Non-contrast Images Research Articles

Performance of an automated deep learning algorithm to identify hepatic steatosis within noncontrast computed tomography scans among people with and without HIV.

Hepatic steatosis (fatty liver disease) affects 25% of the world's population, particularly people with HIV (PWH). Pharmacoepidemiologic studies to identify medications associated with steatosis have not been conducted because methods to evaluate liver fat within digitized images have not been developed. We determined the accuracy of a deep learning algorithm (automatic liver attenuation region-of-interest-based measurement [ALARM]) to identify steatosis within clinically obtained noncontrast abdominal CT images compared to manual radiologist review and evaluated its performance by HIV status. We performed a cross-sectional study to evaluate the performance of ALARM within noncontrast abdominal CT images from a sample of patients with and without HIV in the US Veterans Health Administration. We evaluated the ability of ALARM to identify moderate-to-severe hepatic steatosis, defined by mean absolute liver attenuation <40 Hounsfield units (HU), compared to manual radiologist assessment. Among 120 patients (51 PWH) who underwent noncontrast abdominal CT, moderate-to-severe hepatic steatosis was identified in 15 (12.5%) persons via ALARM and 12 (10%) by radiologist assessment. Percent agreement between ALARM and radiologist assessment of absolute liver attenuation <40 HU was 95.8%. Sensitivity, specificity, positive predictive value, and negative predictive value of ALARM were 91.7% (95%CI, 51.5%-99.8%), 96.3% (95%CI, 90.8%-99.0%), 73.3% (95%CI, 44.9%-92.2%), and 99.0% (95%CI, 94.8%-100%), respectively. No differences in performance were observed by HIV status. ALARM demonstrated excellent accuracy for moderate-to-severe hepatic steatosis regardless of HIV status. Application of ALARM to radiographic repositories could facilitate real-world studies to evaluate medications associated with steatosis and assess differences by HIV status.

Deep learning-based prognostic model using non-enhanced cardiac cine MRI for outcome prediction in patients with heart failure.

To evaluate the performance of a deep learning-based multi-source model for survival prediction and risk stratification in patients with heart failure. Patients with heart failure with reduced ejection fraction (HFrEF) who underwent cardiac magnetic resonance between January 2015 and April 2020 were retrospectively included in this study. Baseline electronic health record data, including clinical demographic information, laboratory data, and electrocardiographic information, were collected. Short-axis non-contrast cine images of the whole heart were acquired to estimate the cardiac function parameters and the motion features of the left ventricle. Model accuracy was evaluated using the Harrell's concordance index. All patients were followed up for major adverse cardiac events (MACEs), and survival prediction was assessed using Kaplan-Meier curves. A total of 329 patients were evaluated (age 54 ± 14years; men, 254) in this study. During a median follow-up period of 1041days, 62 patients experienced MACEs and their median survival time was 495days. When compared with conventional Cox hazard prediction models, deep learning models showed better survival prediction performance. Multi-data denoising autoencoder (DAE) model reached the concordance index of 0.8546 (95% CI: 0.7902-0.8883). Furthermore, when divided into phenogroups, the multi-data DAE model could significantly discriminate between the survival outcomes of the high-risk and low-risk groups compared with other models (p < 0.001). The proposed deep learning (DL) model based on non-contrast cardiac cine magnetic resonance imaging could independently predict the outcome of patients with HFrEF and showed better prediction efficiency than conventional methods. The proposed multi-source deep learning model based on cardiac magnetic resonance enables survival prediction in patients with heart failure. • A multi-source deep learning model based on non-contrast cardiovascular magnetic resonance (CMR) cine images was built to make robust survival prediction in patients with heart failure. • The ground truth definition contains electronic health record data as well as DL-based motion data, and cardiac motion information is extracted by optical flow method from non-contrast CMR cine images. • The DL-based model exhibits better prognostic value and stratification performance when compared with conventional prediction models and could aid in the risk stratification in patients with HF.

Differentiation of geniculate ganglion venous malformation from schwannoma: dynamic T1-weighted imaging provides unique diagnostic value.

To distinguish geniculate ganglion venous malformation (GGVM) from schwannoma (GGS) by using high-resolution CT (HRCT), routine MRI, and dynamic T1-weighted imaging (T1WI) characteristics. Surgically confirmed GGVMs and GGSs between 2016 and 2021 were retrospectively included. Preoperative HRCT, routine MR, and dynamic T1WI were performed on all patients. Clinical data, imaging characteristics including lesion size, involvement of facial nerve (FN), signal intensity, enhancement pattern on dynamic T1WI, and bone destruction on HRCT were evaluated. Logistic regression model was developed to identify independent factors for GGVMs, and the diagnostic performance was accessed by receiving operative curve (ROC) analysis. Histological characteristics were explored for both GGVMs and GGSs. Twenty GGVMs and 23 GGSs with mean age of 31 were included. On dynamic T1WI, 18 GGVMs (18/20) showed "pattern A" enhancement (a progressive filling enhancement), while all 23 GGSs showed "pattern B" enhancement (a gradual whole-lesion enhancement) (p < 0.001). Thirteen GGVMs (13/20) showed the "honeycomb" sign whereas all GGS (23/23) showed extensive bone changes on HRCT (p < 0.001). Lesion size, involvement of FN segment, signal intensity on non-contrast T1WI and T2-weighted imaging (T2WI), and homogeneity on enhanced T1WI were obviously differed between two lesions (p < 0.001, p = 0.002, p < 0.001, p = 0.01, p = 0.02, respectively). Regression model showed the "honeycomb" sign and "pattern A" enhancement were independent risk factors. Histologically, GGVM was characterized by interwoven dilated and tortuous veins, while GGS was characterized by abundant spindle cells with dense arterioles or capillaries. The "honeycomb" sign on HRCT and "pattern A" enhancement on dynamic T1WI are the most promising imaging characteristics for differentiating GGVM from GGS. The characteristic sign and enhancement pattern on HRCT and dynamic T1-weighted imaging allow preoperative differentiation of geniculate ganglion venous malformation and schwannoma feasible, which will improve clinical management and benefit patient prognosis. • The "honeycomb" sign on HRCT is a reliable finding to differentiate GGVM from GGS. • GGVM typically shows "pattern A" enhancement (focal enhancement of the tumor on early dynamic T1WI, followed by progressive contrast filling of the tumor in the delayed phase), while "pattern B" enhancement (gradual heterogeneous or homogeneous enhancement of the whole lesion) is observed in GGS on dynamic T1WI.

OPTIMALISASI KUALITAS CITRA ANATOMI DENGAN VARIASI IDOSE DAN INTERATIVE MODEL RECONTRUCTION (IMR) PADA MSCT UROGRAPHY NON KONTRAS DENGAN TEKNIK TRACKING

Background: The urinary tract (urinary system) consists of the two kidneys and ureters, the urinary bladder, and the ureters. There are several ways to examine abnormalities in the urinary tract radiologically. including the current CT scan, CT Scan (MSCT) is one of the modalities to evaluate and diagnose urinary tract disorders. MSCT of the urinary tract without using positive contrast media is able to identify images of the urinary tract in cases of urolithiasis. iDose and Interative Model Reconstruction (IMR) are the second generation of IR algorithms from the previous generation, namely iDose. This technology can also lower the dose to the patient and also improve the quality of the resulting image.&#x0D; Method: This type of research is an experiment with an observational approach with comparisons. This type of research is an experiment with data obtained from a comparison of noise values and the quality of anatomical images from tracking images with iDose and IMR variations. And an assessment was carried out by 3 radiologists. Place of data collection in radiology RSUD RAA Soewondo Pati. Time for data collection From April to November 2022. in assessing anatomical images using statistical tests. Data analysis was carried out using the Wilcoxon statistical test method because the data were ordinal with paired groups. The Wilcoxon statistical test was used to test the hypothesis and see the level of difference in anatomical image information. The level of significance (level of significance) is 95% or α&gt; 0.05 and is done by assessing the p-value. For a significant level of assessment p &lt;0.05 then Ho is rejected and p&gt; 0.05 then Ho is accepted.&#x0D; Results: Friedman test on each anatomical part of the urinary tract system on Non-Contrast Urography MSCT images with Tracking Techniques with iDose 1-6 and IMR 1-3 variations, there are differences in anatomical image information with a p value of .000 (p &lt;0.05). In the anatomy of the renal parenchyma, the highest mean rank is iDose 3 with a value of 8.15. In pelvic anatomy and kidney calices, the highest mean rank is at iDose 6 with a value of 8.05. In ureteral anatomy, the highest mean rank is iDose 3 with a value of 8.05. In the anatomy of the perirenal space, the highest mean rank is iDose 3 with a value of 8.20. Based on the results of the mean rank Friedman test to find out the optimal I Dose and IMR values, it was obtained iDose 1 with a mean rank value of 4.65, iDose 2 with a mean rank value of 4.95, iDose 3 with a mean rank value of 8.3, iDose 4 with a mean rank value of mean rank 6.4, iDose 5 with a mean rank value of 6.55, iDose 6 with a mean rank value of 7.75, IMR 1 with a mean rank value of 1.65, IMR 2 with a mean rank value of 2.05, and IMR 3 with a mean value rating 2.7.&#x0D; Conclusion: Changes in the values of iDose and IMR variations affect the anatomical image information of Non-Contrast MSCT Urography with Tracking Techniques, this is based on the results of the mean rank Friedman test performed on each anatomy of the renal parenchyma, pelvic calices, ureters and perirenal space which shows a difference. Based on the results of Friedman's mean rank test conducted on the entire anatomy of the renal parenchyma, ureters, and perirenal space, it showed that the optimal iDose and IMR variation values were iDose 3 with a mean rank value of 8.3.

Differential diagnosis of adrenal adenomas and metastases using spectral parameters in dual-layer detector spectral CT

ObjectiveTo assess the diagnostic value of spectral parameters in differentiating adrenal adenomas from metastases based on dual-layer detector spectral CT (DLSCT).Materials and methodsPatients with adenomas or metastases who underwent enhanced DLSCT of the adrenals were enrolled. The CT values of virtual non-contrast images (CTVNC), iodine density (ID) values, and Z-effective (Z-eff) values, the normalized iodine density (NID) values, slopes of spectral HU curves (s-SHC), and iodine-to-CTVNC ratios of the tumors were measured in each phase. Receiver operating characteristic (ROC) curves were used to compare the diagnostic values.ResultsNinety-nine patients with 106 adrenal lesions (63 adenomas, 43 metastases) were included. In the venous phase, all spectral parameters were significantly different between adenomas and metastases (all p < 0.05). The combined spectral parameters showed a better diagnostic performance in the venous phase than in other phase (p < 0.05). The iodine-to-CTVNC value had a larger area under the ROC curve (AUC) than the other spectral parameters in the differential diagnosis of adenomas and metastases, with a diagnostic sensitivity and specificity of 74.4% and 91.9%, respectively. In the differential diagnosis of lipid-rich adenomas, lipid-poor adenomas and metastases, the CTVNC value and s-SHC value also had a larger AUC than the other spectral parameters, with a diagnostic sensitivity of 97.7%, 79.1% and specificity of 91.2%, 93.1%, respectively.ConclusionOn DLSCT, the combined spectral parameters in the venous phase could help better distinguish adrenal adenomas from metastases. The iodine-to-CTVNC, CTVNC and s-SHC values had the highest AUC values in differentiating adenomas, lipid-rich adenomas and lipid-poor adenomas from metastases, respectively.

Cardiac Virtual Noncontrast Images for Calcium Quantification with Photon-counting Detector CT

Purpose To assess the accuracy of aortic valve calcium (AVC), mitral annular calcium (MAC), and coronary artery calcium (CAC) quantification and risk stratification using virtual noncontrast (VNC) images from late enhancement photon-counting detector CT as compared with true noncontrast images. Materials and Methods This retrospective, institutional review board–approved study evaluated patients undergoing photon-counting detector CT between January and September 2022. VNC images were reconstructed from late enhancement cardiac scans at 60, 70, 80, and 90 keV using quantum iterative reconstruction (QIR) strengths of 2–4. AVC, MAC, and CAC were quantified on VNC images and compared with quantification of AVC, MAC, and CAC on true noncontrast images using Bland-Altman analyses, regression models, intraclass correlation coefficients (ICC), and Wilcoxon tests. Agreement between severe aortic stenosis likelihood categories and CAC risk categories determined from VNC and true noncontrast images was assessed by weighted κ analysis. Results Ninety patients were included (mean age, 80 years ± 8 [SD]; 49 male patients). Scores were similar on true noncontrast images and VNC images at 80 keV for AVC and MAC, regardless of QIR strengths, and VNC images at 70 keV with QIR 4 for CAC (all P > .05). The best results were achieved using VNC images at 80 keV with QIR 4 for AVC (mean difference, 3; ICC = 0.992; r = 0.98) and MAC (mean difference, 6; ICC = 0.998; r = 0.99), and VNC images at 70 keV with QIR 4 for CAC (mean difference, 28; ICC = 0.996; r = 0.99). Agreement between calcification categories was excellent on VNC images at 80 keV for AVC (κ = 0.974) and on VNC images at 70 keV for CAC (κ = 0.967). Conclusion VNC images from cardiac photon-counting detector CT enables patient risk stratification and accurate quantification of AVC, MAC, and CAC. Keywords: Coronary Arteries, Aortic Valve, Mitral Valve, Aortic Stenosis, Calcifications, Photon-counting Detector CT Supplemental material is available for this article © RSNA, 2023

Monitoring Muscle Perfusion in Rodents During Short-Term Ischemia Using Power Doppler Ultrasound.

The aim of this work was to evaluate the reliability of power Doppler ultrasound (PD-US) measurements made without contrast enhancement to monitor temporal changes in peripheral blood perfusion. On the basis of pre-clinical rodent studies, we found that combinations of spatial registration and clutter filtering techniques applied to PD-US signals reproducibly tracked blood perfusion in skeletal muscle. Perfusion is monitored while modulating hindlimb blood flow. First, in invasive studies, PD-US measurements in deep muscle with laser speckle contrast imaging (LSCI) of superficial tissues made before, during and after short-term arterial clamping were compared. Then, in non-invasive studies, a pressure cuff was employed to generate longer-duration hindlimb ischemia. Here, B-mode imaging was also applied to measure flow-mediated dilation of the femoral artery while, simultaneously, PD-US was used to monitor downstream muscle perfusion to quantify reactive hyperemia. Measurements in adult male and female mice and rats, some with exercise conditioning, were included to explore biological variables. PD-US methods are validated through comparisons with LSCI measurements. As expected, no significant differences were found between sexes or fitness levels in flow-mediated dilation or reactive hyperemia estimates, although post-ischemic perfusion was enhanced with exercise conditioning, suggesting there could be differences between the hyperemic responses of conduit and resistive vessels. Overall, we found non-contrast PD-US imaging can reliably monitor relative spatiotemporal changes in muscle perfusion. This study supports the development of PD-US methods for monitoring perfusion changes in patients at risk for peripheral artery disease.

Using deep learning to recognize local advanced rectal cancer in CT images.

e15624 Background: Colorectal cancer was the most prevalent malignant disease in developed country and cause 45% mortality rate. Rectal cancer accounted 40% of this disease and is difficult to treat due to its anatomical position. Preoperative neoadjuvant radio-chemotherapy for local advanced rectal cancer can increase circumferential resection margin (CRM) which lead to lower local recurrence rate. MRI was suggested as staging methods for CRM status and nodal evaluation. However, MRI resources were much less than CT scans. CT scans was prevalent and can provide substantial agreement in CRM evaluation under experienced specialist. This study tries to use deep learning to detect local advanced rectal cancer in CT images at initiation of treatment as advisement for neoadjuvant chemoradiotherapy. Methods: From 2010.10.1~2021.12.31, rectal cancer patients with cT3-4 lesion was included. Collect high quality CT images with DICOM files in tumor segment. cT1-2, low rectal lesions, non-CRC cases were not included. Non-contrast and artificial defect images were also excluded. CT images were labeled as” diseased “ when CRM were threatened ( &lt; 2mm). All images were labeled according to judgment of 2 specialist. The data were separated into 2 parts. One for AI model training and testing, another for external validation. The training testing dataset was achieved by deep learning neuro network and evaluating model accuracy performance. Then the model was applied into external validation dataset for real-world testing, evaluating coherent rate between AI and the Dr. decision. Furthermore, to see the cancer survival outcomes according to AI model prediction results. Results: From 2010.10.1~2022.12.31, 1256 rectal cancer cases images were included. 706 cases (889 CT series, 5958 images) with cT3-4 lesions and high quality DICOM files were eligible. 620 cases (803 series, 5318 images) with cT3-4 lesions were classified in training validation set. 1907 (35.9%) images were diseased, 3411 (64.1%) images were normal. 86 clinical stage 2-3 cases (640 images) directly underwent resection during 2014.12.1-2017.5.31 were classified as external validation group. For recognizing CRM+ local advanced rectal cancer by EfficientNetB0, the algorithm achieved an area under the receiver operating characteristic curve (AUC): 0.91 (86.1% accuracy). For External validation set, the sensitivity for AI to detect CRM+ by Dr was 80%; 88% (by images; by cases), the specificity was 68%; 66% (by images; by cases). The survival curve (local recurrence rate, DFS, OS) was also superior in AI- group than AI+ group which was comparable with the results of Dr. Conclusions: Deep Learning can detect local advanced rectal cancer in CT images and prediction results also implicate poor disease outcome.

Dual-Energy CT Evaluation of Gastrointestinal Bleeding

Gastrointestinal (GI) bleeding is a potentially life-threatening condition accounting for more than 300 000 annual hospitalizations. Multidetector abdominopelvic CT angiography is commonly used in the evaluation of patients with GI bleeding. Given that many patients with severe overt GI bleeding are unlikely to tolerate bowel preparation, and inpatient colonoscopy is frequently limited by suboptimal preparation obscuring mucosal visibility, CT angiography is recommended as a first-line diagnostic test in patients with severe hematochezia to localize a source of bleeding. Assessment of these patients with conventional single-energy CT systems typically requires the performance of a noncontrast series followed by imaging during multiple postcontrast phases. Dual-energy CT (DECT) offers several potential advantages for performing these examinations. DECT may eliminate the need for a noncontrast acquisition by allowing the creation of virtual noncontrast (VNC) images from contrast-enhanced data, affording significant radiation dose reduction while maintaining diagnostic accuracy. VNC images can help radiologists to differentiate active bleeding, hyperattenuating enteric contents, hematomas, and enhancing masses. Additional postprocessing techniques such as low-kiloelectron voltage virtual monoenergetic images, iodine maps, and iodine overlay images can increase the conspicuity of contrast material extravasation and improve the visibility of subtle causes of GI bleeding, thereby increasing diagnostic confidence and assisting with problem solving. GI bleeding can also be diagnosed with routine single-phase DECT scans by constructing VNC images and iodine maps. Radiologists should also be aware of the potential pitfalls and limitations of DECT. ©RSNA, 2023 Quiz questions for this article are available through the Online Learning Center.

Exploring Texture Analysis to Optimize Bladder Preservation in Muscle Invasive Bladder Cancer.

To explore if texture analysis of Muscle Invasive Bladder Cancer (MIBC) can aid in better patient selection for bladder preservation. Pretreatment noncontrast CT images of 41 patients of MIBC treated with bladder preservation were included. The visible tumor was contoured on all slices by a single observer. The primary endpoint was to identify texture parameters associated with disease recurrence posttreatment. The secondary endpoints included intra and interobserver variability, single and multislice analysis, and differentiating the texture features of normal bladder and tumor. For interobserver variability of bladder tumor texture features, 3 observers contoured the visible tumor on all slices independently. Observer 1 contoured again at an interval of 1 month for intraobserver variability. The median follow-up was 30 months with 12 patients having a recurrence. In the primary endpoint analysis, the mean of the pixels at Spatial Scaling Filter (SSF) 2 for the no recurrence group and recurrence group was 6.44 v 13.73 respectively (P=.031) and the same at SSF-3 was 11.95 and 22.32 respectively (P=.034). The texture features that could significantly differentiate tumor and normal bladder were mean, standard deviation and kurtosis of the pixels at SSF-2 and entropy and kurtosis of the pixels at SSF-3. Overall, there was an excellent intra and interobserver concordance in texture features. Only multislice analysis and not single-slice could differentiate recurrence and no recurrence posttreatment. Texture analysis can be explored as a modality for patient selection for bladder preservation along with the established clinical parameters to improve outcomes.

Review of Diagnostic Modalities for Adrenal Incidentaloma.

Adrenal incidentalomas are common findings in clinical practice, with a prevalence of up to 4.2% in radiological studies. Due to the large number of focal lesions in the adrenal glands, it can be challenging to make a definitive diagnosis and determine the appropriate management. The purpose of this review is to present current diagnostic modalities used to preoperatively distinguish between adrenocortical adenoma (ACA) and adrenocortical cancer (ACC). Proper management and diagnosis are crucial in avoiding unnecessary adrenalectomies, which occur in over 40% of cases. A literature analysis was conducted to compare ACA and ACC using imaging studies, hormonal evaluation, pathological workup, and liquid biopsy. Before deciding on surgical treatment, the nature of the tumor can be accurately determined using noncontrast CT imaging combined with tumor size and metabolomics. This approach helps to narrow down the group of patients with adrenal tumors who require surgical treatment due to the suspected malignant nature of the lesion.

Non-contrast enhancement of brachial plexus magnetic resonance imaging with compressed sensing

PurposeTo observe the quality of brachial plexus (BP) images obtained from magnetic resonance imaging (MRI) with 3D T2 STIR SPACE sequence and compressed sensing (CS) and to compare the results with BP images from the same sequence without CS. MethodsIn this study, compressed sensing was applied to acquire non-contrast BP images from ten healthy volunteers with 3D T2 STIR SPACE sequence to shorten acquisition time without sacrificing image quality. The acquisition time of scanning with CS was compared to that without CS. The quantitative signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated and compared using paired t-test to determine the quality of images with and without CS. The qualitative assessment by three experienced radiologists was performed using a scoring scale from 1 (poor) to 5 (excellent) and analyzed for interobserver agreement on image quality. ResultsThe increasing SNR and CNR of images with CS were found in nine regions of BP images (p < 0.001) with faster acquisition time. The result of paired t-test (p < 0.001) illustrates the significant difference between images with CS compared to images without CS. The assessment of observers also shows higher scores for images with CS compared to images without CS. ConclusionsThis study demonstrates that CS can effectively increase the visibility of images and image boundaries, SNR, and CNR of BP images obtained with 3D T2 STIR SPACE sequence with the good interobserver agreement and within clinically optimal acquisition time compared to images from similar sequence without CS.

Dual-energy CT of the pancreas: comparison between virtual non-contrast images and true non-contrast images in the detection of pancreatic lesion.

To evaluate the image quality and diagnostic performance for pancreatic lesion between true non-contrast (TNC) and virtual non-contrast (VNC) images obtained from the dual-energy computed tomography (DECT). One hundred six patients with pancreatic mass underwent contrast-enhanced DECT examinations were retrospectively included in this study. VNC images of the abdomen were generated from late arterial (aVNC) and portal (pVNC) phases. For quantitative analysis, the attenuation differences and reproducibility of abdominal organs were compared between TNC and aVNC/pVNC measurements. Qualitatively image quality was assessed by two radiologists using a five-point scale, and they independently compared the detection accuracy of pancreatic lesions between TNC and aVNC/pVNC images. The volume CT dose index (CTDIvol) and size-specific dose estimates (SSDE) were recorded to evaluate the potential dose reduction when using VNC reconstruction to replace the unenhanced phase. A total of 78.38% (765/976) of the attenuation measurement pairs were reproducible between TNC and aVNC images, and 71.0% (693/976) between TNC and pVNC images. In triphasic examinations, a total of 108 pancreatic lesions were found in 106 patients, and no significant difference in detection accuracy was found between TNC and VNC images (p = 0.587-0.957). Qualitatively, image quality was rated diagnostic (score ≥ 3) in all the VNC images. Calculated CTDIvol and SSDE reduction of about 34% could be achieved by omitting the non-contrast phase. VNC images of DECT provide diagnostic image quality and accurate pancreatic lesions detection, which are a promising alternative to unenhanced phase with a substantial reduction of radiation exposure in clinical routine.

Advanced Faster RCNN: a non-contrast CT-based algorithm for detecting pancreatic lesions in multiple disease stages

To propose a non-contrast CT-based algorithm for automated and accurate detection of pancreatic lesions at a low cost. With Faster RCNN as the benchmark model, an advanced Faster RCNN (aFaster RCNN) model for pancreatic lesions detection based on plain CT was constructed. The model uses the residual connection network Resnet50 as the feature extraction module to extract the deep image features of pancreatic lesions. According to the morphology of pancreatic lesions, 9 anchor frame sizes were redesigned to construct the RPN module. A new Bounding Box regression loss function was proposed to constrain the training process of RPN module regression subnetwork by comprehensively considering the constraints of the lesion shape and anatomical structure. Finally, a detection frame was generated using the detector in the second stage. The data from a total of 728 cases of pancreatic diseases from 4 clinical centers in China were used for training (518 cases, 71.15%) and testing (210 cases, 28.85%) of the model. The performance of aFaster RCNN was verified through ablation experiments and comparison experiments with 3 classical target detection models SSD, YOLO and CenterNet. The aFaster RCNN model for pancreatic lesion detection achieved recall rates of 73.64% at the image level and 92.38% at the patient level, with an average precision of 45.29% and 53.80% at the image and patient levels, respectively, which were higher than those of the 3 models for comparison. The proposed method can effectively extract the imaging features of pancreatic lesions from non-contrast CT images to detect the pancreatic lesions.

Improving visualization of free fibula flap perforators and reducing radiation dose in dual-energy CT angiography.

The precise assessment of the perforators of the fibula free flap (FFF) is crucial for minimizing procedure-related complications when harvesting the FFF in patients with maxillofacial lesions. This study aims to investigate the utility of virtual noncontrast (VNC) images for radiation dose saving and to determine the optimal energy level of virtual monoenergetic imaging (VMI) reconstructions in dual-energy computed tomography (DECT) for visualization of the perforators of the fibula free flap (FFF). Data from 40 patients with maxillofacial lesions who received lower extremity DECT examinations in the noncontrast and arterial phase were collected in this retrospective, cross-sectional study. To compare VNC images from the arterial phase with true non-contrast images in a DECT protocol (M_0.5-TNC) and to compare VMI images with 0.5 linear images blending from the arterial phase (M_0.5-C), the attenuation, noise, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and subjective image quality were assessed in different arteries, muscles, and fat tissues. Two readers evaluated the image quality and visualization of the perforators. The dose-length product (DLP) and CT volume dose index (CTDIvol) were used to determine the radiation dose. Objective and subjective analyses showed no significant difference between the M_0.5-TNC and VNC images in the arteries and muscles (P>0.09 to P>0.99), and VNC imaging could reduce 50% of the radiation dose (P<0.001). Compared with those of the M_0.5-C images, the attenuation and CNR of VMI reconstructions at 40 kiloelectron volt (keV) and 60 keV were higher (P<0.001 to P=0.04). Noise was similar at 60 keV (all P>0.99) and increased at 40 keV (all P<0.001), and the SNR in arteries was increased at 60 keV (P<0.001 to P=0.02) in VMI reconstructions compared with those in the M_0.5-C images. The subjective scores in VMI reconstructions at 40 and 60 keV was higher than those in M_0.5-C images (all P<0.001). The image quality at 60 keV was superior to that at 40 keV (P<0.001), and there was no difference in the visualization of the perforators between 40 and 60 keV (P=0.31). VNC imaging is a reliable technique for replacing M_0.5-TNC and provides radiation dose saving. The image quality of the 40-keV and 60-keV VMI reconstructions was higher than that of the M_0.5-C images, and 60 keV provided the best assessment of perforators in the tibia.

Artificial intelligence using a deep learning versus expert computed tomography human reading in calcium score and coronary artery calcium data and reporting system classification.

Artificial intelligence (AI) applied to cardiac imaging may provide improved processing, reading precision and advantages of automation. Coronary artery calcium (CAC) score testing is a standard stratification tool that is rapid and highly reproducible. We analyzed CAC results of 100 studies in order to determine the accuracy and correlation between the AI software (Coreline AVIEW, Seoul, South Korea) and expert level-3 computed tomography (CT) human CAC interpretation and its performance when coronary artery disease data and reporting system (coronary artery calcium data and reporting system) classification is applied. A total of 100 non-contrast calcium score images were selected by blinded randomization and processed with the AI software versus human level-3 CT reading. The results were compared and the Pearson correlation index was calculated. The CAC-DRS classification system was applied, and the cause of category reclassification was determined using an anatomical qualitative description by the readers. The mean age was age 64.5 years, with 48% female. The absolute CAC scores between AI versus human reading demonstrated a highly significant correlation (Pearson coefficient R = 0.996); however, despite these minimal CAC score differences, 14% of the patients had their CAC-DRS category reclassified. The main source of reclassification was observed in CAC-DRS 0-1, where 13 were recategorized, particularly between studies having a CAC Agatston score of 0 versus 1. Qualitative description of the errors showed that the main cause of misclassification was AI underestimation of right coronary calcium, AI overestimation of right ventricle densities and human underestimation of right coronary artery calcium. Correlation between AI and human values is excellent with absolute numbers. When the CAC-DRS classification system was adopted, there was a strong correlation in the respective categories. Misclassified were predominantly in the category of CAC = 0, most often with minimal values of calcium volume. Additional algorithm optimization with enhanced sensitivity and specificity for low values of calcium volume will be required to enhance AI CAC score utilization for minimal disease. Over a broad range of calcium scores, AI software for calcium scoring had an excellent correlation compared to human expert reading and in rare cases determined calcium missed by human interpretation.

A State-of-the-Art Review of Intra-Operative Imaging Modalities Used to Quality Assure Endovascular Aneurysm Repair.

Endovascular aortic aneurysm repair (EVAR) is the preferred method for elective abdominal aortic aneurysm (AAA) repair. However, the success of this technique depends greatly on the technologies available. Intra-operative imaging is essential but can come with limitations. More complex interventions lead to longer operating times, fluoroscopy times, and greater contrast doses. A number of intra-operative imaging modalities to quality assure the success of EVAR have been developed. A systematic literature search was performed with separate searches conducted for each imaging modality in the study: computed tomography (CT), digital subtraction angiography (DSA), fusion, ultrasound, intra-operative positioning system (IOPS), and non-contrast imaging. CT was effective at detecting complications but commonly resulted in increased radiation and contrast dose. The effectiveness of DSA can be increased, and radiation exposure reduced, through the use of adjunctive technologies. We found that 2D-3D fusion was non-inferior to 3D-3D and led to reduced radiation and contrast dose. Non-contrast imaging occasionally led to higher doses of radiation. Ultrasound was particularly effective in the detection of type II endoleaks with reduced radiation and contrast use but was often operator dependent. Unfortunately, no papers made it past full text screening for IOPS. All of the imaging techniques discussed have advantages and disadvantages, and clinical context is relevant to guide imaging choice. Fusion and ultrasound in particular show promise for the future.

Obesity-Related Pitfalls of Virtual versus True Non-Contrast Imaging-An Intraindividual Comparison in 253 Oncologic Patients.

Dual-source dual-energy CT (DECT) facilitates reconstruction of virtual non-contrast images from contrast-enhanced scans within a limited field of view. This study evaluates the replacement of true non-contrast acquisition with virtual non-contrast reconstructions and investigates the limitations of dual-source DECT in obese patients. A total of 253 oncologic patients (153 women; age 64.5 ± 16.2 years; BMI 26.6 ± 5.1 kg/m2) received both multi-phase single-energy CT (SECT) and DECT in sequential staging examinations with a third-generation dual-source scanner. Patients were allocated to one of three BMI clusters: non-obese: <25 kg/m2 (n = 110), pre-obese: 25-29.9 kg/m2 (n = 73), and obese: >30 kg/m2 (n = 70). Radiation dose and image quality were compared for each scan. DECT examinations were evaluated regarding liver coverage within the dual-energy field of view. While arterial contrast phases in DECT were associated with a higher CTDIvol than in SECT (11.1 vs. 8.1 mGy; p < 0.001), replacement of true with virtual non-contrast imaging resulted in a considerably lower overall dose-length product (312.6 vs. 475.3 mGy·cm; p < 0.001). The proportion of DLP variance predictable from patient BMI was substantial in DECT (R2 = 0.738) and SECT (R2 = 0.620); however, DLP of SECT showed a stronger increase in obese patients (p < 0.001). Incomplete coverage of the liver within the dual-energy field of view was most common in the obese subgroup (17.1%) compared with non-obese (0%) and pre-obese patients (4.1%). DECT facilitates a 30.8% dose reduction over SECT in abdominal oncologic staging examinations. Employing dual-source scanner architecture, the risk for incomplete liver coverage increases in obese patients.

Functional lung imaging using novel and emerging MRI techniques.

Respiratory diseases are leading causes of death and disability in the world. While early diagnosis is key, this has proven difficult due to the lack of sensitive and non-invasive tools. Computed tomography is regarded as the gold standard for structural lung imaging but lacks functional information and involves significant radiation exposure. Lung magnetic resonance imaging (MRI) has historically been challenging due to its short T2 and low proton density. Hyperpolarised gas MRI is an emerging technique that is able to overcome these difficulties, permitting the functional and microstructural evaluation of the lung. Other novel imaging techniques such as fluorinated gas MRI, oxygen-enhanced MRI, Fourier decomposition MRI and phase-resolved functional lung imaging can also be used to interrogate lung function though they are currently at varying stages of development. This article provides a clinically focused review of these contrast and non-contrast MR imaging techniques and their current applications in lung disease.

Preoperative Novel Magnetic Resonance Fusion Imaging for Carotid Endarterectomy Patients with Contrast Contraindications: A Pilot Study

For carotid endarterectomy (CEA) patients with renal dysfunction and allergies to contrast media, we developed a preoperative novel method of noncontrast 3-dimensional magnetic resonance fusion imaging (NC-3DMRFI) which could describe well blood vessels, plaques, and bony structures even in surgical position. In this study, we examined the usefulness of this method. We extracted noncontrast magnetic resonance images of bones, blood vessels, and plaques to create a 3-dimensionalusion image. An image acquired in the normal position and another in the surgical position during CEA were used to create a fusion image. We compared the fusion imaging results with the intraoperative findings of 6 patients with contrast contraindications received CEA. Preoperative NC-3DMRFI could clearly show the positions of the carotid bifurcation, the distal end of plaque, and the bony structure in 5 of the 6 cases. Intraoperative findings and preoperative fusion imaging results were comparable in all cases where fusion images could be created. The fusion imaging in the surgical position during CEA was useful for preoperative examination, and the surgical space could be secured in the case of a high cervical location. This pilot study showed our novel NC-3DMRFI method is useful for preoperative simulation in CEA, especially in patients with renal dysfunction and allergies to contrast media.