedic Association scale, and tract-specific mi-crostructural changes in DCM patients was also explored. Results: The study identified diffusion-derived abnormalities in the gray matter, dor-sal and lateral tracts congruent with trans-synaptic degeneration and demyelination in chronic degenerative spinal cord compression with more profound alterations in DCM than NMDC. Diffusion metrics were affected in the C3-6 area as well as above the com-pression level at C3 with more profound rostral deficits in DCM than NMDC. Alterations in lateral motor and dorsal sensory tracts correlated with motor and sensory evoked po-tentials, respectively, whereas electromyography outcomes corresponded with gray mat-ter microstructure. DCM disability corresponded with microstructure alteration in lateral columns. Conclusions: Outcomes imply the necessity of high- resolution tract-specific diffusion MRI for monitoring degenerative spinal pathology in longitudinal studies

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Federated Learning Enables Big Data for Rare Cancer Boundary Detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Quantitative MR Markers in Non-Myelopathic Spinal Cord Compression: A Narrative Review

Degenerative spinal cord compression is a frequent pathological condition with increasing prevalence throughout aging. Initial non-myelopathic cervical spinal cord compression (NMDC) might progress over time into potentially irreversible degenerative cervical myelopathy (DCM). While quantitative MRI (qMRI) techniques demonstrated the ability to depict intrinsic tissue properties, longitudinal in-vivo biomarkers to identify NMDC patients who will eventually develop DCM are still missing. Thus, we aim to review the ability of qMRI techniques (such as diffusion MRI, diffusion tensor imaging (DTI), magnetization transfer (MT) imaging, and magnetic resonance spectroscopy (1H-MRS)) to serve as prognostic markers in NMDC. While DTI in NMDC patients consistently detected lower fractional anisotropy and higher mean diffusivity at compressed levels, caused by demyelination and axonal injury, MT and 1H-MRS, along with advanced and tract-specific diffusion MRI, recently revealed microstructural alterations, also rostrally pointing to Wallerian degeneration. Recent studies also disclosed a significant relationship between microstructural damage and functional deficits, as assessed by qMRI and electrophysiology, respectively. Thus, tract-specific qMRI, in combination with electrophysiology, critically extends our understanding of the underlying pathophysiology of degenerative spinal cord compression and may provide predictive markers of DCM development for accurate patient management. However, the prognostic value must be validated in longitudinal studies

Time-Efficient Perfusion Imaging Using DCE- and DSC-MRI

Dynamic contrast enhanced MRI (DCE-MRI) and dynamic susceptibility contrast MRI (DSC-MRI) are perfusion imaging techniques used mainly for clinical and preclinical measurement of vessel permeability and capillary blood flow, respectively. It is advantageous to apply both methods to exploit their complementary information about the perfusion status of the tissue. We propose a novel acquisition method that combines advantages of the current simultaneous and sequential acquisition. The proposed method consists of a DCE-MRI acquisition interrupted by DSC-MRI acquisition. A new method for processing of the DCE-MRI data is proposed which takes the interleaved acquisition into account. Analysis of both the DCE- and DSC-MRI data is reformulated so that they are approximated by the same pharmacokinetic model (constrained distributed capillary adiabatic tissue homogeneity model). This provides a straightforward evaluation of the methodology as some of the estimated DCE- and DSC-MRI perfusion parameters should be identical. Evaluation on synthetic data showed an acceptable precision and no apparent bias introduced by the interleaved character of the DCE-MRI acquisition. Intravascular perfusion parameters obtained from clinical glioma data showed a fairly high correlation of blood flow estimates from DCE- and DSC-MRI, however, an unknown scaling factor was still present mainly because of the tissue-specific r2*$r_2^*$ relaxivity. The results show validity of the proposed acquisition method. They also indicate that simultaneous processing of both DCE- and DSC-MRI data with joint estimation of some perfusion parameters (included in both DCE- and DSC-MRI) might be possible to increase the reliability of the DCE- and DSC-MRI methods alone

Erratum: Diffusion magnetic resonance imaging reveals tract‐specific microstructural correlates of electrophysiological impairments in non‐myelopathic and myelopathic spinal cord compression

ABSTRACT: Background and purpose: Non- myelopathic degenerative cervical spinal cord compres-sion (NMDC) frequently occurs throughout aging and may progress to potentially irre-versible degenerative cervical myelopathy (DCM). Whereas standard clinical magnetic resonance imaging (MRI) and electrophysiological measures assess compression sever-ity and neurological dysfunction, respectively, underlying microstructural deficits still have to be established in NMDC and DCM patients. The study aims to establish tract- specific diffusion MRI markers of electrophysiological deficits to predict the progression of asymptomatic NMDC to symptomatic DCM. Methods: High-resolution 3 T diffusion MRI was acquired for 103 NMDC and 21 DCM patients compared to 60 healthy controls to reveal diffusion alterations and relation-ships between tract-specific diffusion metrics and corresponding electrophysiological measures and compression severity. Relationship between the degree of DCM disability, assessed by the modified Japanese Orthopaedic Association scale, and tract-specific mi-crostructural changes in DCM patients was also explored. Results: The study identified diffusion-derived abnormalities in the gray matter, dor-sal and lateral tracts congruent with trans-synaptic degeneration and demyelination in chronic degenerative spinal cord compression with more profound alterations in DCM than NMDC. Diffusion metrics were affected in the C3-6 area as well as above the com-pression level at C3 with more profound rostral deficits in DCM than NMDC. Alterations in lateral motor and dorsal sensory tracts correlated with motor and sensory evoked po-tentials, respectively, whereas electromyography outcomes corresponded with gray mat-ter microstructure. DCM disability corresponded with microstructure alteration in lateral columns. Conclusions: Outcomes imply the necessity of high- resolution tract-specific diffusion MRI for monitoring degenerative spinal pathology in longitudinal studies

Federated learning enables big data for rare cancer boundary detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Open-access quantitative MRI data of the spinal cord and reproducibility across participants, sites and manufacturers

In a companion paper by Cohen-Adad et al. we introduce the spine generic quantitative MRI protocol that provides valuable metrics for assessing spinal cord macrostructural and microstructural integrity. This protocol was used to acquire a single subject dataset across 19 centers and a multi-subject dataset across 42 centers (for a total of 260 participants), spanning the three main MRI manufacturers: GE, Philips and Siemens. Both datasets are publicly available via git-annex. Data were analysed using the Spinal Cord Toolbox to produce normative values as well as inter/intra-site and inter/intra-manufacturer statistics. Reproducibility for the spine generic protocol was high across sites and manufacturers, with an average inter-site coefficient of variation of less than 5% for all the metrics. Full documentation and results can be found at https://spine-generic.rtfd.io/. The datasets and analysis pipeline will help pave the way towards accessible and reproducible quantitative MRI in the spinal cord