Generalizing Diffusion Tensor Imaging of the Physis and Metaphysis.

BACKGROUND: Current methods to predict height potential are inaccurate. Predicting height by using MRI of the physeal cartilage has shown promise but the applicability of this technique in different imaging setups has not been well-evaluated. PURPOSE: To assess variability in diffusion tensor imaging of the physis and metaphysis (DTI-P/M) of the distal femur between different scanners, imaging parameters, tractography software, and resolution. STUDY TYPE: Prospective. POPULATION/SUBJECTS: Eleven healthy subjects (five males and six females ages 10-16.94). FIELD STRENGTH/SEQUENCE: 3 T; DTI single shot echo planar sequences. ASSESSMENT: Physeal DTI tract measurements of the distal femur were compared between different scanners, imaging parameters, tractography settings, interpolation correction, and tractography software. STATISTICAL TESTS: Bland-Altman, Spearman correlation, linear regression, and Shapiro-Wilk tests. Threshold for statistical significance was set at P = 0.05. RESULTS: DTI tract values consistently showed low variability with different imaging and analysis settings. Vendor to vendor comparison exhibited strong correlation (ρ = 0.93) and small but significant bias (bias -5.76, limits of agreement [LOA] -24.31 to 12.78). Strong correlation and no significant difference were seen between technical replicates of the General Electric MRI scanner (ρ = 1, bias 0.17 [LOA -1.5 to 1.2], P = 0.42) and the Siemens MRI scanner (ρ = 0.89, bias = 0.56, P = 0.71). Different voxel sizes (1 × 1 × 2 mm3 vs. 2 × 2 × 3 mm3) did not significantly affect DTI values (bias = 1.4 [LOA -5.7 to 8.4], P = 0.35) but maintained a strong correlation (ρ = 0.82). Gap size (0 mm vs. 0.6 mm) significantly affects tract volume (bias = 1.8 [LOA -5.4 to 1.8]) but maintains a strong correlation (ρ = 0.93). Comparison of tractography algorithms generated significant differences in tract number, length, and volume while maintaining correlation (ρ = 0.86, 0.99, 0.93, respectively). Comparison of interobserver variability between different tractography software also revealed significant differences while maintaining high correlation (ρ = 0.85-0.98). DATA CONCLUSION: DTI of the pediatric physis cartilage shows high reproducibility between different imaging and analytic parameters. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: Stage 1.

Assessing the readability of patient-targeted online information on musculoskeletal radiology procedures.

OBJECTIVE: To assess the readability of patient-targeted online information on musculoskeletal radiology procedures. METHODS: Eleven common musculoskeletal radiology procedures were queried in three online search engines (Google, Yahoo!, Bing). All unique patient-targeted websites were identified (n = 384) from the first three pages of search results. The reading grade level of each website was calculated using 6 separate validated metrics for readability assessment. Analysis of word and sentence complexity was also performed. Results were compared between academic vs. non-academic websites and between websites found on different pages of the search results. Statistics were performed using a t test. RESULTS: The mean reading grade level across all procedures was 10th-14th grade. Webpages for nerve block were written at a higher reading grade level on non-academic websites (p = 0.025). There was no difference in reading grade levels between academic and non-academic sources for all other procedures. There was no difference in reading grade levels between websites found on the first page of search results compared with the second and third pages. Across all websites, 16-22% of the words used had 3+ syllables and 31-43% of the words used had 6+ characters (complex words); 13-24% of the sentences used had 22+ words (complex sentences). CONCLUSION: Patient-targeted online information on musculoskeletal radiology procedures are written at the 10th-14th grade reading level, which is well beyond the AMA and NIH recommendation. Readability can be lowered by decreasing text complexity through limitation of high-syllable words and reduction in word and sentence length.

Deep Learning-Assisted Diffusion Tensor Imaging for Evaluation of the Physis and Metaphysis.

Diffusion tensor imaging of physis and metaphysis can be used as a biomarker to predict height change in the pediatric population. Current application of this technique requires manual segmentation of the physis which is time-consuming and introduces interobserver variability. UNET Transformers (UNETR) can be used for automatic segmentation to optimize workflow. Three hundred and eighty-five DTI scans from 191 subjects with mean age of 12.6 years ± 2.01 years were retrospectively used for training and validation. The mean Dice correlation coefficient was 0.81 for the UNETR model and 0.68 for the UNET. Manual extraction and segmentation took 15 min per volume, whereas both deep learning segmentation techniques took < 1 s per volume and were deterministic, always producing the same result for a given input. Intraclass correlation coefficient (ICC) for ROI-derived femur diffusion metrics was excellent for tract count (0.95), volume (0.95), and FA (0.97), and good for tract length (0.87). The results support the hypothesis that a hybrid UNETR model can be trained to replace the manual segmentation of physeal DTI images, therefore automating the process.

Biophysical characterization of the interaction between FAAP20-UBZ4 domain and Rev1-BRCT domain.

FAAP20 (Fanconi anemia-associated protein 20) is a subunit of the Fanconi anemia (FA) core complex that repairs interstrand cross-links. To understand the molecular basis for the FA core complex-mediated recruitment of Rev1 to the DNA lesion, we characterized the interactions among FAAP20-UBZ4, Rev1-BRCT, and ubiquitin using NMR. We found that FAAP20-UBZ4 binds not only ubiquitin but also Rev1-BRCT. Mapping the protein-protein interactions showed that FAAP20-UBZ4 has distinct binding surfaces for ubiquitin and Rev1-BRCT. In addition, the chemical exchange patterns indicated that the interaction between FAAP20-UBZ4 and ubiquitin might enhance the binding affinity between FAAP20-UBZ4 and Rev1-BRCT. These results provide new insight into the Rev1 recognition mechanism by FAAP20.