Population-averaged MRI atlases for automated image processing and assessments of lumbar paraspinal muscles

The assessment of paraspinal muscle epimuscular fat in participants with and without low back pain: A case-control study

It remains unclear whether paraspinal muscle fatty infiltration in low back pain (LBP) is i) solely intramuscular, ii) is lying outside the epimysium between the muscle and fascial plane (epimuscular) or iii) or combination of both, as imaging studies often use different segmentation protocols that are not thoroughly described. Epimuscular fat possibly disturbs force generation of paraspinal muscles, but is seldomly explored. This project aimed to 1) compare epimuscular fat in participants with and without chronic LBP, and 2) determine whether epimuscular fat is different across lumbar spinal levels and associated with BMI, age, sex and LBP status, duration or intensity. Fat and water lumbosacral MRIs of 50 chronic LBP participants and 41 healthy controls were used. The presence and extent of epimuscular fat for the paraspinal muscle group (erector spinae and multifidus) was assessed using a qualitative score (0-5 scale; 0 = no epimuscular fat and 5 = epimuscular fat present along the entire muscle) and quantitative manual segmentation method. Chi-squared tests evaluated associations between qualitative epimuscular fat ratings and LBP status at each lumbar level. Bivariate and partial spearman's rho correlation assessed relationships between quantitative and qualitative epimuscular fat with participants' characteristics. Epimuscular fat was more frequent at the L4-L5 (X2 = 13.781, p = 0.017) and L5-S1 level (X2 = 27.825, p < 0.001) in participants with LBP compared to controls, which was not found for the higher lumbar levels. The total qualitative score (combined from all levels) showed a significant positive correlation with BMI, age, sex (female) and LBP status (r = 0.23-0.55; p < 0.05). Similarly, the total area of epimuscular fat (quantitative measure) was significantly correlated with BMI, age and LBP status (r = 0.26-0.57; p < 0.05). No correlations were found between epimuscular fat and LBP duration or intensity. Paraspinal muscle epimuscular fat is more common in chronic LBP patients. The functional implications of epimuscular fat should be further explored.

PILLAR: ParaspInaL muscLe segmentAtion pRoject - a comprehensive online resource to guide manual segmentation of paraspinal muscles from magnetic resonance imaging

BACKGROUND

There is an increasing interest in assessing paraspinal morphology and composition in relation to low back pain (LBP). However, variations in methods and segmentation protocols contribute to the inconsistent findings in the literature. We present an on-line resource, the ParaspInaL muscLe segmentAtion pRoject (PILLAR, https://projectpillar.github.io/ ), to provide a detailed description and visual guide of a segmentation protocol by using the publicly available ITK-SNAP software and discuss related challenges when performing paraspinal lumbar muscles segmentations from magnetic resonance imaging (MRI).

METHODS

T2-weighted and corresponding fat-water IDEAL axial MRI from 3 males and 3 females (2 chronic LBP and 1 control for each sex) were used to demonstrate our segmentation protocol for each lumbar paraspinal muscle (erector spinae, lumbar multifidus, quadratus lumborum and psoas) and lumbar spinal level (L1-L5).

RESULTS

Proper segmentation requires an understanding of the anatomy of paraspinal lumbar muscles and the variations in paraspinal muscle morphology and composition due to age, sex, and the presence of LBP or related spinal pathologies. Other challenges in segmentation includes the presence and variations of intramuscular and epimuscular fat, and side-to-side asymmetry.

CONCLUSION

The growing interest to assess the lumbar musculature and its role in the development and recurrence of LBP prompted the need for comprehensive and easy-to-follow resources, such as the PILLAR project to reduce inconsistencies in segmentation protocols. Standardizing manual muscle measurements from MRI will facilitate comparisons between studies while the field is progressively moving towards the automatization of paraspinal muscle measurements for large cohort studies.

The association between morphological characteristics of paraspinal muscle and spinal disorders

BACKGROUND

Spinal disorders affect millions of people worldwide, and can cause significant disability and pain. The paraspinal muscles, located on either side of the spinal column, play a crucial role in the movement, support, and stabilization of the spine. Many spinal disorders can affect paraspinal muscles, as evidenced by changes in their morphology, including hypertrophy, atrophy, and degeneration.

OBJECTIVES

The objectives of this review were to examine the current literature on the relationship between the paraspinal muscles and spinal disorders, summarize the methods used in previous studies, and identify areas for future research.

METHODS

We reviewed studies on the morphological characteristics of the paravertebral muscle and discussed their relationship with spinal disorders, as well as the current limitations and future research directions.

RESULTS

The paraspinal muscles play a critical role in spinal disorders and are important targets for the treatment and prevention of spinal disorders. Clinicians should consider the role of the paraspinal muscles in the development and progression of spinal disorders and incorporate assessments of the paraspinal muscle function in clinical practice.

CONCLUSION

The findings of this review highlight the need for further research to better understand the relationship between the paraspinal muscles and spinal disorders, and to develop effective interventions to improve spinal health and reduce the burden of spinal disorders.

Paraspinal muscle imaging measurements for common spinal disorders: review and consensus-based recommendations from the ISSLS degenerative spinal phenotypes group

PURPOSE

Paraspinal muscle imaging is of growing interest related to improved phenotyping, prognosis, and treatment of common spinal disorders. We reviewed issues related to paraspinal muscle imaging measurement that contribute to inconsistent findings between studies and impede understanding.

METHODS

Three key contributors to inconsistencies among studies of paraspinal muscle imaging measurements were reviewed: failure to consider possible mechanisms underlying changes in paraspinal muscles, lack of control of confounding factors, and variations in spinal muscle imaging modalities and measurement protocols. Recommendations are provided to address these issues to improve the quality and coherence of future research.

RESULTS

Possible pathophysiological responses of paraspinal muscle to various common spinal disorders in acute or chronic phases are often overlooked, yet have important implications for the timing, distribution, and nature of changes in paraspinal muscle. These considerations, as well as adjustment for possible confounding factors, such as sex, age, and physical activity must be considered when planning and interpreting paraspinal muscle measurements in studies of spinal conditions. Adoption of standardised imaging measurement protocols for paraspinal muscle morphology and composition, considering the strengths and limitations of various imaging modalities, is critically important to interpretation and synthesis of research.

CONCLUSION

Study designs that consider physiological and pathophysiological responses of muscle, adjust for possible confounding factors, and use common, standardised measures are needed to advance knowledge of the determinants of variations or changes in paraspinal muscle and their influence on spinal health.

Statistical morphological analysis reveals characteristic paraspinal muscle asymmetry in unilateral lumbar disc herniation

Growing evidence suggests an association of lumbar paraspinal muscle morphology with low back pain (LBP) and lumbar pathologies. Unilateral spinal disorders provide unique models to study this association, with implications for diagnosis, prognosis, and management. Statistical shape analysis is a technique that can identify signature shape variations related to phenotypes but has never been employed in studying paraspinal muscle morphology. We present the first investigation using this technique to reveal disease-related paraspinal muscle asymmetry, using MRIs of patients with a single posterolateral disc herniation at the L5-S1 spinal level and unilateral leg pain. Statistical shape analysis was conducted to reveal disease- and phenotype-related morphological variations in the multifidus and erector spinae muscles at the level of herniation and the one below. With the analysis, shape variations associated with disc herniation were identified in the multifidus on the painful side at the level below the pathology while no pathology-related asymmetry in cross-sectional area (CSA) and fatty infiltration was found in either muscle. The results demonstrate higher sensitivity and spatial specificity for the technique than typical CSA and fatty infiltration measures. Statistical shape analysis holds promise in studying paraspinal muscle morphology to improve our understanding of LBP and various lumbar pathologies.

Analysis of the Curative Effect of Posterior Approach on Lumbar Brucellar Spondylitis with Abscess through Magnetic Resonance Imaging under Improved Watershed Algorithm

To explore the performance of improved watershed algorithm in processing magnetic resonance imaging (MRI) images and the effect of the processed images on the treatment of lumbar brucellar spondylitis (BS) with abscess by the posterior approach, the watershed algorithm was improved by adding constraints such as noise reduction and regional area attribute. 50 patients with abscessed lumbar disc herniation admitted to the hospital from January 2018 to January 2019 were selected, and all of them were examined by MRI. They were rolled into two groups in random. The treatment group (n = 25) accepted surgery with the aid of MRI images processed by the improved watershed algorithm, and the control group (Ctrl group) (n = 25) accepted surgery with the aid of unprocessed MRI images. The improved watershed algorithm can accurately segment the spine, and the segmentation results were relatively excellent. In contrast with the unprocessed MRI image, that processed by the improved watershed algorithm had a positive effect on the operation. In contrast with the Ctrl group, the visual analogue scale pain score (VAS), oxygen desaturation index (ODI), erythrocyte sedimentation rate (ESR), and high sensitivity C-reactive protein (CRP) were obviously lower (p < 0.05). The improved watershed algorithm proposed performs better in MRI image processing and can effectively enhance the resolution of MRI images. At the same time, the posterior approach has a good effect in the treatment of lumbar BS with abscess and is worthy of clinical promotion.

A Deep-Learning-Based, Fully Automated Program to Segment and Quantify Major Spinal Components on Axial Lumbar Spine Magnetic Resonance Images

OBJECTIVE

The paraspinal muscles have been extensively studied on axial lumbar magnetic resonance imaging (MRI) for better understanding of back pain; however, the acquisition of measurements mainly relies on manual segmentation, which is time consuming. The study objective was to develop and validate a deep-learning-based program for automated acquisition of quantitative measurements for major lumbar spine components on axial lumbar MRIs, the paraspinal muscles in particular.

METHODS

This study used a cross-sectional observational design. From the Hangzhou Lumbar Spine Study, T2-weighted axial MRIs at the L4-5 disk level of 120 participants (aged 54.8 years [SD = 15.0]) were selected to develop the deep-learning-based program Spine Explorer (Tulong). Another 30 axial lumbar MRIs were automatically measured by Spine Explorer and then manually measured using ImageJ to acquire quantitative size and compositional measurements for bilateral multifidus, erector spinae, and psoas muscles; the disk; and the spinal canal. Intersection-over-union and Dice score were used to evaluate the performance of automated segmentation. Intraclass coefficients and Bland-Altman plots were used to examine intersoftware agreements for various measurements.

RESULTS

After training, Spine Explorer (Tulong) measures an axial lumbar MRI in 1 second. The intersections-over-union were 83.3% to 88.4% for the paraspinal muscles and 92.2% and 82.1% for the disk and spinal canal, respectively. For various size and compositional measurements of paraspinal muscles, Spine Explorer (Tulong) was in good agreement with ImageJ (intraclass coefficient = 0.85 to approximately 0.99).

CONCLUSION

Spine Explorer (Tulong) is automated, efficient, and reliable in acquiring quantitative measurements for the paraspinal muscles, the disk, and the canal, and various size and compositional measurements were simultaneously obtained for the lumbar paraspinal muscles.

IMPACT

Such an automated program might encourage further epidemiological studies of the lumbar paraspinal muscle degeneration and enhance paraspinal muscle assessment in clinical practice.

Quantitative identification and segmentation repeatability of thoracic spinal muscle morphology

OBJECTIVE

MRI derived spinal-muscle morphology measurements have potential diagnostic, prognostic, and therapeutic applications in spinal health. Muscle morphology in the thoracic spine is an important determinant of kyphosis severity in older adults. However, the literature on quantification of spinal muscles to date has been limited to cervical and lumbar regions. Hence, we aim to propose a method to quantitatively identify regions of interest of thoracic spinal muscle in axial MR images and investigate the repeatability of their measurements.

METHODS

Middle (T4-T5) and lower (T8-T9) thoracic levels of six healthy volunteers (age 26 ± 6 years) were imaged in an upright open scanner (0.5T MROpen, Paramed, Genoa, Italy). A descriptive methodology for defining the regions of interest of trapezius, erector spinae, and transversospinalis in axial MR images was developed. The guidelines for segmentation are laid out based on the points of origin and insertion, probable size, shape, and the position of the muscle groups relative to other recognizable anatomical landmarks as seen from typical axial MR images. 2D parameters such as muscle cross-sectional area (CSA) and muscle position (radius and angle) with respect to the vertebral body centroid were computed and 3D muscle geometries were generated. Intra and inter-rater segmentation repeatability was assessed with intraclass correlation coefficient (ICC (3,1)) for 2D parameters and with dice coefficient (DC) for 3D parameters.

RESULTS

Intra and inter-rater repeatability for 2D and 3D parameters for all muscles was generally good/excellent (average ICC (3,1) = 0.9 with ranges of 0.56-0.98; average DC = 0.92 with ranges from 0.85-0.95).

CONCLUSION

The guidelines proposed are important for reliable MRI-based measurements and allow meaningful comparisons of muscle morphometry in the thoracic spine across different studies globally. Good segmentation repeatability suggests we can further investigate the effect of posture and spinal curvature on muscle morphology in the thoracic spine.

Inconsistent descriptions of lumbar multifidus morphology: A scoping review

Background

Lumbar multifidus (LM) is regarded as the major stabilizing muscle of the spine. The effects of exercise therapy in low back pain (LBP) are attributed to this muscle. A current literature review is warranted, however, given the complexity of LM morphology and the inconsistency of anatomical descriptions in the literature.

Methods

Scoping review of studies on LM morphology including major anatomy atlases. All relevant studies were searched in PubMed (Medline) and EMBASE until June 2019. Anatomy atlases were retrieved from multiple university libraries and online. All studies and atlases were screened for the following LM parameters: location, imaging methods, spine levels, muscle trajectory, muscle thickness, cross-sectional area, and diameter. The quality of the studies and atlases was also assessed using a five-item evaluation system.

Results

In all, 303 studies and 19 anatomy atlases were included in this review. In most studies, LM morphology was determined by MRI, ultrasound imaging, or drawings – particularly for levels L4–S1. In 153 studies, LM is described as a superficial muscle only, in 72 studies as a deep muscle only, and in 35 studies as both superficial and deep. Anatomy atlases predominantly depict LM as a deep muscle covered by the erector spinae and thoracolumbar fascia. About 42% of the studies had high quality scores, with 39% having moderate scores and 19% having low scores. The quality of figures in anatomy atlases was ranked as high in one atlas, moderate in 15 atlases, and low in 3 atlases.

Discussion

Anatomical studies of LM exhibit inconsistent findings, describing its location as superficial (50%), deep (25%), or both (12%). This is in sharp contrast to anatomy atlases, which depict LM predominantly as deep muscle. Within the limitations of the self-developed quality-assessment tool, high-quality scores were identified in a majority of studies (42%), but in only one anatomy atlas.

Conclusions

We identified a lack of standardization in the depiction and description of LM morphology. This could affect the precise understanding of its role in background and therapy in LBP patients. Standardization of research methodology on LM morphology is recommended. Anatomy atlases should be updated on LM morphology.

Paraspinal Muscle Segmentation Based on Deep Neural Network

The accurate segmentation of the paraspinal muscle in Magnetic Resonance (MR) images is a critical step in the automated analysis of lumbar diseases such as chronic low back pain, disc herniation and lumbar spinal stenosis. However, the automatic segmentation of multifidus and erector spinae has not yet been achieved due to three unusual challenges: (1) the muscle boundary is unclear; (2) the gray histogram distribution of the target overlaps with the background; (3) the intra- and inter-patient shape is variable. We propose to tackle the problem of the automatic segmentation of paravertebral muscles using a deformed U-net consisting of two main modules: the residual module and the feature pyramid attention (FPA) module. The residual module can directly return the gradient while preserving the details of the image to make the model easier to train. The FPA module fuses different scales of context information and provides useful salient features for high-level feature maps. In this paper, 120 cases were used for experiments, which were provided and labeled by the spine surgery department of Shengjing Hospital of China Medical University. The experimental results show that the model can achieve higher predictive capability. The dice coefficient of the multifidus is as high as 0.949, and the Hausdorff distance is 4.62 mm. The dice coefficient of the erector spinae is 0.913 and the Hausdorff distance is 7.89 mm. The work of this paper will contribute to the development of an automatic measurement system for paraspinal muscles, which is of great significance for the treatment of spinal diseases.

Are Magnetic Resonance Imaging Technologies Crucial to Our Understanding of Spinal Conditions?

Persistent spinal (traumatic and nontraumatic) pain is common and contributes to high societal and personal costs globally. There is an acknowledged urgency for new and interdisciplinary approaches to the condition, and soft tissues, including skeletal muscles, the spinal cord, and the brain, are rightly receiving increased attention as important biological contributors. In reaction to the recent suspicion and questioned value of imaging-based findings, this paper serves to recognize the promise that the technological evolution of imaging techniques, and particularly magnetic resonance imaging, is allowing in characterizing previously less visible morphology. We emphasize the value of quantification and data analysis of several contributors in the biopsychosocial model for understanding spinal pain. Further, we highlight emerging evidence regarding the pathobiology of changes to muscle composition (eg, atrophy, fatty infiltration), as well as advancements in neuroimaging and musculoskeletal imaging techniques (eg, fat-water imaging, functional magnetic resonance imaging, diffusion imaging, magnetization transfer imaging) for these important soft tissues. These noninvasive and objective data sources may complement known prognostic factors of poor recovery, patient self-report, diagnostic tests, and the "-omics" fields. When combined, advanced "big-data" analyses may assist in identifying associations previously not considered. Our clinical commentary is supported by empirical findings that may orient future efforts toward collaborative conversation, hypothesis generation, interdisciplinary research, and translation across a number of health fields. Our emphasis is that magnetic resonance imaging technologies and research are crucial to the advancement of our understanding of the complexities of spinal conditions. J Orthop Sports Phys Ther 2019;49(5):320-329. Epub 26 Mar 2019. doi:10.2519/jospt.2019.8793.