The effect of forearm rotation on the bone mineral density measurements of the distal radius.

INTRODUCTION: Forearm dual-energy X-ray absorptiometry (DXA) is often performed in clinics where central DXA is unavailable. Accurate bone mineral density (BMD) measurement is crucial for clinical assessment. Forearm rotation can affect BMD measurements, but this effect remains uncertain. Thus, we aimed to conduct a simulation study using CT images to clarify the effect of forearm rotation on BMD measurements. MATERIALS AND METHODS: Forearm CT images of 60 women were analyzed. BMD was measured at the total, ultra-distal (UD), mid-distal (MD), and distal 33% radius regions with the radius located at the neutral position using digitally reconstructed radiographs generated from CT images. Then, the rotation was altered from - 30° to 30° (supination set as positive) with a one-degree increment, and the percent BMD changes from the neutral position were quantified for all regions at each angle for each patient. RESULTS: The maximum mean BMD changes were 5.8%, 7.0%, 6.2%, and 7.2% for the total, UD, MD, and distal 33% radius regions, respectively. The analysis of the absolute values of the percent BMD changes from the neutral position showed that BMD changes of all patients remained within 2% when the rotation was between - 5° and 7° for the total region, between - 3° and 2° for the UD region, between - 4° and 3° for the MD region, and between - 3° and 1° for the distal 33% radius region. CONCLUSION: Subtle rotational changes affected the BMD measurement of each region. The results showed the importance of forearm positioning when measuring the distal radius BMD.

4D-foot analysis on effect of arch support on ankle, subtalar, and talonavicular joint kinematics.

BACKGROUND: It has been difficult to study the effects of arch support on multiple joints simultaneously. Herein, we evaluated foot and ankle kinematics using a fully automated analysis system, "4D-Foot," consisting of a biplane X-ray imager and two-dimensional‒three-dimensional registration, with automated image segmentation and landmark detection tools. METHODS: We evaluated the effect of arch support on ankle, subtalar, and talonavicular joint kinematics in five healthy female volunteers without a clinical history of foot and ankle disorders. Computed tomography images of the foot and ankle and X-ray videos of walking barefoot and with arch support were acquired. A kinematic analysis using the "4D-Foot" system was performed. The ankle, subtalar, and talonavicular joint kinematics were quantified from heel-strike to foot-off, with and without arch support. RESULTS: For the ankle joint, significant differences were observed in dorsi/plantarflexion, inversion/eversion, and internal/external rotation in the late midstance phase. The dorsi/plantarflexion and inversion/eversion motions were smaller with arch support. For the subtalar joint, a significant difference was observed in all the dynamic motions in the heel-strike and late midstance phases. For the talonavicular joint, significant differences were observed in inversion/eversion and internal/external rotation in heel-strike and the late midstance phases. For the subtalar and talonavicular joints, the motion was larger with arch support. An extremely strong correlation was observed when the motion of the subtalar and talonavicular joints was compared for each condition and motion. CONCLUSIONS: The results indicated that the arch support decreased the ankle motion and increased the subtalar and talonavicular joint motions. Additionally, our study demonstrated that the in vivo subtalar and talonavicular joints revealed a strong correlation, suggesting that the navicular and calcaneal bones were moving similarly to the talus and that the arch support stabilizes the ankle joint and compensatively increases the subtalar and talonavicular joint motions.

The Effect of Region of Interest on Measurement of Bone Mineral Density of the Proximal Femur: Simulation Analysis Using CT Images.

While accurate measurement of bone mineral density (BMD) is essential in the diagnosis of osteoporosis and in evaluating the treatment of osteoporosis, it is unclear how region of interest (ROI) settings affect measurement of BMD at the total proximal femur region. In this study, we performed a simulation analysis to clarify the effect on BMD measurement of changing the ROI using hip computed tomography (CT) images of 75 females (mean age, 62.4 years). Digitally reconstructed radiographs of the proximal femur region were generated from CT images to calculate the change in BMD when the proximal boundary of the ROI was altered by 0-10 mm, and when the distal boundary of the ROI was altered by 0-30 mm. Further, changes in BMD were compared across BMD classification groups. A mean BMD increase of 0.62% was found for each 1-mm extension of the distal boundary. A mean BMD decrease of 0.18% was found for each 1-mm alteration of the proximal boundary. Comparing BMD classification groups, patients with osteoporosis and osteopenia demonstrated greater BMD changes than patients with normal BMD for the distal boundary (0.68%, 0.64%, and 0.54%, respectively) and patients with osteoporosis demonstrated greater BMD changes than patients with osteoporosis and normal BMD for the proximal boundary (0.37%, 0.13%, and 0.03%, respectively). In conclusion, our study found that a consistent ROI setting, especially on the distal boundary, is necessary for the accurate measurement of total proximal femur BMD. Based on the findings, we recommend confirming that the ROI setting shown on the BMD result form is consistent with changes in serial BMD.

Validation study of the CT-based cross-sectional evaluation of muscular atrophy and fatty degeneration around the pelvis and the femur.

BACKGROUND: Muscle atrophy and degeneration around hip joint have been evaluated using computer tomography (CT) or magnetic resonance imaging two-dimensionally (2D). However, it is unclear how these 2D measurements of muscle atrophy and degeneration reflect their three-dimensional (3D) measurements. The purpose of this study is to examine the validity of the 2D evaluation of muscular atrophy and fatty degeneration compared to 3D evaluation. METHODS: The study included computed tomography (CT) images of 20 patients with unilateral hip joint disorders. We manually segmented the edges of 14 muscles around the pelvis and the femur. The correlation coefficient between the 3D and 2D measurements at the anatomic landmarks was calculated. Furthermore, the 2D evaluation was performed at 1 cm interval from each anatomic landmark to determine the cross-section that correlated most strongly with the 3D measurements. A strong correlation in Spearman's rank correlation coefficient (r) was defined as r > 0.7. RESULTS: Nine (64%) and 10 (71%) muscles had a strong correlation coefficient between the 3D and 2D anatomic landmark measurements of muscular atrophy and degeneration, respectively. The maximum distance between the cross-section with the highest correlation coefficient and the anatomical landmark was 11 cm. CONCLUSIONS: More than half of muscles exhibited a strong correlation between the 3D and 2D anatomic landmark measurements of muscular atrophy and degeneration. To improve the correlation coefficient, the position of the cross-section was needed to be changed.

The learning effect of using stereoscopic vision in the early phase of laparoscopic surgical training for novices.

BACKGROUND: Recently to improve depth perception, the performance of three-dimensional (3D) laparoscopic surgeries has increased. However, the effects of laparoscopic training using 3D are still unclear. This study aimed to clarify the effects of using a 3D monitor among novices in the early phase of training. METHODS: Participants were 40 novices who had never performed laparoscopic surgery (20 medical students and 20 junior residents). Three laparoscopic phantom tasks (task 1: touching markers on a flat disk with a rod; task 2: straight rod transfer through a single loop; and task 3: curved rod transfer through two loops) in the training box were performed ten times, respectively. Performances were recorded by an optical position tracker. The participants were randomly divided into two groups: one group performed each task five times initially under a 2D system (2D start group), and the other group performed each task five times under a 3D system (3D start group). Both groups then performed the same task five times. After the trial, we evaluated the performance scores (operative time, path length of forceps, and technical errors) and the learning curves for both groups. RESULTS: Scores for all tasks performed under the 3D system were significantly better than scores for tasks using the 2D system. Scores for each task in the 2D start group improved after switching to the 3D system. However, scores for each task in the 3D start group were worse after switching to the 2D system, especially scores related to technical errors. CONCLUSIONS: The stereoscopic vision improved laparoscopic surgical techniques of novices from the early phase of training. However, the performance of novices trained only by 3D worsened by changing to the 2D environment.

The Posterior Capsular Ligamentous Complex Contributes to Hip Joint Stability in Distraction.

BACKGROUND: Laxity of soft tissues after total hip arthroplasty is considered to be a cause of accelerated wear of bearing surfaces and dislocation. The purpose of this study is to assess the contribution of the anterior and posterior capsular ligamentous complexes and the short external rotators, except the quadratus femoris, on the stability of the hip against axial traction. METHODS: The study subjects comprised 7 fresh cadavers with 12 normal hip joints. In 6 hips, soft tissues surrounding the hip joint were resected in the following order to simulate the anterior approach: anterior capsule, posterior capsule, piriformis, conjoined tendon, and external obturator. In the remaining 6 hips, soft tissues were resected in the following order to simulate the posterior approach: piriformis, conjoined tendon, external obturator, posterior capsule, and anterior capsule. Soft tissue tension was measured by applying traction amounting to 250 N with joints in the neutral position. RESULTS: The separation distance between the femoral head and acetabulum during axial leg traction significantly increased from 4.0 to 14.5 mm on average after circumferential resection of the capsule via the anterior approach. Subsequent resection of the short external rotators increased the separation distance up to 19.0 mm, but the differences did not reach statistical significance. Resection of the short external rotators via the posterior approach did not significantly increase the separation distance; it significantly increased from 6.0 to 11.4 mm after the resection of the anterior capsule and further to 20.5 mm after the resection of the posterior capsule. CONCLUSION: The posterior capsule, in addition to the anterior capsule, significantly contributes to hip joint stability in distraction regardless of whether the short external rotators, except the quadratus femoris, were preserved or resected.

Can Anatomic Measurements of Stem Anteversion Angle Be Considered as the Functional Anteversion Angle?

BACKGROUND: Stem anteversion angle is important in the combined anteversion theory to avoid implant impingement after total hip arthroplasty (THA). However, anatomic measurements of stem anteversion angle may not represent functional anteversion of the femur if the femur undergoes axial rotation. Herein, the femoral rotational angle (FRA) was measured in supine and standing positions before and after THA to evaluate the difference between anatomic and functional measurements. METHODS: A total of 191 hips (174 patients) treated with THA for osteoarthritis were analyzed in this retrospective, case-controlled study. The FRA was measured as the angle between the posterior condylar line and the line through the bilateral anterior superior iliac spines (positive for external rotation) and was measured preoperatively and postoperatively in supine and standing positions with computed tomography segmentation and landmark localization of the pelvis and the femur followed by intensity-based 2D-3D registration. The number of cases in which the absolute FRA remained <15° in both positions was also calculated. RESULTS: The average ± standard deviation preoperative FRA was 0.3° ± 8.3° in the supine position and -4.5° ± 8.8° during standing; the postoperative FRA was -3.8° ± 9.0° in supine and -14.3° ± 8.3° during standing. There were 134 cases (70%) in which the preoperative absolute FRA remained <15° in both positions while only 85 hips (45%) remained <15°, postoperatively. CONCLUSION: Substantial variability was seen in the FRA, especially during the postoperative period. These results suggest that the anatomic stem anteversion angle may not represent the functional anteversion of the femur.

Change in Pelvic Sagittal Inclination From Supine to Standing Position Before Hip Arthroplasty.

BACKGROUND: Cup anteversion and inclination are important for avoiding implant impingement and dislocation in total hip arthroplasty. However, functional cup anteversion and cup inclination also change as the pelvic sagittal inclination (PSI) changes. Therefore, PSI in both supine and standing positions was measured in a large cohort in this study. METHODS: A total of 422 patients (median age, 61; range, 15-87) who underwent total hip arthroplasty were the subjects of this study. There were 83 patients with primary osteoarthritis (OA), 274 patients with developmental dysplasia-derived secondary OA, 48 patients with osteonecrosis, and 17 patients with rapidly destructive coxopathy (RDC). Preoperative PSI in supine and standing positions was measured by automated computed topography segmentation and landmark localization of the pelvis followed by intensity-based 2D-3D registration, and the number of cases in which PSI changed more than 10° posteriorly was calculated. Hip disease, sex, and age were analyzed if they were related to a PSI change of more than 10°. RESULTS: The median PSI was 5.1° (interquartile range, 0.4°-9.4°) in supine and -1.3° (interquartile range, -6.5° to 4.2°) in standing position. There were 79 cases (19%) in which the PSI changed more than 10° posteriorly from supine to standing. Elder age and patients with primary OA and RDC were revealed to be the related factors. CONCLUSION: PSI changed more than 10° posteriorly from supine to standing in 19% of cases. Age and diagnosis of primary OA and RDC were the significant factors for the posterior rotation.

Preliminary development of a workstation for craniomaxillofacial surgical procedures: introducing a computer-assisted planning and execution system.

INTRODUCTION: Facial transplantation represents one of the most complicated scenarios in craniofacial surgery because of skeletal, aesthetic, and dental discrepancies between donor and recipient. However, standard off-the-shelf vendor computer-assisted surgery systems may not provide custom features to mitigate the increased complexity of this particular procedure. We propose to develop a computer-assisted surgery solution customized for preoperative planning, intraoperative navigation including cutting guides, and dynamic, instantaneous feedback of cephalometric measurements/angles as needed for facial transplantation and other related craniomaxillofacial procedures. METHODS: We developed the Computer-Assisted Planning and Execution (CAPE) workstation to assist with planning and execution of facial transplantation. Preoperative maxillofacial computed tomography (CT) scans were obtained on 4 size-mismatched miniature swine encompassing 2 live face-jaw-teeth transplants. The system was tested in a laboratory setting using plastic models of mismatched swine, after which the system was used in 2 live swine transplants. Postoperative CT imaging was obtained and compared with the preoperative plan and intraoperative measures from the CAPE workstation for both transplants. RESULTS: Plastic model tests familiarized the team with the CAPE workstation and identified several defects in the workflow. Live swine surgeries demonstrated utility of the CAPE system in the operating room, showing submillimeter registration error of 0.6 ± 0.24 mm and promising qualitative comparisons between intraoperative data and postoperative CT imaging. CONCLUSIONS: The initial development of the CAPE workstation demonstrated that integration of computer planning and intraoperative navigation for facial transplantation are possible with submillimeter accuracy. This approach can potentially improve preoperative planning, allowing ideal donor-recipient matching despite significant size mismatch, and accurate surgical execution for numerous types of craniofacial and orthognathic surgical procedures.

Automatic detection of midfacial fractures in facial bone CT images using deep learning-based object detection models.

BACKGROUND: Midfacial fractures are among the most frequent facial fractures. Surgery is recommended within 2 weeks of injury, but this time frame is often extended because the fracture is missed on diagnostic imaging in the busy emergency medicine setting. Using deep learning technology, which has progressed markedly in various fields, we attempted to develop a system for the automatic detection of midfacial fractures. The purpose of this study was to use this system to diagnose fractures accurately and rapidly, with the intention of benefiting both patients and emergency room physicians. METHODS: One hundred computed tomography images that included midfacial fractures (e.g., maxillary, zygomatic, nasal, and orbital fractures) were prepared. In each axial image, the fracture area was surrounded by a rectangular region to create the annotation data. Eighty images were randomly classified as the training dataset (3736 slices) and 20 as the validation dataset (883 slices). Training and validation were performed using Single Shot MultiBox Detector (SSD) and version 8 of You Only Look Once (YOLOv8), which are object detection algorithms. RESULTS: The performance indicators for SSD and YOLOv8 were respectively: precision, 0.872 and 0.871; recall, 0.823 and 0.775; F1 score, 0.846 and 0.82; average precision, 0.899 and 0.769. CONCLUSIONS: The use of deep learning techniques allowed the automatic detection of midfacial fractures with good accuracy and high speed. The system developed in this study is promising for automated detection of midfacial fractures and may provide a quick and accurate solution for emergency medical care and other settings.

Automatic hip osteoarthritis grading with uncertainty estimation from computed tomography using digitally-reconstructed radiographs.

PURPOSE: Progression of hip osteoarthritis (hip OA) leads to pain and disability, likely leading to surgical treatment such as hip arthroplasty at the terminal stage. The severity of hip OA is often classified using the Crowe and Kellgren-Lawrence (KL) classifications. However, as the classification is subjective, we aimed to develop an automated approach to classify the disease severity based on the two grades using digitally-reconstructed radiographs from CT images. METHODS: Automatic grading of the hip OA severity was performed using deep learning-based models. The models were trained to predict the disease grade using two grading schemes, i.e., predicting the Crowe and KL grades separately, and predicting a new ordinal label combining both grades and representing the disease progression of hip OA. The models were trained in classification and regression settings. In addition, the model uncertainty was estimated and validated as a predictor of classification accuracy. The models were trained and validated on a database of 197 hip OA patients, and externally validated on 52 patients. The model accuracy was evaluated using exact class accuracy (ECA), one-neighbor class accuracy (ONCA), and balanced accuracy. RESULTS: The deep learning models produced a comparable accuracy of approximately 0.65 (ECA) and 0.95 (ONCA) in the classification and regression settings. The model uncertainty was significantly larger in cases with large classification errors ( P < 6 e - 3 ). CONCLUSIONS: In this study, an automatic approach for grading hip OA severity from CT images was developed. The models have shown comparable performance with high ONCA, which facilitates automated grading in large-scale CT databases and indicates the potential for further disease progression analysis. Classification accuracy was correlated with the model uncertainty, which would allow for the prediction of classification errors. The code will be made publicly available at https://github.com/NAIST-ICB/HipOA-Grading .

Hybrid representation-enhanced sampling for Bayesian active learning in musculoskeletal segmentation of lower extremities.

PURPOSE: Manual annotations for training deep learning models in auto-segmentation are time-intensive. This study introduces a hybrid representation-enhanced sampling strategy that integrates both density and diversity criteria within an uncertainty-based Bayesian active learning (BAL) framework to reduce annotation efforts by selecting the most informative training samples. METHODS: The experiments are performed on two lower extremity datasets of MRI and CT images, focusing on the segmentation of the femur, pelvis, sacrum, quadriceps femoris, hamstrings, adductors, sartorius, and iliopsoas, utilizing a U-net-based BAL framework. Our method selects uncertain samples with high density and diversity for manual revision, optimizing for maximal similarity to unlabeled instances and minimal similarity to existing training data. We assess the accuracy and efficiency using dice and a proposed metric called reduced annotation cost (RAC), respectively. We further evaluate the impact of various acquisition rules on BAL performance and design an ablation study for effectiveness estimation. RESULTS: In MRI and CT datasets, our method was superior or comparable to existing ones, achieving a 0.8% dice and 1.0% RAC increase in CT (statistically significant), and a 0.8% dice and 1.1% RAC increase in MRI (not statistically significant) in volume-wise acquisition. Our ablation study indicates that combining density and diversity criteria enhances the efficiency of BAL in musculoskeletal segmentation compared to using either criterion alone. CONCLUSION: Our sampling method is proven efficient in reducing annotation costs in image segmentation tasks. The combination of the proposed method and our BAL framework provides a semi-automatic way for efficient annotation of medical image datasets.

Reproduction of forearm rotation dynamic using intensity-based biplane 2D-3D registration matching method.

This study aimed to reproduce and analyse the in vivo dynamic rotational motion of the forearm and to clarify forearm motion involvement and the anatomical function of the interosseous membrane (IOM). The dynamic forearm rotational motion of the radius and ulna was analysed in vivo using a novel image-matching method based on fluoroscopic and computed tomography images for intensity-based biplane two-dimensional-three-dimensional registration. Twenty upper limbs from 10 healthy volunteers were included in this study. The mean range of forearm rotation was 150 ± 26° for dominant hands and 151 ± 18° for non-dominant hands, with no significant difference observed between the two. The radius was most proximal to the maximum pronation relative to the ulna, moved distally toward 60% of the rotation range from maximum pronation, and again proximally toward supination. The mean axial translation of the radius relative to the ulna during forearm rotation was 1.8 ± 0.8 and 1.8 ± 0.9 mm for dominant and non-dominant hands, respectively. The lengths of the IOM components, excluding the central band (CB), changed rotation. The transverse CB length was maximal at approximately 50% of the rotation range from maximum pronation. Summarily, this study describes a detailed method for evaluating in vivo dynamic forearm motion and provides valuable insights into forearm kinematics and IOM function.

Attention induction for a CT volume classification of COVID-19.

PURPOSE: This study proposes a method to draw attention toward the specific radiological findings of coronavirus disease 2019 (COVID-19) in CT images, such as bilaterality of ground glass opacity (GGO) and/or consolidation, in order to improve the classification accuracy of input CT images. METHODS: We propose an induction mask that combines a similarity and a bilateral mask. A similarity mask guides attention to regions with similar appearances, and a bilateral mask induces attention to the opposite side of the lung to capture bilaterally distributed lesions. An induction mask for pleural effusion is also proposed in this study. ResNet18 with nonlocal blocks was trained by minimizing the loss function defined by the induction mask. RESULTS: The four-class classification accuracy of the CT images of 1504 cases was 0.6443, where class 1 was the typical appearance of COVID-19 pneumonia, class 2 was the indeterminate appearance of COVID-19 pneumonia, class 3 was the atypical appearance of COVID-19 pneumonia, and class 4 was negative for pneumonia. The four classes were divided into two subgroups. The accuracy of COVID-19 and pneumonia classifications was evaluated, which were 0.8205 and 0.8604, respectively. The accuracy of the four-class and COVID-19 classifications improved when attention was paid to pleural effusion. CONCLUSION: The proposed attention induction method was effective for the classification of CT images of COVID-19 patients. Improvement of the classification accuracy of class 3 by focusing on features specific to the class remains a topic for future work.

The effect of patient positioning on measurements of bone mineral density of the proximal femur: a simulation study using computed tomographic images.

The patient's position may affect the bone mineral density (BMD) measurements; however, the extent of this effect is undefined. This CT image-based simulation study quantified changes in BMD induced by hip flexion, adduction, and rotations to recommend appropriate patient positioning when acquiring dual-energy x-ray absorptiometry images. PURPOSE: Several studies have analyzed the effect of hip rotation on the measurement of bone mineral density (BMD) of the proximal femur by dual-energy x-ray absorptiometry (DXA). However, as the effects of hip flexion and abduction on BMD measurements remain uncertain, a computational simulation study using CT images was performed in this study. METHODS: Hip CT images of 120 patients (33 men and 87 women; mean age, 82.1 ± 9.4 years) were used for analysis. Digitally reconstructed radiographs of the proximal femur region were generated from CT images to calculate the BMD of the proximal femur region. BMD at the neutral position was quantified, and the percent changes in BMD when hip internal rotation was altered from -30° to 15°, when hip flexion was altered from 0° to 30°, and when hip abduction was altered from -15° to 30° were quantified. Analyses were automatically performed with a 1° increment in each direction using computer programming. RESULTS: The alteration of hip angles in each direction affected BMD measurements, with the largest changes found for hip flexion (maximum change of 17.7% at 30° flexion) and the smallest changes found for hip rotation (maximum change of 2.2% at 15° internal rotation). The BMD measurements increased by 0.34% for each 1° of hip abduction, and the maximum change was 12.3% at 30° abduction. CONCLUSION: This simulation study quantified the amount of BMD change induced by altering the hip position. Based on these results, we recommend that patients be positioned carefully when acquiring DXA images.

Decomposition of musculoskeletal structures from radiographs using an improved CycleGAN framework.

This paper presents methods of decomposition of musculoskeletal structures from radiographs into multiple individual muscle and bone structures. While existing solutions require dual-energy scan for the training dataset and are mainly applied to structures with high-intensity contrast, such as bones, we focused on multiple superimposed muscles with subtle contrast in addition to bones. The decomposition problem is formulated as an image translation problem between (1) a real X-ray image and (2) multiple digitally reconstructed radiographs, each of which contains a single muscle or bone structure, and solved using unpaired training based on the CycleGAN framework. The training dataset was created via automatic computed tomography (CT) segmentation of muscle/bone regions and virtually projecting them with geometric parameters similar to the real X-ray images. Two additional features were incorporated into the CycleGAN framework to achieve a high-resolution and accurate decomposition: hierarchical learning and reconstruction loss with the gradient correlation similarity metric. Furthermore, we introduced a new diagnostic metric for muscle asymmetry directly measured from a plain X-ray image to validate the proposed method. Our simulation and real-image experiments using real X-ray and CT images of 475 patients with hip diseases suggested that each additional feature significantly enhanced the decomposition accuracy. The experiments also evaluated the accuracy of muscle volume ratio measurement, which suggested a potential application to muscle asymmetry assessment from an X-ray image for diagnostic and therapeutic assistance. The improved CycleGAN framework can be applied for investigating the decomposition of musculoskeletal structures from single radiographs.

Automatic orbital segmentation using deep learning-based 2D U-net and accuracy evaluation: A retrospective study.

The purpose of this study was to verify whether the accuracy of automatic segmentation (AS) of computed tomography (CT) images of fractured orbits using deep learning (DL) is sufficient for clinical application. In the surgery of orbital fractures, many methods have been reported to create a 3D anatomical model for use as a reference. However, because the orbit bone is thin and complex, creating a segmentation model for 3D printing is complicated and time-consuming. Here, the training of DL was performed using U-Net as the DL model, and the AS output was validated with Dice coefficients and average symmetry surface distance (ASSD). In addition, the AS output was 3D printed and evaluated for accuracy by four surgeons, each with over 15 years of clinical experience. One hundred twenty-five CT images were prepared, and manual orbital segmentation was performed in all cases. Ten orbital fracture cases were randomly selected as validation data, and the remaining 115 were set as training data. AS was successful in all cases, with good accuracy: Dice, 0.860 ± 0.033 (mean ± SD); ASSD, 0.713 ± 0.212 mm. In evaluating AS accuracy, the expert surgeons generally considered that it could be used for surgical support without further modification. The orbital AS algorithm developed using DL in this study is extremely accurate and can create 3D models rapidly at low cost, potentially enabling safer and more accurate surgeries.

Artificial intelligence-based volumetric analysis of muscle atrophy and fatty degeneration in patients with hip osteoarthritis and its correlation with health-related quality of life.

PURPOSE: Artificial intelligence (AI) technologies have enabled precise three-dimensional analysis of individual muscles on computed tomography (CT) or magnetic resonance images via automatic segmentation. This study aimed to perform three-dimensional assessments of pelvic and thigh muscle atrophy and fatty degeneration in patients with unilateral hip osteoarthritis using CT and to evaluate the correlation with health-related quality of life (HRQoL). METHODS: The study included one man and 43 women. Six muscle groups were segmented, and the muscle atrophy ratio was calculated volumetrically. The degree of fatty degeneration was defined as the difference between the mean CT values (Hounsfield units [HU]) of the healthy and affected sides. HRQoL was evaluated using the Western Ontario and McMaster Universities Osteoarthritis (WOMAC) index and the Japanese Orthopaedic Association Hip Disease Evaluation Questionnaire (JHEQ). RESULTS: The mean muscle atrophy rate was 16.3%, and the mean degree of muscle fatty degeneration was 7.9 HU. Multivariate correlation analysis revealed that the WOMAC stiffness subscale was significantly related to fatty degeneration of the hamstrings, the WOMAC physical function subscale was significantly related to fatty degeneration of the iliopsoas muscle, and the JHEQ movement subscale was significantly related to fatty degeneration of the hip adductors. CONCLUSION: We found that fatty degeneration of the hamstrings, iliopsoas, and hip adductor muscles was significantly related to HRQoL in patients with hip osteoarthritis. These findings suggest that these muscles should be targeted during conservative rehabilitation for HOA and perioperative rehabilitation for THA.

Rotational-motion measurement of the sacroiliac joint using upright MRI scanning and intensity-based registration: is there a sex difference?

PURPOSE: The sacroiliac joint (SIJ) has attracted increasing attention as a source of low back and groin pain, but the kinematics of SIJ against standing load and its sex difference remain unclear due to the difficulty of in vivo load study. An upright magnetic resonance imaging (MRI) system can provide in vivo imaging both in the supine and standing positions. The reliability of the mobility of SIJ against the standing load was evaluated and its sex difference was examined in healthy young volunteers using an upright MRI. METHOD: Static (reliability) and kinematic studies were performed. In the static study, a dry bone of pelvic ring embedded in gel form and frozen in the plastic box was used. In the kinematic study, 19 volunteers (10 males, 9 females) with a mean age of 23.9 years were included. The ilium positions for the sacrum in supine and standing positions were measured against the pelvic coordinates to evaluate the mobility of the SIJ. RESULTS: In the static study, the residual error of the rotation of the SIJ study was < 0.2°. In the kinematic study, the mean values of SIJ sagittal rotation from supine to standing position in males and females were - 0.9° ± 0.7° (mean ± standard deviation) and - 1.7° ± 0.8°, respectively. The sex difference was statistically significant (p = 0.04). The sagittal rotation of the SIJ showed a significant correlation with the sacral slope. CONCLUSION: The residual error for measuring the SIJ rotation using the upright MRI was < 0.2°. The young healthy participants showed sex differences in the sagittal rotation of the SIJ against the standing load and the females showed a larger posterior rotation of the ilium against the sacrum from the supine to standing position than the males. Therefore, upright MRI is useful to investigate SIJ motion.

Development of a system to assess the two- and three-dimensional bone mineral density of the lumbar vertebrae from clinical quantitative CT images.

This study developed a system to quantify the lumbar spine's bone mineral density (BMD) in two and three dimensions for osteoporosis screening using quantitative CT images. Measuring the two-dimensional BMD could reproduce the BMD measurement performed in dual-energy X-ray absorptiometry, and an accurate diagnosis of osteoporosis was possible. PURPOSE: To date, the assessment of bone mineral density (BMD) using CT images has been made in three dimensions, leading to errors in detecting osteoporosis based on the two-dimensional assessments of BMD using dual-energy X-ray absorptiometry (DXA-BMD). Herein, we aimed to develop a system that measures two- and three-dimensional lumbar BMD from quantitative CT images and validated the accuracy of the system in diagnosing osteoporosis with regard to the DXA classification. METHODS: Fifty-nine pairs of spinal CT and DXA images were analyzed. First, the three-dimensional BMD was measured at the axial slice of the L1 vertebra on CT images (L1-vBMD). Then, the L1-L4 vertebrae were segmented from the CT images to measure the three-dimensional BMD at the trabecular region of the L1-L4 vertebral bodies (CT-vBMD). Lastly, the segmented vertebrae were projected onto the coronal plane to measure the two-dimensional BMD (CT-aBMD). Each parameter was correlated with DXA-BMD, and the receiver operating characteristic (ROC) curve to diagnose osteoporosis was assessed. RESULTS: The correlation coefficients of DXA-BMD with L1-vBMD, CT-vBMD, and CT-aBMD were 0.364, 0.456, and 0.911, respectively (all p < 0.01). In the ROC curve analysis to diagnose osteoporosis, the area under the curve for CT-aBMD (0.941) was significantly higher than those for L1-vBMD (0.582) and CT-vBMD (0.657) (both p < 0.01). CONCLUSION: Compared with L1-vBMD and CT-vBMD, CT-aBMD could accurately predict DXA-BMD and detect patients with osteoporosis. Given that our method can quantify BMD in both two and three dimensions, it could be used to screen for osteoporosis from quantitative CT images.