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.

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.

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.

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.

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.

[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).

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.

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.

[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.

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.

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.