Deformation in transcatheter heart valves: Clinical implications and considerations

Transcatheter aortic valve replacement (TAVR) has emerged as a preferred treatment modality for aortic stenosis, marking a significant advancement in cardiac interventions. Transcatheter heart valves (THVs) have also received approval for treating failed bioprosthetic valves and rings across aortic, mitral, tricuspid, and pulmonic positions. Unlike surgically implanted valves, which are sewn into the annulus, THVs are anchored through relative oversizing. Although THVs are designed to function optimally in a fully expanded state, they exhibit a certain degree of tolerance to underexpansion. However, significant deformation beyond this tolerance can adversely affect the valve's hemodynamics and durability, ultimately impacting patient outcomes. Such post-implantation deviations from the valve's intended three-dimensional design are influenced by a variety of physiological and anatomical factors unique to each patient and procedure, leading to underexpansion, eccentric expansion, and vertical deformation. These deformation patterns increase leaflet stress and strain, potentially causing fatigue and damage. This review article delves into the extent of THV deformation, its impact on leaflet function, hypoattenuating leaflet thickening, and structural valve degeneration. It provides an in-depth analysis of deformation specifics in different procedural contexts, including TAVR in native aortic stenosis, aortic and mitral valve-in-valve procedures, and redo-TAVR. Additionally, the review discusses strategies to mitigate THV deformation during the procedure, offering insights into potential solutions to these challenges.

Systematic training of LI-RADS CT v2018 improves interobserver agreements and performances in LR categorization for focal liver lesions

AIM

To retrospectively explored whether systematic training in the use of Liver Imaging Reporting and Data System (LI-RADS) v2018 on computed tomography (CT) can improve the interobserver agreements and performances in LR categorization for focal liver lesions (FLLs) among different radiologists.

MATERIALS AND METHODS

A total of 18 visiting radiologists and the liver multiphase CT images of 70 hepatic observations in 63 patients at high risk of HCC were included in this study. The LI-RADS v2018 training procedure included three thematic lectures, with an interval of 1 month. After each seminar, the radiologists had 1 month to adopt the algorithm into their daily work. The interobserver agreements and performances in LR categorization for FLLs among the radiologists before and after training were compared.

RESULTS

After training, the interobserver agreements in classifying the LR categories for all radiologists were significantly increased for most LR categories (P < 0.001), except for LR-1 (P = 0.053). After systematic training, the areas under the curve (AUCs) for LR categorization performance for all participants were significantly increased for most LR categories (P < 0.001), except for LR-1 (P = 0.062).

CONCLUSION

Systematic training in the use of the LI-RADS can improve the interobserver agreements and performances in LR categorization for FLLs among radiologists with different levels of experience.

Effectiveness of low dose computed tomography to detect fractures in paediatric suspected physical abuse: a systematic review

PURPOSE

The skeletal survey X-ray series is the current 'gold standard' when investigating suspected physical abuse (SPA) of children, in addition to a non-contrast computed tomography (CT) brain scan. This systematic literature review synthesised findings of published research to determine if low dose computed tomography (LDCT) could detect subtle fractures and therefore replace the skeletal survey X-ray series in the investigation of SPA in children aged under 3 years.

METHODS

Five electronic databases and grey literature were systematically searched from their inception to 28 April 2022. Primary studies were included where the population comprised paediatric patients up to 16 years and LDCT was used to detect fractures associated with SPA. Studies involving imaging investigations of the head, standard dose CT examinations or accidental trauma were excluded.

RESULTS

Three studies met the inclusion criteria, all of which were case series. These studies did not report many of the criteria required to compare the accuracy of LDCT to X-ray, i.e. they did not meet the criteria for a diagnostic accuracy test. Therefore, it is difficult to conclude from the case series if LDCT is accurate enough to replace X-rays.

CONCLUSION

Due to the gap in current literature, a phantom study and subsequent post-mortem CT study are recommended as the primary investigative methods to assess the ability of low-dose CT to identify the subtle fractures associated with SPA and to calculate how low the achievable CT dose can be.

Cholangiocarcinoma imaging: from diagnosis to response assessment

Cholangiocarcinoma (CCA), a highly aggressive primary liver cancer arising from the bile duct epithelium, represents a substantial proportion of hepatobiliary malignancies, posing formidable challenges in diagnosis and treatment. Notably, the global incidence of intrahepatic CCA has seen a rise, necessitating a critical examination of diagnostic and management strategies, especially due to presence of close imaging mimics such as hepatocellular carcinoma (HCC) and combined hepatocellular carcinoma-cholangiocarcinoma (cHCC-CCA). Hence, it is imperative to understand the role of various imaging modalities such as ultrasound, computed tomography (CT), and magnetic resonance imaging (MRI), elucidating their strengths, and limitations in diagnostic precision and staging accuracy. Beyond conventional approaches, there is emerging significance of functional imaging tools including positron emission tomography (PET)-CT and diffusion-weighted (DW)-MRI, providing pivotal insights into diagnosis, therapeutic assessment, and prognostic evaluation. This comprehensive review explores the risk factors, classification, clinical features, and role of imaging in the holistic spectrum of diagnosis, staging, management, and restaging for CCA, hence serving as a valuable resource for radiologists evaluating CCA.

Evaluation of four computed tomography reconstruction algorithms using a coronary artery phantom

Background

Despite advancements in coronary computed tomography angiography (CTA), challenges in positive predictive value and specificity remain due to limited spatial resolution. The purpose of this experimental study was to investigate the effect of 2nd generation deep learning-based reconstruction (DLR) on the quantitative and qualitative image quality in coronary CTA.

Methods

A vessel model with stepwise non-calcified plaque was scanned using 320-detector CT. Image reconstruction was performed using four techniques: hybrid iterative reconstruction (HIR), model-based iterative reconstruction (MBIR), DLR, and 2nd generation DLR. The luminal peak CT number, contrast-to-noise ratio (CNR), and edge rise slope (ERS) were quantitatively evaluated via profile curve analysis. Two observers qualitatively graded the graininess, lumen sharpness, and overall lumen visibility on the basis of the degree of confidence for the stenosis severity using a five-point scale.

Results

The image noise with HIR, MBIR, DLR, and 2nd generation DLR was 23.0, 21.0, 16.9, and 9.5 HU, respectively. The corresponding CNR (25% stenosis) was 15.5, 15.9, 22.1, and 38.3, respectively. The corresponding ERS (25% stenosis) was 203.2, 198.6, 228.9, and 262.4 HU/mm, respectively. Among the four reconstruction methods, the 2nd generation DLR achieved the significantly highest CNR and ERS values. The score of 2nd generation DLR in all evaluation points (graininess, sharpness, and overall lumen visibility) was higher than those of the other methods (overall vessel visibility score, 2.6±0.5, 3.8±0.6, 3.7±0.5, and 4.6±0.5 with HIR, MBIR, DLR, and 2nd generation DLR, respectively).

Conclusions

2nd generation DLR provided better CNR and ERS in coronary CTA than HIR, MBIR, and previous-generation DLR, leading to the highest subjective image quality in the assessment of vessel stenosis.

Utility of MRI-based vertebral bone quality scores and CT-based Hounsfield unit values in vertebral bone mineral density assessment for patients with diffuse idiopathic skeletal hyperostosis

This study investigated bone mineral density assessment for patients with DISH. DXA-based T-scores overestimated bone quality, while MRI-based VBQ scores and CT-based HU values provided accurate assessments, particularly for advanced degenerative cases. This enhances accurate evaluation of BMD, crucial for clinical decision-making.

PURPOSE

To investigate the diagnostic effectiveness of DXA, MRI, and CT in assessing bone mineral density (BMD) for diffuse idiopathic skeletal hyperostosis (DISH) patients.

METHODS

Retrospective analysis of 105 DISH patients and 116 age-matched controls with lumbar spinal stenosis was conducted. BMD was evaluated using DXA-based T-scores, MRI-based vertebral bone quality (VBQ) scores, and CT-based Hounsfield unit (HU) values. Patients were categorized into three BMD subgroups. Lumbar osteophyte categories were determined by Mata score. Demographics, clinical data, T-scores, VBQ scores, and HU values were collected. Receiver operating characteristic (ROC) analysis identified VBQ and HU thresholds for diagnosing normal BMD using DXA in controls. Correlations between VBQ, HU, and lumbar T-score were analyzed.

RESULTS

Age, gender, and BMI showed no significant differences between DISH and control groups. DISH patients had higher T-score (L1-4), the lowest T-score, and Mata scores. VBQ and HU did not significantly differ between groups. In controls, VBQ and HU effectively diagnosed normal BMD (AUC = 0.857 and 0.910, respectively) with cutoffs of 3.0 for VBQ and 104.3 for HU. DISH had higher normal BMD prevalence using T-scores (69.5% vs. 58.6%, P < 0.05), but no significant differences using VBQ (57.1% vs. 56.2%, P > 0.05) and HU (58.1% vs. 57.8%, P > 0.05). Correlations revealed moderate correlations between HU and T-scores (L1-4) in DISH (r = 0.642, P < 0.001) and strong in controls (r = 0.846, P < 0.001). Moderate negative correlations were observed between VBQ and T-scores (L1-4) in DISH (r =  - 0.450, P < 0.001) and strong in controls (r =  - 0.813, P < 0.001).

CONCLUSION

DXA-based T-scores may overestimate BMD in DISH. VBQ scores and HU values could effectively complement BMD assessment, particularly in DISH patients or those with advanced lumbar degeneration.

Educational simulator for mastoidectomy considering mechanical properties using 3D printing and its usability evaluation

Complex temporal bone anatomy complicates operations; thus, surgeons must engage in practice to mitigate risks, improving patient safety and outcomes. However, existing training methods often involve prohibitive costs and ethical problems. Therefore, we developed an educational mastoidectomy simulator, considering mechanical properties using 3D printing. The mastoidectomy simulator was modeled on computed tomography images of a patient undergoing a mastoidectomy. Infill was modeled for each anatomical part to provide a realistic drilling sensation. Bone and other anatomies appear in assorted colors to enhance the simulator's educational utility. The mechanical properties of the simulator were evaluated by measuring the screw insertion torque for infill specimens and cadaveric temporal bones and investigating its usability with a five-point Likert-scale questionnaire completed by five otolaryngologists. The maximum insertion torque values of the sigmoid sinus, tegmen, and semicircular canal were 1.08 ± 0.62, 0.44 ± 0.42, and 1.54 ± 0.43 N mm, displaying similar-strength infill specimens of 40%, 30%, and 50%. Otolaryngologists evaluated the quality and usability at 4.25 ± 0.81 and 4.53 ± 0.62. The mastoidectomy simulator could provide realistic bone drilling feedback for educational mastoidectomy training while reinforcing skills and comprehension of anatomical structures.

Lumbar disc herniation: Epidemiology, clinical and radiologic diagnosis WFNS spine committee recommendations

Objective

To formulate the most current, evidence-based recommendations regarding the epidemiology, clinical diagnosis, and radiographic diagnosis of lumbar herniated disk (LDH).

Methods

A systematic literature search in PubMed, MEDLINE, and CENTRAL was performed from 2012 to 2022 using the search terms "herniated lumbar disc", "epidemiology", "prevention" "clinical diagnosis", and "radiological diagnosis". Screening criteria resulted in 17, 16, and 90 studies respectively that were analyzed regarding epidemiology, clinical diagnosis, and radiographic diagnosis of LDH. Using the Delphi method and two rounds of voting at two separate international meetings, ten members of the WFNS (World Federation of Neurosurgical Societies) Spine Committee generated eleven final consensus statements.

Results

The lifetime risk for symptomatic LDH is 1-3%; of these, 60-90% resolve spontaneously. Risk factors for LDH include genetic and environmental factors, strenuous activity, and smoking. LDH is more common in males and in 30-50 year olds. A set of clinical tests, including manual muscle testing, sensory testing, Lasegue sign, and crossed Lasegue sign are recommended to diagnose LDH. Magnetic resonance imaging (MRI) is the gold standard for confirming suspected LDH.

Conclusions

These eleven final consensus statements provide current, evidence-based guidelines on the epidemiology, clinical diagnosis, and radiographic diagnosis of LDH for practicing spine surgeons worldwide.

Phantom study for CT artifacts of dental titanium implants and zirconia upper structures: the effects of occlusal plane angle setting and SEMAR algorithm

OBJECTIVES

The single-energy metal artifact reduction (SEMAR) algorithm effectively reduces metal artifacts in computed tomography (CT). The study aimed to evaluate the effect of the occlusal plane angle on metal artifacts caused by dental implants and zirconia upper structures, and the effectiveness of SEMAR for CT prognostic evaluation.

METHODS

Part of a bovine rib was used as the mandibular implant phantom. First, the phantom immersed in a water tank was scanned using CT to obtain the control image under certain conditions. Subsequently, three titanium implant bodies were implanted in a straight line into the phantom, and a zirconia superstructure was attached. CT scans were performed. The CT-reconstructed images were obtained with and without SEMAR processing. Twelve regions of interest (ROIs) were set at the same site on each sagittal image, and the CT values were measured at all the ROIs. The CT values of the ROIs in the control images and those of the ROIs with and without SEMAR were compared.

RESULTS

The variations in the occlusal plane angle during CT imaging negligibly affected the number of regions in which metal artifacts appeared. SEMAR improved the CT value of the trabecular bone, which was affected by metal artifacts.

CONCLUSION

This study showed that the occlusal plane angle occasionally did not affect the area of metal artifacts caused by dental implants or zirconia upper structures. Other results indicate that SEMAR is effective for accurately evaluating the alveolar bone around the implant body by reducing metal artifacts.

Reducing Postoperative CT Imaging for Children With Complicated Appendicitis: A Pediatric Surgical Quality Collaborative Quality Improvement Project

BACKGROUND

Despite widespread initiatives to reduce ionizing radiation for appendicitis diagnosis, computed tomography (CT) scanning postoperatively remains common. The Pediatric Surgery Quality Collaborative (PSQC) aimed to identify differences between children's hospitals with high and low postoperative CT usage for complicated appendicitis.

METHODS

Using National Surgery Quality Improvement Program Pediatric data from PSQC children's hospitals, we compared postoperative CT imaging for complicated appendicitis (April 2020-March 2021). Key stakeholders from 11 hospitals (5 low CT utilization, 6 high CT utilization) participated in semi-structured interviews regarding postoperative imaging. Qualitative analysis of transcripts was performed deductively and inductively based on the Theoretical Domains Framework (TDF).

RESULTS

Five of twelve TDF domains were most prominent in influencing CT use: skills, beliefs about capabilities, intentions/goals, memory and decision processes, and environment. Children's hospitals with lower rates of postoperative CT use tended to: trust and educate the ultrasound technicians; believe US strengths outweigh weaknesses; image no sooner than 7 days postoperatively; have access to sufficient quality improvement resources; maintain trusting relationships between specialties; and prioritize radiation stewardship.

CONCLUSION

Hospitals at extremes of postoperative CT use for complicated appendicitis reveal strategies for improvement, which include imaging protocol development and adherence, quality improvement resource availability, interdisciplinary collaboration, and promoting radiation stewardship.

LEVEL OF EVIDENCE

Level V.

Molecular mechanism of quality changes in solid endosperm of tender coconut during room temperature storage based on transcriptome and metabolome

Tender coconut (TC) is popular around the world. Postharvest storage of TC leads to a decline in its appearance quality and flavor in both liquid endosperm (LE) and solid endosperm (SE). While LE is the most consumed part and remains in a liquid state, SE is the only cellular tissue directly connected to LE and may be the main contributor to flavor deterioration during storage. This study focused on investigating SE changes during TC storage at 25 °C using computed tomographic technology, transcriptome and metabolome analyses. The results showed increased thickness and density, elevated protein and fat contents, and decreased reducing and soluble sugars in SE of TC during storage. Integrated transcriptome and metabolome analysis revealed that these changes were mainly associated with the gene transcription levels involved in amino acid, carbohydrate and lipid metabolisms, along with specific metabolites. These findings offer valuable insights for controlling TC quality during storage.

3D visualization and printing: An "Anatomical Engineering" trend revealing underlying morphology via innovation and reconstruction towards future of veterinary anatomy

The amalgamation of veterinary anatomy, technology and innovation has led to development of latest technological advancement in the field of veterinary medicine, i.e., three-dimensional (3D) imaging and reconstruction. 3D visualization technique followed by 3D reconstruction has been proven to enhance non-destructive 3D visualization grossly or microscopically, e.g., skeletal muscle, smooth muscle, ligaments, cartilage, connective tissue, blood vessels, nerves, lymph nodes, and glands. The core aim of this manuscript is to document non-invasive 3D visualization methods being adopted currently in veterinary anatomy to reveal underlying morphology and to reconstruct them by 3D softwares followed by printing, its applications, current challenges, trends and future opportunities. 3D visualization methods such as MRI, CT scans and micro-CT scans are utilised in revealing volumetric data and underlying morphology at microscopic levels as well. This will pave a way to transform and re-invent the future of teaching in veterinary medicine, in clinical cases as well as in exploring wildlife anatomy. This review provides novel insights into 3D visualization and printing as it is the future of veterinary anatomy, thus making it spread to become the plethora of opportunities for whole veterinary science.

Head-to-head comparison of [11C]methionine PET, [11C]choline PET, and 4-dimensional CT as second-line scans for detection of parathyroid adenomas in primary hyperparathyroidism

PURPOSE

Accurate preoperative localization is imperative to guide surgery in primary hyperparathyroidism (pHPT). It remains unclear which second-line imaging technique is most effective after negative first-line imaging. In this study, we compare the diagnostic effectiveness of [11C]methionine PET/CT, [11C]choline PET/CT, and four dimensional (4D)-CT head-to-head in patients with pHPT, to explore which of these imaging techniques to use as a second-line scan.

METHODS

We conducted a powered, prospective, blinded cohort study in patients with biochemically proven pHPT and prior negative or discordant first-line imaging consisting of ultrasonography and 99mTc-sestamibi. All patients underwent [11C]methionine PET/CT, [11C]choline PET/CT, and 4D-CT. At first, all scans were interpreted by a nuclear medicine physician, and a radiologist who were blinded from patient data and all imaging results. Next, a non-blinded scan reading was performed. The scan results were correlated with surgical and histopathological findings. Serum calcium values at least 6 months after surgery were used as gold standard for curation of HPT.

RESULTS

A total of 32 patients were included in the study. With blinded evaluation, [11C]choline PET/CT was positive in 28 patients (88%), [11C]methionine PET/CT in 23 (72%), and 4D-CT in 15 patients (47%), respectively. In total, 30 patients have undergone surgery and 32 parathyroid lesions were histologically confirmed as parathyroid adenomas. Based on the blinded evaluation, lesion-based sensitivity of [11C]choline PET/CT, [11C]methionine PET/CT, and 4D-CT was respectively 85%, 67%, and 39%. The sensitivity of [11C]choline PET/CT differed significantly from that of [11C]methionine PET/CT and 4D-CT (p = 0.031 and p < 0.0005, respectively).

CONCLUSION

In the setting of pHPT with negative first-line imaging, [11C]choline PET/CT is superior to [11C]methionine PET/CT and 4D-CT in localizing parathyroid adenomas, allowing correct localization in 85% of adenomas. Further studies are needed to determine cost-benefit and efficacy of these scans, including the timing of these scans as first- or second-line imaging techniques.

Tomographic Image Reconstruction Using an Advanced Score Function (ADSF)

Computed tomography (CT) reconstructs volumetric images using X-ray projection data acquired from multiple angles around an object. For low-dose or sparse-view CT scans, the classic image reconstruction algorithms often produce severe noise and artifacts. To address this issue, we develop a novel iterative image reconstruction method based on maximum a posteriori (MAP) estimation. In the MAP framework, the score function, i.e., the gradient of the logarithmic probability density distribution, plays a crucial role as an image prior in the iterative image reconstruction process. By leveraging the Gaussian mixture model, we derive a novel score matching formula to establish an advanced score function (ADSF) through deep learning. Integrating the new ADSF into the image reconstruction process, a new ADSF iterative reconstruction method is developed to improve image reconstruction quality. The convergence of the ADSF iterative reconstruction algorithm is proven through mathematical analysis. The performance of the ADSF reconstruction method is also evaluated on both public medical image datasets and clinical raw CT datasets. Our results show that the ADSF reconstruction method can achieve better denoising and deblurring effects than the state-of-the-art reconstruction methods, showing excellent generalizability and stability.

A 3D lung lesion variational autoencoder

In this study, we develop a 3D beta variational autoencoder (beta-VAE) to advance lung cancer imaging analysis, countering the constraints of conventional radiomics methods. The autoencoder extracts information from public lung computed tomography (CT) datasets without additional labels. It reconstructs 3D lung nodule images with high quality (structural similarity: 0.774, peak signal-to-noise ratio: 26.1, and mean-squared error: 0.0008). The model effectively encodes lesion sizes in its latent embeddings, with a significant correlation with lesion size found after applying uniform manifold approximation and projection (UMAP) for dimensionality reduction. Additionally, the beta-VAE can synthesize new lesions of varying sizes by manipulating the latent features. The model can predict multiple clinical endpoints, including pathological N stage or KRAS mutation status, on the Stanford radiogenomics lung cancer dataset. Comparisons with other methods show that the beta-VAE performs equally well in these tasks, suggesting its potential as a pretrained model for predicting patient outcomes in medical imaging.

CT whole lung radiomic nomogram: a potential biomarker for lung function evaluation and identification of COPD

BACKGROUND

Computed tomography (CT) plays a great role in characterizing and quantifying changes in lung structure and function of chronic obstructive pulmonary disease (COPD). This study aimed to explore the performance of CT-based whole lung radiomic in discriminating COPD patients and non-COPD patients.

METHODS

This retrospective study was performed on 2785 patients who underwent pulmonary function examination in 5 hospitals and were divided into non-COPD group and COPD group. The radiomic features of the whole lung volume were extracted. Least absolute shrinkage and selection operator (LASSO) logistic regression was applied for feature selection and radiomic signature construction. A radiomic nomogram was established by combining the radiomic score and clinical factors. Receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA) were used to evaluate the predictive performance of the radiomic nomogram in the training, internal validation, and independent external validation cohorts.

RESULTS

Eighteen radiomic features were collected from the whole lung volume to construct a radiomic model. The area under the curve (AUC) of the radiomic model in the training, internal, and independent external validation cohorts were 0.888 [95% confidence interval (CI) 0.869-0.906], 0.874 (95%CI 0.844-0.904) and 0.846 (95%CI 0.822-0.870), respectively. All were higher than the clinical model (AUC were 0.732, 0.714, and 0.777, respectively, P < 0.001). DCA demonstrated that the nomogram constructed by combining radiomic score, age, sex, height, and smoking status was superior to the clinical factor model.

CONCLUSIONS

The intuitive nomogram constructed by CT-based whole-lung radiomic has shown good performance and high accuracy in identifying COPD in this multicenter study.

Sd-net: a semi-supervised double-cooperative network for liver segmentation from computed tomography (CT) images

INTRODUCTION

The automatic segmentation of the liver is a crucial step in obtaining quantitative biomarkers for accurate clinical diagnosis and computer-aided decision support systems. This task is challenging due to the frequent presence of noise and sampling artifacts in computerized tomography (CT) images, as well as the complex background, variable shapes, and blurry boundaries of the liver. Standard segmentation of medical images based on full-supervised convolutional networks demands accurate dense annotations. Such a learning framework is built on laborious manual annotation with strict requirements for expertise, leading to insufficient high-quality labels.

METHODS

To overcome such limitation and exploit massive weakly labeled data, we relaxed the rigid labeling requirement and developed a semi-supervised double-cooperative network (SD- Net). SD-Net is trained to segment the complete liver volume from preoperative abdominal CT images by using limited labeled datasets and large-scale unlabeled datasets. Specifically, to enrich the diversity of unsupervised information, we construct SD-Net consisting of two collaborative network models. Within the supervised training module, we introduce an adaptive mask refinement approach. First, each of the two network models predicts the labeled dataset, after which adaptive mask refinement of the difference predictions is implemented to obtain more accurate liver segmentation results. In the unsupervised training module, a dynamic pseudo-label generation strategy is proposed. First each of the two models predicts unlabeled data and the better prediction is considered as pseudo-labeling before training.

RESULTS AND DISCUSSION

Based on the experimental findings, the proposed method achieves a dice score exceeding 94%, indicating its high level of accuracy and its suitability for everyday clinical use.

Evaluation of data uncertainty for deep-learning-based CT noise reduction using ensemble patient data and a virtual imaging trial framework

Deep learning-based image reconstruction and noise reduction (DLIR) methods have been increasingly deployed in clinical CT. Accurate assessment of their data uncertainty properties is essential to understand the stability of DLIR in response to noise. In this work, we aim to evaluate the data uncertainty of a DLIR method using real patient data and a virtual imaging trial framework and compare it with filtered-backprojection (FBP) and iterative reconstruction (IR). The ensemble of noise realizations was generated by using a realistic projection domain noise insertion technique. The impact of varying dose levels and denoising strengths were investigated for a ResNet-based deep convolutional neural network (DCNN) model trained using patient images. On the uncertainty maps, DCNN shows more detailed structures than IR although its bias map has less structural dependency, which implies that DCNN is more sensitive to small changes in the input. Both visual examples and histogram analysis demonstrated that hotspots of uncertainty in DCNN may be associated with a higher chance of distortion from the truth than IR, but it may also correspond to a better detection performance for some of the small structures.

Accuracy of solid portion size measured on multiplanar volume rendering images for assessing invasiveness in lung adenocarcinoma manifesting as subsolid nodules

Background

The solid component of subsolid nodules (SSNs) is closely associated with the invasiveness of lung adenocarcinoma, and its accurate assessment is crucial for selecting treatment method. Therefore, this study aimed to evaluate the accuracy of solid component size within SSNs measured on multiplanar volume rendering (MPVR) and compare it with the dimensions of invasive components on pathology.

Methods

A pilot study was conducted using a chest phantom to determine the optimal MPVR threshold for the solid component within SSN, and then clinical validation was carried out by retrospective inclusion of patients with pathologically confirmed solitary SSN from October 2020 to October 2021. The radiological tumor size on MPVR and solid component size on MPVR (RSSm) and on lung window (RSSl) were measured. The size of the tumor and invasion were measured on the pathological section, and the invasion, fibrosis, and inflammation within SSNs were also recorded. The measurement difference between computed tomography (CT) and pathology, inter-observer and inter-measurement agreement were analyzed. Receiver operating characteristic (ROC) analysis and Bland-Altman plot were performed to evaluate the diagnostic efficiency of MPVR.

Results

A total of 142 patients (mean age, 54±11 years, 39 men) were retrospectively enrolled in the clinical study, with 26 adenocarcinomas in situ, 92 minimally invasive adenocarcinomas (MIAs), and 24 invasive adenocarcinomas (IAs). The RSSl was significantly smaller than pathological invasion size with fair inter-measurement agreement [intraclass correlation coefficient (ICC) =0.562, P<0.001] and moderate interobserver agreement (ICC =0.761, P<0.001). The RSSm was significantly larger than pathological invasion size with the excellent inter-measurement agreement (ICC =0.829, P<0.001) and excellent (ICC =0.952, P<0.001) interobserver agreement. ROC analysis showed that the cutoff value of RSSm for differentiating adenocarcinoma in situ from MIA and MIA from IA was 1.85 and 6.45 mm (sensitivity: 93.8% and 95.5%, specificity: 85.7% and 88.2%, 95% confidence internal: 0.914-0.993 and 0.900-0.983), respectively. The positive predictive value-and negative predictive value of MPVR in predicting invasiveness were 92.8% and 100%, respectively.

Conclusions

Using MPVR to predict the invasive degree of SSN had high accuracy and good inter-observer agreement, which is superior to lung window measurements and helpful for clinical decision-making.

Fully automatic deep learning-based lung parenchyma segmentation and boundary correction in thoracic CT scans

PURPOSE

The proposed work aims to develop an algorithm to precisely segment the lung parenchyma in thoracic CT scans. To achieve this goal, the proposed technique utilized a combination of deep learning and traditional image processing algorithms. The initial step utilized a trained convolutional neural network (CNN) to generate preliminary lung masks, followed by the proposed post-processing algorithm for lung boundary correction.

METHODS

First, the proposed method trained an improved 2D U-Net CNN model with Inception-ResNet-v2 as its backbone. The model was trained on 32 CT scans from two different sources: one from the VESSEL12 grand challenge and the other from AIIMS Delhi. Further, the model's performance was evaluated on a test dataset of 16 CT scans with juxta-pleural nodules obtained from AIIMS Delhi and the LUNA16 challenge. The model's performance was assessed using evaluation metrics such as average volumetric dice coefficient (DSCavg), average IoU score (IoUavg), and average F1 score (F1avg). Finally, the proposed post-processing algorithm was implemented to eliminate false positives from the model's prediction and to include juxta-pleural nodules in the final lung masks.

RESULTS

The trained model reported a DSCavg of 0.9791 ± 0.008, IoUavg of 0.9624 ± 0.007, and F1avg of 0.9792 ± 0.004 on the test dataset. Applying the post-processing algorithm to the predicted lung masks obtained a DSCavg of 0.9713 ± 0.007, IoUavg of 0.9486 ± 0.007, and F1avg of 0.9701 ± 0.008. The post-processing algorithm successfully included juxta-pleural nodules in the final lung mask.

CONCLUSIONS

Using a CNN model, the proposed method for lung parenchyma segmentation produced precise segmentation results. Furthermore, the post-processing algorithm addressed false positives and negatives in the model's predictions. Overall, the proposed approach demonstrated promising results for lung parenchyma segmentation. The method has the potential to be valuable in the advancement of computer-aided diagnosis (CAD) systems for automatic nodule detection.

CT features based preoperative predictors of aggressive pathology for clinical T1 solid renal cell carcinoma and the development of nomogram model

BACKGROUND

We aimed to identify preoperative predictors of aggressive pathology for cT1 solid renal cell carcinoma (RCC) by combining clinical features with qualitative and quantitative CT parameters, and developed a nomogram model.

METHODS

We conducted a retrospective study of 776 cT1 solid RCC patients treated with partial nephrectomy (PN) or radical nephrectomy (RN) between 2018 and 2022. All patients underwent four-phase contrast-enhanced CT scans and the CT parameters were obtained by two experienced radiologists using region of interest (ROI). Aggressive pathology was defined as patients with nuclear grade III-IV; upstage to pT3a; type II papillary renal cell carcinoma (pRCC), collecting duct or renal medullary carcinoma, unclassified RCC or sarcomatoid/rhabdoid features. Univariate and multivariate logistic analyses were used to determine significant predictors and develop the nomogram model. To evaluate the accuracy and clinical utility of the nomogram model, we used the receiver operating characteristic (ROC) curve, calibration plot, decision curve analysis (DCA), risk stratification, and subgroup analysis.

RESULTS

Of the 776 cT1 solid RCC patients, 250 (32.2%) had aggressive pathological features. The interclass correlation coefficient (ICC) of CT parameters accessed by two reviewers ranged from 0.758 to 0.982. Logistic regression analyses showed that neutrophil-to-lymphocyte ratio (NLR), distance to the collecting system, CT necrosis, tumor margin irregularity, peritumoral neovascularity, and RER-NP were independent predictive factors associated with aggressive pathology. We built the nomogram model using these significant variables, which had an area under the curve (AUC) of 0.854 in the ROC curve.

CONCLUSIONS

Our research demonstrated that preoperative four-phase contrast-enhanced CT was critical for predicting aggressive pathology in cT1 solid RCC, and the constructed nomogram was useful in guiding patient treatment and postoperative follow-up.

Association between CT-based adipose variables, preoperative blood biochemical indicators and pathological T stage of clear cell renal cell carcinoma

Background

Clear cell renal cell carcinoma (ccRCC) is corelated with tumor-associated material (TAM), coagulation system and adipocyte tissue, but the relationships between them have been inconsistent. Our study aimed to explore the cut-off intervals of variables that are non-linearly related to ccRCC pathological T stage for providing clues to understand these discrepancies, and to effectively preoperative risk stratification.

Methods

This retrospective analysis included 218 ccRCC patients with a clear pathological T stage between January 1st, 2014, and November 30th, 2021. The patients were categorized into two cohorts based on their pathological T stage: low T stage (T1 and T2) and high T stage (T3 and T4). Abdominal and perirenal fat variables were measured based on preoperative CT images. Blood biochemical indexes from the last time before surgery were also collected. The generalized sum model was used to identify cut-off intervals for nonlinear variables.

Results

In specific intervals, fibrinogen levels (FIB) (2.63-4.06 g/L) and platelet (PLT) counts (>200.34 × 109/L) were significantly positively correlated with T stage, while PLT counts (<200.34 × 109/L) were significantly negatively correlated with T stage. Additionally, tumor-associated material exhibited varying degrees of positive correlation with T stage at different cut-off intervals (cut-off value: 90.556 U/mL).

Conclusion

Preoperative PLT, FIB and TAM are nonlinearly related to pathological T stage. This study is the first to provide specific cut-off intervals for preoperative variables that are nonlinearly related to ccRCC T stage. These intervals can aid in the risk stratification of ccRCC patients before surgery, allowing for developing a more personalized treatment planning.

A two-stage deep-learning framework for CT denoising based on a clinically structure-unaligned paired data set

Background

In low-dose computed tomography (LDCT) lung cancer screening, soft tissue is hardly appreciable due to high noise levels. While deep learning-based LDCT denoising methods have shown promise, they typically rely on structurally aligned synthesized paired data, which lack consideration of the clinical reality that there are no aligned LDCT and normal-dose CT (NDCT) images available. This study introduces an LDCT denoising method using clinically structure-unaligned but paired data sets (LDCT and NDCT scans from the same patients) to improve lesion detection during LDCT lung cancer screening.

Methods

A cohort of 64 patients undergoing both LDCT and NDCT was randomly divided into training (n=46) and testing (n=18) sets. A two-stage training approach was adopted. First, Gaussian noise was added to NDCT data to create simulated LDCT data for generator training. Then, the model was trained on a clinically structure-unaligned paired data set using a Wasserstein generative adversarial network (WGAN) framework with the initial generator weights obtained during the first stage of training. An attention mechanism was also incorporated into the network.

Results

Validated on a clinical CT data set, our proposed method outperformed other available methods [CycleGAN, Pixel2Pixel, block-matching and three-dimensional filtering (BM3D)] in noise removal and detail retention tasks in terms of the peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and root mean square error (RMSE) metrics. Compared with the results produced by BM3D, our method yielded an average improvement of approximately 7% in terms of the three evaluation indicators. The probability density profile of the denoised CT output produced using our method best fit the reference NDCT scan. Additionally, our two-stage model outperformed the one-stage WGAN-based model in both objective and subjective evaluations, further demonstrating the higher effectiveness of our two-stage training approach.

Conclusions

The proposed method performed the best in removing noise from LDCT scans and exhibited good detail retention, which could potentially enhance the lesion detection and characterization effects obtained for soft tissues in the scanning scope of LDCT lung cancer screening.

Three-dimensional CT cinematic rendering of adrenal masses: Role in tumor analysis and management

The adrenal gland is home to an array of complex physiological and neoplastic disease processes. While dedicated adrenal computed tomography (CT) is the gold standard imaging modality for adrenal lesions, there exists significant overlap among imaging features of adrenal pathology. This can often make radiological diagnosis and subsequent determination of the optimal surgical approach challenging. Cinematic rendering (CR) is a novel CT post-processing technique that utilizes advanced light modeling to generate highly photorealistic anatomic visualization. This generates unique prospects in the evaluation of adrenal masses. As one of the first large tertiary care centers to incorporate CR into routine diagnostic workup, our preliminary experience with using CR has been positive, and we have found CR to be a valuable adjunct during surgical planning. Herein, we highlight the unique utility of CR techniques in the workup of adrenal lesions and provide commentary on the opportunities and obstacles associated with the application of this novel display method in this setting.

High-Altitude Pulmonary Edema Combined with Spontaneous Pneumomediastinum: A Case Report

Background

High-altitude pulmonary edema (HAPE) is a serious life-threatening disease that occurs after rapid ascent to high altitude; its main early-stage presentations include fatigue, headache, low-grade fever, dyspnea, and cough. X-ray and computed tomography (CT) images show pulmonary shadows and patches, which may be localized (initial right lung field predomination) or generalized to the bilateral lung base.

Case Presentation

In this report, we present a case of a 25-year-old man diagnosed with HAPE combined with spontaneous pneumomediastinum. After a quick descent and effective medical treatment, this patient made a full recovery. The case may provide helpful information for the prevention and treatment of this disease since an increased number of people, especially young men, currently travel and work at high altitudes.

Conclusion

After accurate clinical diagnosis with the help of CT or X-ray, immediate descent and appropriate oxygen supplementation are the most effective treatments for HAPE at high altitude.

Quantification of soft tissue artifacts using CT registration and subject-specific multibody modeling

The potential use of gait analysis for quantitative preoperative planning in total hip arthroplasty (THA) has previously been demonstrated. However, the joint kinematic data measured through this process tend to be unreliable for surgical planning due to distortions caused by soft tissue artifacts (STAs). In this study, we developed a novel motion capture framework by combining computed tomography (CT)-based postural calibration and subject-specific multibody dynamics modeling to prevent the effect of STAs in measuring hip kinematics. Three subjects with femoroacetabular impingement syndrome were recruited, and CT data for each patient were collected by attaching marker clusters near the hip. A subject-specific multibody hip joint model was developed based on reconstructed CT data. Spring-dashpot network calculations were performed to minimize the distance between the anatomical landmark and its corresponding infrared reflective marker. The STAs of the thigh was described as six degrees of freedom viscoelastic bushing elements, and their parameter values were identified via smooth orthogonal decomposition. Least squares optimization was used to modify the pelvic rotations to compensate for the rigid components of STAs. The results showed that CT-assisted motion tracking enabled the successful identification of STA influences in gait and squat positions. Furthermore, STA effects were found to alter maximal pelvis tilt and hip rotations during a squat. Compared to other techniques, such as dual fluoroscopic imaging, the adopted framework does not require additional medical imaging for patients undergoing robot-assisted THA surgery and is thus a practical way of evaluating hip joint kinematics for preoperative surgical planning.

Feasibility study of using low-kilovoltage, prospective gating, high-pitch, dual-source computed tomography prior to transcatheter aortic valve replacement: analysis of image quality and radiation dose

Background

Computed tomography (CT) scans before transcatheter aortic valve replacement (TAVR) are used to evaluate the aortic valve and guide the selection of appropriate valve stents. Accurate imaging evaluation can ensure the success rate of surgery while reducing the incidence of complications. Multiple studies have adopted a protocol of coronary artery, aortic valve, and total aortic scan, with the patients receiving higher radiation doses. The aim of this study was to evaluate the image quality, radiation dose, and diagnostic performance of dual-source computed tomography (DSCT) with high-pitch spiral scanning for TAVR.

Methods

A total of 240 patients being evaluated for TAVR were continuously enrolled. Based on the differences in electrocardiography (ECG) gating and tube voltage, the patients were divided into 4 groups: group A, 70-kV prospective ECG gating, high-pitch helical; group B, 70-kV retrospective ECG gating; group C, 100-kV prospective ECG gating, high-pitch helical; and group D, 120-kV prospective ECG gating, high-pitch helical. Image quality was evaluated on a 4-point scale. The image signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated for objective evaluation. The radiation doses of all patients were recorded. The image quality and radiation dose of each group were compared.

Results

There were no differences in age, body mass index (BMI), subjective image quality scores, CT values between the aorta and the coronary artery, or image CNR between the 4 groups. The mean radiation doses of groups A-D were 4.13±0.69, 4.79±0.58, 12.00±1.62, and 15.01±1.90 mSv, respectively. The mean radiation dose in group A (70-kV prospective ECG gating) decreased significantly (P<0.05).

Conclusions

Using low-kilovoltage, high-pitch DSCT can provide comparable image quality for TAVR evaluation and significantly reduce the radiation dose.

Novel method for calculating the effective dose using size-specific dose estimates conversion factors in abdomen-pelvis computed tomography

We propose a novel method for calculating the effective dose that closely reflects the individual attenuation, utilizing two conversion coefficients. A total of 180 adult patients who underwent abdomen-pelvis computed tomography were categorized into six groups based on sex and body type. The effective dose was calculated by multiplying the dose-length product with the effective dose conversion coefficient and the size-specific dose estimate conversion factor. The effective dose calculated using a simulation-based dose calculator (WAZA-ARI) was employed as the reference value. The following values, obtained through both methods, were compared within each category: distribution of the effective dose, median effective dose, and relative difference in median effective dose across additional body mass index (BMI) categories. For male patients, no significant disparity was observed in the median effective doses calculated using the two methods. The relative differences in median effective doses across additional BMI categories ranged from - 5 to 6%. Conversely, among female patients, the median effective dose calculated using our method slightly undercut that calculated using WAZA-ARI, with relative differences ranging from - 16 to - 9%. Additionally, relative differences in median effective dose across additional BMI categories ranged from - 18 to - 7%. The median effective dose differed slightly depending on the calculation method because of the different reference phantoms applied in dose calculations. Our proposed method is sensitive to individual size and helps compute a size-specific effective dose.

Deep learning-assisted LI-RADS grading and distinguishing hepatocellular carcinoma (HCC) from non-HCC based on multiphase CT: a two-center study

OBJECTIVES

To develop a deep learning (DL) method that can determine the Liver Imaging Reporting and Data System (LI-RADS) grading of high-risk liver lesions and distinguish hepatocellular carcinoma (HCC) from non-HCC based on multiphase CT.

METHODS

This retrospective study included 1049 patients with 1082 lesions from two independent hospitals that were pathologically confirmed as HCC or non-HCC. All patients underwent a four-phase CT imaging protocol. All lesions were graded (LR 4/5/M) by radiologists and divided into an internal (n = 886) and external cohort (n = 196) based on the examination date. In the internal cohort, Swin-Transformer based on different CT protocols were trained and tested for their ability to LI-RADS grading and distinguish HCC from non-HCC, and then validated in the external cohort. We further developed a combined model with the optimal protocol and clinical information for distinguishing HCC from non-HCC.

RESULTS

In the test and external validation cohorts, the three-phase protocol without pre-contrast showed κ values of 0.6094 and 0.4845 for LI-RADS grading, and its accuracy was 0.8371 and 0.8061, while the accuracy of the radiologist was 0.8596 and 0.8622, respectively. The AUCs in distinguishing HCC from non-HCC were 0.865 and 0.715 in the test and external validation cohorts, while those of the combined model were 0.887 and 0.808.

CONCLUSION

The Swin-Transformer based on three-phase CT protocol without pre-contrast could feasibly simplify LI-RADS grading and distinguish HCC from non-HCC. Furthermore, the DL model have the potential in accurately distinguishing HCC from non-HCC using imaging and highly characteristic clinical data as inputs.

CLINICAL RELEVANCE STATEMENT

The application of deep learning model for multiphase CT has proven to improve the clinical applicability of the Liver Imaging Reporting and Data System and provide support to optimize the management of patients with liver diseases.

KEY POINTS

• Deep learning (DL) simplifies LI-RADS grading and helps distinguish hepatocellular carcinoma (HCC) from non-HCC. • The Swin-Transformer based on the three-phase CT protocol without pre-contrast outperformed other CT protocols. • The Swin-Transformer provide help in distinguishing HCC from non-HCC by using CT and characteristic clinical information as inputs.

3D print model for surgical planning in a case of recurrent osteoblastic osteosarcoma of the left maxilla. A case report

Osteosarcoma (OS) of the head and neck is a rare and aggressive disease characterized by the formation of osteoid by malignant osteoblasts. The mandible or maxilla are the most common sites of presentation. Radiologically, these tumors show considerable, destructive growth with periosteal reaction, which can suggest the diagnosis of OS. 3D printing, as an emerging technology, can play a role in orthopedic oncology by providing patient-specific 3D printed models to improve surgical planning and facilitate patient understanding. We present the case of a male in his early 30s with a final histological diagnosis of recurrent osteosarcoma of the left maxilla, where a 3D printed model was helpful for the diagnostic workup, surgical planning, and the procedure.

Correlation Analysis Between Computed Tomography and Magnetic Resonance Imaging for Assessing Thoracic Pedicle Morphology

OBJECTIVE

This study aimed to determine the accuracy and reliability of using magnetic resonance imaging (MRI) to characterize thoracic pedicle morphology in the normal population.

METHODS

Computed tomography (CT) and MRI datasets of 63 surgically treated patients were included in the study. Bilateral T3, T6, T9, and T12 vertebral levels were evaluated for the type of pedicle, pedicle chord length, and pedicle isthmic diameter on both MRI and CT scans. Pedicles were classified according to Sarwahi et al. into type A (normal pedicle), >4 mm cancellous channel; type B, 2-4 mm channel; type C, completely cortical channels >2 mm; and type D, <2 mm cortical bone channel.

RESULTS

Of 504 pedicles, 294 were classified as type A, 173 as type B, 24 as type C, and 13 as type D based on a CT scan. MRI had an overall accuracy rate of 92.86% for detecting type A, 96.53% for type B, and 100% for type C and type D compared with CT scans. Regarding the thoracic levels, MRI had an overall concordance of 97.98% at T3 level, 94.43% at T6 level, 98.11% at T9 level, and 99.3% at T12 level with CT readings. Comparing measurements between MRI and CT studies for pedicle isthmic diameter and pedicle chord length showed moderate to good reliability at all measured levels.

CONCLUSIONS

Pedicle measurements obtained by MRI may be used to estimate pedicle dimensions, allowing surgeons to preoperatively determine pedicle screw sizes based on MRI scans alone.

Bone mineral density estimation from a plain X-ray image by learning decomposition into projections of bone-segmented computed tomography

Osteoporosis is a prevalent bone disease that causes fractures in fragile bones, leading to a decline in daily living activities. Dual-energy X-ray absorptiometry (DXA) and quantitative computed tomography (QCT) are highly accurate for diagnosing osteoporosis; however, these modalities require special equipment and scan protocols. To frequently monitor bone health, low-cost, low-dose, and ubiquitously available diagnostic methods are highly anticipated. In this study, we aim to perform bone mineral density (BMD) estimation from a plain X-ray image for opportunistic screening, which is potentially useful for early diagnosis. Existing methods have used multi-stage approaches consisting of extraction of the region of interest and simple regression to estimate BMD, which require a large amount of training data. Therefore, we propose an efficient method that learns decomposition into projections of bone-segmented QCT for BMD estimation under limited datasets. The proposed method achieved high accuracy in BMD estimation, where Pearson correlation coefficients of 0.880 and 0.920 were observed for DXA-measured BMD and QCT-measured BMD estimation tasks, respectively, and the root mean square of the coefficient of variation values were 3.27 to 3.79% for four measurements with different poses. Furthermore, we conducted extensive validation experiments, including multi-pose, uncalibrated-CT, and compression experiments toward actual application in routine clinical practice.

A novel fusion algorithm for benign-malignant lung nodule classification on CT images

The accurate recognition of malignant lung nodules on CT images is critical in lung cancer screening, which can offer patients the best chance of cure and significant reductions in mortality from lung cancer. Convolutional Neural Network (CNN) has been proven as a powerful method in medical image analysis. Radiomics which is believed to be of interest based on expert opinion can describe high-throughput extraction from CT images. Graph Convolutional Network explores the global context and makes the inference on both graph node features and relational structures. In this paper, we propose a novel fusion algorithm, RGD, for benign-malignant lung nodule classification by incorporating Radiomics study and Graph learning into the multiple Deep CNNs to form a more complete and distinctive feature representation, and ensemble the predictions for robust decision-making. The proposed method was conducted on the publicly available LIDC-IDRI dataset in a 10-fold cross-validation experiment and it obtained an average accuracy of 93.25%, a sensitivity of 89.22%, a specificity of 95.82%, precision of 92.46%, F1 Score of 0.9114 and AUC of 0.9629. Experimental results illustrate that the RGD model achieves superior performance compared with the state-of-the-art methods. Moreover, the effectiveness of the fusion strategy has been confirmed by extensive ablation studies. In the future, the proposed model which performs well on the pulmonary nodule classification on CT images will be applied to increase confidence in the clinical diagnosis of lung cancer.

Perspective of Neuroimaging-Based on Epilepsy Monitoring Technology

Epilepsy is a long-standing illness defined by short episodes of aberrant brain activity caused by abrupt cell discharges. The illness is not communicable and might linger for a long period. Epilepsy affects roughly 50 million individuals worldwide, making it a prevalent neurological illness. Epilepsy monitoring is the most significant element of epilepsy diagnosis and also plays an important role in diagnosing the origin of epilepsy, assessing prognosis, and directing therapy. This paper details the principles and basic algorithmic models of commonly used neuroimaging techniques and describes the role of different monitoring techniques in the diagnosis and treatment of epilepsy. The paper compares the advantages and disadvantages of different monitoring techniques in their application and explores a comprehensive and less restrictive epilepsy monitoring protocol for readers and relevant researchers. Currently, electroencephalography (EEG) is the most common technique for monitoring epilepsy, and its most basic algorithmic models are independent component analysis (ICA) and discrete wavelet analysis (DWA), which are used for aspects such as noise removal and feature extraction. This article is dedicated to helping the reader or relevant researcher to gain a more comprehensive and systematic understanding of current neuroimaging techniques and medical devices. Furthermore, it seeks to forecast future research directions based on current difficulties in the area. The purpose of this study is to give a useful reference for future research in the field of epilepsy monitoring.

Evolution of neuroimaging findings in angioinvasive cerebral aspergillosis in a pediatric patient with leukemia during long-term observation

The central nervous system is one of the most common sites of aspergillosis involvement in immunocompromised people, just after sinopulmonary infections. Neuroimaging modalities are crucial for the diagnosis of cerebral aspergillosis (CA). Here, we describe a rare case of concurrent mixed aspergillosis infection with Aspergillus fumigatus and Aspergillus niger in a 2-year-old leukemic boy. The first neuroimaging finding, which was followed by focal seizures, was recognized as extensive cerebral hemorrhage in the absence of thrombocytopenia and coagulopathy. As the patient survived for more than 4 months after diagnosis, we were able to perform a neuroimaging evaluation during long-term observation. In serial neuroimaging studies, a secondary fungal abscess was observed at the site of hemorrhagic infarctions. Finally, the patient died from bacterial sepsis. In this case study, we try to categorize the neuroimaging findings of CA into distinct phases to better understand how CA changes over time.

Quality Improvement Campaign Improved Utilization of Rapid Sequence MRI for Diagnosis of Pediatric Appendicitis

INTRODUCTION

Appendicitis is the most common indication for emergency general surgery in the pediatric population. Computed tomography (CT) or magnetic resonance imaging (MRI) may be used for diagnosis when ultrasound findings are equivocal. However, CT involves unnecessary radiation exposure if MRI is available. After introducing a rapid sequence MRI (rsMRI) appendicitis protocol at our institution, CT was still preferentially used. We therefore implemented a quality improvement (QI) campaign to reduce the rate of CTs and increase the rate of rsMRI. Here, we assess the effectiveness of the QI campaign while evaluating potential barriers to using rsMRI.

METHODS

We conducted a mixed methods study, first performing stakeholder interviews which informed the design of a QI campaign initiated in May 2021 and a midway feedback survey in December 2021. A retrospective cohort study was then performed of children evaluated for appendicitis at our institution between January 1, 2016, and April 30, 2022. CT and rsMRI rates were compared before and after QI campaign implementation.

RESULTS

There was a significant decrease in rate of CTs and increase in rate of rsMRIs performed following the initiation of the QI campaign (p < 0.0001). The rate of CT scans decreased by a factor of 0.4 while the rate of rsMRI increased by a factor of 9.5.

CONCLUSION

A successful QI campaign was initiated at our institution, resulting in decreased utilization of CT and increased use of rsMRI for the evaluation of suspected appendicitis. These results highlight the potential impact of QI projects.

LEVEL OF EVIDENCE

III.

Neuroradiology of acute pathologies in adults with hematologic malignancies: a pictorial review

Hematopoietic and lymphoid tumors are a heterogeneous group of diseases including lymphomas, multiple myeloma (MM), and leukemias. These diseases are associated with systemic involvement and various clinical presentations including acute neurological deficits. Adult patients with hematologic malignancies (HM) are at risk for developing a wide array of acute conditions involving the nervous system. HM in adults may present as tumoral masses responsible for mass effect, possibly resulting in acute neurological signs and symptoms caused by tumor growth with compression of central nervous system (CNS) structures. Moreover, as result of the hematologic disease itself or due to systemic treatments, hematologic patients are at risk for vascular pathologies, such as ischemic, thrombotic, and hemorrhagic disorders due to the abnormal coagulation status. The onset of these disorders is often with acute neurologic signs or symptoms. Lastly, it is well known that patients with HM can have impaired function of the immune system. Thus, CNS involvement due to immune-related diseases such as mycotic, parasitic, bacterial, and viral infections linked to immunodeficiency, together with immune reconstitution inflammatory syndrome, are frequently seen in hematologic patients. Knowledge of the etiology and expected CNS imaging findings in patients with HM is of great importance to reach a fast and correct diagnosis and guide treatment choices. In this manuscript, we review the computed tomography (CT) and magnetic resonance findings of these conditions which can be related to the disease itself and/or to their treatments.

The role of iodinated contrast media in computed tomography structured Reporting and Data Systems (RADS): a narrative review

Background and Objective

In recent years, there has been a large-scale dissemination of guidelines in radiology in the form of Reporting & Data Systems (RADS). The use of iodinated contrast media (ICM) has a fundamental role in enhancing the diagnostic capabilities of computed tomography (CT) but poses certain risks. The scope of the present review is to summarize the current role of ICM only in clinical reporting guidelines for CT that have adopted the "RADS" approach, focusing on three specific questions per each RADS: (I) what is the scope of the scoring system; (II) how is ICM used in the scoring system; (III) what is the impact of ICM enhancement on the scoring.

Methods

We analyzed the original articles for each of the latest versions of RADS that can be used in CT [PubMed articles between January, 2005 and March, 2023 in English and American College of Radiology (ACR) official website].

Key Content and Findings

We found 14 RADS suitable for use in CT out of 28 RADS described in the literature. Four RADS were validated by the ACR: Colonography-RADS (C-RADS), Liver Imaging-RADS (LI-RADS), Lung CT Screening-RADS (Lung-RADS), and Neck Imaging-RADS (NI-RADS). One RADS was validated by the ACR in collaboration with other cardiovascular scientific societies: Coronary Artery Disease-RADS 2.0 (CAD-RADS). Nine RADS were proposed by other scientific groups: Bone Tumor Imaging-RADS (BTI-RADS), Bone‑RADS, Coronary Artery Calcium Data & Reporting System (CAC-DRS), Coronavirus Disease 2019 Imaging-RADS (COVID-RADS), COVID-19-RADS (CO-RADS), Interstitial Lung Fibrosis Imaging-RADS (ILF-RADS), Lung-RADS (LU-RADS), Node-RADS, and Viral Pneumonia Imaging-RADS (VP-RADS).

Conclusions

This overview suggests that ICM is not strictly necessary for the study of bones and calcifications (CAC-DRS, BTI-RADS, Bone-RADS), lung parenchyma (Lung-RADS, LU-RADS, COVID-RADS, CO-RADS, VP-RADS and ILF-RADS), and in CT colonography (C-RADS). On the other hand, ICM plays a key role in CT angiography (CAD-RADS), in the study of liver parenchyma (LI-RADS), and in the evaluation of soft tissues and lymph nodes (NI-RADS, Node-RADS). Future studies are needed in order to evaluate the impact of the new iodinated and non-iodinate contrast media, artificial intelligence tools and dual energy CT in the assignment of RADS scores.

Applications of new radiological scores: the Node-rads in colon cancer staging

PURPOSE

The study focuses on the evaluation of the new Node Reporting and Data System 1.0 (Node-rads) scoring accuracy in the assessment of metastatic lymph nodes (LN) in patients with colon carcinoma.

MATERIAL AND METHODS

From April 2021 to May 2022, retrospective chart reviews were performed on 67 preoperative CT (Computed Tomography) of patients undergoing excisional surgery for colon cancer at the Polyclinic of Bari, Italy. Primary endpoints were to assess lymph node size and configuration to express the likelihood of a metastatic site adopting the Node-rads score system, whose categories of risk are defined from 1 (very low) to 5 (very high). The nodal postsurgical histological evaluation was the gold standard. The relationship between Node-rads score, LN size, configuration criteria (texture, border and shape) and the presence of histological metastases was statistically evaluated.

RESULTS

All surgical specimens examined had correlation with Node-rads score. They were significantly more likely to present nodes micrometastasis those patients with (a) spherical LN shape (82.8%), (b) with lymph node necrosis (100%), (c) irregular borders (87%) and (d) the LN short axis more than 10 mm (61.9%).

CONCLUSIONS

Our experience highlights how the Node-rads system proposes an intuitive and effective definition of criteria to standardize the lymph node radiological reports in colon cancer disease. Further studies are needed to streamline the classification of the nodal and peripheral LN in all the oncological imaging.

Identification of anatomical types of segmental bronchi in left superior and lingular lobes using multi-slice CT

PURPOSE

The objectives of this study were to evaluate various branching patterns of segmental bronchi in the left superior and lingular lobes and to survey the anatomical diversity and sex-related differences of these branches in a large sample of the study population.

MATERIALS AND METHODS

Overall, 10,000 participants (5428 males, and 4572 females, mean age 50 ± 13.5 years [SD] years; age range: 3-91 years) who underwent multi-slice CT (MSCT) scans between September 2019 and December 2021 were retrospectively included. Using the syngo.via post-processing workstation, the data were applied to generate three-dimensional (3D) and virtual bronchoscopy (VB) simulations of a bronchial tree. The reconstructed images were then interpreted to identify and categorize distinct bronchial patterns in the left superior and lingular lobes. Cross-tabulation analysis and the Pearson Chi-square (χ2) test were used to calculate the constituent ratios of bronchial branch types and determine their significance between male and female groups.

RESULTS

Our results revealed mainly four distinct types for the left superior lobe (LSL) bronchial tree, i.e., (B1 + 2, B3, 76.13%); (B1 + 2 + 3, 17.32%); (B1 + 3, B2, 5.74%); (B1a + B3, B1b + B2, 0.81%) and two types for the left lingular lobe (LLL) bronchial tree, i.e., (B4, B5, 91.05%); (B4, B5, B*, 8.95%). There were no significant sex-related differences in the proportion of bronchial branches in LLL (P > 0.05). However, sex-related differences were significant in the proportion of bronchial branches in LSL (P < 0.05).

CONCLUSION

The current study has validated the presence of segmental bronchial variations in the left superior and lingular lobes. These findings may have a crucial effect on the diagnosis of symptomatic patients, as well as in carrying out procedures such as lung resections, endotracheal intubation, and bronchoscopies.

Diagnostic performance of a deep learning-based method in differentiating malignant from benign subcentimeter (≤10 mm) solid pulmonary nodules

Background

This study assessed the diagnostic performance of a deep learning (DL)-based model for differentiating malignant subcentimeter (≤10 mm) solid pulmonary nodules (SSPNs) from benign ones in computed tomography (CT) images compared against radiologists with 10 and 15 years of experience in thoracic imaging (medium-senior seniority).

Methods

Overall, 200 SSPNs (100 benign and 100 malignant) were retrospectively collected. Malignancy was confirmed by pathology, and benignity was confirmed by follow-up or pathology. CT images were fed into the DL model to obtain the probability of malignancy (range, 0-100%) for each nodule. According to the diagnostic results, enrolled nodules were classified into benign, malignant, or indeterminate. The accuracy and diagnostic composition of the model were compared with those of the radiologists using the McNemar-Bowker test. Enrolled nodules were divided into 3-6-, 6-8-, and 8-10-mm subgroups. For each subgroup, the diagnostic results of the model were compared with those of the radiologists.

Results

The accuracy of the DL model, in differentiating malignant and benign SSPNs, was significantly higher than that of the radiologists (71.5% vs. 38.5%, P<0.001). The DL model reported more benign or malignant deterministic results and fewer indeterminate results. In subgroup analysis of nodule size, the DL model also yielded higher performance in comparison with that of the radiologists, providing fewer indeterminate results. The accuracy of the two methods in the 3-6-, 6-8-, and 8-10-mm subgroups was 75.5% vs. 28.3% (P<0.001), 62.0% vs. 28.2% (P<0.001), and 77.6% vs. 55.3% (P=0.001), respectively, and the indeterminate results were 3.8% vs. 66.0%, 8.5% vs. 66.2%, and 2.6% vs. 35.5% (all P<0.001), respectively.

Conclusions

The DL-based method yielded higher performance in comparison with that of the radiologists in differentiating malignant and benign SSPNs. This DL model may reduce uncertainty in diagnosis and improve diagnostic accuracy, especially for SSPNs smaller than 8 mm.

Exploring the displacement characteristics of Garden III femoral neck fractures and the reliability, validity, and value of the anteroposterior Garden Index in assessing displacement severity

BACKGROUND

Femoral neck fractures represent a significant public health concern, particularly in the elderly population. A thorough understanding and assessment of these fractures are deemed essential for optimal treatment and management. Displacement characteristics of Garden III femoral neck fractures were explored in this study, and the reliability, validity, and clinical utility of the anteroposterior Garden Index in evaluating displacement severity were investigated.

METHODS

Patients diagnosed with Garden III femoral neck fractures were included in this study. The anteroposterior Garden Index was computed from X-ray images by three experienced orthopedic doctors. Additionally, the contact area of the fracture endpoint and displacement of the femoral neck were evaluated using 128-slice 3D CT scans. Inter-observer and retest reliability of the Garden Index measurements were assessed, along with its correlation with CT measurements.

RESULTS

In this study, a total of 110 patients with Garden III femoral neck fractures were analyzed, showcasing an almost equal gender distribution and an age range spanning from 20 to 88 years. An average Garden Index of 135° (± 16°) was observed. The intra-observer repeatability of the Garden Index was found to exceed 90%. A significant positive correlation was identified between the Garden Index and the contact surface area of the fracture endpoint (r = 0.82, P < 0.001), while a significant negative correlation was noted with the upward displacement of the femoral neck (r = - 0.79, P < 0.001).

CONCLUSIONS

The anteroposterior Garden Index has been demonstrated to have promising potential as a reliable and valid tool for assessing the displacement severity of Garden III femoral neck fractures. Nonetheless, further research is needed to elucidate its relationship with other fracture characteristics and to enhance its criterion and construct validity.

Improved stereo perception in coronary angiography using the X-ray tube as the viewpoint and validation with 3D printed models

Coronary angiography (CAG) provides two-dimensional images, but a clinician who is experienced in percutaneous coronary interventions can use information from these images to interpret spatial depth and infer the three-dimensional (3D) locations of vessels. We hypothesized that CAG results were equivalent to the mirror image of a coronary artery perspective projection, and a stereo perception could be easily established when the viewpoint of the angiogram was the X-ray tube instead of the detector. To eliminate the influence of heartbeat and respiration, a 3D-printed a coronary artery model was constructed for analysis. The effects of gantry movements during digital subtraction angiography (DSA) on the image were used to identify factors that affected DSA image transformation. Then, based on these factors, DSA imaging was simulated using UG NX software with three methods: (i) a perspective projection with the detector as the viewpoint; (ii) a parallel projection; and (iii) a mirror image of the perspective projection with the X-ray tube as the viewpoint. Finally, the resulting 3D images were compared with the DSA image. Our mirror image of the coronary artery perspective projection that used the X-ray tube as the viewpoint fused precisely with the CAG results and provided exact simulations of all the effects of DSA gantry movements on the DSA image. CAG results were equivalent to the mirror image of coronary artery perspective projection, and the stereo perception was easily established using the X-ray tube as the viewpoint.

Artificial Intelligence Automation of Proptosis Measurement: An Indicator for Pediatric Orbital Abscess Surgery

INTRODUCTION

To evaluate the ability of artificial intelligence (AI) software to quantify proptosis for identifying patients who need surgical drainage.

METHODS

We pursued a retrospective study including 56 subjects with a clinical diagnosis of subperiosteal orbital abscess (SPOA) secondary to sinusitis at a tertiary pediatric hospital from 2002 to 2016. AI computer software was developed to perform 3D visualization and quantitative assessment of proptosis from computed tomography (CT) images acquired at the time of hospital admission. The AI software automatically computed linear and volume metrics of proptosis to provide more practice-consistent and informative measures. Two experienced physicians independently measured proptosis using the interzygomatic line method on axial CT images. The AI software and physician proptosis assessments were evaluated for association with eventual treatment procedures as standalone markers and in combination with the standard predictors.

RESULTS

To treat the SPOA, 31 of 56 (55%) children underwent surgical intervention, including 18 early surgeries (performed within 24 h of admission), and 25 (45%) were managed medically. The physician measurements of proptosis were strongly correlated (Spearman r = 0.89, 95% CI 0.82-0.93) with 95% limits of agreement of ± 1.8 mm. The AI linear measurement was on average 1.2 mm larger (p = 0.007) and only moderately correlated with the average physicians' measurements (r = 0.53, 95% CI 0.31-0.69). Increased proptosis of both AI volumetric and linear measurements were moderately predictive of surgery (AUCs of 0.79, 95% CI 0.68-0.91, and 0.78, 95% CI 0.65-0.90, respectively) with the average physician measurement being poorly to fairly predictive (AUC of 0.70, 95% CI 0.56-0.84). The AI proptosis measures were also significantly greater in the early as compared to the late surgery groups (p = 0.02, and p = 0.04, respectively). The surgical and medical groups showed a substantial difference in the abscess volume (p < 0.001).

CONCLUSION

AI proptosis measures significantly differed from physician assessments and showed a good overall ability to predict the eventual treatment. The volumetric AI proptosis measurement significantly improved the ability to predict the likelihood of surgery compared to abscess volume alone. Further studies are needed to better characterize and incorporate the AI proptosis measurements for assisting in clinical decision-making.

Characterizing thoracic morphology variation to develop representative 3D models for applications in chest trauma

BACKGROUND

Rib fracture(s) occurs in 85% of blunt chest trauma cases. Increasing evidence supports that surgical intervention, particularly for multiple fractures, may improve outcomes. Thoracic morphology diversity across ages and sexes is important to consider in the design and use of surgical intervention devices in chest trauma. However, research on non-average thoracic morphology is lacking.

METHODS

The rib cage was segmented from patient computed tomography (CT) scans to create 3D point clouds. These point clouds were uniformly oriented and chest height, width, and depth were measured. Size categorization was determined by grouping each dimension into small, medium, and large tertiles. From small and large size combinations, subgroups were extracted to develop thoracic 3D models of the rib cage and surrounding soft tissue.

RESULTS

The study population included 141 subjects (48% male) ranging from age 10-80 with ∼20 subjects/age decade. Mean chest volume increased with age by 26% from the age groups 10-20 to 60-70, with 11% of this increase occurring between the youngest groups of 10-20 and 20-30. Across all ages, chest dimensions were ∼10% smaller in females and chest volume was highly variable (SD: ±3936.5 cm3). Representative thoracic models of four males (ages 16, 24, 44, 48) and three females (ages 19, 50, 53) were developed to characterize morphology associated with combinations of small and large chest dimensions.

CONCLUSIONS

The seven models developed cover a broad range of non-average thoracic morphologies and can serve as a basis for informing device design, surgical planning, and injury risk assessments.

Tract seeding in indwelling pleural catheter placement for the drainage of malignant pleural effusions: Incidence and related clinical and imaging factors

BACKGROUND

The incidence of tract seeding after the placement of indwelling pleural catheter (IPC) for malignant pleural effusion drainage has been variable in the literature.

RESEARCH QUESTION

To evaluate the incidence of IPC-related cancer tract seeding and find out related demographic, clinical or imaging factors to the tract seeding.

STUDY DESIGN AND METHODS

This retrospective study included 124 consecutive patients seen between January 2011 and December 2021 who underwent IPC placement for malignant pleural effusion drainage. Chest radiographs before IPC placement and serial chest CT studies were obtained. One patient was diagnosed pathologically, and the other patients were diagnosed as tract seeding radiologically. The incidence of and related factors to tract seeding were assessed by reviewing medical records and imaging studies.

RESULTS

The incidence of IPC tract seeding was 21.7% (27 of 124 malignant effusions). Of 27 patients, 15 had primary lung cancer and remaining 12 had extra-thoracic malignancy. Adenocarcinoma (19 of 27, 70.3%) either from the lung (N = 12) or extra-thoracic malignancy (N = 7) was the most common cell type. Mean time elapsed until tract seeding occurrence after IPC placement was 96 days (ranges; 28-306 days). The survival in seeding group after IPC placement was 185 days (ranges, 32-457 days). On odd ratio analysis, the presence of mediastinal pleural thickening (OR [95% CI]; 9.79 (2.67-35.84), p = 0.001) was significantly related to the occurrence of tract seeding. Neither tumor volume within pleural space (p = 0.168), duration of IPC indwelling (p = 0.142), days of survival after IPC placement (p = 0.26), nor pleural effusion amount (p = 0.481) was related to the tract seeding.

INTERPRETATION

IPC tract seeding is seen in 27 (21.7%) of 124 malignant pleural effusion patients, particularly with adenocarcinoma cytology. CT features of mediastinal pleural thickening are related to the occurrence of tract seeding.

Diagnostic efficacy of chest CT imaging in diagnosis of COVID-19 cases based on duration of symptoms

Background

Chest computed tomography (CT) imaging provides results more rapidly and with higher sensitivity than reverse transcription polymerase chain reaction in diagnosis of COVID-19.

Aim

To evaluate diagnostic efficacy of chest CT imaging in diagnosis of COVID-19 cases based on age and duration of symptoms.

Materials and Methods

A retrospective study conducted during December 2020 to June 2021 in a tertiary care hospital, India. Total 495 patients with typical clinical symptoms of COVID-19, reverse transcription polymerase chain reaction positive for COVID-19 and had undergone chest CT imaging were included. Descriptive statistical analysis was performed for all the variables. Receiver operating characteristic curve analysis was used to determine threshold value of chest CT severity score (CT_SS) based on duration of symptoms and age to diagnose COVID-19.

Results

Mean age of patients was 61.86 ± 10.77 years and 367 (71.4%) patients were male. Ground glass opacities were observed in 456 (92.1%) patients and in 332 (67.1%) patients, multilobes were affected. Total CT_SS showed positive correlation with age (r = 0.257) and duration of symptoms (r = 0.625). Total CT_SS >6 after a duration of 2 days of symptoms identified COVID-19 cases with sensitivity 90.8% (95% confidence interval [CI]: 87.5%-93.5%) and specificity 84.6% (95% CI: 76.2%-90.9%). Total CT_SS >11 in patients aged more than 60 years identified COVID-19 cases with sensitivity 47.4% (95% CI: 41.2%-53.6%) and specificity 87.3% (95% CI: 82.3%-91.4%).

Conclusion

Threshold value of CT_SS determined will help to expedite diagnosis of COVID-19 patients by the clinicians in an early stage especially in India and other developing countries which have a high patient volume and limited health resources.

Which head element is more effective for cement augmentation of TFNA? Helical blade versus lag screw

BACKGROUND

Early fixation and rehabilitation is the gold standard treatment for intertrochanteric femur fractures. Cement augmentation through perforated head elements has been developed to avoid postoperative complications such as cut-out or cut-through. The purpose of this study was to compare two head elements in terms of cement distribution using computed tomography (CT) and to examine their initial fixation and clinical outcomes.

METHODS

Elderly patients who had intertrochanteric fractures were treated with a trochanteric fixation nail advanced (TFNA) helical blade (Blade group) or a TFNA lag screw (Screw group). In both groups, 4.2 mL of cement was injected under an image intensifier (1.8 mL of cement was directed cranially and 0.8 mL each caudally, anteriorly, and posteriorly). Patient demographics and clinical outcome were investigated post-operatively. Cement distribution from the center of the head element was evaluated with CT. Maximum penetration depth (MPD) were measured in the coronal and sagittal planes. On each axial plane, the cross-sectional areas in the cranial, caudal, anterior and posterior directions were calculated. The sum of cross-sectional areas (successive 36 slices) was defined as the volume of the head element.

RESULTS

The Blade group included 14 patients, and the Screw group included 15 patients. In the Blade group, MPD in the anterior and caudal direction was significantly greater than that in the posterior direction (p < 0.01). In the Screw group, volume in the cranial and posterior direction was significantly greater than that in the Blade group (p = 0.03). Subsequently, the total volume in the Screw group was significantly larger than that in the Blade group (p < 0.01). No significant correlation was detected between bone mineral density, T score, young adult mean, and total cement volume. Change in radiographic parameters and clinical outcome such as Parker score and visual analog scale were similar in both groups. No patients suffered from cut-out / cut through or non-union.

CONCLUSIONS

The position of cement distribution through the lag screw is different from that through the helical blade, and the total volume of the head element is significantly larger in the lag screw. Both groups had similarly effective results in terms of mechanical stability after surgery, postoperative pain and early phase of rehabilitation.

TRIAL REGISTRATION

Current Controlled Trials ISRCTN45341843, 24/12/2022, Retrospectively registered.

Comparison of computed tomography and magnetic resonance imaging findings and histopathological features of macrotrabecular-massive hepatocellular carcinoma

Background

Macrotrabecular-massive hepatocellular carcinoma (MTM-HCC) is a novel subtype of HCC, one of eight distinct subtypes, that accounts for 5% of all cases of HCC and is associated with a worse prognosis. Preoperative diagnosis of MTM-HCCs using imaging findings can facilitate patient treatment decision-making. The purpose of this study was to describe computed tomography (CT) and magnetic resonance imaging (MRI) findings of MTM-HCCs and compare these findings with histopathological features.

Methods

This retrospective case-control study was performed at Shenzhen People's Hospital. The cohort included 17 patients with surgically confirmed MTM-HCCs and 232 patients with surgically confirmed non-MTM-HCCs who were enrolled by searching the pathological database from January 2018 to June 2022. CT and MRI findings were retrospectively analyzed and compared with pathological features. Student's t-test or Mann-Whitney U test for continuous variables and χ² test or Fisher's exact test for categorical variables were implemented to compare imaging manifestations between MTM-HCCs and non-MTM-HCCs, as appropriate.

Results

Seventeen tumors with a mean diameter of 8.58±2.83 cm were identified in the 17 patients. In addition to the typical findings of hepatocellular carcinomas (HCCs), such as arterial phase hyperenhancement (APHE), wash out, restricted diffusion, capsule and non-uptake at the hepatobiliary phase (HBP), the most common findings in MTM-HCCs were necrosis in 11 patients (64.7%, 11/17), followed by intratumoral arteries in 6 patients (35.3%, 6/17), peritumoral arterial transitive enhancement in 3 patients (17.6%, 3/17) and peritumoral hypointensive areas at the HBP in 3 of 8 patients (37.5%, 3/8) who received gadolinium-ethoxybenzyl-diethylenetriamine penta-acetic acid (Gd-EOB-DTPA) enhancement. The tumor size of non-MTM-HCCs was 5.26±1.94 cm, which was smaller than the 8.58±2.83 cm of MTM-HCCs (P<0.001). The frequency of necrosis and intratumoral arteries was significantly higher in MTM-HCCs than in non-MTM-HCCs (necrosis: 64.7% vs. 34.6%, P=0.012; intratumoral arteries: 47.1% vs. 19.7%, P=0.008).

Conclusions

MTM-HCCs tend to be large in size with intratumoral artery and intratumoral necrosis, which are characteristics that may distinguish them from non-MTM-HCCs.

Identification of Heterotopic Mineralization and Adjacent Pathology in the Equine Fetlock Region by Low-Field Magnetic Resonance Imaging, Cone-Beam and Fan-Beam Computed Tomography

Heterotopic mineralization in equine distal limbs has been considered an incidental finding and little is known about its imaging features. The study aimed to identify heterotopic mineralization and adjacent pathology in the fetlock region with cone-beam (CB) computed tomography (CT), fan-beam (FB) CT, and low-field magnetic resonance imaging (MRI). Images from 12 equine cadaver limbs were examined for heterotopic mineralization and adjacent pathology and verified by macro-examination. Retrospective review of the CBCT/MR images from 2 standing horses was also included. CBCT and FBCT identified twelve mineralization's with homogeneous hyperattenuation: oblique-sesamoidean-ligament (5) without macroscopic abnormality; deep-digital-flexor-tendon (1) and suspensory-branch (6) with macroscopic abnormalities. MRI failed to identify all mineralization's, but detected suspensory branch splitting, and T2 and STIR hyperintensity in 4 suspensory-branches and 3 oblique-sesamoidean-ligaments. Macro-examination found corresponding disruption/splitting and discoloration. All modalities identified 7 ossified fragments showing cortical/trabecular pattern: capsular (1), palmar sagittal ridge (1), proximal phalanx (2) without macroscopic abnormality, and proximal sesamoid bones (3). On MRI, fragments were most identifiable on T1 images. All abaxial avulsions had suspensory-branch splitting on T1 images with T2 and STIR hyperintensity. Macro-examination showed ligament disruption/splitting and discoloration. Suspensory-branch/intersesamoidean ligament mineralization's were identified by CBCT in standing cases; 1 had associated T2 hyperintensity. Both CT systems were generally superior in identifying heterotopic mineralization's than MRI, while MRI provided information on soft tissue pathology related to the lesions, which may be important for management.