A fast, accurate, and reliable reconstruction method of the lumbar spine vertebrae using positional MRI

Sequence based local-global information fusion framework for vertebrae detection under pathological and FOV variation challenges

Automated vertebrae detection (identification and localization) aims to identify vertebrae and locate their centroids in medical images, which is a critical step of spinal computer-aided systems. However, due to unpredictable field-of-view and various pathology cases, the image content is diverse and the vertebral morphology can be abnormal in a variety of ways, which challenges the designed systems. In this paper, we propose an effective sequence-based framework robust to various tough cases for accurate vertebrae identification and localization. Our method consists of three sub-modules: (1) Local Feature Extraction (LFE) module designs a shape-compatible category-balanced sampler to collect patches to train a convolution neural network, which extracts representative local features and generates score maps. (2) Discriminative Sequential Image Description (DSID) module proposes a node screening strategy for reliable vertebral feature sequence construction based on feature maps and score maps. This effectively prevents false positives and false negatives in light-weighted dense prediction schemes and fuses local features into a hierarchical discriminative description of given images. (3) Spinal Pattern Exploitation (SPE) module designs an end-balanced relative position learning scheme to fuse hierarchical local-global information for comprehensively exploiting spinal patterns to overcome the FOV and pathological variation challenges in vertebrae detection. Extensive experiments on a challenging dataset consisting of 450 spinal MRIs show that the identification rate of FSDF reaches 0.974 ±0.025 and the localization error is only 4.742 ±2.928 pixels, which demonstrates the effectiveness of our method with pathological and field-of-view variations and its superiority over other state-of-the-art methods.

Please be upstanding - A narrative review of evidence comparing upright to supine lumbar spine MRI

OBJECTIVES

The objective of the review was to examine the evidence comparing upright to supine MRI of the lumbar spine.

KEY FINDINGS

A literature search identified 14 articles comparing data where subjects had been scanned in both supine and upright positions on the same scanner. Lumbar spine anatomy is dynamic and therefore subject to morphological changes when transitioning from the supine to the upright position. There is strong evidence to suggest structural changes in spinal morphology due to radiographic positioning, and that upright positioning is better for evaluating spondylolisthesis.

CONCLUSION

It has been demonstrated that the scanning position is important in the outcome of the MRI examination of the lumbar spine. With this in mind, it would be beneficial for guidance to be written and adopted to improve the consistency and quality of scanning.

IMPLICATIONS FOR PRACTICE

As upright MRI occupies a niche in the scanning sector, many professionals are unaware of its capabilities. This article aims to increase awareness of the use of upright MRI in evaluating the lumbar spine.

The Effectiveness of Regulations and Behavioral Interventions on Head Impacts and Concussions in Youth, High-School, and Collegiate Football: A Systematized Review

The purpose of this study was to assess the effectiveness of regulations and behavioral interventions on head impacts and concussions in youth, high-school, and collegiate football, using a systematic search strategy to identify relevant literature. Six databases were searched using key search terms related to three categories: football, head-injuries, and interventions. Studies that met inclusion criteria were included in the study and underwent data extraction. Twenty articles met inclusion criteria and were included in the final systematized review. Of the 20 included studies, 8 studies evaluated interventions in high-school football, 5 studies evaluated interventions in collegiate football, 6 studies evaluated interventions in youth football, and 1 study evaluated interventions in both, high-school and collegiate football. The four categories of interventions and regulations included rule changes, training, education/instruction/coaching tactical changes, and tackle football alternatives. Studies evaluating the effectiveness of interventions and regulations on reducing head impact exposures or head injuries have shown mixed results. Some regulations may be more effective than others, but methodological design and risk of bias pose limitations to generalize effects.

Assessment of Hip Translation In Vivo in Patients With Femoracetabular Impingement Syndrome Using 3-Dimensional Computed Tomography

Purpose

To determine the 3-dimensional (3D) in vivo hip translation in patients with symptomatic femoroacetabular impingement syndrome (FAIS) using 3D computed tomography (CT) models with the hip in neutral and FABER (flexion, abduction, and the external rotation) positions and to identify patient predictors associated with the degree of hip translation.

Methods

Seventy-eight patients with FAIS and cam lesions underwent CT scans in neutral and FABER positions. Demographics including age, sex, and body mass index (BMI) were recorded for each patient. The cam deformity was characterized both in plain x-ray film and 3D. Translation between both positions was calculated using a validated high-precision 3D-3D registration technique. Univariate and multivariate regression analyses sought factors correlated with translation.

Results

The mean age of the patients included in the analysis was 36.3 ± 9.2 years, with 51% of the study group being female. The mean 3D femoral head center translation was 0.84 ± 0.37 mm, decomposed into vectors on standard anatomical directions as 0.13 ± 0.58 mm medial, 0.10 ± 0.54 mm posterior, and 0.08 ± 0.46 mm inferior. Multivariate analysis demonstrated that total translation was associated with larger alpha angles (β = 0.014; 95% confidence interval [CI] 0.003-0.024; P = .013), and greater BMI (β = 0.033; 95% CI 0.001-0.065; P = .042). Furthermore, posterior–inferior translation was associated with BMI (β = 0.032; 95% CI 0.003-0.061; P = .031), whereas medial–lateral translation is associated with the female sex (β = 0.388; 95% CI 0.124-0.634; P = .002), and smaller head radius (β = –0.068; 95% CI –0.128 to –0.007; P = .029).

Conclusions

As a provocative maneuver, FABER positioning in patients with FAIS resulted in an average measurable translation of the femoral head center in the posterior, medial, and inferior direction. Factors including sex, BMI, and alpha angle predicted the degree of translation.

Clinical Relevance

The current study demonstrates that there is measurable hip translation between the neutral and FABER positions in patients with symptomatic FAIS, which may cause hip microinstability. Furthermore, the study found an association between hip translation and both modifiable and nonmodifiable factors. This may indicate the need for more comprehensive preoperative surgical planning, intraoperative dynamic examination of the hip, and consideration of capsular plication in certain patients.

Joint Vertebrae Identification and Localization in Spinal CT Images by Combining Short- and Long-Range Contextual Information

Automatic vertebrae identification and localization from arbitrary computed tomography (CT) images is challenging. Vertebrae usually share similar morphological appearance. Because of pathology and the arbitrary field-of-view of CT scans, one can hardly rely on the existence of some anchor vertebrae or parametric methods to model the appearance and shape. To solve the problem, we argue that: 1) one should make use of the short-range contextual information, such as the presence of some nearby organs (if any), to roughly estimate the target vertebrae; and 2) due to the unique anatomic structure of the spine column, vertebrae have fixed sequential order, which provides the important long-range contextual information to further calibrate the results. We propose a robust and efficient vertebrae identification and localization system that can inherently learn to incorporate both the short- and long-range contextual information in a supervised manner. To this end, we develop a multi-task 3-D fully convolutional neural network to effectively extract the short-range contextual information around the target vertebrae. For the long-range contextual information, we propose a multi-task bidirectional recurrent neural network to encode the spatial and contextual information among the vertebrae of the visible spine column. We demonstrate the effectiveness of the proposed approach on a challenging data set, and the experimental results show that our approach outperforms the state-of-the-art methods by a significant margin.