BACKGROUND: Inflammatory bowel disease (IBD) is a progressive and debilitating inflammatory disease of the gastrointestinal tract (GIT). Despite recent advances, precise treatment and noninvasive monitoring remain challenging.
METHODS: Herein, we developed orally-administered, colitis-targeting and hyaluronic acid (HA)-modified, core-shell curcumin (Cur)- and cerium oxide (CeO2)-loaded nanoprobes (Cur@PC-HA/CeO2 NPs) for computed tomography (CT) imaging-guided treatment and monitoring of IBD in living mice.
RESULTS: Following oral administration, high-molecular-weight HA maintains integrity with little absorption in the upper GIT, and then actively accumulates at local colitis sites owing to its colitis-targeting ability, leading to specific CT enhancement lasting for 24 h. The retained NPs are further degraded by hyaluronidase in the colon to release Cur and CeO2, thereby exerting anti-inflammatory and antioxidant effects. Combined with the ability of NPs to regulate intestinal flora, the oral NPs result in substantial relief in symptoms. Following multiple treatments, the gradually decreasing range of the colon with high CT attenuation correlates with the change in the clinical biomarkers, indicating the feasibility of treatment response and remission.
CONCLUSIONS: This study provides a proof-of-concept for the design of a novel theranostic integration strategy for concomitant IBD treatment and the real-time monitoring of treatment responses.

暂无摘要。

OBJECTIVE: This study aims to investigate the anatomical structure of the C6 pedicle and lateral mass in children aged 0-14 years using CT imaging, providing detailed insights into their growth and development.
METHODS: We conducted a comprehensive measurement of C6. Measurements included width, length, and height of the pedicles, as well as the length, width, and thickness of the lateral masses, and several angular metrics. Regression analysis was performed to understand the growth trends, and statistical analyses were carried out to identify differences between age groups, genders, and sides.
RESULTS: In children younger than four years, the pedicle width exceeds its height, influencing the diameter of the pedicle screws. By age two to three, the pedicle height and lateral mass thickness reaches 3.0 mm, allowing for the use of 3.0 mm diameter screws. The pedicle transverse angle remains stable. Most parameters showed no significant differences between the left and right sides. Size parameters exhibited significant larger in males than females at ages 0-1, 3-7, and 10-12 years. Regression analysis revealed that the growth trends of size parameters follow cubic or polynomial curves. Most angular metrics follow cubic fitting curves without a clear trend of change with age.
CONCLUSIONS: This study provides a detailed analysis of the anatomical development of the C6 pedicle and lateral masses in children, offering valuable insights for pediatric cervical spine surgeries. The findings highlight the importance of considering age-specific anatomical variations when planning posterior surgical fixation, specifically at C6. It is necessary for us to perform thin-layer CT scans on children and carefully measure various indicators before surgery.

Pulmonary sclerosing pneumocytoma (PSP) is a rare, benign tumor. Given the challenges of a bronchoscopic diagnosis, surgery is performed during the early stages of the disease. Therefore, little is known about the growth pattern of PSP. This case of PSP was not diagnosed despite bronchoscopy, resulting in lung resection eight years after the anomaly was first identified on computed tomography (CT). This report compares the long-term follow-up of CT and pathological findings and discusses the difficulty in making a diagnosis using a bronchoscopic forceps biopsy to aid in future PSP diagnoses and treatment planning.

X-ray computed tomography (CT) imaging technology has become an indispensable diagnostic tool in clinical examination. However, it poses a risk of ionizing radiation, making the reduction of radiation dose one of the current research hotspots in CT imaging. Sparse-view imaging, as one of the main methods for reducing radiation dose, has made significant progress in recent years. In particular, sparse-view reconstruction methods based on deep learning have shown promising results. Nevertheless, efficiently recovering image details under ultra-sparse conditions remains a challenge. To address this challenge, this paper proposes a high-frequency enhanced and attention-guided learning Network (HEAL). HEAL includes three optimization strategies to achieve detail enhancement: Firstly, we introduce a dual-domain progressive enhancement module, which leverages fidelity constraints within each domain and consistency constraints across domains to effectively narrow the solution space. Secondly, we incorporate both channel and spatial attention mechanisms to improve the network\'s feature-scaling process. Finally, we propose a high-frequency component enhancement regularization term that integrates residual learning with direction-weighted total variation, utilizing directional cues to effectively distinguish between noise and textures. The HEAL network is trained, validated and tested under different ultra-sparse configurations of 60 views and 30 views, demonstrating its advantages in reconstruction accuracy and detail enhancement.

Several lung diseases lead to alterations in regional lung mechanics, including ventilator- and radiation-induced lung injuries. Such alterations can lead to localized underventilation of the affected areas, resulting in the overdistension of the surrounding healthy regions. Thus, there has been growing interest in quantifying the dynamics of the lung parenchyma using regional biomechanical markers. Image registration through dynamic imaging has emerged as a powerful tool to assess lung parenchyma\'s kinematic and deformation behaviors during respiration. However, the difficulty in validating the image registration estimation of lung deformation, primarily due to the lack of ground-truth deformation data, has limited its use in clinical settings. To address this barrier, we developed a method to convert a finite-element (FE) mesh of the lung into a phantom computed tomography (CT) image, advantageously possessing ground-truth information included in the FE model. The phantom CT images generated from the FE mesh replicated the geometry of the lung and large airways that were included in the FE model. Using spatial frequency response, we investigated the effect of \" imaging parameters\" such as voxel size (resolution) and proximity threshold values on image quality. A series of high-quality phantom images generated from the FE model simulating the respiratory cycle will allow for the validation and evaluation of image registration-based estimations of lung deformation. In addition, the present method could be used to generate synthetic data needed to train machine-learning models to estimate kinematic biomarkers from medical images that could serve as important diagnostic tools to assess heterogeneous lung injuries.

BACKGROUND: Forty to fifty percent of women and 13%-22% of men experience an osteoporosis-related fragility fracture in their lifetimes. After the age of 50 years, the risk of hip fracture doubles in every 10 years. x-Ray based DXA is currently clinically used to diagnose osteoporosis and predict fracture risk. However, it provides only 2-D representation of bone and is associated with other technical limitations. Thus, alternative methods are needed.
OBJECTIVE: To develop and evaluate an ultra-low dose (ULD) hip CT-based automated method for assessment of volumetric bone mineral density (vBMD) at proximal femoral subregions.
METHODS: An automated method was developed to segment the proximal femur in ULD hip CT images and delineate femoral subregions. The computational pipeline consists of deep learning (DL)-based computation of femur likelihood map followed by shape model-based femur segmentation and finite element analysis-based warping of a reference subregion labeling onto individual femur shapes. Finally, vBMD is computed over each subregion in the target image using a calibration phantom scan. A total of 100 participants (50 females) were recruited from the Genetic Epidemiology of COPD (COPDGene) study, and ULD hip CT imaging, equivalent to 18 days of background radiation received by U.S. residents, was performed on each participant. Additional hip CT imaging using a clinical protocol was performed on 12 participants and repeat ULD hip CT was acquired on another five participants. ULD CT images from 80 participants were used to train the DL network; ULD CT images of the remaining 20 participants as well as clinical and repeat ULD CT images were used to evaluate the accuracy, generalizability, and reproducibility of segmentation of femoral subregions. Finally, clinical CT and repeat ULD CT images were used to evaluate accuracy and reproducibility of ULD CT-based automated measurements of femoral vBMD.
RESULTS: Dice scores of accuracy (n = 20), reproducibility (n = 5), and generalizability (n = 12) of ULD CT-based automated subregion segmentation were 0.990, 0.982, and 0.977, respectively, for the femoral head and 0.941, 0.970, and 0.960, respectively, for the femoral neck. ULD CT-based regional vBMD showed Pearson and concordance correlation coefficients of 0.994 and 0.977, respectively, and a root-mean-square coefficient of variation (RMSCV) (%) of 1.39% with the clinical CT-derived reference measure. After 3-digit approximation, each of Pearson and concordance correlation coefficients as well as intraclass correlation coefficient (ICC) between baseline and repeat scans were 0.996 with RMSCV of 0.72%. Results of ULD CT-based bone analysis on 100 participants (age (mean ± SD) 73.6 ± 6.6 years) show that males have significantly greater (p < 0.01) vBMD at the femoral head and trochanteric regions than females, while females have moderately greater vBMD (p = 0.05) at the medial half of the femoral neck than males.
CONCLUSIONS: Deep learning, combined with shape model and finite element analysis, offers an accurate, reproducible, and generalizable algorithm for automated segmentation of the proximal femur and anatomic femoral subregions using ULD hip CT images. ULD CT-based regional measures of femoral vBMD are accurate and reproducible and demonstrate regional differences between males and females.

We present an adult patient, a 39-year-old female, with chief complaints of pain in the umbilical region. The patient was further evaluated by radiological investigations and was diagnosed with small bowel intussusception caused by submucosal lipoma as the lead point. She had undergone ileal resection and anastomosis of the affected segment. The postoperative period was uncomplicated, and the patient continued with regular oral intake. The histopathological analysis revealed it to be adipose tissue with no features of atypia. This case shows the rare presentation of small bowel intussusception due to a submucosal lipoma. It emphasizes the significance of diagnostic imaging tools for diagnosis and the need for surgery for proper administration.

暂无摘要。

OBJECTIVE: Most recently transformer models became the state of the art in various medical image segmentation tasks and challenges, outperforming most of the conventional deep learning approaches. Picking up on that trend, this study aims at applying various transformer models to the highly challenging task of colorectal cancer (CRC) segmentation in CT imaging and assessing how they hold up to the current state-of-the-art convolutional neural network (CNN), the nnUnet. Furthermore, we wanted to investigate the impact of the network size on the resulting accuracies, since transformer models tend to be significantly larger than conventional network architectures.
METHODS: For this purpose, six different transformer models, with specific architectural advancements and network sizes were implemented alongside the aforementioned nnUnet and were applied to the CRC segmentation task of the medical segmentation decathlon.
RESULTS: The best results were achieved with the Swin-UNETR, D-Former, and VT-Unet, each transformer models, with a Dice similarity coefficient (DSC) of 0.60, 0.59 and 0.59, respectively. Therefore, the current state-of-the-art CNN, the nnUnet could be outperformed by transformer architectures regarding this task. Furthermore, a comparison with the inter-observer variability (IOV) of approx. 0.64 DSC indicates almost expert-level accuracy. The comparatively low IOV emphasizes the complexity and challenge of CRC segmentation, as well as indicating limitations regarding the achievable segmentation accuracy.
CONCLUSIONS: As a result of this study, transformer models underline their current upward trend in producing state-of-the-art results also for the challenging task of CRC segmentation. However, with ever smaller advances in total accuracies, as demonstrated in this study by the on par performances of multiple network variants, other advantages like efficiency, low computation demands, or ease of adaption to new tasks become more and more relevant.