Quantitative analysis of fiber tractography in cervical spondylotic myelopathy

Background context: Diffusion tensor fiber tractography is an emerging tool for the visualization of spinal cord microstructure. However, there are few quantitative analyses of the damage in the nerve fiber tracts of the myelopathic spinal cord. Purpose: The aim of this study was to develop a quantitative approach for fiber tractography analysis in cervical spondylotic myelopathy (CSM). Study design/setting: Prospective study on a series of patients. Materials and methods: A total of 22 volunteers were recruited with informed consent, including 15 healthy subjects and 7 CSM patients. The clinical severity of CSM was evaluated using modified Japanese Orthopedic Association (JOA) score. The microstructure of myelopathic cervical cord was analyzed using diffusion tensor imaging. Diffusion tensor imaging was performed with a 3.0-T magnetic resonance imaging scanner using pulsed gradient, spin-echo, echo-planar imaging sequence. Fiber tractography was generated via TrackVis with fractional anisotropy threshold set at 0.2 and angle threshold at 40. Region of interest (ROI) was defined to cover C4 level only or the whole-length cervical spinal cord from C1 to C7 for analysis. The length and density of tracked nerve bundles were measured for comparison between healthy subjects and CSM patients. Results: The length of tracked nerve bundles significantly shortened in CSM patients compared with healthy subjects (healthy: 6.85-77.90 mm, CSM: 0.68-62.53 mm). The density of the tracked nerve bundles was also lower in CSM patients (healthy: 086±0.03, CSM: 0.80±0.06, p<.05). Although the definition of ROI covering C4 only or whole cervical cord appeared not to affect the trend of the disparity between healthy and myelopathic cervical cords, the density of the tracked nerve bundle through whole myelopathic cords was in an association with the modified JOA score in CSM cases (r=0.949, p=.015), yet not found with ROI at C4 only (r=0.316, p=.684). Conclusions: The quantitative analysis of fiber tractography is a reliable approach to detect cervical spondylotic myelopathic lesions compared with healthy spinal cords. It could be employed to delineate the severity of CSM. © 2013 Published by Elsevier Inc. All rights reserved.postprin

A combination of functional magnetic resonance imaging and diffusion tensor image to explore structure-function relationship in healthy and myelopathic spinal cord

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Quantitative assessment of column-specific degeneration in cervical spondylotic myelopathy based on diffusion tensor tractography

Purpose Cervical spondylotic myelopathy (CSM) is a common spinal cord disorder in the elderly. Diffusion tensor imaging (DTI) has been shown to be of great value for evaluating the microstructure of nerve tracts in the spinal cord. Currently, the quantitative assessment of the degeneration on the specific tracts in CSM is still rare. The aim of the present study was to use tractography-based quantification to investigate the column-specific degeneration in CSM. Methods A total of 43 volunteers were recruited with written informed consent, including 20 healthy subjects and 23 CSM patients. Diffusion MRI was taken by 3T MRI scanner. Fiber tractography was performed using TrackVis to reconstruct the white matter tracts of the anterior, lateral and posterior column on the bilateral sides. The DTI metrics acquired from tractography, including fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD) and radial diffusivity (RD), were compared between healthy subjects and CSM patients. Results Compared to healthy subjects, FA was found significantly lower in the lateral (Healthy 0.64 ± 0.07 vs. CSM 0.53 ± 0.08) and posterior column (Healthy 0.67 ± 0.08 vs. CSM 0.47 ± 0.08) (p < 0.001), while MD, AD and RD were significantly higher in the anterior, lateral and posterior column in CSM (p < 0.05). Conclusion Loss of microstructural integrity was detected in the lateral and posterior column in CSM. Tractography-based quantification was capable of evaluating the subtle pathological insult within white matter on a column-specific basis, which exhibited potential clinical value for in vivo evaluation of the severity of CSM. © 2014 Springer-Verlag Berlin Heidelberg.postprin

HARDI-ZOOMit protocol improves specificity to microstructural changes in presymptomatic myelopathy

ABSTRACT: Diffusion magnetic resonance imaging (dMRI) proved promising in patients with non-myelopathic degenerative cervical cord compression (NMDCCC), i.e., without clinically manifested myelopathy. Aim of the study is to present a fast multi-shell HARDI-ZOOMit dMRI protocol and validate its usability to detect microstructural myelopathy in NMDCCC patients. In 7 young healthy volunteers, 13 age-comparable healthy controls, 18 patients with mild NMDCCC and 15 patients with severe NMDCCC, the protocol provided higher signal-to-noise ratio, enhanced visualization of white/gray matter structures in microstructural maps, improved dMRI metric reproducibility, preserved sensitivity (SE = 87.88%) and increased specificity (SP = 92.31%) of control-patient group differences when compared to DTI-RESOLVE protocol (SE = 87.88%, SP = 76.92%). Of the 56 tested microstructural parameters, HARDI-ZOOMit yielded significant patient-control differences in 19 parameters, whereas in DTI-RESOLVE data, differences were observed in 10 parameters, with mostly lower robustness. Novel marker the white-gray matter diffusivity gradient demonstrated the highest separation. HARDI-ZOOMit protocol detected larger number of crossing fibers (5–15% of voxels) with physiologically plausible orientations than DTI-RESOLVE protocol (0–8% of voxels). Crossings were detected in areas of dorsal horns and anterior white commissure. HARDI-ZOOMit protocol proved to be a sensitive and practical tool for clinical quantitative spinal cord imaging

Potential use of diffusion tensor imaging in level diagnosis of multilevel cervical spondylotic myelopathy

STUDY DESIGN.: A prospective study on a series of consecutive patients. OBJECTIVE.: To investigate the use of diffusion tensor imaging (DTI) and orientation entropy in level localization in patients diagnosed with multilevel cervical spondylotic myelopathy (CSM). SUMMARY OF BACKGROUND DATA.: Multilevel CSM presents complex neurological signs that make level localization difficult. DTI is recently found to be able to assess the microstructural changes of the white matter caused by cord compression. METHODS.: Sixteen patients with CSM with multilevel compression were recruited. The level(s) responsible for the clinical symptoms were determined by detailed neurological examination, T2-weighted (T2W) magnetic resonance imaging (MRI), and DTI. On T2W MRI, anterior-posterior compression ratio and increased signal intensities were used to determine the affected level(s). The level diagnosis results from T2W MRI, increased signal intensities, DTI, and combination method were correlated to that of neurological examination on a level-to-level basis, respectively. The accuracy, sensitivity, and specificity were calculated. RESULTS.: When correlated with the clinical level determination, the weighted orientation entropy-based DTI analysis was found to have higher accuracy (82.76% vs. 75.86%) and sensitivity (84.62% vs. 76.92%) than those of the anterior-posterior compression ratio. The increased signal intensities have the highest specificity (100.00%) but the lowest accuracy (58.62%) and sensitivity (53.85%). When combined with the level diagnosis result of wOE with that of anterior-posterior compression ratio, it demonstrated the highest accuracy and sensitivity that were 93.10% and 96.15%, respectively, and equal specificity (66.67%) with using them individually. CONCLUSION.: DTI can be a useful tool to determine the pathological spinal cord levels in multilevel CSM. This information from orientation entropy-based DTI analysis, in addition to conventional MRI and clinical neurological assessment, should help spine surgeons in deciding the optimal surgical strategy. Copyright © 2014 Lippincott Williams & Wilkins.postprin

Prognosis for Degenerative Cervical Myelopathy: A Computer Learning Approach on the AOspine Database

Description du problème : La myélopathie cervicale dégénérative (MCD) est une condition particulière liée à l’âge touchant environ 600 adultes par million à travers le monde [1]. Elle résulte d’une compression spontanée de la moelle épinière, causée par une excroissance os-seuse d’une vertèbre cervicale ou bien d’un des disques intervertébraux. Dans les deux cas, la moelle se retrouve comprimée et le patient commence à perdre sensations et contrôle mo-teur. Cette maladie a un très fort impact socio-économique. En e˙et, les patients perdent graduellement l’usage de leurs membres, les empêchant de vivre et de travailler au fur et à mesure que la compression augmente [2] [3]. Cette maladie est principalement diagnostiquée à l’aide d’évaluations cliniques du contrôle moteur et l’utilisation d’imagerie par résonance magnétique. Le problème reste toutefois com-plexe, car la compression peut être asymptomatique, et de faibles compressions sont encore diÿciles à détecter avec les méthodes d’imagerie actuelle. Certains patients peuvent attendre de long mois avant d’avoir un diagnostic [4]. Fort heureusement, une méthode existe afin de limiter la future perte de contrôle moteur : il s’agit de la chirurgie décompressive. Cette chirurgie peut prendre plusieurs formes et est décidée au cas par cas. Il n’existe pas encore de consensus sur les détails et les approches de telles opérations. Toutefois, cette opération compte de très nombreux risques (p. ex., pa-ralysie C5) [3]. La moelle épinière est en e˙et une zone sensible, et le chirurgien doit aller travailler au plus proche de cette autoroute nerveuse vitale. De nombreuses complications peuvent suivre. Bien que le résultat possible semble excéder les risques, la réalité est bien di˙érente, car seulement 40 % des opérations ont un réel impact sur le rétablissement des patients.Le pronostic postopératoire est une tâche compliquée et l’opération est donc choi-sie par défaut. La question demeure sur la possibilité d’établir ce pronostic de manière plus précise. Il n’existe pour l’instant que peu d’étude se penchant sur l’analyse quantitative de données cliniques des patients afin d’obtenir un pronostic postopératoire. L’une des pistes d’exploration serait d’utiliser des méthodes d’intelligence artificielle afin de créer un modèle capable d’aider les chirurgiens dans leur prise de décision. Cela passe par l’exploitation de données cliniques et IRM. Durant les années précédentes, seulement deux études sont appa-rues exploitant des données similaires dans un but identique. Ces études sont principalement centrées sur l’analyse des données cliniques [5] [6]. Objectifs : L’objectif de cette étude est de créer ou d’exploiter un modèle existant afin de vérifier si l’intelligence artificielle pourrait apporter des solutions à ce problème. Parmi les sous-objectifs de ce travail, le but est notamment de vérifier les hypothèses suivantes : • L’exploitation d’IRM conjointement avec les données cliniques apporte de meilleurs résultats • Il est possible d’établir un modèle de pronostic postopératoire pour la myélopathie cervicale dégénérative. Méthodes et matériel : Nous avons à notre disposition une base donnée de 759 sujets pour lesquels nous avons 135 points de données cliniques ainsi que, pour une partie des sujets, des images IRM de modalité T2 et T1 avec les vues axiales et sagittales. Ces informations cliniques regroupent di˙érentes données médicales et courantes sur le patient telles que l’âge, le sexe, etc... Ces données ont été acquises prospectivement durant une précédente étude : AOSpine [7]. Ces données proviennent de di˙érents centres et o˙rent donc une bonne hété-rogénéité au niveau du contraste des images, simulant correctement des données réelles. Cela est important pour évaluer la capacité de généralisation du modèle. Ces patients sou˙rent tous de MCD et ont été opérés dans les di˙érents centres. Parmi ces données, nous avons di˙érents scores d’évaluation de leur capacité de contrôle moteur ainsi que de leur sensation (MJOa,SF6D, . . . ). Ces scores cliniques ont été établis avant l’opération ainsi que 6, 12 et 24 mois après. La di˙érence entre le score préopératoire et le score postopératoire servira de cible. Le score le plus important semble être celui établi sur l’échelle de la modified japanese orthopedic association (MJOa). La di˙érence entre le score préopératoire et le score postopé-ratoire pourra être classifiée en 2 catégories selon la di˙érence minimale significative qui est de 2 points [8]. Une augmentation du score de 2 traduirait donc une amélioration de l’état des patients après chirurgie. Les données contiennent également des informations sur l’opération subie par le patient qui ne sont a priori pas disponibles dans le cadre de pronostic préopératoire. Toutefois, cela pourrait être utile pour étudier l’impact de ces données sur les performances de notre modèle prédictif. Les données cliniques ont tout d’abord été manuellement analysées afin d’essayer d’en ex-traire uniquement les données importantes ainsi que de retirer les données postopératoires dans un premier temps. Cela a permis d’établir un score de base sur un modèle classique type «random forest» fourni dans le package «scikit-learn» de python. Plusieurs modèles de machine learning ont alors été testés pour établir un score maximum atteignable avec ces données. Nous avons également ajouté les données opératoires dans nos modèles afin d’éva-luer leur impact sur les performances du modèle. Par la suite, di˙érents modèles de réseaux de neurones artificiels, principalement convolu-tionnels,ont été créées afin d’e˙ectuer l’analyse automatique des images IRM. Ces images n’étaient pas les images originales, mais le résultat d’un prétraitement à l’aide de la «spinal cord toolbox» [9]. Di˙érents modèles furent établis : le premier utilisait uniquement les IRM T2 sagittal, le suivant les IRM T2 et T1 sagittal, et le dernier fonctionnait les données T1 et T2 sagittales ainsi que les données cliniques. La partie du réseau traitant les données cliniques fut également testée seule pour vérifier ses performances face au modèle de machine learning établi à l’étape précédente. Le pipeline donne une précision de prédiction de 72,5 % ( soit une amélioration de 8 % par rapport a la baseline) avec une aire sous la courbe (ASC) de 0,73 pour le modèle basé unique-ment sur des données cliniques. Toutefois, cela dépend fortement de la quantité de données disponibles. Les modèles d’apprentissage profond ont tendance à overfit ou underfit les don-nées montrant un manque de généralisabilité du modèle, ce qui pourrait s’expliquer par le nombre réduit d’IRM disponibles. L’ajout des données extraites semble fournir au modèle une plus grande capacité puisque l’amélioration par rapport à la baseline atteint 8 % avec une précision de 65,2 % et une ASC de 0,69 avec moins de sujets que le premier modèle.----------ABSTRACT Description of the problem: Spinal injuries may impair patients’ motor control as the spinal cord represents a nervous highway connecting the brain and the limb. Some of these in-juries arise from accidents others occur progressively; such includes Degenerative Cervical Myelopathy (DCM). Cervical myelopathy is caused by the compression of the spinal cord by an outgrowth of a vertebral body or intervertebral disc, yielding symptoms such as sen-sorimotor dysfunction or pain. Cervical myelopathy is degenerative, which implies that it only gets worse as time goes by, and the compression increases. This condition is a cause of surgery among 40 adults per million yearly [1]. The diagnosis for this condition is made possible through clinical motor skill testing and magnetic resonance imaging (MRI). However, diagnosis is still a complex problem as the compression can be asymptomatic or, in some cases, not easily visible in MR images at its early stages. The diagnosis can take months, if not years, for some patients. [4] The current study proposes a decompressive surgery which aims at removing the object causing the compression, or at least, part of it. The goal here is to avoid further compression of the spinal cord and alleviate the existing ones. This operation can take various forms (anterior or posterior approach, bone graft, bone fusion) and includes many risks (e.g., C5 palsy) [3] as this touches the spinal cord, that is, highly sensitive and important to the body. The details of each surgery are then determined case by case by the surgeon as no consensus on the aspects of such operation exists [10]. This exposes a complicated scenario in predicting the outcome of the surgery. Even though the benefits seem to outweigh the risk, the operation is only successful in 35 % [11] of the cases. This success rate is based on the improvement of the patients’ sensorimotor skills. As it is complex to predict the outcome, surgery is often seen as a default option; however, there is an open question about the possibility of predicting the outcome of the surgery. To the best of my knowledge, only a handful of studies that utilize quantitative clinical data analysis in predicting post-surgery prognosis exist. One of the leading techniques is the use of data science and artificial intelligence to design a model that will be able to establish this prognosis and assist surgeons in the decision process with AO spine [7] clinical data. The first study exploring this concept was published in 2019 [5], and, since then, only two studies have been conducted to improve on the methodology [6]. These studies mainly focus on the analysis of clinical data.Objectives: Primary Goal: The primary goal of this study is to develop a model based on artificial intelligence (AI) that can be used in patient prognosis and treatment of DCM The sub-objectives for the study are: • To establish the eÿcacy of using MRI in conjunction with clinical data in providing better results in treatment and prognosis. • To establish a postoperative prognostic model for DCM. Material and method: The database consists of 769 subjects, most of whom have T2 and T1 MRI images with an axial and sagittal view and 135 clinical data points. These data was acquired prospectively during the AOSpine study. These data came from numerous centers and o˙er a functional heterogeneity in image contrast, making it close to a real-life scenario. The inclusion criteria for patients used as participants in this study were to be su˙ering from DCM and have undergone surgery. Among these data, we have di˙erent clinical evaluations of their sensorimotor capacities according to various scales (MJOa, SF6D. . . ). These scores were established before the operation as well as 6, 12, and 24 months after the surgery. The goal is to predict the di˙erence between pre-surgery and post-surgery scores. We evaluated which score is the most relevant among the three measures at 6, 12, and 24 months. This target can be classified into two categories according to the minimum clinically significant di˙erence [8], which is two. The Modified Japanese Orthopedic Association (MJOA) has been used in this study to record and gauge the results. An increase of 2 points or more would, therefore, reflect an improvement in the condition of the patient after the surgery. The data also contain information from the surgery performed on the patient, which isn’t available in the preoperative prognosis. However, this may be useful to study the impact of these data on the performance of our predictive model. Three approaches were tested in order to try to improve on the current prognosis. The first one aims at exploiting clinical data, the second one aimed at leveraging deep learning method to use images as well as clinical data as input, the third one exploit features extracted from Magnetic Resonance (MR) images. Clinical data was processed through machine learning. This was done after a pre-processing step aimed at removing the non-relevant features in order to get the best results possible. To process available MRI data jointly with clinical data, two di˙erent strategies were implemented. The first one is geared towards the use of deep learning and the exploita-tion of artificial neural networks, which were fed pre-processed sagittal images and clinical data. The models used were a Resnet and a custom model to use both T1w and T2w MR im-ages. The model is based on three di˙erent modules. Two of them are similar and were used to process and encode features from the image. They were created with convolutional filter and inception modules. The second one relies on feature extraction from the axial images through a semi-automatic processing pipeline. These features were then added to the existing clinical one to improve the ability of the model to generalize to the unseen cases. All models were tested for accuracy on unseen data. Data were split between training, validation, and testing (80%,10%,10%, respectively). Results show an accuracy of 72.5 % ( 8 % improvement from the baseline) with an area under the curve (AUC) of 0.75 for the model-based solely on clinical data. This is, however, heavily dependent on the quantity of available data. Deep learning models tend to overfit or underfit the data showing a lack of generalizability from the model, which could be explained by the reduced number of available MRI. The added extracted feature seems to provide the model with valuable insight as the improvement from the baseline reaches 8 % with an accuracy of 65.2% and an AUC of 0.68 with less subject than the first model

Magnetisation transfer imaging in the study of early relapsing-remitting multiple sclerosis.

Multiple sclerosis is a common cause of neurological disability in the young adult, but, at present, disease modifying medication may have little, if any, effect upon long term clinical impairment. For this reason, there is a continuing need to understand the mechanisms that lead to long term disability and - in the context of clinical trials - to develop reliable surrogate markers of disease progression. It may be especially useful to describe the early evolution of abnormality within normal-appearing white matter (NAWM) and grey matter firstly because pathology in early MS may be a key determinant of later disability, and secondly because there is only a modest relationship between white matter lesion load and clinical impairment. This thesis presents a series of studies, investigating NAWM and grey matter abnormality in a cohort of patients with early relapsing-remitting MS. A key question was whether MRI measures were able to detect accumulating abnormality in NAWM and grey matter early in the clinical course. An initial investigation, using Ti relaxation time estimation, did not detect strong evidence for a net change over time. Attention was therefore turned to the magnetisation transfer ratio (MTR) and results from a series of studies, investigating NAWM, grey matter and thalamic MTR abnormalities in early relapsing-remitting MS are presented. Of note, a clinically relevant reduction in grey matter MTR was apparent, and there was evidence for increasing MTR abnormality in the grey matter, NAWM and the thalamus over a two year follow-up period. In part three of this thesis, a model for the MT effect is used to estimate two underlying MT parameters - the semi-solid proton fraction (/) and the semi-solid T2 (T2B) in sixty patients with clinically-definite MS. The aim was to assess the clinical relevance of these novel parameters

Orientation entropy analysis of diffusion tensor in healthy and myelopathic spinal cord

The majority of nerve fibers in the spinal cord run longitudinally, playing an important role in connecting the brain to the peripheral nerves. There is a growing interest in applying diffusion tensor imaging (DTI) to the evaluation of spinal cord microarchitecture. The current study sought to compare the organization of longitudinal nerve fibers between healthy and myelopathic spinal cords using entropy-based analysis of principal eigenvector mapping. A total of 22 subjects were recruited, including 14 healthy subjects, seven cervical myelopathy (CM) patients with single-level compression, and one patient suffering from multi-level compression. Diffusion tensor magnetic resonance (MR) images of the cervical spinal cord were obtained using a pulsed gradient, spin-echo echo-planar imaging (SE-EPI) sequence with a 3T MR system. Regions of interest (ROIs) were drawn manually to cover the spinal cord, and Shannon entropy was calculated in principal eigenvector maps. The results revealed no significant differences in orientation entropy values along the whole length of cervical spinal cord in healthy subjects (C2-3: 0.73 ± 0.05; C3-4: 0.71 ± 0.07; C4-5: 0.72 ± 0.048; C5-6: 0.71 ± 0.07; C6-7: 0.72 ± 0.07). In contrast, orientation entropy values in myelopathic cord were significantly higher at the compression site (0.91 ± 0.03), and the adjacent levels (above: 0.85 ± 0.03; below: 0.83 ± 0.05). This study provides a novel approach to analyze the orientation information in diffusion MR images of healthy and diseased spinal cord. These results indicate that orientation entropy can be applied to determine the contribution of each compression level to the overall disorganization of principal nerve tracts of myelopathic spinal cord in cases with multi-level compression. © 2011 Elsevier Inc.link_to_subscribed_fulltex