Biceps Pulley Lesions: Diagnostic Accuracy of Nonarthrographic Shoulder MRI and the Value of Various Diagnostic Signs.

BACKGROUND: There is limited data in the literature regarding the role of nonarthrographic MRI for detecting biceps pulley (BP) lesions. PURPOSE: To assess the accuracy of nonarthrographic MRI for detecting BP lesions, and to evaluate the diagnostic value of various MRI signs (superior glenohumeral ligament discontinuity/nonvisibility, long head of biceps (LHB) displacement sign or subluxation/dislocation, LHB tendinopathy, and supraspinatus and subscapularis tendon lesions) in detecting such lesions. STUDY TYPE: Retrospective. POPULATION: 84 patients (32 in BP-lesion group and 52 in BP-intact group-as confirmed by arthroscopy). FIELD STRENGTH/SEQUENCE: 1.5-T, T1-weighted turbo spin echo (TSE), T2-weighted TSE, and proton density-weighted TSE spectral attenuated inversion recovery (SPAIR) sequences. ASSESSMENT: Three radiologists independently reviewed all MRI data for the presence of BP lesions and various MRI signs. The MRI signs and final MRI diagnoses were tested for accuracy regarding detecting BP lesions using arthroscopy results as the reference standard. Furthermore, the inter-reader agreement (IRA) between radiologists was determined. STATISTICAL TESTS: Student's t-tests, Chi-squared, and Fisher's exact tests, and 4-fold table test were used. The IRA was calculated using Kappa statistics. A P-value <0.05 was considered statistically significant. RESULTS: The sensitivity, specificity, and accuracy of nonarthrographic MRI for detecting BP lesions were 65.6%-78.1%, 90.4%-92.3%, and 81%-86.9%, respectively. The highest accuracy was noticed for the LHB displacement sign (84.5%-86.9%), and the highest sensitivity was registered for the LHB tendinopathy sign (87.5%). Furthermore, the highest specificity was observed for the LHB displacement sign and LHB subluxation/dislocation sign (98.1%-100%). The IRA regarding final MRI diagnosis and MRI signs of BP lesions was good to very good (κ = 0.76-0.98). DATA CONCLUSION: Nonarthrographic shoulder MRI may show good diagnostic accuracy for detecting BP lesions. The LHB displacement sign could serve as the most accurate and specific sign for diagnosis of BP lesions. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 2.

MRI-Based Classification for Tibial Spine Fracture: Detection Efficacy, Classification Accuracy, and Reliability.

RATIONALE AND OBJECTIVES: Recently, a new MRI-based classification for evaluating tibial spine fractures (TSFs) was developed to aid in treating these injuries. Our objective was to assess the detection efficacy, classification accuracy, and reliability of this classification in detecting and grading TSFs, as well as its impact on treatment strategy, compared to the Meyers and McKeever (MM) classification. MATERIALS AND METHODS: A retrospective study included 68 patients with arthroscopically confirmed TSFs. All patients had plain radiography and conventional MRI of the affected knee before arthroscopy. Three experienced radiologists independently reviewed all plain radiographs and MRI data and graded each patient according to MM and MRI-based classifications. The detection efficacy, classification accuracy, and inter-rater agreement of both classifications were evaluated and compared, using arthroscopic findings as the gold standard. RESULTS: The final analysis included 68 affected knees. Compared to the MM classification, the MRI-based classification produced 22.0% upgrade of TSFs and 11.8% downgrade of TSFs. According to the reviewers, the fracture classification accuracy of the MRI-based classification (91.2-95.6%) was significantly higher than that of the MM classification (73.5-76.5%, p = 0.002-0.01). The fracture detection rate of MRI-based classification (94.1-98.5%) was non-significantly higher than that of the MM classification (83.8-89.7%, p = 0.07-0.4). The soft tissue injury detection accuracy for MRI-based classification was 91.2-94.1%. The inter-rater reliability for grading TSFs was substantial for both the MM classification (κ = 0.69) and MRI-based classification (κ = 0.79). CONCLUSION: MRI-based classification demonstrates greater accuracy and reliability compared to MM classification for detecting and grading TSFs and associated soft tissue injuries.

Histological alterations of small intestine and growth performance of broiler chicks after in ovo copper injection at 10 days of embryogenesis period.

This study aimed to assess impacts of early in ovo injection (at 10 days of incubation) of copper (sulfate, acetate, or nanoparticles) on histomorphometric parameters of small intestine and growth performance of post-hatched chicks. Fertile eggs (n = 462) were distributed to seven groups (3 replicates, 22 eggs in each). The first group as a control, the 2nd, 3rd and 4th groups injected with 100 µL deionized water containing 8 µg/egg of Cu (sulfate, acetate and nanoparticles, respectively), and the 5th, 6th and 7th groups injected with 100 µL deionized water containing 16 µg/egg of the same Cu sources above. Results illustrated that in ovo administration of Cu sulfate and Cu acetate significantly improved histological parameters of small intestine parts of newly hatched chicks compared with the control. Cu sulfate and nano-Cu significantly augmented body weight gain compared with the control. In ovo Cu injection showed a nonsignificant improvement in feed conversion ratio. The highest level (16 µg/egg) of different sources was better than the lowest level (8 µg/egg) in most results. In conclusion, it is recommended that in ovo injection of Cu (16 µg/egg) can improve the growth performance (Cu sulfate and nano-Cu) and the small intestine histomorphometry parameters (Cu sulfate and Cu acetate) of broiler chicks.

Effect of in ovo copper injection on body weight, immune response, blood biochemistry and carcass traits of broiler chicks at 35 days of age.

The current study was conducted to investigate effects of copper (sulfate, acetate and nano) in ovo injection at 10 days of the embryogenesis period on body weight (BW), immunity, biochemical parameters and carcass traits of broiler chicks at 35 days of age. A total number of 462 fertile eggs were used in seven groups, each group containing 66 eggs in three replicates. The experimental design was as follows: the group 1 as a control, while groups 2, 3 and 4 injected with 8 µg/egg of Cu sulfate, Cu acetate and nano Cu, respectively, and groups 5, 6 and 7 injected with 16 µg/egg of Cu sulfate, Cu acetate and nano Cu, respectively. Results stated that BW was increased in Cu-injected groups, except groups of Cu acetate, but plasma constituents, carcass and relative weight of organs did not affect. Cu level (8 µg/egg) had better results than Cu level (16 µg/egg). No differences between among groups in relative weights of spleen and bursa and immune response. In conclusion, it is recommended that in ovo injection of different sources of Cu can augment the BW and did not harmfully affect immunity, carcass traits and biochemical parameters of broiler chicks.

A novel no-sensors 3D model reconstruction from monocular video frames for a dynamic environment.

Occlusion awareness is one of the most challenging problems in several fields such as multimedia, remote sensing, computer vision, and computer graphics. Realistic interaction applications are suffering from dealing with occlusion and collision problems in a dynamic environment. Creating dense 3D reconstruction methods is the best solution to solve this issue. However, these methods have poor performance in practical applications due to the absence of accurate depth, camera pose, and object motion.This paper proposes a new framework that builds a full 3D model reconstruction that overcomes the occlusion problem in a complex dynamic scene without using sensors' data. Popular devices such as a monocular camera are used to generate a suitable model for video streaming applications. The main objective is to create a smooth and accurate 3D point-cloud for a dynamic environment using cumulative information of a sequence of RGB video frames. The framework is composed of two main phases. The first uses an unsupervised learning technique to predict scene depth, camera pose, and objects' motion from RGB monocular videos. The second generates a frame-wise point cloud fusion to reconstruct a 3D model based on a video frame sequence. Several evaluation metrics are measured: Localization error, RMSE, and fitness between ground truth (KITTI's sparse LiDAR points) and predicted point-cloud. Moreover, we compared the framework with different widely used state-of-the-art evaluation methods such as MRE and Chamfer Distance. Experimental results showed that the proposed framework surpassed the other methods and proved to be a powerful candidate in 3D model reconstruction.