MR Imaging of the Lumbar Spine: Relation of Posterior Soft-Tissue Edema-Like Signal and Body Weight

Knowing the material properties of the musculoskeletal soft tissue could be important to develop rehabilitation therapy and surgical procedures. However, there is a lack of devices and information on the viscoelastic properties of soft tissues around the lumbar spine. The goal of this study was to develop a portable quantifying device for providing strain and stress curves of muscles and ligaments around the lumbar spine at various stretching speeds. Each sample was conditioned and applied for 20 repeatable cyclic 5 mm stretch-and-relax trials in the direction and perpendicular direction of the fiber at 2, 3 and 5 mm/s. Our device successfully provided the stress and strain curve of the samples and our results showed that there were significant effects of speed on the young’s modulus of the samples (p < 0.05). Compared to the expensive commercial device, our lower-cost device provided comparable stress and strain curves of the sample. Based on our device and findings, various sizes of samples can be measured and viscoelastic properties of the soft tissues can be obtained. Our portable device and approach can help to investigate young’s modulus of musculoskeletal soft tissues conveniently, and can be a basis for developing a material testing device in a surgical room or various lab environments.

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Coronal STIR sequence, a simple adjustment to routine MRI protocol for extra-spinal sciatica and sciatica-like symptoms

Egyptian Journal of Radiology and Nuclear Medicine ◽

10.1186/s43055-019-0096-6 ◽

2019 ◽

Vol 50 (1) ◽

Author(s):

Rania Zeitoun ◽

Mohammed Saleh Ali Mohieddin

Keyword(s):

Young Adults ◽

Soft Tissue ◽

Mr Imaging ◽

Age Groups ◽

Field Of View ◽

Mri Protocol ◽

Stir Sequence ◽

Tissue Abnormalities ◽

Additional Value ◽

Bone Abnormalities

Abstract Background The value of adding coronal STIR images to MR imaging of sciatica aiming to detect extra-spinal abnormalities. Results Additional coronal STIR images detected extra-spinal abnormalities in 20% of the patients, thereby downgraded the normal studies from 21 to 13%. The extra-spinal abnormalities included bone abnormalities (36.4%), soft tissue abnormalities (4.5%), neurological abnormalities (2.3%), gynecological abnormalities (50%), and miscellaneous (6.8%). In 6.9% of patients, the extra-spinal abnormalities explained the patients’ pain and influenced their management. Extra-spinal causes of pain significantly correlated to positive trauma and neoplasm history, normal routine protocol images, and absent nerve root impingement. Extra-spinal abnormalities were more prevalent in age groups (20–39 years). Conclusion Coronal STIR images (field of view: mid abdomen to the lesser trochanters) identify extra-spinal abnormalities that maybe overlooked on routine MRI protocol. It is of additional value in young adults, trauma, neoplasm, and negative routine images.

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Gravity determines the direction of nerve roots sedimentation in the lumbar spinal canal

BMC Musculoskeletal Disorders ◽

10.1186/s12891-021-04032-y ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Jun Yang ◽

Zhiyun Feng ◽

Nian Chen ◽

Zhenhua Hong ◽

Yongyu Zheng ◽

...

Keyword(s):

Lumbar Spine ◽

Mr Imaging ◽

Spinal Canal ◽

Ventral Side ◽

Lateral Position ◽

Dorsal Side ◽

Mr Images ◽

Nerve Roots ◽

Cross Sectional ◽

Dural Sac

Abstract Objectives To investigate the role of gravity in the sedimentation of lumbar spine nerve roots using magnetic resonance (MR) imaging of various body positions. Methods A total of 56 patients, who suffered from back pain and underwent conventional supine lumbar spine MR imaging, were selected from sanmen hospital database. All the patients were called back to our hospital to perform MR imaging in prone position or lateral position. Furthermore, the sedimentation sign (SedSign) was determined based on the suspension of the nerve roots in the dural sac on cross-sectional MR images, and 31 cases were rated as positive and another 25 cases were negative. Results The mean age of negative SedSign group was significantly younger than that of positive SedSign group (51.7 ± 8.7 vs 68.4 ± 10.5, P < 0.05). The constitutions of clinical diagnosis were significantly different between patients with a positive SedSign and those with a negative SedSign (P < 0.001). Overall, nerve roots of the vast majority of patients (48/56, 85.7%) subsided to the ventral side of the dural sac on the prone MR images, although that of 8 (14.3%) patients remain stay in the dorsal side of dural sac. Nerve roots of only one patient with negative SedSign did not settle to the ventral dural sac, while this phenomenon occurred in 7 patients in positive SedSign group (4% vs 22.6%, P < 0.001). In addition, the nerve roots of all the five patients subsided to the left side of dural sac on lateral position MR images. Conclusions The nerve roots sedimentation followed the direction of gravity. Positive SedSign may be a MR sign of lumbar pathology involved the spinal canal.

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MR IMAGING OF THE TRAUMATIZED LUMBAR SPINE

Magnetic Resonance Imaging Clinics of North America ◽

10.1016/s1064-9689(21)00577-8 ◽

1999 ◽

Vol 7 (3) ◽

pp. 589-602

Author(s):

Peter C. Young ◽

Cheryl A. Petersilge

Keyword(s):

Lumbar Spine ◽

Mr Imaging

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Development and External Validation of Deep-Learning-Based Tumor Grading Models in Soft-Tissue Sarcoma Patients Using MR Imaging

Cancers ◽

10.3390/cancers13122866 ◽

2021 ◽

Vol 13 (12) ◽

pp. 2866

Author(s):

Fernando Navarro ◽

Hendrik Dapper ◽

Rebecca Asadpour ◽

Carolin Knebel ◽

Matthew B. Spraker ◽

...

Keyword(s):

Deep Learning ◽

Soft Tissue ◽

Mr Imaging ◽

Characteristic Curve ◽

External Validation ◽

Soft Tissue Sarcomas ◽

Treatment Decision ◽

Receiver Operator Characteristic Curve ◽

Low Grade ◽

Tumor Grading

Background: In patients with soft-tissue sarcomas, tumor grading constitutes a decisive factor to determine the best treatment decision. Tumor grading is obtained by pathological work-up after focal biopsies. Deep learning (DL)-based imaging analysis may pose an alternative way to characterize STS tissue. In this work, we sought to non-invasively differentiate tumor grading into low-grade (G1) and high-grade (G2/G3) STS using DL techniques based on MR-imaging. Methods: Contrast-enhanced T1-weighted fat-saturated (T1FSGd) MRI sequences and fat-saturated T2-weighted (T2FS) sequences were collected from two independent retrospective cohorts (training: 148 patients, testing: 158 patients). Tumor grading was determined following the French Federation of Cancer Centers Sarcoma Group in pre-therapeutic biopsies. DL models were developed using transfer learning based on the DenseNet 161 architecture. Results: The T1FSGd and T2FS-based DL models achieved area under the receiver operator characteristic curve (AUC) values of 0.75 and 0.76 on the test cohort, respectively. T1FSGd achieved the best F1-score of all models (0.90). The T2FS-based DL model was able to significantly risk-stratify for overall survival. Attention maps revealed relevant features within the tumor volume and in border regions. Conclusions: MRI-based DL models are capable of predicting tumor grading with good reproducibility in external validation.

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