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The assessment of the postoperative spinal alignment: MRI adds up on accuracy.

ABSTRACT: INTRODUCTION: The sagittal profile of conventionally and surgically treated scoliotic spines is usually analyzed via lateral views of whole-spine X-rays in an upright position. Due to a more hypokyphotic configuration of scoliotic spines, the view onto the upper thoracic vertebrae is often difficult. We investigated whether additional supine MRI measurement supports valid kyphosis angle measurement. PATIENTS AND METHODS: Twenty patients with either short (n = 10, Halm-Zielke, VDS) or long spondylodesis (n = 10, dorsoventral) were assessed 5 years after surgery with standing radiographs and supine whole-spine MRI. RESULTS: Up to 90% of the upper thoracic vertebrae were invisible on radiographs, whereas MRI allowed visibility of almost many vertebrae. No significant difference in thoracal kyphosis angles could be observed in the comparison of X-ray and MRI data. CONCLUSION: Thoracal kyphosis measurement of postoperative spines in MRI is a valid diagnostic tool with reliability comparable to that of X-ray. These results cannot be transferred to lumbar lordosis measurement and transferred only partly to coronal COBB angle measurement.

SUBMITTER: Bernstein P

PROVIDER: S-EPMC3326118 | biostudies-literature | 2012 Apr

REPOSITORIES: biostudies-literature

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The assessment of the postoperative spinal alignment: MRI adds up on accuracy.

Bernstein Peter P Hentschel Susanne S Platzek Ivan I Zwingenberger Stefan S Weigel Sebastian S Hühne Sebastian S Seifert Jens J

European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society 20111217 4

<h4>Introduction</h4>The sagittal profile of conventionally and surgically treated scoliotic spines is usually analyzed via lateral views of whole-spine X-rays in an upright position. Due to a more hypokyphotic configuration of scoliotic spines, the view onto the upper thoracic vertebrae is often difficult. We investigated whether additional supine MRI measurement supports valid kyphosis angle measurement.<h4>Patients and methods</h4>Twenty patients with either short (n = 10, Halm-Zielke, VDS) o ...[more]

PMID: 22179752

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Project description:BackgroundThe assessment of spinal stiffness by manual palpation in clinical settings has demonstrated both poor accuracy and reliability. More recently, mechanical methods for assessment of spinal stiffness have demonstrated superior accuracy and reliability. However, mechanical methods of spinal stiffness assessment can be expensive, time consuming and/or unsuited to clinical practice. While a new device has been designed to address these issues (VerteTrack), its benchtop performance remains unknown.AimTo measure the bench-top performance of VerteTrack.MethodsA series of laboratory-based experiments were conducted in February 2018 to investigate the accuracy (precision and bias) of load and displacement measurements obtained by VerteTrack and then were compared against an appropriate reference standard. Measurements of both multiple-level continuous assessment (multiple spinal levels measured), and single-level assessment (single spinal level measured) were performed on a viscoelastic foam medium (AIREX® balance beam, Switzerland) and the resulting stiffness calculated.ResultsVerteTrack demonstrated high precision at all loads and displacements. There was minimal systematic measurement bias identified for applied versus reference load (mean bias = - 0.123 N; 95%CI - 0.182 to 0.428 N, p < .001), and no systematic measurement bias for measured versus reference displacement (mean difference = 0.02 mm; 95%CI - 0.09 to 0.14 mm, p < .001). The magnitude of stiffness obtained during multiple-level continuous assessment was on average 0.25 N/mm (2.79%) less than that for single-level assessment (95%CI - 0.67 to 0.17 N/mm, p < .001).ConclusionsVerteTrack demonstrated high accuracy (high precision, low bias) under bench-top conditions. The difference in stiffness found between multiple versus single spinal levels should be considered in the research context, but is unlikely to be clinically relevant. The results of this study demonstrate that VerteTrack may be suitable for both single and multi-level spinal stiffness measurements in-vivo.

Project description:Early detection of Alzheimer's disease (AD) is essential for developing effective treatments. Neuroimaging techniques like Magnetic Resonance Imaging (MRI) have the potential to detect brain changes before symptoms emerge. Structural MRI can detect atrophy related to AD, but it is possible that functional changes are observed even earlier. We therefore examined the potential of Magnetoencephalography (MEG) to detect differences in functional brain activity in people with Mild Cognitive Impairment (MCI) - a state at risk of early AD. We introduce a framework for multimodal combination to ask whether MEG data from a resting-state provides complementary information beyond structural MRI data in the classification of MCI versus controls. More specifically, we used multi-kernel learning of support vector machines to classify 163 MCI cases versus 144 healthy elderly controls from the BioFIND dataset. When using the covariance of planar gradiometer data in the low Gamma range (30-48 Hz), we found that adding a MEG kernel improved classification accuracy above kernels that captured several potential confounds (e.g., age, education, time-of-day, head motion). However, accuracy using MEG alone (68%) was worse than MRI alone (71%). When simply concatenating (normalized) features from MEG and MRI into one kernel (Early combination), there was no advantage of combining MEG with MRI versus MRI alone. When combining kernels of modality-specific features (Intermediate combination), there was an improvement in multimodal classification to 74%. The biggest multimodal improvement however occurred when we combined kernels from the predictions of modality-specific classifiers (Late combination), which achieved 77% accuracy (a reliable improvement in terms of permutation testing). We also explored other MEG features, such as the variance versus covariance of magnetometer versus planar gradiometer data within each of 6 frequency bands (delta, theta, alpha, beta, low gamma, or high gamma), and found that they generally provided complementary information for classification above MRI. We conclude that MEG can improve on the MRI-based classification of MCI.

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The assessment of the postoperative spinal alignment: MRI adds up on accuracy.

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The assessment of the postoperative spinal alignment: MRI adds up on accuracy.

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