Evaluation of Incidental Findings on Abdominopelvic CT: Potential Benefit of Photon-counting Detector CT Over Conventional Single-energy CT.

PURPOSE: To assese of potential benefint of photon-counting detector CT (PCD-CT) over conventional single-energy CT (CSE-CT) on accurate diagnosis of incidental findings with high clinical significance (IFHCS). MATERIALS AND METHODS: This retrospective study included 365 patients who initially underwent abdominopelvic contrast-enhanced CT (AP-CECT) without non-enhancement (PCD-CT: 187 and CSE-CT: 178). We selected IFHCS and evaluated their diagnosability using CE-CT alone. IFHCSs that could not be diagnosed with only CE-CT were evaluated using additional PCD-CT postprocessing techniques, including virtual non-contrast image, low keV image, and iodine map. A PCD-CT scanner (NAEOTOM Alpha, Siemens Healthineer, Erlangen, Germany) was used. RESULTS: Thirty-nine IFHCSs (PCD-CT: 22 and CSE-CT: 17) were determined in this study. Seven IFHCSs in each group were able to diagnose with only CE-CT. Fifteen IFHCSs were able to diagnose using the additional PCD-CT postprocessing technique, which was useful for detecting and accurately diagnosing 68.2% (15/22) of lesions and 65% (13/20) of patients. All IFHCSs were accurately diagonosed with PCD-CT. CONCLUSION: PCD-CT was useful for characterizing IFHCSs that are indeterminate at CSE-CT. PCD-CT offered potential benefit of PCD-CT over conventional single-energy CT on evaluation of IFHCS on only abdominopelvic CT.

View adaptive unified self-supervised technique for abdominal organ segmentation.

Automatic abdominal organ segmentation is an essential prerequisite for accurate volumetric analysis, disease diagnosis, and tracking by medical practitioners. However, the deformable shapes, variable locations, overlapping with nearby organs, and similar contrast make the segmentation challenging. Moreover, the requirement of a large manually labeled dataset makes it harder. Hence, a semi-supervised contrastive learning approach is utilized to perform the automatic abdominal organ segmentation. Existing 3D deep learning models based on contrastive learning are not able to capture the 3D context of medical volumetric data along three planes/views: axial, sagittal, and coronal views. In this work, a semi-supervised view-adaptive unified model (VAU-model) is proposed to make the 3D deep learning model as view-adaptive to learn 3D context along each view in a unified manner. This method utilizes the novel optimization function that assists the 3D model to learn the 3D context of volumetric medical data along each view in a single model. The effectiveness of the proposed approach is validated on the three types of datasets: BTCV, NIH, and MSD quantitatively and qualitatively. The results demonstrate that the VAU model achieves an average Dice score of 81.61% which is a 3.89% improvement compared to the previous best results for pancreas segmentation in multi-organ dataset BTCV. It also achieves an average Dice score of 77.76% and 76.76% for the pancreas under the single organ non-pathological NIH dataset, and pathological MSD dataset.

Focused abdominal ultrasound.

A focused point-of-care abdominal ultrasound is an examination performed at the patient's location and interpreted within the clinical context. This review gives an overview of this examination modality. The objective is to rapidly address predefined dichotomised questions about the presence of an abdominal aortic aneurysm, gallstones, cholecystitis, hydronephrosis, urinary retention, free intraperitoneal fluid, and small bowel obstruction. FAUS is a valuable tool for emergency physicians to promptly confirm various conditions upon the patients' arrival, thus reducing the time to diagnosis and in some cases eliminating the need for other imaging.

Abdominal multi-organ segmentation in Multi-sequence MRIs based on visual attention guided network and knowledge distillation.

PURPOSE: The segmentation of abdominal organs in magnetic resonance imaging (MRI) plays a pivotal role in various therapeutic applications. Nevertheless, the application of deep-learning methods to abdominal organ segmentation encounters numerous challenges, especially in addressing blurred boundaries and regions characterized by low-contrast. METHODS: In this study, a multi-scale visual attention-guided network (VAG-Net) was proposed for abdominal multi-organ segmentation based on unpaired multi-sequence MRI. A new visual attention-guided (VAG) mechanism was designed to enhance the extraction of contextual information, particularly at the edge of organs. Furthermore, a new loss function inspired by knowledge distillation was introduced to minimize the semantic disparity between different MRI sequences. RESULTS: The proposed method was evaluated on the CHAOS 2019 Challenge dataset and compared with six state-of-the-art methods. The results demonstrated that our model outperformed these methods, achieving DSC values of 91.83 ± 0.24% and 94.09 ± 0.66% for abdominal multi-organ segmentation in T1-DUAL and T2-SPIR modality, respectively. CONCLUSION: The experimental results show that our proposed method has superior performance in abdominal multi-organ segmentation, especially in the case of small organs such as the kidneys.

Can thoraco-abdominal organ boundaries be accurately determined from X-ray and anthropometric surface scans? Implications for body armour system coverage and design.

To optimise soldier protection within body armour systems, knowledge of the boundaries of essential thoraco-abdominal organs is necessary to inform coverage requirements. However, existing methods of organ boundary identification are costly and time consuming, limiting widespread adoption for use on soldier populations. The aim of this study was to evaluate a novel method of using 3D organ models to identify essential organ boundaries from low dose planar X-rays and 3D external surface scans of the human torso. The results revealed that, while possible to reconstruct 3D organs using template 3D organ models placed over X-ray images, the boundary data (relating to the size and position of each organ) obtained from the reconstructed organs differed significantly from MRI organ data. The magnitude of difference varied between organs. The most accurate anatomical boundaries were the left, right, and inferior boundaries of the heart, and lateral boundaries for the liver and spleen. Visual inspection of the data demonstrated that 11 of 18 organ models were successfully integrated within the 3D space of the participant's surface scan. These results suggest that, if this method is further refined and evaluated, it has potential to be used as a tool for estimating body armour coverage requirements.

Impact of Breathing Control Training Program on sonographic quantification of abdominal vasculature.

OBJECTIVES: To compare vascular scanning parameters (vessel diameter, peak systolic velocity, end-diastolic velocity, and resistive index) and scanning time before and after breathing control training program for selected abdominal vessels. METHODS: This study was pre and post quasi-experimental. The researchers designed a breathing training program that gives participants instructions through a video describing breathing maneuvers. Data were collected at the ultrasound laboratory/College of Health and Rehabilitation Sciences in Princess Nourah bint Abdul Rahman University, Riyadh, Saudi Arabia from January 2023 to November 2023. About 49 volunteers at the university participated in the study. Scanning was performed two times for the right renal artery, upper abdominal aorta, inferior vena cava, and superior mesenteric artery. Scanning time was measured before and after the program as well. A paired sample t-test was used to compare the parameters means and time before and after the program. RESULTS: The program had a significant effect on the following parameters: right renal artery peak systolic velocity (p=0.042), upper abdominal aortic peak systolic velocity, and resistive index (p=0.014, p=0.014 respectively), superior mesenteric artery and inferior vena cava diameters (p=0.010 and p=0.020). The scanning time was reduced significantly (p<0.001). CONCLUSION: The breathing training program saves time and improves ultrasound measurement quality. Hospitals and health centers should consider the importance of breathing control training programs before abdominal scanning.

CNN application for automated determination of the patient's size to obtain the size-specific dose estimated in CT.

Introduction. The currently available dosimetry techniques in computed tomography can be inaccurate which overestimate the absorbed dose. Therefore, we aimed to provide an automated and fast methodology to more accurately calculate the SSDE usingDwobtained by using CNN from thorax and abdominal CT study images.Methods. The SSDE was determined from the 200 records files. For that purpose, patients' size was measured in two ways: (a) by developing an algorithm following the AAPM Report No. 204 methodology; and (b) using a CNN according to AAPM Report No. 220.Results. The patient's size measured by the in-house software in the region of thorax and abdomen was 27.63 ± 3.23 cm and 28.66 ± 3.37 cm, while CNN was 18.90 ± 2.6 cm and 21.77 ± 2.45 cm. The SSDE in thorax according to 204 and 220 reports were 17.26 ± 2.81 mGy and 23.70 ± 2.96 mGy for women and 17.08 ± 2.09 mGy and 23.47 ± 2.34 mGy for men. In abdomen was 18.54 ± 2.25 mGy and 23.40 ± 1.88 mGy in women and 18.37 ± 2.31 mGy and 23.84 ± 2.36 mGy in men.Conclusions. Implementing CNN-based automated methodologies can contribute to fast and accurate dose calculations, thereby improving patient-specific radiation safety in clinical practice.

Robust multishot diffusion-weighted imaging of the abdomen with region-based shot rejection.

PURPOSE: Diffusion-weighted (DW) imaging provides a useful clinical contrast, but is susceptible to motion-induced dephasing caused by the application of strong diffusion gradients. Phase navigators are commonly used to resolve shot-to-shot motion-induced phase in multishot reconstructions, but poor phase estimates result in signal dropout and Apparent Diffusion Coefficient (ADC) overestimation. These artifacts are prominent in the abdomen, a region prone to involuntary cardiac and respiratory motion. To improve the robustness of DW imaging in the abdomen, region-based shot rejection schemes that selectively weight regions where the shot-to-shot phase is poorly estimated were evaluated. METHODS: Spatially varying weights for each shot, reflecting both the accuracy of the estimated phase and the degree of subvoxel dephasing, were estimated from the phase navigator magnitude images. The weighting was integrated into a multishot reconstruction using different formulations and phase navigator resolutions and tested with different phase navigator resolutions in multishot DW-echo Planar Imaging acquisitions of the liver and pancreas, using conventional monopolar and velocity-compensated diffusion encoding. Reconstructed images and ADC estimates were compared qualitatively. RESULTS: The proposed region-based shot rejection reduces banding and signal dropout artifacts caused by physiological motion in the liver and pancreas. Shot rejection allows conventional monopolar diffusion encoding to achieve median ADCs in the pancreas comparable to motion-compensated diffusion encoding, albeit with a greater spread of ADCs. CONCLUSION: Region-based shot rejection is a linear reconstruction that improves the motion robustness of multi-shot DWI and requires no sequence modifications.

Distortionless, free-breathing, and respiratory resolved 3D diffusion weighted imaging of the abdomen.

PURPOSE: Abdominal imaging is frequently performed with breath holds or respiratory triggering to reduce the effects of respiratory motion. Diffusion weighted sequences provide a useful clinical contrast but have prolonged scan times due to low signal-to-noise ratio (SNR), and cannot be completed in a single breath hold. Echo-planar imaging (EPI) is the most commonly used trajectory for diffusion weighted imaging but it is susceptible to off-resonance artifacts. A respiratory resolved, three-dimensional (3D) diffusion prepared sequence that obtains distortionless diffusion weighted images during free-breathing is presented. Techniques to address the myriad of challenges including: 3D shot-to-shot phase correction, respiratory binning, diffusion encoding during free-breathing, and robustness to off-resonance are described. METHODS: A twice-refocused, M1-nulled diffusion preparation was combined with an RF-spoiled gradient echo readout and respiratory resolved reconstruction to obtain free-breathing diffusion weighted images in the abdomen. Cartesian sampling permits a sampling density that enables 3D shot-to-shot phase navigation and reduction of transient fat artifacts. Theoretical properties of a region-based shot rejection are described. The region-based shot rejection method was evaluated with free-breathing (normal and exaggerated breathing), and respiratory triggering. The proposed sequence was compared in vivo with multishot DW-EPI. RESULTS: The proposed sequence exhibits no evident distortion in vivo when compared to multishot DW-EPI, robustness to B0 and B1 field inhomogeneities, and robustness to motion from different respiratory patterns. CONCLUSION: Acquisition of distortionless, diffusion weighted images is feasible during free-breathing with a b-value of 500 s/mm2, scan time of 6 min, and a clinically viable reconstruction time.

Vascular compression syndromes in the abdomen and pelvis: a concise pictorial review.

Vascular compression syndromes are a diverse group of pathologies that can manifest asymptomatically and incidentally in otherwise healthy individuals or symptomatically with a spectrum of presentations. Due to their relative rarity, these syndromes are often poorly understood and overlooked. Early identification of these syndromes can have a significant impact on subsequent clinical management. This pictorial review provides a concise summary of seven vascular compression syndromes within the abdomen and pelvis including median arcuate ligament (MAL) syndrome, superior mesenteric artery (SMA) syndrome, nutcracker syndrome (NCS), May-Thurner syndrome (MTS), ureteropelvic junction obstruction (UPJO), vascular compression of the ureter, and portal biliopathy. The demographics, pathophysiology, predisposing factors, and expected treatment for each compression syndrome are reviewed. Salient imaging features of each entity are illustrated through imaging examples using multiple modalities including ultrasound, fluoroscopy, CT, and MRI.

Fully automated explainable abdominal CT contrast media phase classification using organ segmentation and machine learning.

BACKGROUND: Contrast-enhanced computed tomography (CECT) provides much more information compared to non-enhanced CT images, especially for the differentiation of malignancies, such as liver carcinomas. Contrast media injection phase information is usually missing on public datasets and not standardized in the clinic even in the same region and language. This is a barrier to effective use of available CECT images in clinical research. PURPOSE: The aim of this study is to detect contrast media injection phase from CT images by means of organ segmentation and machine learning algorithms. METHODS: A total number of 2509 CT images split into four subsets of non-contrast (class #0), arterial (class #1), venous (class #2), and delayed (class #3) after contrast media injection were collected from two CT scanners. Seven organs including the liver, spleen, heart, kidneys, lungs, urinary bladder, and aorta along with body contour masks were generated by pre-trained deep learning algorithms. Subsequently, five first-order statistical features including average, standard deviation, 10, 50, and 90 percentiles extracted from the above-mentioned masks were fed to machine learning models after feature selection and reduction to classify the CT images in one of four above mentioned classes. A 10-fold data split strategy was followed. The performance of our methodology was evaluated in terms of classification accuracy metrics. RESULTS: The best performance was achieved by Boruta feature selection and RF model with average area under the curve of more than 0.999 and accuracy of 0.9936 averaged over four classes and 10 folds. Boruta feature selection selected all predictor features. The lowest classification was observed for class #2 (0.9888), which is already an excellent result. In the 10-fold strategy, only 33 cases from 2509 cases (∼1.4%) were misclassified. The performance over all folds was consistent. CONCLUSIONS: We developed a fast, accurate, reliable, and explainable methodology to classify contrast media phases which may be useful in data curation and annotation in big online datasets or local datasets with non-standard or no series description. Our model containing two steps of deep learning and machine learning may help to exploit available datasets more effectively.

[Common and New Regional Anaesthesia Techniques Under Review - From Head to Abdomen]. / Alte und neue Regionalanästhesieverfahren unter Review ­ Kopf bis Bauch.

By implementation of sonography regional anesthesia became more relevant in the daily practice of anesthesia and pain therapy. Due to visualized needle guidance ultrasound supports more safety during needle placement. Thereby new truncal blocks got enabled. Next to the blocking of specific nerve structures, plane blocks got established which can also be described as interfascial compartment blocks. The present review illustrates published and established blocks in daily practice concerning indications and the procedural issues. Moreover, the authors explain potential risks, complications and dosing of local anesthetics.

Opportunistic Identification of Vertebral Compression Fractures on CT Scans of the Chest and Abdomen, Using an AI Algorithm, in a Real-Life Setting.

This study evaluated the performance of a vertebral fracture detection algorithm (HealthVCF) in a real-life setting and assessed the impact on treatment and diagnostic workflow. HealthVCF was used to identify moderate and severe vertebral compression fractures (VCF) at a Danish hospital. Around 10,000 CT scans were processed by the HealthVCF and CT scans positive for VCF formed both the baseline and 6-months follow-up cohort. To determine performance of the algorithm 1000 CT scans were evaluated by specialized radiographers to determine performance of the algorithm. Sensitivity was 0.68 (CI 0.581-0.776) and specificity 0.91 (CI 0.89-0.928). At 6-months follow-up, 18% of the 538 patients in the retrospective cohort were dead, 78 patients had been referred for a DXA scan, while 25 patients had been diagnosed with osteoporosis. A higher mortality rate was seen in patients not known with osteoporosis at baseline compared to patients known with osteoporosis at baseline, 12.8% versus 22.6% (p = 0.003). Patients receiving bisphosphonates had a lower mortality rate (9.6%) compared to the rest of the population (20.9%) (p = 0.003). HealthVCF demonstrated a poorer performance than expected, and the tested version is not generalizable to the Danish population. Based on its specificity, the HealthVCF can be used as a tool to prioritize resources in opportunistic identification of VCF's. Implementing such a tool on its own only resulted in a small number of new diagnoses of osteoporosis and referrals to DXA scans during a 6-month follow-up period. To increase efficiency, the HealthVCF should be integrated with Fracture Liaison Services (FLS).

Introducing fitting models for estimating age-specific dose and effective dose in paediatric patients undergoing head, chest and abdomen-pelvis imaging protocols: a patient study.

INTRODUCTION: Concerns regarding the adverse consequences of radiation have increased due to the expanded application of computed tomography (CT) in medical practice. Certain studies have indicated that the radiation dosage depends on the anatomical region, the imaging technique employed and patient-specific variables. The aim of this study is to present fitting models for the estimation of age-specific dose estimates (ASDE), in the same direction of size-specific dose estimates, and effective doses based on patient age, gender and the type of CT examination used in paediatric head, chest and abdomen-pelvis imaging. METHODS: A total of 583 paediatric patients were included in the study. Radiometric data were gathered from DICOM files. The patients were categorised into five distinct groups (under 15 years of age), and the effective dose, organ dose and ASDE were computed for the CT examinations involving the head, chest and abdomen-pelvis. Finally, the best fitting models were presented for estimation of ASDE and effective doses based on patient age, gender and the type of examination. RESULTS: The ASDE in head, chest, and abdomen-pelvis CT examinations increases with increasing age. As age increases, the effective dose in head and abdomen-pelvis CT scans decreased. However, for chest scans, the effective dose initially showed a decreasing trend until the first year of life; after that, it increases in correlation with age. CONCLUSIONS: Based on the presented fitting model for the ASDE, these CT scan quantities depend on factors such as patient age and the type of CT examination. For the effective dose, the gender was also included in the fitting model. By utilising the information about the scan type, region and age, it becomes feasible to estimate the ASDE and effective dose using the models provided in this study.

Convolutional LSTM model for cine image prediction of abdominal motion.

Objective.In this study, we tackle the challenge of latency in magnetic resonance linear accelerator (MR-Linac) systems, which compromises target coverage accuracy in gated real-time radiotherapy. Our focus is on enhancing motion prediction precision in abdominal organs to address this issue. We developed a convolutional long short-term memory (convLSTM) model, utilizing 2D cine magnetic resonance (cine-MR) imaging for this purpose.Approach.Our model, featuring a sequence-to-one architecture with six input frames and one output frame, employs structural similarity index measure (SSIM) as loss function. Data was gathered from 17 cine-MRI datasets using the Philips Ingenia MR-sim system and an Elekta Unity MR-Linac equivalent sequence, focusing on regions of interest (ROIs) like the stomach, liver, pancreas, and kidney. The datasets varied in duration from 1 to 10 min.Main results.The study comprised three main phases: hyperparameter optimization, individual training, and transfer learning with or without fine-tuning. Hyperparameters were initially optimized to construct the most effective model. Then, the model was individually applied to each dataset to predict images four frames ahead (1.24-3.28 s). We evaluated the model's performance using metrics such as SSIM, normalized mean square error, normalized correlation coefficient, and peak signal-to-noise ratio, specifically for ROIs with target motion. The average SSIM values achieved were 0.54, 0.64, 0.77, and 0.66 for the stomach, liver, kidney, and pancreas, respectively. In the transfer learning phase with fine-tuning, the model showed improved SSIM values of 0.69 for the liver and 0.78 for the kidney, compared to 0.64 and 0.37 without fine-tuning.Significance. The study's significant contribution is demonstrating the convLSTM model's ability to accurately predict motion for multiple abdominal organs using a Unity-equivalent MR sequence. This advancement is key in mitigating latency issues in MR-Linac radiotherapy, potentially improving the precision and effectiveness of real-time treatment for abdominal cancers.

Point-of-care abdominal ultrasound in pediatric and neonatal intensive care units.

A spectrum of critical abdominal pathological conditions that might occur in neonates and children warrants real-time point-of-care abdominal ultrasound (abdominal POCUS) assessment. Abdominal radiographs have limited value with low sensitivity and specificity in many cases and have no value in assessing abdominal organ perfusion and microcirculation (Rehan et al. in Clin Pediatr (Phila) 38(11):637-643, 1999). The advantages of abdominal POCUS include that it is non-invasive, easily available, can provide information in real-time, and can guide therapeutic intervention (such as paracentesis and urinary bladder catheterization), making it a crucial tool for use in pediatric and neonatal abdominal emergencies (Martínez Biarge et al. in J Perinat Med 32(2):190-194, 2004) and (Alexander et al. in Arch Dis Child Fetal Neonatal Ed 106(1):F96-103, 2021).  Conclusion: Abdominal POCUS is a dynamic assessment with many ultrasound markers of gut injury by two dimensions (2-D) and color Doppler (CD) compared to the abdominal X-ray; the current evidence supports the superiority of abdominal POCUS over an abdominal X-ray in emergency situations. However, it should still be considered an adjunct rather than replacing abdominal X-rays due to its limitations and operator constraints (Alexander et al. in Arch Dis Child Fetal Neonatal Ed 106(1):F96-103, 2021). What is Known: • Ultrasound is an important modality for the assessment of abdominal pathologies. What is New: • The evidence supports the superiority of abdominal POCUS over an abdominal X-ray in emergency abdominal situations in the neonatal and pediatric intensive care units.