As an umbrella term, artificial intelligence (AI) covers machine learning and deep learning. This review aimed to elaborate on these terms to act as a primer for radiologists to learn more about the algorithms commonly used in musculoskeletal radiology. It also aimed to familiarize them with the common practices and issues in the use of AI in this domain.

Purpose Clinical management of disc degeneration in patients with chronic low back pain (cLBP) is hampered by the challenge of distinguishing pathologic changes relating to pain from physiologic changes related to aging. The goal of this study was to use imaging biomarkers of disc biochemical composition to distinguish degenerative changes associated with cLBP from normal aging. Methods T1ρ MRI data were acquired from 133 prospectively enrolled subjects for this observational study (80 cLBP, 53 controls; mean ± SD age = 43.9 ± 13.4 years; 61 females, 72 males). The mean T1ρ relaxation time in the nucleus pulposus (NP-T1ρ; n = 650 discs) was used as a quantitative biomarker of disc biochemical composition. Linear regression was used to assess associations between NP-T1ρ and age, sex, spinal level, and study group, and their interactions. Results NP-T1ρ values were lower in cLBP patients than controls (70.8 ± 22.8 vs. 76.4 ± 22.2 ms, p = 0.009). Group differences were largest at L5–S1 (ΔT1ρ cLBP-control = −11.3 ms, p < 0.0001), representing biochemical deterioration typically observed over a 9–12 year period (NP-T1ρ declined by 0.8–1.1 ms per year [95% CI]). Group differences were large in younger patients and diminished with age. Finally, the age-dependence of disc degeneration was stronger in controls than cLBP patients. Conclusion Aging effects on the biochemical composition of the L5–S1 disc may involve a relatively uniform set of factors from which many cLBP patients deviate. NP-T1ρ values at L5–S1 may be highly relevant to clinical phenotyping, particularly in younger individuals.

Low back pain (LBP) is a global health burden, and up to 90% of affected patients are diagnosed with non-specific LBP. A structure increasingly recognized as a potential contributor to LBP is paraspinal musculature (PSM). In this study we revealed that the fat fraction (FF) of PSM derived from chemical shift encoding-based water-fat MRI (CSE-MRI) is associated with the Goutallier classification (GC) at each lumbar level as well as with self-reported pain. In contrast, other parameters (muscle volume, lumbar indentation value [LIV], muscle-fat index [MFI]) do not significantly correlate to the FF, despite being frequently used to evaluate PSM quality.

Background: T2* relaxation times in the spinal cartilage endplate (CEP) measured using ultra-short echo time magnetic resonance imaging (UTE MRI) reflect aspects of biochemical composition that influence the CEP's permeability to nutrients. Deficits in CEP composition measured using T2* biomarkers from UTE MRI are associated with more severe intervertebral disc degeneration in patients with chronic low back pain (cLBP). The goal of this study was to develop an objective, accurate, and efficient deep-learning-based method for calculating biomarkers of CEP health using UTE images. Methods: Multi-echo UTE MRI of the lumbar spine was acquired from a prospectively enrolled crosssectional and consecutive cohort of 83 subjects spanning a wide range of ages and cLBP-related conditions. CEPs from the L4-S1 levels were manually segmented on 6,972 UTE images and used to train neural networks utilizing the u-net architecture. CEP segmentations and mean CEP T2* values derived from manually- and model-generated segmentations were compared using Dice scores, sensitivity, specificity, Bland-Altman, and receiver-operator characteristic (ROC) analysis. Signal-to-noise (SNR) and contrast-tonoise (CNR) ratios were calculated and related to model performance. Results: Compared with manual CEP segmentations, model-generated segmentations achieved sensitives of 0.80-0.91, specificities of 0.99, Dice scores of 0.77-0.85, area under the receiver-operating characteristic curve values of 0.99, and precision-recall (PR) AUC values of 0.56-0.77, depending on spinal level and sagittal image position. Mean CEP T2* values and principal CEP angles derived from the model-predicted segmentations had low bias in an unseen test dataset (T2* bias =0.33 +/- 2.37 ms, angle bias =0.36 +/- 2.65 degrees). To simulate a hypothetical clinical scenario, the predicted segmentations were used to stratify CEPs into high, medium, and low T2* groups. Group predictions had diagnostic sensitivities of 0.77-0.86 and specificities of Conclusions: The trained deep learning models enable accurate, automated CEP segmentations and T2*

BackgroundParaspinal musculature (PSM) is increasingly recognized as a contributor to low back pain (LBP), but with conventional MRI sequences, assessment is limited. Chemical shift encoding‐based water–fat MRI (CSE‐MRI) enables the measurement of PSM fat fraction (FF), which may assist investigations of chronic LBP.PurposeTo investigate associations between PSM parameters from conventional MRI and CSE‐MRI and between PSM parameters and pain.Study TypeProspective, cross‐sectional.PopulationEighty‐four adults with chronic LBP (44.6 ± 13.4 years; 48 males).Field Strength/Sequence3‐T, T1‐weighted fast spin‐echo and iterative decomposition of water and fat with echo asymmetry and least squares estimation sequences.AssessmentT1‐weighted images for Goutallier classification (GC), muscle volume, lumbar indentation value, and muscle‐fat index, CSE‐MRI for FF extraction (L1/2–L5/S1). Pain was self‐reported using a visual analogue scale (VAS). Intra‐ and/or interreader agreement was assessed for MRI‐derived parameters.Statistical TestsMixed‐effects and linear regression models to 1) assess relationships between PSM parameters (entire cohort and subgroup with GC grades 0 and 1; statistical significance α = 0.0025) and 2) evaluate associations of PSM parameters with pain (α = 0.05). Intraclass correlation coefficients (ICCs) for intra‐ and/or interreader agreement.ResultsThe FF showed excellent intra‐ and interreader agreement (ICC range: 0.97–0.99) and was significantly associated with GC at all spinal levels. Subgroup analysis suggested that early/subtle changes in PSM are detectable with FF but not with GC, given the absence of significant associations between FF and GC (P‐value range: 0.036 at L5/S1 to 0.784 at L2/L3). Averaged over all spinal levels, FF and GC were significantly associated with VAS scores.Data ConclusionIn the absence of FF, GC may be the best surrogate for PSM quality. Given the ability of CSE‐MRI to detect muscle alterations at early stages of PSM degeneration, this technique may have potential for further investigations of the role of PSM in chronic LBP.Level of Evidence2Technical Efficacy Stage2

Purpose The composition of the subchondral bone marrow and cartilage endplate (CEP) could affect intervertebral disc health by influencing vertebral perfusion and nutrient diffusion. However, the relative contributions of these factors to disc degeneration in patients with chronic low back pain (cLBP) have not been quantified. The goal of this study was to use compositional biomarkers derived from quantitative MRI to establish how CEP composition (surrogate for permeability) and vertebral bone marrow fat fraction (BMFF, surrogate for perfusion) relate to disc degeneration. Methods MRI data from 60 patients with cLBP were included in this prospective observational study (28 female, 32 male; age = 40.0 ± 11.9 years, 19–65 [mean ± SD, min–max]). Ultra-short echo-time MRI was used to calculate CEP T2* relaxation times (reflecting biochemical composition), water-fat MRI was used to calculate vertebral BMFF, and T1ρ MRI was used to calculate T1ρ relaxation times in the nucleus pulposus (NP T1ρ, reflecting proteoglycan content and degenerative grade). Univariate linear regression was used to assess the independent effects of CEP T2* and vertebral BMFF on NP T1ρ. Mixed effects multivariable linear regression accounting for age, sex, and BMI was used to assess the combined relationship between variables . Results CEP T2* and vertebral BMFF were independently associated with NP T1ρ ( p = 0.003 and 0.0001, respectively). After adjusting for age, sex, and BMI, NP T1ρ remained significantly associated with CEP T2* ( p = 0.0001) but not vertebral BMFF ( p = 0.43). Conclusion Poor CEP composition plays a significant role in disc degeneration severity and can affect disc health both with and without deficits in vertebral perfusion.

Background: Bone marrow fat (BMF) fraction quantification in vertebral bodies is used as a novel imaging biomarker to assess and characterize chronic lower back pain. However, manual segmentation of vertebral bodies is time consuming and laborious. Purpose: (1) Develop a deep learning pipeline for segmentation of vertebral bodies using quantitative water-fat MRI. (2) Compare BMF measurements between manual and automatic segmentation methods to assess performance. Materials and Methods: In this retrospective study, MR images using a 3D spoiled gradient-recalled echo (SPGR) sequence with Iterative Decomposition of water and fat with Echo Asymmetry and Least-squares estimation (IDEAL) reconstruction algorithm were obtained in 57 subjects (28 women, 29 men, mean age, 47.2 ± 12.6 years). An artificial network was trained for 100 epochs on a total of 165 lumbar vertebrae manually segmented from 31 subjects. Performance was assessed by analyzing the receiver operating characteristic curve, precision-recall, F1 scores, specificity, sensitivity, and similarity metrics. Bland-Altman analysis was used to assess performance of BMF fraction quantification using the predicted segmentations. Results: The deep learning segmentation method achieved an AUC of 0.92 (CI 95%: 0.9186, 0.9195) on a testing dataset (n = 24 subjects) on classification of pixels as vertebrae. A sensitivity of 0.99 and specificity of 0.80 were achieved for a testing dataset, and a mean Dice similarity coefficient of 0.849 ± 0.091. Comparing manual and automatic segmentations on fat fraction maps of lumbar vertebrae (n = 124 vertebral bodies) using Bland-Altman analysis resulted in a bias of only −0.605% (CI 95% = −0.847 to −0.363%) and agreement limits of −3.275% and +2.065%. Automatic segmentation was also feasible in 16 ± 1 s. Conclusion: Our results have demonstrated the feasibility of automated segmentation of vertebral bodies using deep learning models on water-fat MR (Dixon) images to define vertebral regions of interest with high specificity. These regions of interest can then be used to quantify BMF with comparable results as manual segmentation, providing a framework for completely automated investigation of vertebral changes in CLBP.

Studying the function of a gene of interest can be achieved by manipulating its level of expression, such as decreasing its expression with knockdown cell lines or increasing its expression with overexpression cell lines. Transient and stable transfection are two methods that are often used for exogenous gene expression. Transient transfection is only useful for short-term expression, whereas stable transfection allows exogenous genes to be integrated into the host cell genome where it will be continuously expressed. As a result, stable transfection is usually employed for research into long-term genetic regulation. Here we describe a simple protocol to generate a stable cell line overexpressing tagged death receptor 3 (DR3) to explore DR3 function. We picked single clones after a retroviral infection in order to maintain the homogeneity and purity of the stable cell lines. The stable cell lines generated using this protocol render DR3-deficient HT29 cells sensitive to antimitotic drugs, thus reconstituting the apoptotic response in HT29 cells. Moreover, the FLAG tag on DR3 compensates for the unavailability of good DR3 antibody and facilitates the biochemical study of the molecular mechanism by which antimitotic agents induce apoptosis.

Innate behaviors are genetically encoded, but their underlying molecular mechanisms remain largely unknown. Predator odor 2,4,5-trimethyl-3-thiazoline (TMT) and its potent analog 2-methyl-2-thiazoline (2MT) are believed to activate specific odorant receptors to elicit innate fear/defensive behaviors in naive mice. Here, we conduct a large-scale recessive genetics screen of ethylnitrosourea (ENU)-mutagenized mice. We find that loss of Trpa1, a pungency/irritancy receptor, diminishes TMT/2MT and snake skin-evoked innate fear/defensive responses. Accordingly, Trpa1 -/- mice fail to effectively activate known fear/stress brain centers upon 2MT exposure, despite their apparent ability to smell and learn to fear 2MT. Moreover, Trpa1 acts as a chemosensor for 2MT/TMT and Trpa1-expressing trigeminal ganglion neurons contribute critically to 2MT-evoked freezing. Our results indicate that Trpa1-mediated nociception plays a crucial role in predator odor-evoked innate fear/defensive behaviors. The work establishes the first forward genetics screen to uncover the molecular mechanism of innate fear, a basic emotion and evolutionarily conserved survival mechanism.

In the solid state, 4-methoxy-N'-(2,2,2-trichloroethanimidoyl)benzene-1-carboximidamide, C(10)H(10)Cl(3)N(3)O, (I), N'-(2,2,2-trichloroethanimidoyl)benzene-1-carboximidamide, C(9)H(8)Cl(3)N(3), (II), 4-chloro-N'-(2,2,2-trichloroethanimidoyl)benzene-1-carboximidamide, C(9)H(7)Cl(4)N(3), (III), 4-bromo-N'-(2,2,2-trichloroethanimidoyl)benzene-1-carboximidamide, C(9)H(7)BrCl(3)N(3), (IV), and 4-trifluoromethyl-N'-(2,2,2-trichloroethanimidoyl)benzene-1-carboximidamide, C(10)H(7)Cl(3)F(3)N(3), (V), display strong intramolecular N-H...N hydrogen bonding across the chelate ring and also intramolecular N-H...Cl contacts. Additional intermolecular hydrogen bonds link the molecules into chains, double chains or sheets in all cases except for compound (V). For compound (II), there are three independent molecules per asymmetric unit.