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.

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.

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.

[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.

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.

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.

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).

Fast abdominal magnetic resonance elastography with simultaneous encoding of three-dimensional displacements.

Three-dimensional (3D) magnetic resonance elastography (MRE) is more accurate than two-dimensional (2D) MRE; however, it requires long-term acquisition. This study aimed to reduce the acquisition time of abdominal 3D MRE using a new sample interval modulation (short-SLIM) approach that can acquire all three motions faster while reducing the prolongation of echo time and flow compensation. To this end, two types of phantom studies and an in vivo test of the liver in three healthy volunteers were performed to compare the performances of conventional spin-echo echo-planar (SE-EPI) MRE, conventional SLIM and short-SLIM. One phantom study measured the mean amplitude and shear modulus within the overall region of a homogeneous phantom by changing the mechanical vibration power to assess the robustness to the lowered phase-to-noise ratio in short-SLIM. The other measured the mean shear modulus in the stiff and background materials of a phantom with an embedded stiffer rod to assess the performance of short-SLIM for complex wave patterns with wave interference. The Spearman's rank correlation coefficient was used to assess similarity of elastograms in the rod-embedded phantom and liver between methods. The results of the phantom study changing the vibration power indicated that there was little difference between conventional MRE and short-SLIM. Moreover, the elastogram pattern and the mean shear modulus in the rod-embedded phantom in conventional SLIM and short-SLIM did not change for conventional MRE; the liver test also showed a small difference between the acquisition techniques. This study demonstrates that short-SLIM can provide MRE results comparable to those of conventional MRE. Short-SLIM can reduce the total acquisition time by a factor of 2.25 compared to conventional 3D MRE time, leading to an improvement in the accuracy of shear modulus estimation by suppressing the patient movements.

Influence of spectral shaping and tube voltage modulation in ultralow-dose computed tomography of the abdomen.

PURPOSE: Unenhanced abdominal CT constitutes the diagnostic standard of care in suspected urolithiasis. Aiming to identify potential for radiation dose reduction in this frequent imaging task, this experimental study compares the effect of spectral shaping and tube voltage modulation on image quality. METHODS: Using a third-generation dual-source CT, eight cadaveric specimens were scanned with varying tube voltage settings with and without tin filter application (Sn 150, Sn 100, 120, 100, and 80 kVp) at three dose levels (3 mGy: standard; 1 mGy: low; 0.5 mGy: ultralow). Image quality was assessed quantitatively by calculation of signal-to-noise ratios (SNR) for various tissues (spleen, kidney, trabecular bone, fat) and subjectively by three independent radiologists based on a seven-point rating scale (7 = excellent; 1 = very poor). RESULTS: Irrespective of dose level, Sn 100 kVp resulted in the highest SNR of all tube voltage settings. In direct comparison to Sn 150 kVp, superior SNR was ascertained for spleen (p ≤ 0.004) and kidney tissue (p ≤ 0.009). In ultralow-dose scans, subjective image quality of Sn 100 kVp (median score 3; interquartile range 3-3) was higher compared with conventional imaging at 120 kVp (2; 2-2), 100 kVp (1; 1-2), and 80 kVp (1; 1-1) (all p < 0.001). Indicated by an intraclass correlation coefficient of 0.945 (95% confidence interval: 0.927-0.960), interrater reliability was excellent. CONCLUSIONS: In abdominal CT with maximised dose reduction, tin prefiltration at 100 kVp allows for superior image quality over Sn 150 kVp and conventional imaging without spectral shaping.

Fully-automated multi-organ segmentation tool applicable to both non-contrast and post-contrast abdominal CT: deep learning algorithm developed using dual-energy CT images.

A novel 3D nnU-Net-based of algorithm was developed for fully-automated multi-organ segmentation in abdominal CT, applicable to both non-contrast and post-contrast images. The algorithm was trained using dual-energy CT (DECT)-obtained portal venous phase (PVP) and spatiotemporally-matched virtual non-contrast images, and tested using a single-energy (SE) CT dataset comprising PVP and true non-contrast (TNC) images. The algorithm showed robust accuracy in segmenting the liver, spleen, right kidney (RK), and left kidney (LK), with mean dice similarity coefficients (DSCs) exceeding 0.94 for each organ, regardless of contrast enhancement. However, pancreas segmentation demonstrated slightly lower performance with mean DSCs of around 0.8. In organ volume estimation, the algorithm demonstrated excellent agreement with ground-truth measurements for the liver, spleen, RK, and LK (intraclass correlation coefficients [ICCs] > 0.95); while the pancreas showed good agreements (ICC = 0.792 in SE-PVP, 0.840 in TNC). Accurate volume estimation within a 10% deviation from ground-truth was achieved in over 90% of cases involving the liver, spleen, RK, and LK. These findings indicate the efficacy of our 3D nnU-Net-based algorithm, developed using DECT images, which provides precise segmentation of the liver, spleen, and RK and LK in both non-contrast and post-contrast CT images, enabling reliable organ volumetry, albeit with relatively reduced performance for the pancreas.

Improving abdominal image segmentation with overcomplete shape priors.

The extraction of abdominal structures using deep learning has recently experienced a widespread interest in medical image analysis. Automatic abdominal organ and vessel segmentation is highly desirable to guide clinicians in computer-assisted diagnosis, therapy, or surgical planning. Despite a good ability to extract large organs, the capacity of U-Net inspired architectures to automatically delineate smaller structures remains a major issue, especially given the increase in receptive field size as we go deeper into the network. To deal with various abdominal structure sizes while exploiting efficient geometric constraints, we present a novel approach that integrates into deep segmentation shape priors from a semi-overcomplete convolutional auto-encoder (S-OCAE) embedding. Compared to standard convolutional auto-encoders (CAE), it exploits an over-complete branch that projects data onto higher dimensions to better characterize anatomical structures with a small spatial extent. Experiments on abdominal organs and vessel delineation performed on various publicly available datasets highlight the effectiveness of our method compared to state-of-the-art, including U-Net trained without and with shape priors from a traditional CAE. Exploiting a semi-overcomplete convolutional auto-encoder embedding as shape priors improves the ability of deep segmentation models to provide realistic and accurate abdominal structure contours.

Development, evaluation, and overview of standardized training phantoms for abdominal ultrasound-guided interventions.

PURPOSE: Ultrasound (US) represents the primary approach for abdominal diagnosis and is regularly used to guide diagnostic and therapeutic interventions (INVUS). Due to possible serious INVUS complications, structured training concepts are required. Phantoms can facilitate teaching, but their use is currently restricted by complex manufacturing and short durability of the materials. Hence, the aim of this study was the development and evaluation of an optimized abdominal INVUS phantom. MATERIALS AND METHODS: Phantom requirements were defined in a structured research process: Skin-like surface texture, homogeneous matrix with realistic tissue properties, implementation of lesions and abscess cavities in different sizes and depths as well as a modular production process allowing for customized layouts. The phantom prototypes were evaluated in certified ultrasound courses. RESULTS: In accordance with the defined specifications, a new type of matrix was developed and cast in multiple layers including different target materials. The phantom structure is based on features of liver anatomy and includes solid focal lesions, vessels, and abscess formations. For a realistic biopsy procedure, ultrasound-proof material was additionally included to imitate bone. The evaluation was performed by US novices (n=40) and experienced participants (n=41). The majority (73/81) confirmed realistic visualization of the lesions. The 3D impression was rated as "very good" in 64% of cases (52/81) and good in 31% (25/81). Overall, 86% (70/81) of the participants certified high clinical relevance of the phantom. CONCLUSION: The presented INVUS phantom concept allows standardized and realistic training for interventions.

[Application Research of Vector Flow Technique on Convex Array Ultrasonic Probe of Abdomen].

Vector flow imaging (VFI) is an innovative ultrasound flow measurement technology. Compared with the traditional color Doppler and spectral Doppler, VFI has the advantages of independence of angle correction and direct acquisition of real-time amplitude and direction of flow. Transverse oscillation (TO) method is one of the effective methods for vector flow imaging. However, a complete and detailed algorithm validation process based on commercial ultrasound machines is still lacking due to more complex convex probes. This study starts with introducing the imaging process and principle of transverse oscillation vector flow technique, and calculates the error between the set velocity value and the measured velocity value through the simulation experiment, and verifies the error between the set velocity value and the measured velocity value through the Doppler flow phantom experiment. Among them, the velocity value measured by the TO vector flow technique in the simulation experiment is 0.48 m/s and the preset value is 0.50 m/s, the error between them is -4%. The velocity values are 8.33, 11.14, 14.44 and 16.67 cm/s measured by the Doppler flow phantom experiment, the actual velocity values are 7.97, 10.78, 14.06 and 17.34 cm/s, the errors between them are all within ±5%. Both experiments verify the feasibility of using vector flow technique on abdominal convex probe.

Characterization and quantification of fluid in the abdomen by ultrasound and magnetic resonance imaging in children with clinical suspicion of appendicitis.

PURPOSE: While regarded as a secondary sign of pediatric appendicitis, the frequency of physiologic intra-abdominal fluid in children with suspected but absent appendicitis is unknown. Ex vivo: to assess the validity of US/MRI measurements of free fluid. In vivo: in suspected pediatric appendicitis, to assess the amount of abdominal fluid by US and MRI, determine performance characteristics of US in fluid detection and identify fluid volume ranges in confirmed appendicitis. METHODS: Ex vivo: criterion validity of US and MRI for fluid volume measurements was tested using tissue-mimicking phantoms filled with different volumes of distilled water. In vivo: all participants from a previous prospective study of suspected appendicitis were evaluated by US; MRI was performed after equivocal USs. Qualitative and quantitative analyses of abdominal fluid and correlation of fluid presence with appendicitis were performed. RESULTS: Ex vivo: no difference was found between phantom-fluid amount and measured volume using the formula for volume of an ellipsoid for US (P=0.19) or MRI (P=0.08). In vivo: intra-abdominal fluid was present in 212/591 (35.9%) patients; 75/212 patients with fluid (35.4%) had appendicitis, 60 (28.3%) had alternate diagnoses, and 77 (36.3%) had physiologic fluid. Sensitivity and specificity of US for fluid detection were 84% (95% CI 71-93) and 65% (95% CI 52-77), respectively. In children with versus without appendicitis, the respective ranges of fluid volume were 0.7-1148.8 ml and 0.8-318 ml. CONCLUSION: The volume of an ellipsoid formula is a valid method for quantifying intra-abdominal fluid. The sole presence of intra-abdominal fluid on US does not support the diagnosis of pediatric appendicitis.

Prehospital ultrasound scanning for abdominal free fluid detection in trauma patients: a systematic review and meta-analysis.

INTRODUCTION: Focused assessment with sonography for trauma helps detect abdominal free fluid. Prehospital ultrasound scanning is also important because the early diagnosis of hemoperitoneum may reduce the time to definitive treatment in the hospital. This study investigated whether prehospital ultrasound scanning can help detect abdominal free fluid. MATERIALS AND METHODS: In this systematic review, relevant databases were searched for studies investigating prehospital ultrasound examinations for abdominal free fluid in trauma patients. The prehospital ultrasound results were compared with computed tomography, surgery, or hospital ultrasound examination data. The pooled sensitivity and specificity values were analyzed using forest plots. The overall predictive power was calculated by the summary receiver operating characteristic curve. The quality of the included studies was assessed using the quality assessment of diagnostic accuracy studies tool. The Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) was performed to assess the certainty of evidence. RESULT: This meta-analysis comprised six studies that included 1356 patients. The pooled sensitivity and specificity values were 0.596 (95% confidence interval [CI] = 0.345-0.822) and 0.970 (95% CI = 0.953-0.983), respectively. The pooled area under the summary receiver operating characteristic curve was 0.998. The quality assessment tool showed favorable results. In the GRADE analysis, the quality of evidence was very low for sensitivity and high for specificity when prehospital ultrasound was used for hemoperitoneum diagnosis. CONCLUSION: The specificity of abdominal free fluid detection using prehospital ultrasound examinations in trauma patients was very high.

Hyperpolarized 13 C metabolic imaging of the human abdomen with spatiotemporal denoising.

PURPOSE: Improving the quality and maintaining the fidelity of large coverage abdominal hyperpolarized (HP) 13 C MRI studies with a patch based global-local higher-order singular value decomposition (GL-HOVSD) spatiotemporal denoising approach. METHODS: Denoising performance was first evaluated using the simulated [1-13 C]pyruvate dynamics at different noise levels to determine optimal kglobal and klocal parameters. The GL-HOSVD spatiotemporal denoising method with the optimized parameters was then applied to two HP [1-13 C]pyruvate EPI abdominal human cohorts (n = 7 healthy volunteers and n = 8 pancreatic cancer patients). RESULTS: The parameterization of kglobal = 0.2 and klocal = 0.9 denoises abdominal HP data while retaining image fidelity when evaluated by RMSE. The kPX (conversion rate of pyruvate-to-metabolite, X = lactate or alanine) difference was shown to be <20% with respect to ground-truth metabolic conversion rates when there is adequate SNR (SNRAUC > 5) for downstream metabolites. In both human cohorts, there was a greater than nine-fold gain in peak [1-13 C]pyruvate, [1-13 C]lactate, and [1-13 C]alanine apparent SNRAUC . The improvement in metabolite SNR enabled a more robust quantification of kPL and kPA . After denoising, we observed a 2.1 ± 0.4 and 4.8 ± 2.5-fold increase in the number of voxels reliably fit across abdominal FOVs for kPL and kPA quantification maps. CONCLUSION: Spatiotemporal denoising greatly improves visualization of low SNR metabolites particularly [1-13 C]alanine and quantification of [1-13 C]pyruvate metabolism in large FOV HP 13 C MRI studies of the human abdomen.

Interactive content-based image retrieval with deep learning for CT abdominal organ recognition.

Objective.Recognizing the most relevant seven organs in an abdominal computed tomography (CT) slice requires sophisticated knowledge. This study proposed automatically extracting relevant features and applying them in a content-based image retrieval (CBIR) system to provide similar evidence for clinical use.Approach.A total of 2827 abdominal CT slices, including 638 liver, 450 stomach, 229 pancreas, 442 spleen, 362 right kidney, 424 left kidney and 282 gallbladder tissues, were collected to evaluate the proposed CBIR in the present study. Upon fine-tuning, high-level features used to automatically interpret the differences among the seven organs were extracted via deep learning architectures, including DenseNet, Vision Transformer (ViT), and Swin Transformer v2 (SwinViT). Three images with different annotations were employed in the classification and query.Main results.The resulting performances included the classification accuracy (94%-99%) and retrieval result (0.98-0.99). Considering global features and multiple resolutions, SwinViT performed better than ViT. ViT also benefited from a better receptive field to outperform DenseNet. Additionally, the use of hole images can obtain almost perfect results regardless of which deep learning architectures are used.Significance.The experiment showed that using pretrained deep learning architectures and fine-tuning with enough data can achieve successful recognition of seven abdominal organs. The CBIR system can provide more convincing evidence for recognizing abdominal organs via similarity measurements, which could lead to additional possibilities in clinical practice.