Oral enzyme-responsive nanoprobes for targeted theranostics of inflammatory bowel disease.

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. CONCLUSION: 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.

Quantitative Three-Dimensional Imaging Analysis of HfO2 Nanoparticles in Single Cells via Deep Learning Aided X-ray Nano-Computed Tomography.

It is crucial for understanding mechanisms of drug action to quantify the three-dimensional (3D) drug distribution within a single cell at nanoscale resolution. Yet it remains a great challenge due to limited lateral resolution, detection sensitivities, and reconstruction problems. The preferable method is using X-ray nano-computed tomography (Nano-CT) to observe and analyze drug distribution within cells, but it is time-consuming, requiring specialized expertise, and often subjective, particularly with ultrasmall metal nanoparticles (NPs). Furthermore, the accuracy of batch data analysis through conventional processing methods remains uncertain. In this study, we used radioenhancer ultrasmall HfO2 nanoparticles as a model to develop a modular and automated deep learning aided Nano-CT method for the localization quantitative analysis of ultrasmall metal NPs uptake in cancer cells. We have established an ultrasmall objects segmentation method for 3D Nano-CT images in single cells, which can highly sensitively analyze minute NPs and even ultrasmall NPs in single cells. We also constructed a localization quantitative analysis method, which may accurately segment the intracellularly bioavailable particles from those of the extracellular space and intracellular components and NPs. The high bioavailability of HfO2 NPs in tumor cells from deeper penetration in tumor tissue and higher tumor intracellular uptake provide mechanistic insight into HfO2 NPs as advanced radioenhancers in the combination of quantitative subcellular image analysis with the therapeutic effects of NPs on 3D tumor spheroids and breast cancer. Our findings unveil the substantial uptake rate and subcellular quantification of HfO2 NPs by the human breast cancer cell line (MCF-7). This revelation explicates the notable efficacy and safety profile of HfO2 NPs in tumor treatment. These findings demonstrate that this 3D imaging technique promoted by the deep learning algorithm has the potential to provide localization quantitative information about the 3D distributions of specific molecules at the nanoscale level. This study provides an approach for exploring the subcellular quantitative analysis of NPs in single cells, offering a valuable quantitative imaging tool for minute amounts or ultrasmall NPs.

Machine Learning Models Can Define Clinically Relevant Bone Density Subgroups based on Patient Specific Calibrated CT Scans in Patients Undergoing Reverse Shoulder Arthroplasty.

BACKGROUND: Reduced bone density is recognized as a predictor for potential complications in reverse shoulder arthroplasty (RSA). While humeral and glenoid planning based on preoperative computed tomography (CT) scans assist in implant selection and position, reproducible methods for quantifying the patients' bone density are currently not available. The purpose of this study was to perform bone density analyses including patient specific calibration in an RSA cohort based on preoperative CT imaging. It was hypothesized that preoperative CT bone density measures would provide objective quantification of the patients' humeral bone quality. METHODS: This study consisted of three parts, (1) analysis of a patient-specific calibration method in cadaveric CT scans, (2) retrospective application in a clinical RSA cohort, and (3) clustering and classification with machine learning models. Forty cadaveric shoulders were scanned in a clinical CT and compared regarding calibration with density phantoms, air muscle, and fat (patient-specific) or standard Hounsfield unit. Post-scan patient-specific calibration was used to improve the extraction of three-dimensional regions of interest for retrospective bone density analysis in a clinical RSA cohort (n=345). Machine learning models were used to improve the clustering (Hierarchical Ward) and classification (Support Vector Machine (SVM)) of low bone densities in the respective patients. RESULTS: The patient-specific calibration method demonstrated improved accuracy with excellent intraclass correlation coefficients (ICC) for cylindrical cancellous bone densities (ICC>0.75). Clustering partitioned the training data set into a high-density subgroup consisting of 96 patients and a low-density subgroup consisting of 146 patients, showing significant differences between these groups. The SVM showed optimized prediction accuracy of low and high bone densities compared to conventional statistics in the training (accuracy=91.2%; AUC=0.967) and testing (accuracy=90.5 %; AUC=0.958) data set. CONCLUSION: Preoperative CT scans can be used to quantify the proximal humeral bone quality in patients undergoing RSA. The use of machine learning models and patient-specific calibration on bone mineral density demonstrated that multiple 3D bone density scores improved the accuracy of objective preoperative bone quality assessment. The trained model could provide preoperative information to surgeons treating patients with potentially poor bone quality.

Pulmonary Sclerosing Pneumocytoma: A Case Revealed During 8-Year of Follow-up with CT imaging.

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.

Anatomical study of the C6 pedicle and lateral mass in children aged 0-14 years based on CT imaging.

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. CONCLUSION: 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.

The clinical potential of radiomics to predict hematoma expansion in spontaneous intracerebral hemorrhage: a narrative review.

Spontaneous intracerebral hemorrhage (sICH) is associated with significant morbidity and mortality, with subsequent hematoma expansion (HE) linked to worse neurologic outcomes. Accurate, real-time predictions of the risk of HE could enable tailoring management-including blood pressure control or surgery-based on individual patient risk. Although multiple radiographic markers of HE have been proposed based on standard imaging, their clinical utility remains limited by a reliance on subjective interpretation of often ambiguous findings and a poor overall predictive power. Radiomics refers to the quantitative analysis of medical images that can be combined with machine-learning algorithms to identify predictive features for a chosen clinical outcome with a granularity beyond human limitations. Emerging data have supported the potential utility of radiomics in the prediction of HE after sICH. In this review, we discuss the current clinical management of sICH, the impact of HE and standard imaging predictors, and finally, the current data and potential future role of radiomics in HE prediction and management of patients with sICH.

Mapping wrist motion: 3D CT analysis after scapholunate ligament transection.

The injury of the scapholunate (SL) ligament is common in wrist traumas leading to pain and reduced wrist function. The wrist's unique joint design and possible underlying theories as the carpal row theory were subject to earlier investigations studying wrist kinematics. Nevertheless, a comprehensive understanding of how SL ligament injuries affect wrist biomechanics is still lacking. Through a quantitative analysis of carpal bone motion patterns, we evaluated the impact on wrist kinematics occurring after SL ligament injury. We conducted a study using computer tomography imaging to analyse wrist kinematics after SL ligament transection in 21 fresh-frozen anatomical specimens. The collected data were then transformed into 3D models, employing both standardized global and object coordinate systems. The study encompassed the evaluation of rotation and translation for each individual carpal bone, as well as the ulna, and all metacarpal bones in reference to the radius. The study showed a significant increase in rotation towards palmar (p < 0.01), particularly notable for the scaphoid, following transection of the SL ligament during palmar flexion. Ulnar deviation did not significantly affect rotation or translation, and radial deviation also showed no significant changes in rotation or translation. The study highlights the significance of the SL ligament in wrist kinematics, revealing that SL ligament tears lead to changes in wrist motion. While we observed significant rotational changes for the scaphoid, other carpal bones showed less pronounced alterations, emphasizing the complexity of wrist biomechanics.

[18F]-florbetaben PET/CT is sensitive for cardiac AL amyloidosis.

Often differential diagnosis between AL and ATTR amyloidosis is difficult. Concerning ATTR, sensitive diagnostic tool, as diphosphonate scintigraphy, was validated, instead of no imaging approach is as accurate in AL. Cardiac ultrasound and circulating biomarkers may raise the clinical suspicion but biopsy remains the only option for diagnosis. We aimed to explore the sensitivity of 18F-Florbetaben PET respect to blood tests or periumbilical fat (POF), cardiac, bone marrow (BM) or other tissues biopsies in a cohort of 33 patients.

Transformers for colorectal cancer segmentation in CT imaging.

PURPOSE: 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. CONCLUSION: 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.

Synthesis and Preclinical Evaluation of [18F]AlF-NOTA-Asp2-PEG2-Folate as a Novel Folate-Receptor-Targeted Tracer for PET Imaging.

Recently, the folate receptor (FR) has become an exciting target for the diagnosis of FR-positive malignancies. Nevertheless, suboptimal in vivo pharmacokinetic properties, particularly high uptake in the renal and hepatobiliary systems, are important limiting factors for the clinical translation of most FR-based radiotracers. In this study, we developed a novel 18F-labeled FR-targeted positron emission tomography (PET) tracer [18F]AlF-NOTA-Asp2-PEG2-Folate modified with a hydrophilic linker (-Asp2-PEG2) to optimize its pharmacokinetic properties and conducted a comprehensive preclinical assessment. The [18F]AlF-NOTA-Asp2-PEG2-Folate was manually synthesized within 30 min with a non-decay-corrected radiochemical yield of 16.3 ± 2.0% (n = 5). Among KB cells, [18F]AlF-NOTA-Asp2-PEG2-Folate exhibited high specificity and affinity for FR. PET/CT imaging and biodistribution experiments in KB tumor-bearing mice showed decent tumor uptake (1.7 ± 0.3% ID/g) and significantly decreased uptake in kidneys and liver (22.2 ± 2.1 and 0.3 ± 0.1% ID/g at 60 min p.i., respectively) of [18F]AlF-NOTA-Asp2-PEG2-Folate, compared to the known tracer [18F]AlF-NOTA-Folate (78.6 ± 5.1 and 5.3 ± 0.5 % ID/g at 90 min p.i., respectively). The favorable properties of [18F]AlF-NOTA-Asp2-PEG2-Folate, including its efficient synthesis, decent tumor uptake, relatively low renal uptake, and rapid clearance from most normal organs, portray it as a promising PET tracer for FR-positive tumors.

Submucosal Lipoma-Induced Small Bowel Intussusception: A Case Study of Surgical Intervention and Management in an Adult Female.

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.

Ultra-low dose hip CT-based automated measurement of volumetric bone mineral density at proximal femoral subregions.

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. PURPOSE: 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. CONCLUSION: 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.

The first-in-human preclinical evaluation of the new probe [123I]I-PSMA-7 for real-time intraoperative targeted biopsy and SPECT/CT imaging in prostate cancer.

PURPOSE: PSMA/PET has been increasingly used to detect PCa, and PSMA/PET-guided biopsy has shown promising results. However, it cannot be confirmed immediately whether the tissues are the targeted area. In this study, we aimed to develop a novel probe, [123I]I-PSMA-7. First, we hope that [123I]I-PSMA-7 can provide instant confirmation for prostate biopsy. Second, we hope it will help detect PCa. METHODS: We synthesized a high-affinity probe, [123I]I-PSMA-7, and evaluated its properties. We included ten patients with suspected PCa and divided them into two groups. The injection and biopsy were approximately 24 h apart. The activity in biopsy lesions was measured as the cpm by a γ-counter. Moreover, we enrolled 3 patients to evaluate the potential of [123I]I-PSMA-7 for detecting PCa. RESULTS: Animal experiments verified the safety, targeting and effectiveness of [123I]I-PSMA-7, and the tumor-to-muscle ratio was greatest at 24 h, which confirmed the results of this study in humans. After injection of 185MBq [123I]I-PSMA-7, 18/55 cores were positive, and the cpm was significantly greater (4345 ± 3547 vs. 714 ± 547, P < 0.001), with an AUC of 0.97 and a cutoff of 1312 (sens/spec of 94.40%/91.90%). At a lower dose, 10/55 biopsy cores were cancerous, and the cpm was 2446 ± 1622 vs. 153 ± 112 (P < 0.001). The AUC was 1, with a cutoff value of 490 (sens/spec of 100%). When the radiopharmaceuticals were added to 370 MBq, we achieved better SPECT/CT imaging. CONCLUSION: With the aid of [123I]I-PSMA-7 and via cpm-based biopsy, we can reduce the number of biopsies to a minimum operation. [123I]I-PSMA-7 PSMA SPECT/CT can also provide good imaging results. TRIAL REGISTRATION: Chinese Clinical trial registry ChiCTR2300069745, Registered 24 March 2023.

HEAL: High-Frequency Enhanced and Attention-Guided Learning Network for Sparse-View CT Reconstruction.

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.

Preclinical characterization of endotoxin-induced uveitis models using OCT, PET/CT and proteomics.

Uveitis is a group of inflammatory ocular pathologies. Endotoxin-Induced Uveitis (EIU) model represent a well-known model induced by administration of Lipopolysaccharide (LPS). The aim is to characterize two models of EIU through two routes of administration with novel noninvasive imaging techniques. 29 rats underwent Intraocular Pressure (IOP) measurements, Optical Coherence Tomography (OCT), proteomic analysis, and Positron Emission Tomography and Computed Tomography (PET/CT). Groups included healthy controls (C), BSS administered controls (Ci), systemically induced EIU with LPS (LPSs), and intravitreally induced EIU with LPS (LPSi) for IOP, OCT, and proteomic studies. For 18F-FDG PET/CT study, animals were divided into FDG-C, FDG-LPSs and FDG-LPSi groups and scanned using a preclinical PET/CT system. LPSi animals exhibited higher IOP post-induction compared to C and LPSs groups. LPSi showed increased cellular infiltrate, fibrotic membranes, and iris inflammation. Proinflammatory proteins were more expressed in EIU models, especially LPSi. PET/CT indicated higher eye uptake in induced models compared to FDG-C. FDG-LPSi showed higher eye uptake than FDG-LPSs but systemic uptake was higher in FDG-LPSs due to generalized inflammation. OCT is valuable for anterior segment assessment in experimental models. 18F-FDG PET/CT shows promise as a noninvasive biomarker for ocular inflammatory diseases. Intravitreal induction leads to higher ocular inflammation. These findings offer insights for future inflammatory disease research and drug studies.

In-silico CT lung phantom generated from finite-element mesh.

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.