The Role of Preoperative Abdominal Ultrasound in the Preparation of Patients Undergoing Primary Metabolic and Bariatric Surgery: A Machine Learning Algorithm on 4418 Patients' Records.

BACKGROUND: The utility of preoperative abdominal ultrasonography (US) in evaluating patients with obesity before metabolic bariatric surgery (MBS) remains ambiguously defined. METHOD: Retrospective analysis whereby patients were classified into four groups based on ultrasound results. Group 1 had normal findings. Group 2 had non-significant findings that did not affect the planned procedure. Group 3 required additional or follow-up surgeries without changing the surgical plan. Group 4, impacting the procedure, needed further investigations and was subdivided into 4A, delaying surgery for more assessments, and 4B, altering or canceling the procedure due to critical findings. Machine learning techniques were utilized to identify variables. RESULTS: Four thousand four hundred eighteen patients' records were analyzed. Group 1 was 45.7%. Group 2, 35.7%; Group 3, 17.0%; Group 4, 1.5%, Group 4A, 0.8%; and Group 4B, 0.7%, where surgeries were either canceled (0.3%) or postponed (0.4%). The hyperparameter tuning process identified a Decision Tree classifier with a maximum tree depth of 7 as the most effective model. The model demonstrated high effectiveness in identifying patients who would benefit from preoperative ultrasound before MBS, with training and testing accuracies of 0.983 and 0.985. It also showed high precision (0.954), recall (0.962), F1 score (0.958), and an AUC of 0.976. CONCLUSION: Our study found that preoperative ultrasound demonstrated clinical utility for a subset of patients undergoing metabolic bariatric surgery. Specifically, 15.9% of the cohort benefited from the identification of chronic calculous cholecystitis, leading to concomitant cholecystectomy. Additionally, surgery was postponed in 1.4% of the cases due to other findings. While these findings indicate a potential benefit in certain cases, further research, including a cost-benefit analysis, is necessary to fully evaluate routine preoperative ultrasound's overall utility and economic impact in this patient population.

Three-dimensional trans-rectal and trans-abdominal ultrasound image fusion for the guidance of gynecologic brachytherapy procedures: a proof of concept study.

High dose-rate brachytherapy is a treatment technique for gynecologic cancers where intracavitary applicators are placed within the patient's pelvic cavity. To ensure accurate radiation delivery, localization of the applicator at the time of insertion is vital. This study proposes a novel method for acquiring, registering, and fusing three-dimensional (3D) trans-abdominal and 3D trans-rectal ultrasound (US) images for visualization of the pelvic anatomy and applicators during gynecologic brachytherapy. The workflow was validated using custom multi-modal pelvic phantoms and demonstrated during two patient procedures. Experiments were performed for three types of intracavitary applicators: ring-and-tandem, ring-and-tandem with interstitial needles, and tandem-and-ovoids. Fused 3D US images were registered to magnetic resonance (MR) and computed tomography (CT) images for validation. The target registration error (TRE) and fiducial localization error (FLE) were calculated to quantify the accuracy of our fusion technique. For both phantom and patient images, TRE and FLE across all modality registrations (3D US versus MR or CT) resulted in mean ± standard deviation of 4.01 ± 1.01 mm and 0.43 ± 0.24 mm, respectively. This work indicates proof of concept for conducting further clinical studies leveraging 3D US imaging as an accurate, accessible alternative to advanced modalities for localizing brachytherapy applicators.

Fast prediction of personalized abdominal organ doses from CT examinations by radiomics feature-based machine learning models.

The X-rays emitted during CT scans can increase solid cancer risks by damaging DNA, with the risk tied to patient-specific organ doses. This study aims to establish a new method to predict patient specific abdominal organ doses from CT examinations using minimized computational resources at a fast speed. The CT data of 247 abdominal patients were selected and exported to the auto-segmentation software named DeepViewer to generate abdominal regions of interest (ROIs). Radiomics feature were extracted based on the selected CT data and ROIs. Reference organ doses were obtained by GPU-based Monte Carlo simulations. The support vector regression (SVR) model was trained based on the radiomics features and reference organ doses to predict abdominal organ doses from CT examinations. The prediction performance of the SVR model was tested and verified by changing the abdominal patients of the train and test sets randomly. For the abdominal organs, the maximal difference between the reference and the predicted dose was less than 1 mGy. For the body and bowel, the organ doses were predicted with a percentage error of less than 5.2%, and the coefficient of determination (R2) reached up to 0.9. For the left kidney, right kidney, liver, and spinal cord, the mean absolute percentage error ranged from 5.1 to 8.9%, and the R2 values were more than 0.74. The SVR model could be trained to achieve accurate prediction of personalized abdominal organ doses in less than one second using a single CPU core.

[Utility of abdominal ultrasonography in the management of ulcerative colitis:proposal of a new scoring system].

In the management of ulcerative colitis (UC), colonoscopy (CS) is considered essential for diagnosis;however, its invasiveness poses a challenge. Conversely, recent advancements in ultrasound diagnostic devices have improved imaging quality for the digestive tract, rendering them valuable in UC management. Therefore, this study aimed to elucidate the correlation between abdominal ultrasonography (AUS) and CS in assessing UC activity. The indices adopted for UC evaluation using AUS were as follows:1) bowel wall stratification, 2) bowel wall thickness, 3) bowel wall flow at power Doppler, 4) presence of increased brightness of inflammatory fat, and 5) presence of mesenteric lymph node swelling greater than 5mm. Subsequently, we developed a new AUS index for UC, termed the UCUS score, which comprises the aforementioned five indices. Finally, we compared the UCUS score with representative endoscopic indices, the Mayo endoscopic sub-score, and the Ulcerative Colitis Endoscopic Index of Severity. The results demonstrated that our proposed UCUS score better reflected disease activity than individual items assessed separately. ROC curve analysis revealed a UCUS score cutoff of 3 points. Therefore, a UCUS score of ≥3 points indicates the need for further examination with CS. Conversely, a score below 3 points suggests low disease activity, and in situations when evaluating treatment effectiveness, AUS could potentially substitute for CS. We believe that the UCUS score is an important source of information to understand the patient's condition and to motivate the patient to undergo endoscopy.

Clinical feasibility of deep learning based synthetic contrast enhanced abdominal CT in patients undergoing non enhanced CT scans.

Our objective was to develop and evaluate the clinical feasibility of deep-learning-based synthetic contrast-enhanced computed tomography (DL-SynCCT) in patients designated for nonenhanced CT (NECT). We proposed a weakly supervised learning with the utilization of virtual non-contrast CT (VNC) for the development of DL-SynCCT. Training and internal validations were performed with 2202 pairs of retrospectively collected contrast-enhanced CT (CECT) images with the corresponding VNC images acquired from dual-energy CT. Clinical validation was performed using an external validation set including 398 patients designated for true nonenhanced CT (NECT), from multiple vendors at three institutes. Detection of lesions was performed by three radiologists with only NECT in the first session and an additionally provided DL-SynCCT in the second session. The mean peak signal-to-noise ratio (PSNR) and structural similarity index map (SSIM) of the DL-SynCCT compared to CECT were 43.25 ± 0.41 and 0.92 ± 0.01, respectively. With DL-SynCCT, the pooled sensitivity for lesion detection (72.0% to 76.4%, P < 0.001) and level of diagnostic confidence (3.0 to 3.6, P < 0.001) significantly increased. In conclusion, DL-SynCCT generated by weakly supervised learning showed significant benefit in terms of sensitivity in detecting abnormal findings when added to NECT in patients designated for nonenhanced CT scans.

Comparison and precision of visceral adipose tissue measurement techniques in a multisite longitudinal study using MRI.

BACKGROUND: Measurement of visceral adipose tissue (VAT) using magnetic resonance imaging (MRI) is considered accurate and safe. Single slice measurements perform similar to volumetric measurements for cross-sectional observation studies but may not perform as well for longitudinal studies. This study compared the performance of single slice to volumetric VAT measurements in a prospective longitudinal study. Consistency of results across sites and over time was also evaluated. METHODS: A total of 935 healthy participants were recruited and scanned with MRI twice, approximately six months apart as part of a randomized, controlled, parallel arm, unblinded study conducted at four clinical centers in the United States. A 3D Dixon MRI sequence was used to image the abdomen, and visceral fat volumes were quantified for the abdomen, reduced coverage volumes (11 and 25 slices), and at single slices positioned at anatomical landmarks. A traveling phantom was scanned twice at all imaging sites. RESULTS: The correlation of single slice VAT measurement to full abdomen volumetric measurements ranged from 0.78 to 0.93 for cross-sectional observation measurements and 0.30 to 0.55 for longitudinal change. Reduced coverage volumetric measurement outperformed single slice measurements but still showed improved precision with more slices with cross-sectional observation and longitudinal correlations of 0.94 and 0.66 for 11 slices and 0.94 and 0.70 for 25 slices, respectively. No significant differences were observed across sites or over time with the traveling phantom and the volume measurements had a standard deviation of 14.1 mL, 2.6% of the measured volume. CONCLUSION: Single slice VAT measurements had significantly lower correlation with abdomen VAT volume for longitudinal change than for cross-sectional observation measurements and may not be suitable for longitudinal studies. Data from multiple sites, different scanners, and over time did not show significant differences.

Optimizing pseudo-spiral sampling for abdominal DCE MRI using a digital anthropomorphic phantom.

PURPOSE: For reliable DCE MRI parameter estimation, k-space undersampling is essential to meet resolution, coverage, and signal-to-noise requirements. Pseudo-spiral (PS) sampling achieves this by sampling k-space on a Cartesian grid following a spiral trajectory. The goal was to optimize PS k-space sampling patterns for abdomin al DCE MRI. METHODS: The optimal PS k-space sampling pattern was determined using an anthropomorphic digital phantom. Contrast agent inflow was simulated in the liver, spleen, pancreas, and pancreatic ductal adenocarcinoma (PDAC). A total of 704 variable sampling and reconstruction approaches were created using three algorithms using different parametrizations to control sampling density, halfscan and compressed sensing regularization. The sampling patterns were evaluated based on image quality scores and the accuracy and precision of the DCE pharmacokinetic parameters. The best and worst strategies were assessed in vivo in five healthy volunteers without contrast agent administration. The best strategy was tested in a DCE scan of a PDAC patient. RESULTS: The best PS reconstruction was found to be PS-diffuse based, with quadratic distribution of readouts on a spiral, without random shuffling, halfscan factor of 0.8, and total variation regularization of 0.05 in the spatial and temporal domains. The best scoring strategy showed sharper images with less prominent artifacts in healthy volunteers compared to the worst strategy. Our suggested DCE sampling strategy also showed high quality DCE images in the PDAC patient. CONCLUSION: Using an anthropomorphic digital phantom, we identified an optimal PS sampling strategy for abdominal DCE MRI, and demonstrated feasibility in a PDAC patient.

T1-weighted Motion Mitigation in Abdominal MRI: Technical Principles, Clinical Applications, Current Limitations, and Future Prospects.

T1-weighted (T1W) pulse sequences are an indispensable component of clinical protocols in abdominal MRI but usually require multiple breath holds (BHs) during the examination, which not all patients can sustain. Patient motion can affect the quality of T1W imaging so that key diagnostic information, such as intrinsic signal intensity and contrast enhancement image patterns, cannot be determined. Patient motion also has a negative impact on examination efficiency, as multiple acquisition attempts prolong the duration of the examination and often remain noncontributory. Techniques for mitigation of motion-related artifacts at T1W imaging include multiple arterial acquisitions within one BH; free breathing with respiratory gating or respiratory triggering; and radial imaging acquisition techniques, such as golden-angle radial k-space acquisition (stack-of-stars). While each of these techniques has inherent strengths and limitations, the selection of a specific motion-mitigation technique is based on several factors, including the clinical task under investigation, downstream technical ramifications, patient condition, and user preference. The authors review the technical principles of free-breathing motion mitigation techniques in abdominal MRI with T1W sequences, offer an overview of the established clinical applications, and outline the existing limitations of these techniques. In addition, practical guidance for abdominal MRI protocol strategies commonly encountered in clinical scenarios involving patients with limited BH abilities is rendered. Future prospects of free-breathing T1W imaging in abdominal MRI are also discussed. ©RSNA, 2024 See the invited commentary by Fraum and An in this issue.

Echinococcal disease can present with giant abdominal cysts at very young age: A case report.

Echinococcus granulosus larvae can cause cystic echinococcosis (CE, also known as hydatid disease) in humans. The latent phase of hydatid disease lasts for years as a result of the slow growth of the cysts, which only become symptomatic when they are large. Therefore, CE is seldomly seen in very young children. Here we present a 4-year-old boy with two giant asymptomatic abdominal cysts. Ultrasound was inconclusive in regard to the nature of the cysts and serology for echinococcosis was negative, rendering CE improbable also in view of the young age. Nevertheless, in the absence of other conclusive explanations, the patient was started on albendazole. A subsequent diagnostic percutaneous puncture with direct microscopy of cyst fluid revealed parasitological evidence of echinococcosis. This case report shows that CE can present with giant cysts also at very young age and should be considered as a possible diagnosis in all children with giant abdominal cysts.

Radiology of fibrosis part II: abdominal organs.

Fibrosis is the aberrant process of connective tissue deposition from abnormal tissue repair in response to sustained tissue injury caused by hypoxia, infection, or physical damage. It can affect almost all organs in the body causing dysfunction and ultimate organ failure. Tissue fibrosis also plays a vital role in carcinogenesis and cancer progression. The early and accurate diagnosis of organ fibrosis along with adequate surveillance are helpful to implement early disease-modifying interventions, important to reduce mortality and improve quality of life. While extensive research has already been carried out on the topic, a thorough understanding of how this relationship reveals itself using modern imaging techniques has yet to be established. This work outlines the ways in which fibrosis shows up in abdominal organs and has listed the most relevant imaging technologies employed for its detection. New imaging technologies and developments are discussed along with their promising applications in the early detection of organ fibrosis.

Comparison of the Size Measurement of Gallbladder Polyps by Three Different Radiologists in Abdominal Ultrasonography.

BACKGROUND: There is little information regarding the size measurement differences in gallbladder (GB) polyps performed by different radiologists on abdominal ultrasonography (US). AIM: To reveal the differences in GB polyp size measurements performed by different radiologists on abdominal US. METHODS: From June to September 2022, the maximum diameter of 228 GB polyps was measured twice on abdominal US by one of three radiologists (a third-year radiology resident [reader A], a radiologist with 7 years of experience in abdominal US [reader B], and an abdominal radiologist with 8 years of experience in abdominal US [reader C]). Intra-reader agreements for polyp size measurements were assessed by intraclass correlation coefficient (ICC). A Bland-Altman plot was used to visualize the differences between the first and second size measurements in each reader. RESULTS: Reader A, reader B, and reader C evaluated 65, 77, and 86 polyps, respectively. The mean size of measured 228 GB polyps was 5.0 ± 1.9 mm. Except for the case where reader A showed moderate intra-reader agreement (0.726) for polyps with size ≤ 5 mm, all readers showed an overall high intra-reader reliability (reader A, ICC = 0.859; reader B, ICC = 0.947, reader C, ICC = 0.948), indicative of good and excellent intra-reader agreements. The 95% limit of agreement of reader A, B, and C was 1.9 mm of the mean in all three readers. CONCLUSIONS: GB polyp size measurement on abdominal US showed good or excellent intra-reader agreements. However, size changes of approximately less than 1.9 mm should be interpreted carefully because these may be within the measurement error.

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique.

Abdominal multi-organ segmentation is one of the most important topics in the field of medical image analysis, and it plays an important role in supporting clinical workflows such as disease diagnosis and treatment planning. In this study, an efficient multi-organ segmentation method called Swin-PSAxialNet based on the nnU-Net architecture is proposed. It was designed specifically for the precise segmentation of 11 abdominal organs in CT images. The proposed network has made the following improvements compared to nnU-Net. Firstly, Space-to-depth (SPD) modules and parameter-shared axial attention (PSAA) feature extraction blocks were introduced, enhancing the capability of 3D image feature extraction. Secondly, a multi-scale image fusion approach was employed to capture detailed information and spatial features, improving the capability of extracting subtle features and edge features. Lastly, a parameter-sharing method was introduced to reduce the model's computational cost and training speed. The proposed network achieves an average Dice coefficient of 0.93342 for the segmentation task involving 11 organs. Experimental results indicate the notable superiority of Swin-PSAxialNet over previous mainstream segmentation methods. The method shows excellent accuracy and low computational costs in segmenting major abdominal organs.

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.

Study of a rapid ultrasonographic technique for the evaluation of the abdominal organs in rabbits.

BACKGROUND: Focused ultrasonographic imaging techniques are commonly used for cats and dogs; however, such techniques have not been described in rabbits. METHODS: Focused abdominal ultrasonography was performed on 12 healthy conscious rabbits using four acoustic windows: xiphisternal, left and right renal and cystic. They were positioned in sternal recumbency on a table top, with a cut-out area to allow access to the ventral abdomen. Ultrasonographic images were obtained using a micro-convex probe (3‒11 MHz), and the organs identified in each image were recorded. RESULTS: The liver, kidneys, stomach, duodenum, jejunum, caecum and colon were identified in all rabbits (12/12). In most rabbits, the following were identified: urinary bladder (11/12), gall bladder (11/12), spleen (10/12) and caudal vena cava or aorta (7/10). The right adrenal gland was identified in five of the 12 rabbits, but the left adrenal gland was identified in only one. The stomach filled at least one view in all rabbits, and the caecum filled the view in nine of 12 rabbits. Other structures thought to be identified included caecal flexures (9/12), appendix (9/12), ampulla coli (3/12), sacculus rotundus (3/12), colonic haustrae (2/12) and pancreas (2/12). LIMITATION: Only neutered individuals were imaged, so the usefulness of the technique for imaging the reproductive organs could not be determined. CONCLUSION: This technique enabled imaging of the major abdominal organs in most rabbits, demonstrating the potential value of focused imaging in this species.

Presentation of microstructural diffusion components by color schemes in abdominal organs.

PURPOSE: Development of a color scheme representation to facilitate the interpretation of tri-exponential DWI data from abdominal organs, where multi-exponential behavior is more pronounced. METHODS: Multi-exponential analysis of DWI data provides information about the microstructure of the tissue under study. The tri-exponential signal analysis generates numerous parameter images that are difficult to analyze individually. Summarized color images can simplify at-a-glance analysis. A color scheme was developed in which the slow, intermediate, and fast diffusion components were each assigned to a different red, green, and blue color channel. To improve the appearance of the image, histogram equalization, gamma correction, and white balance were used, and the processing parameters were adjusted. Examples of the resulting color maps of the diffusion fractions of healthy and pathological kidney and prostate are shown. RESULTS: The color maps obtained by the presented method show the merged information of the slow, intermediate, and fast diffusion components in a single view. A differentiation of the different fractions becomes clearly visible. Fast diffusion regimes, such as in the renal hilus, can be clearly distinguished from slow fractions, such as in dense tumor tissue. CONCLUSION: Combining the diffusion information from tri-exponential DWI analysis into a single color image allows for simplified interpretation of the diffusion fractions. In the future, such color images may provide additional information about the microstructural nature of the tissue under study.

Fat suppression using frequency-sweep RF saturation and iterative reconstruction.

PURPOSE: To introduce an alternative idea for fat suppression that is suited both for low-field applications where conventional fat-suppression approaches become ineffective due to narrow spectral separation and for applications with strong B0 homogeneities. METHODS: Separation of fat and water is achieved by sweeping the frequency of RF saturation pulses during continuous radial acquisition and calculating frequency-resolved images using regularized iterative reconstruction. Voxel-wise signal-response curves are extracted that reflect tissue's response to RF saturation at different frequencies and allow the classification into fat or water. This information is then utilized to generate water-only composite images. The principle is demonstrated in free-breathing abdominal and neck examinations using stack-of-stars 3D balanced SSFP (bSSFP) and gradient-recalled echo (GRE) sequences at 0.55 and 3T. Moreover, a potential extension toward quantitative fat/water separation is described. RESULTS: Experiments with a proton density fat fraction (PDFF) phantom validated the reliability of fat/water separation using signal-response curves. As demonstrated for abdominal imaging at 0.55T, the approach resulted in more uniform fat suppression without loss of water signal and in improved CSF-to-fat signal ratio. Moreover, the approach provided consistent fat suppression in 3T neck exams where conventional spectrally-selective fat saturation failed due to strong local B0 inhomogeneities. The feasibility of simultaneous fat/water quantification has been demonstrated in a PDFF phantom. CONCLUSION: The proposed principle achieves reliable fat suppression in low-field applications and adapts to high-field applications with strong B0 inhomogeneity. Moreover, the principle potentially provides a basis for developing an alternative approach for PDFF quantification.

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.