Leg position effects on the femoral neurovascular bundle location during a direct anterior approach total hip arthroplasty: a radiographic study.

BACKGROUND: Femoral neurovascular injury is a serious complication in a direct anterior approach (DAA) total hip arthroplasty. However, dynamic neurovascular bundle location changes during the approach were not examined. Thus, this study aimed to analyze the effects of leg position on the femoral neurovascular bundle location using magnetic resonance imaging (MRI). METHODS: This study scanned 30 healthy volunteers (15 males and 15 females) with 3.0T MRI in a supine and 30-degree hip extension position with the left leg in a neutral rotation position and the right leg in a 45-degree external extension position. The minimum distance from the edge of the anterior acetabulum to the femoral nerve (dFN), artery, and vein were measured on axial T1-weighted images at the hip center level, as well as the angle to the horizontal line of the femoral nerve (aFN), artery (aFA), and vein from the anterior acetabulum. RESULTS: The dFN in the supine position with external rotation was significantly larger than supine with neutral and extension with external rotation position (20.7, 19.5, and 19.0; p = 0.031 and 0.012, respectively). The aFA in supine with external rotation was significantly larger than in other postures (52.4°, 34.2°, and 36.2°, p < 0.001, respectively). The aFV in supine with external rotation was significantly larger than in supine with a neutral position (52.3° versus 47.7°, p = 0.037). The aFN in supine and external rotation was significantly larger than other postures (54.6, 38.2, and 33.0, p < 0.001, respectively). CONCLUSIONS: This radiographic study revealed that the leg position affected the neurovascular bundle location. These movements can be the risk of direct neurovascular injury or traction.

Development of attenuation correction methods using deep learning in brain-perfusion single-photon emission computed tomography.

PURPOSE: Computed tomography (CT)-based attenuation correction (CTAC) in single-photon emission computed tomography (SPECT) is highly accurate, but it requires hybrid SPECT/CT instruments and additional radiation exposure. To obtain attenuation correction (AC) without the need for additional CT images, a deep learning method was used to generate pseudo-CT images has previously been reported, but it is limited because of cross-modality transformation, resulting in misalignment and modality-specific artifacts. This study aimed to develop a deep learning-based approach using non-attenuation-corrected (NAC) images and CTAC-based images for training to yield AC images in brain-perfusion SPECT. This study also investigated whether the proposed approach is superior to conventional Chang's AC (ChangAC). METHODS: In total, 236 patients who underwent brain-perfusion SPECT were randomly divided into two groups: the training group (189 patients; 80%) and the test group (47 patients; 20%). Two models were constructed using Autoencoder (AutoencoderAC) and U-Net (U-NetAC), respectively. ChangAC, AutoencoderAC, and U-NetAC approaches were compared with CTAC using qualitative analysis (visual evaluation) and quantitative analysis (normalized mean squared error [NMSE] and the percentage error in each brain region). Statistical analyses were performed using the Wilcoxon signed-rank sum test and Bland-Altman analysis. RESULTS: U-NetAC had the highest visual evaluation score. The NMSE results for the U-NetAC were the lowest, followed by AutoencoderAC and ChangAC (P < 0.001). Bland-Altman analysis showed a fixed bias for ChangAC and AutoencoderAC and a proportional bias for ChangAC. ChangAC underestimated counts by 30-40% in all brain regions. AutoencoderAC and U-NetAC produced mean errors of <1% and maximum errors of 3%, respectively. CONCLUSION: New deep learning-based AC methods for AutoencoderAC and U-NetAC were developed. Their accuracy was higher than that obtained by ChangAC. U-NetAC exhibited higher qualitative and quantitative accuracy than AutoencoderAC. We generated highly accurate AC images directly from NAC images without the need for intermediate pseudo-CT images. To verify our models' generalizability, external validation is required.

Multidimensional Deep Learning Reduces False-Positives in the Automated Detection of Cerebral Aneurysms on Time-Of-Flight Magnetic Resonance Angiography: A Multi-Center Study.

Current deep learning-based cerebral aneurysm detection demonstrates high sensitivity, but produces numerous false-positives (FPs), which hampers clinical application of automated detection systems for time-of-flight magnetic resonance angiography. To reduce FPs while maintaining high sensitivity, we developed a multidimensional convolutional neural network (MD-CNN) designed to unite planar and stereoscopic information about aneurysms. This retrospective study enrolled time-of-flight magnetic resonance angiography images of cerebral aneurysms from three institutions from June 2006 to April 2019. In the internal test, 80% of the entire data set was used for model training and 20% for the test, while for the external tests, data from different pairs of the three institutions were used for training and the remaining one for testing. Images containing aneurysms > 15 mm and images without aneurysms were excluded. Three deep learning models [planar information-only (2D-CNN), stereoscopic information-only (3D-CNN), and multidimensional information (MD-CNN)] were trained to classify whether the voxels contained aneurysms, and they were evaluated on each test. The performance of each model was assessed using free-response operating characteristic curves. In total, 732 aneurysms (5.9 ± 2.5 mm) of 559 cases (327, 120, and 112 from institutes A, B, and C; 469 and 263 for 1.5T and 3.0T MRI) were included in this study. In the internal test, the highest sensitivities were 80.4, 87.4, and 82.5%, and the FPs were 6.1, 7.1, and 5.0 FPs/case at a fixed sensitivity of 80% for the 2D-CNN, 3D-CNN, and MD-CNN, respectively. In the external test, the highest sensitivities were 82.1, 86.5, and 89.1%, and 5.9, 7.4, and 4.2 FPs/cases for them, respectively. MD-CNN was a new approach to maintain sensitivity and reduce the FPs simultaneously.