Nonenhanced Images Research Articles

Improving Image Quality and Visualization of Hepatocellular Carcinoma in Arterial Phase Imaging Using Contrast Enhancement-Boost Technique.

This study aimed to evaluate the image quality and visualization of hepatocellular carcinoma (HCC) on arterial phase computed tomography (CT) using the contrast enhancement (CE)-boost technique. This retrospective study included 527 consecutive patients who underwent dynamic liver CT between June 2021 and February 2022. Quantitative and qualitative image analyses were performed on 486 patients after excluding 41 patients. HCC conspicuity was evaluated in 40 of the 486 patients with at least one HCC in the liver. Iodinated images obtained by subtracting nonenhanced images from arterial phase images were combined to generate CE-boost images. For quantitative image analysis, image noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were measured for the liver, pancreas, muscles, and aorta. For qualitative analysis, the overall image quality and noise were graded using a 3-point scale. Artifact, sharpness, and HCC lesion conspicuity were assessed using a 5-point scale. The paired-sample t test was used to compare quantitative measures, whereas the Wilcoxon signed-rank test was used to compare qualitative measures. The mean SNR and CNR of the aorta, liver, pancreas, and muscle were significantly higher, and the image noise was significantly lower in the CE-boost images than in the conventional images (P < 0.001). The mean CNR of HCC was also significantly higher in the CE-boost images than in the conventional images (P < 0.001). In the qualitative analysis, CE-boost images showed higher scores for HCC lesion conspicuity than conventional images (P < 0.001). The overall image quality and visibility of HCC were improved using the CE-boost technique.

Concrete crack classification based on fourier image enhancement and convolutional neural network

This paper investigates the application of Fourier image enhancement combined with Convolutional Neural Networks (CNNs) for detecting cracks in concrete structures. Fourier enhancement is used to preprocess crack images, improving their clarity and reducing noise, which in turn enhances the performance of the CNN in accurately classifying cracks. The results demonstrate that this combination improves the classification accuracy, with the enhanced images achieving a higher accuracy compared to non-enhanced images. Additionally, the study examines the effects of this preprocessing on CNN training time. However, accuracy varies depending on the dataset used, with one dataset reaching a maximum accuracy of 95% after enhancement. These findings highlight the potential of using frequency-domain image enhancement techniques in conjunction with deep learning models for structural health monitoring.

Value of spectral parameters in the differential diagnosis of benign and malignant breast nodules in non-enhanced chest CT.

Dual-layer spectral computed tomography (DSCT) is capable of acquiring both conventional and spectral images during one routine scan, and is widely used for the quantitative and qualitative analyses of substances, differential diagnosis, and disease staging. However, limited research has been conducted on its performance in the differential diagnosis of benign and malignant breast nodules using non-enhanced scans. This study aimed to assess the diagnostic performance of multiple quantitative parameters derived from non-enhanced DSCT in differentiating benign from malignant breast nodules. This retrospective cross-sectional study examined a total of 121 breast nodules from 114 patients (malignant group: n=68; benign group: n=53) identified during chest physical examination or routine admission for the treatment of breast diseases at The First Affiliated Hospital of Soochow University from March 2023 to December 2023. All the patients underwent DSCT scanning and pathological diagnosis. The DSCT quantitative parameters, including the effective atomic number (Zeff), computed tomography (CT) attenuation values at 40-70 keV, and the slope of the spectral Hounsfield unit curve (λHU), in non-enhanced images were measured. The λHU was calculated as follows: λHU = CT70 keV - CT40 keV/30 HU. Additionally, typical radiological features were analyzed. A DSCT parameter diagnostic model and a conventional CT diagnostic model were assessed using receiver operating characteristic (ROC) curves. The Delong test was used to assess and compare the diagnostic performance of each model. The DSCT parameters, including the Zeff (P<0.001), λHU (P<0.001), and CT attenuation values at 40 keV (P<0.001) and 50 keV (P=0.001), as well as the presence of the lobular sign (P<0.001) and spicule sign (P<0.001), exhibited statistically significant differences between the benign and malignant groups. The logistic regression analysis revealed that the Zeff [odds ratio (OR): 9.22; 95% confidence interval (CI): 2.11-40.35; P=0.003], λHU (OR: 0.64; 95% CI: 0.52-0.79; P<0.001), 40 keV CT attenuation value (OR: 8.69; 95% CI: 3.28-23.06; P<0.001), 50 keV CT attenuation value (OR: 0.01; 95% CI: 0.001-0.07; P<0.001), and lobular sign (OR: 3.95; 95% CI: 1.52-10.31; P=0.005) were independent predictors of malignancy. Compared to the benign group, the malignant group had a higher likelihood of presenting with the lobular sign and higher Zeff values but lower λHU values. The ROC curve indicated that the Zeff had the highest diagnostic efficacy [area under the curve (AUC) of the ROC =0.792, 95% CI: 0.71-0.87]. Further, the DSCT parameter diagnostic model had improved diagnostic efficacy with an AUC of 0.899 (95% CI: 0.84-0.96), which was higher than the AUC of the conventional CT diagnostic model (AUC =0.796, 95% CI: 0.72-0.87). The Delong test revealed a statistically significant difference between these two models (P=0.04). DSCT parameters derived from non-enhanced DSCT images, such as the Zeff value and λHU, can be used to differentiate benign and malignant breast nodules, and the differential diagnosis efficacy of the DSCT parameters is higher than that of conventional CT parameters.

Establishing a Foundation for the In Vivo Visualization of Intravascular Blood with Photon-Counting Technology in Spectral Imaging in Cranial CT.

Advances in computed tomography (CT) technology, particularly photon-counting CT (PCCT), are reshaping the possibilities for medical imaging. PCCT in spectral imaging enables the high-resolution visualization of tissues with material-specific accuracy. This study aims to establish a foundational approach for the in vivo visualization of intracranial blood using PCCT, focusing on non-enhanced imaging techniques and spectral imaging capabilities. We employed photon-counting detector within a spectral CT framework to differentiate between venous and arterial intracranial blood. Our analysis included not only monoenergetic +67 keV reconstructions, but also images from virtual non-contrast and iodine phases, enabling detailed assessments of blood's characteristics without the use of contrast agents. Our findings demonstrate the ability of PCCT to provide clear and distinct visualizations of intracranial vascular structures. We quantified the signal-to-noise ratio across different imaging phases and found consistent enhancements in image clarity, particularly in the detection and differentiation of arterial and venous blood. PCCT offers a robust platform for the non-invasive and detailed visualization of intravascular intracranial blood. With its superior resolution and specific imaging capabilities, PCCT lays the groundwork for advancing clinical applications and research, notably in the diagnosis and management of intracranial disorders. This technology promises to improve diagnostic accuracy by enabling more precise imaging assessments.

TIST-Net: style transfer in dynamic contrast enhanced MRI using spatial and temporal information

Objective. Training deep learning models for image registration or segmentation of dynamic contrast enhanced (DCE) MRI data is challenging. This is mainly due to the wide variations in contrast enhancement within and between patients. To train a model effectively, a large dataset is needed, but acquiring it is expensive and time consuming. Instead, style transfer can be used to generate new images from existing images. In this study, our objective is to develop a style transfer method that incorporates spatio-temporal information to either add or remove contrast enhancement from an existing image. Approach. We propose a temporal image-to-image style transfer network (TIST-Net), consisting of an auto-encoder combined with convolutional long short-term memory networks. This enables disentanglement of the content and style latent spaces of the time series data, using spatio-temporal information to learn and predict key structures. To generate new images, we use deformable and adaptive convolutions which allow fine grained control over the combination of the content and style latent spaces. We evaluate our method, using popular metrics and a previously proposed contrast weighted structural similarity index measure. We also perform a clinical evaluation, where experts are asked to rank images generated by multiple methods. Main Results. Our model achieves state-of-the-art performance on three datasets (kidney, prostate and uterus) achieving an SSIM of 0.91 ± 0.03, 0.73 ± 0.04, 0.88 ± 0.04 respectively when performing style transfer between a non-enhanced image and a contrast-enhanced image. Similarly, SSIM results for style transfer from a contrast-enhanced image to a non-enhanced image were 0.89 ± 0.03, 0.82 ± 0.03, 0.87 ± 0.03. In the clinical evaluation, our method was ranked consistently higher than other approaches. Significance. TIST-Net can be used to generate new DCE-MRI data from existing images. In future, this may improve models for tasks such as image registration or segmentation by allowing small training datasets to be expanded.

Imaging and Histopathological Features Of Primary Thymic Neuroendocrine Tumor.

To investigate CT, MRI, and PET/CT features with histopathological findings of primary thymic neuroendocrine tumor. All 9 cases with pathologically proven primary thymic neuroendocrine tumors were reviewed retrospectively. Among them, 7 underwent enhanced CT, 1 with MRI (enhanced) and another with PET/CT scan. Multiple characters were examined, including tumor location, contour, CT attenuation, enhancement pattern, involvement of surrounding structure and lymphadenopathy. Among 9 patients studied, 7 (77%) masses were located in the anterior superior mediastinum, 1 in the anterior superior-middle mediastinum, and 1 in the anterior and middle mediastinum. The maximum diameter (longitudinal) ranged from 4.2 to 23 cm (mean ± standard deviation, 9.5 cm ± 2.8). Four masses had irregular, 3 had lobulated, and 2 had smooth contours, while 8 masses had clear margins and 1 had an ill-defined margin. Six masses showed heterogeneous attenuation with necrotic/cystic component (n=5), calcification (n=2) and hemorrhage(n=1), and 3 showed homogeneous attenuation on the non-enhanced image. After contrast administration, 8 masses showed heterogeneous attenuation, and 1 showed homogeneous attenuation with tumor vessels visible in 4 masses. Among all, 8 masses showed strong enhancement, and 1 showed moderate enhancement in comparison to muscles in the anterior thoracic wall on enhanced images. Involvement of adjacent mediastinal structures was observed in 5 cases. Immunohistochemical analysis showed that the tumor cells were positive for CgA, Syn, CK, CD56 and EMA. Primary NETs are large masses located anterior superior mediastinum, irregular in contour, showing heterogeneous attenuation with necrotic/cystic component and strong heterogeneous enhancement with tumor vessels, compressing local mediastinal structures. In addition, immunohistochemical examination is required in such a diagnosis.

Bone Mineral Density Derived from Cardiac CT Scans: Using Contrast Enhanced Scans for Opportunistic Screening

Purpose: Osteoporosis is under-diagnosed and often co-exists with other diseases. Very low bone mineral density (BMD) indicates risk of osteoporosis and opportunistic screening for low BMD in CT-scans has been suggested. In a non-contrast enhanced thoracic CT scan, the scan-field-of-view includes vertebrae enabling BMD estimation. However, many CT scans are obtained by administration of contrast material. If the impact of contrast enhancement on BMD measurements could be quantified, considerably more patients are eligible for screening. Methods: This study investigated the impact of intravenous contrast on thoracic BMD measurements in cardiac CT scans pre- and post-contrast, including different contrast trigger levels of 130 and 180 Hounsfield units (HU). BMD was measured using quantitative CT with asynchronous calibration. Results: In 195 participants undergoing cardiac CT (mean age 57±9 years, 37 % females) contrast increased mean thoracic BMD from 116±33 mg/cm3 (non-enhanced CT) to 130±38 mg/cm3 (contrast-enhanced CT) (p<0.001). Using clinical cut-off values for very low (<80 mg/cm3) and low BMD (<120 mg/cm3) showed that 24 % (47/195 participants) were misclassified when BMD was measured on contrast-enhanced CT-scans. Of the misclassified patients, 6 % (12/195 participants) were categorized as having low BMD despite having very low BMD on the non-enhanced images. Contrast-CT using a higher contrast trigger level showed a significant increase in BMD compared to the lower trigger level (119±32 vs. 135±40 mg/cm3, p<0.01). Conclusion: For patients undergoing cardiac CT, using contrast-enhanced images to assess BMD entails substantial overestimation. Contrast protocol trigger levels also affect BMD measurements. Adjusting for these factors is needed before contrast-enhanced images can be used clinically. Mini abstract: Osteoporosis is under-diagnosed. Contrast-enhanced CT made to examine other diseases might be utilized simultaneously for bone mineral density (BMD) screening. These scans, however, likely entails overestimation of BMD due to the effect of contrast. Adjusting for this effect is needed before contrast-enhanced images can be implemented clinically for BMD screening.

Virtual non-enhanced dual-energy computed tomography reconstruction: a candidate to replace true non-enhanced computed tomography scans in the setting of suspected liver alveolar echinococcosis.

When a suspected hepatic alveolar echinococcosis (AE) lesion is detected on a contrast enhanced computed tomography (CT) scan, an additional triphasic or non-enhanced CT scan is required to determine the presence of calcification and enhancement. As a result, imaging costs and exposure to ionizing radiation will increase. We can create a non-enhanced series from routine contrast-enhanced images using dual-energy CT (DECT) and virtual non-enhanced (VNE) images. This study's objective is to assess virtual non-enhanced DECT reconstruction as a potential diagnostic tool for hepatic AE. Triphasic CT scans and a routine dual energy venous phase were acquired using a third-generation DECT system. A commercially available software package was used to generate VNE images. Individual evaluations were conducted by two radiologists. The study population consisted of 100 patients (30 AE, 70 other solid liver masses). All AE cases were diagnosed [no false positives/negatives, 95% confidence interval (CI) sensitivity: 91.3%-100%; 95% CI specificity: 95.3%-100%]. Interrater agreement was k: 0.79. In total, 33 (33.00%) of the patients had AE, which was detected using both true non-enhanced (TNE) and VNE images. The mean dose-length product of a standard triphasic CT was significantly higher than biphasic dual-energy VNE images. In terms of diagnostic confidence, VNE images are comparable with actual non-enhanced imaging when evaluating hepatic AE. Further, VNE images could replace TNE images with a substantial radiation dose reduction. Advances in knowledge: hepatic cystic echinococcosis and AE are serious and severe diseases with high fatality rates and a poor prognosis if managed incorrectly, especially AE. Moreover, VNE images produce equal diagnostic confidence to TNE images for assessing liver AE, with a significant reduction in radiation dose.

Spatiotemporal knowledge teacher–student reinforcement learning to detect liver tumors without contrast agents

Detecting Liver tumors without contrast agents (CAs) has shown great potential to advance liver cancer screening. It enables the provision of a reliable liver tumor-detecting result from non-enhanced MR images comparable to the radiologists’ results from CA-enhanced MR images, thus eliminating the high risk of CAs, preventing an experience gap between radiologists and simplifying clinical workflows. In this paper, we proposed a novel spatiotemporal knowledge teacher–student reinforcement learning (SKT-RL) as a safe, speedy, and inexpensive contrast-free technology for liver tumor detection. Our SKT-RL builds a teacher–student framework to realize the exploring of explicit liver tumor knowledge from a teacher network on clear contrast-enhanced images to guide a student network to detect tumors from non-enhanced images directly. Importantly, our STK-RL enables three novelties in aspects of construction, transferring, and optimization to tumor knowledge to improve the guide effect. (1) A new spatiotemporal ternary knowledge set enables the construction of accurate knowledge that allows understanding of DRL’s behavior (what to do) and reason (why to do it) behind reliable detection within each state and between their related historical states. (2) A novel pixel momentum transferring strategy enables detailed and controlled knowledge transfer ability. It transfers knowledge at a pixel level to enlarge the explorable space of transferring and control how much knowledge is transferred to prevent over-rely of the student to the teacher. (3) A phase-trend reward function designs different evaluations according to different detection phases to optimal for each phase in high-precision but also allows reward trend to constraint the evaluation to improve stability. Comprehensive experiments on a generalized liver tumor dataset with 375 patients (including hemangiomas, hepatocellular carcinoma, and normal controls) show that our novel SKT-RL attains a new state-of-the-art performance (improved precision by at least 4% when comparing the six recent advanced methods) in the task of liver tumor detection without CAs. The results proved that our SKT-DRL has greatly promoted the development and deployment of contrast-free liver tumor technology.

Diagnostic performance and interreader agreement of individual and combined non-enhanced and contrast-enhanced MR imaging parameters in adhesive capsulitis of the shoulder

ObjectiveThe aims of our study were to analyze agreement among readers with different levels of expertise and diagnostic performance of individual and combined imaging signs for the diagnosis of adhesive capsulitis of the shoulder.MethodsIn a retrospective study, contrast-enhanced shoulder MRIs of 60 patients with and 120 without clinically diagnosed adhesive capsulitis were evaluated by three readers independently. As non-enhanced imaging signs, readers evaluated signal intensity and thickness of the axillary recess capsule, thickness of the rotator interval capsule and the coracohumeral ligament as well as obliteration of subcoracoid fat. Furthermore, contrast enhancement of axillary recess and rotator interval capsule were evaluated. Data analysis included interreader reliability, ROC analysis, and logistic regression (p < 0.05).ResultsContrast-enhanced parameters showed substantially higher agreement among readers (ICC 0.79–0.80) than non-enhanced parameters (0.37–0.45). AUCs of contrast-enhanced signs (95.1–96.6%) were significantly higher (p < 0.01) than of non-enhanced imaging signs (61.5–85.9%) when considered individually. Combined evaluation of axillary recess signal intensity and thicknesses of axillary recess or rotator interval—when at least one of two signs was rated positive—increased accuracy compared to individual imaging signs, however not statistically significant.ConclusionContrast-enhanced imaging signs show both distinctly higher agreement among readers and distinctly higher diagnostic performance compared to non-enhanced imaging signs based on the imaging protocol used in this study. Combined evaluation of parameters showed a tendency to increase discrimination; however, the effect on diagnosis of ACS was not statistically significant.

Study of T2 mapping in quantifying and discriminating uterine lesions under different magnetic field strengths: 1.5 T vs. 3.0 T

BackgroundMRI is the best imaging tool for the evaluation of uterine tumors, but conventional MRI diagnosis results rely on radiologists and contrast agents (if needed). As a new objective, reproducible and contrast-agent free quantification technique, T2 mapping has been applied to a number of diseases, but studies on the evaluation of uterine lesions and the influence of magnetic field strength are few. Therefore, the aim of this study was to systematically investigate and compare the performance of T2 mapping as a nonenhanced imaging tool in discriminating common uterine lesions between 1.5 T and 3.0 T MRI systems.MethodsA total of 50 healthy subjects and 126 patients with suspected uterine lesions were enrolled in our study, and routine uterine MRI sequences with additional T2 mapping sequences were performed. T2 maps were calculated by monoexponential fitting using a custom code in MATLAB. T2 values of normal uterine structures in the healthy group and lesions (benign: adenomyosis, myoma, endometrial polyps; malignant: cervical cancer, endometrial carcinoma) in the patient group were collected. The differences in T2 values between 1.5 T MRI and 3.0 T MRI in any normal structure or lesion were compared. The comparison of T2 values between benign and malignant lesions was also performed under each magnetic field strength, and the diagnostic efficacies of the T2 value obtained through receiver operating characteristic (ROC) analysis were compared between 1.5 T and 3.0 T.ResultsThe mean T2 value of any normal uterine structure or uterine lesion under 3.0 T MRI was significantly lower than that under 1.5 T MRI (p < 0.05). There were significant differences in T2 values between each lesion subgroup under both 1.5 T and 3.0 T MRI. Moreover, the T2 values of benign lesions (71.1 ± 22.0 ms at 1.5 T and 63.4 ± 19.1 ms at 3.0 T) were also significantly lower than those of malignant lesions (101.1 ± 4.5 ms at 1.5 T and 93.5 ± 5.1 ms at 3.0 T) under both field strengths. In the aspect of differentiating benign from malignant lesions, the area under the curve of the T2 value under 3.0 T (0.94) was significantly higher than that under 1.5 T MRI (0.90) (p = 0.02).ConclusionT2 mapping can be a potential tool for quantifying common uterine lesions, and it has better performance in distinguishing benign from malignant lesions under 3.0 T MRI.

Reduced-Dose Full-Body CT in Lymphoma Follow-up: A Pilot Study.

How to reduce the radiation dose received from full-body CT scans during the follow-up of lymphoma patients is a concern. The aim of the study was to investigate the image quality and radiation dose of reduced-dose full-body computerized tomography (CT) in lymphoma patients during the follow-up. 121 patients were included and divided into conventional CT group (group 1, 120-kVp, n = 61) or reduced-dose CT group (group 2, 100-kVp combined dual-energy CT (DECT), n = 60). 140-kVp polychromatic images and 70-keV monochromatic images were reconstructed from DECT. The abdominal virtual non-enhanced (VNE) images were reconstructed from monochromatic images. Two radiologists rated the overall image quality with a five-point scale and graded the depiction of lesions using a four-point scale. The objective image quality was evaluated using image noise, signal-to-noise ratio, and contrast-to-noise ratio. The radiation dose and image quality were compared between the groups. The comparable subjective image quality was observed between 70-keV and 120-kVp images in the neck, while 120-kVp images showed better objective image quality. 70-keV images showed better objective image quality in the chest. While the subjective image quality of abdominal VNE images was inferior to that of true non-enhanced images, the improved objective image quality was observed in VNE images. In the abdominal arterial phase, similar subjective image quality was observed between the groups. Abdominal 70-keV images in the arterial phase showed improved objective image quality. Similar image quality was obtained in the abdominal venous phase between the groups. The effective radiation dose in group 2 showed a significant reduction. The application of reduced-dose full-body CT can significantly reduce the radiation dose for lymphoma patients during the follow-up while maintaining or improving the image quality.

Multi-level GAN based enhanced CT scans for liver cancer diagnosis

Liver cancer diagnosis requires preprocessing of images with preserved structural details. In this study, a multi-level generative adversarial network (GAN) is proposed to enhance computed tomographic (CT) images. The generated enhanced images are used to perform computer-aided diagnosis between malignant and normal liver. Three publicly available datasets, Ircadb, Sliver07, and LiTS, are used to investigate the performance of the proposed method with qualitative and quantitative analysis, namely performance metrics and computer-aided diagnosis. The mean structure similarity index of 0.45 and peak signal-to-noise ratio of 16.20 dB is achieved for the metric analysis. The AlexNet is adopted to perform binary classification with the testing accuracy of 90.37% and 85.90% for enhanced and non-enhanced images, respectively, which demonstrates the effectiveness of the proposed multi-level GAN in producing enhanced biomedical images with preserved structural details and favorable reduction in artifacts. Moreover, the consistently better performance among three datasets confirms the merits of the proposed multi-level GAN for computer-aided diagnosis.

Reliable brain morphometry from contrast-enhanced T1w-MRI in patients with multiple sclerosis.

Brain morphometry is usually based on non-enhanced (pre-contrast) T1-weighted MRI. However, such dedicated protocols are sometimes missing in clinical examinations. Instead, an image with a contrast agent is often available. Existing tools such as FreeSurfer yield unreliable results when applied to contrast-enhanced (CE) images. Consequently, these acquisitions are excluded from retrospective morphometry studies, which reduces the sample size. We hypothesize that deep learning (DL)-based morphometry methods can extract morphometric measures also from contrast-enhanced MRI. We have extended DL+DiReCT to cope with contrast-enhanced MRI. Training data for our DL-based model were enriched with non-enhanced and CE image pairs from the same session. The segmentations were derived with FreeSurfer from the non-enhanced image and used as ground truth for the coregistered CE image. A longitudinal dataset of patients with multiple sclerosis (MS), comprising relapsing remitting (RRMS) and primary progressive (PPMS) subgroups, was used for the evaluation. Global and regional cortical thickness derived from non-enhanced and CE images were contrasted to results from FreeSurfer. Correlation coefficients of global mean cortical thickness between non-enhanced and CE images were significantly larger with DL+DiReCT (r=0.92) than with FreeSurfer (r=0.75). When comparing the longitudinal atrophy rates between the two MS subgroups, the effect sizes between PPMS and RRMS were higher with DL+DiReCT both for non-enhanced (d=-0.304) and CE images (d=-0.169) than for FreeSurfer (non-enhanced d=-0.111, CE d=0.085). In conclusion, brain morphometry can be derived reliably from contrast-enhanced MRI using DL-based morphometry tools, making additional cases available for analysis and potential future diagnostic morphometry tools.

Abbreviated non-enhanced magnetic resonance imaging in patients with acute necrotizing pancreatitis.

To investigate the performance of T2-weighted abbreviated magnetic resonance imaging (T2W-AMRI) protocol in evaluating patients with acute necrotizing pancreatitis (ANP). A retrospective analysis of consecutive hospitalized patients with ANP who underwent MRI (contrast-enhanced, CE or non-contrast, NC) between January 2017 and November 2020was performed. The T2W-AMRI and complete MRI (cMRI) sequences were anonymized, and subsequently, two separate sets of data (AMRI and cMRI) were created for presentation to the radiologists involved in reading the data. The T2W-AMRI was based on a single-axial T2 half-Fourier acquisition single-shot turbo spin-echo (HASTE) sequence. The pancreatic and extrapancreatic findings were recorded and tabulated independently by two radiologists on T2W-AMRI and cMRI. In addition, the interobserver agreement and association of findings between T2W-AMRI and cMRI were analyzed. Twenty-eight patients (mean age 31.7 ± 12.2years, 17 females) were included. Thirteen patients had CE-cMRI, while the rest underwent NC-cMRI. There was no significant difference in the identification of pancreatic necrosis on T2W-AMRI vs. cMRI (p = 1.00). However, T2W-AMRI underestimated necrosis in one patient. Collections were accurately detected in all patients on T2W-AMRI. The mean size of the collection was larger on cMRI (6.5 ± 3.7cm) than T2W-AMRI (6 ± 3.7cm) with p = 0.006. cMRI detected more patients with disrupted pancreatic duct (n = 9) than T2W-AMRI (n = 6). However, the difference was not statistically significant (p = 0.375). There was a good to an excellent interobserver agreement between the readers for T2W-AMRI (k = 0.62-1). T2W-AMRI may offer a suitable alternative to cMRI in ANP, especially severe disease, as it can be acquired rapidly without the need for contrast injection.

Value of nonenhanced CT combined with laboratory examinations in the diagnosis of acute suppurative cholecystitis treated with percutaneous cholecystostomy: a retrospective study

PurposesIn this study, we aimed to identify the distribution of presenting laboratory and nonenhanced computed tomography (CT) imaging features within 48 h before percutaneous cholecystostomy (PC) and create a model to appropriately guide the diagnosis of acute suppurative cholecystitis (ASC).MethodsThe study population included 204 acute cholecystitis patients who underwent PC. Based on the timing of the last laboratory and CT examinations before PC, the patients were divided into two groups: within 48 h before PC (Group 1, n = 138) and over 48 h before PC (Group 2, n = 63). The clinical features of the ASC patients in the two groups were compared. A multivariable model for the diagnosis of ASC in the patients in Group 1 was developed.ResultsThirty-nine patients in Group 1 had ASC (28.3%). Gallbladder stones, common bile duct stones, gallbladder wall thickness > 2.85 mm, and neutrophil granulocytes > 82.55% were confirmed to be independent risk factors for ASC. The receiver operating characteristic curve of the recurrence prediction model verified its accuracy (area under the curve: 0.803). Compared with the ASC patients in Group 2, the ASC patients in Group 1 had a higher proportion of pericholecystic exudation or fluid (P = 0.013) and thicker gallbladder walls (P = 0.033).ConclusionsUsing nonenhanced CT imaging features and cutoffs for neutrophil granulocytes, we were able to identify a simple algorithm to discriminate ASC. The degree of local inflammation of the gallbladder in ASC patients progressively increases over time, and these changes can be observed on nonenhanced CT images. However, the symptoms of abdominal pain are of little help in estimating the disease duration in elderly patients.

Edge Constraint and Location Mapping for Liver Tumor Segmentation from Nonenhanced Images.

As there is no contrast enhancement, the liver tumor area in nonenhanced MRI exists with blurred edges and low contrast, which greatly affects the speed and accuracy of liver tumor diagnosis. As a result, precise segmentation of liver tumor from nonenhanced MRI has become an urgent and challenging task. In this paper, we propose an edge constraint and localization mapping segmentation model (ECLMS) to accurately segment liver tumor from nonenhanced MRI. It consists of two parts: localization network and dual-branch segmentation network. We build the localization network, which generates prior coarse masks to provide position mapping for the segmentation network. This part enhances the ability of the model to localize liver tumor in nonenhanced images. We design a dual-branch segmentation network, where the main decoding branch focuses on the feature representation in the core region of the tumor and the edge decoding branch concentrates on capturing the edge information of the tumor. To improve the ability of the model for capturing detailed features, sSE blocks and dense upward connections are introduced into it. We design the bottleneck multiscale module to construct multiscale feature representations using kernels of different sizes while integrating the location mapping of tumor. The ECLMS model is evaluated on a private nonenhanced MRI dataset that comprises 215 different subjects. The model achieves the best Dice coefficient, precision, and accuracy of 90.23%, 92.25%, and 92.39%, correspondingly. The effectiveness of our model is demonstrated by experiment results, and our model reaches superior results in the segmentation task of nonenhanced liver tumor compared to existing segmentation methods.

MR imaging detection of adhesive capsulitis of the shoulder: impact of intravenous contrast administration and reader’s experience on diagnostic performance

ObjectiveCorrect identification of adhesive capsulitis of the shoulder (ACS) has an important impact on adequate therapy. The aim of our study was to investigate the influence of intravenous contrast administration and of reader’s experience on sensitivity and specificity of MRI in diagnosing ACS.Materials and methodsA total of 180 patients were included in a retrospective study: 60 subjects with at least 4 of 5 clinical signs of adhesive capsulitis of the shoulder and 120 patients with other shoulder diseases who underwent contrast-enhanced MRI. In a first session, only non-enhanced images and in a second session also contrast-enhanced (CE) series were independently evaluated by three radiologists with various levels of professional experience. Readers were blinded to all clinical information and had to rate the shoulder MRIs for absence or presence of adhesive capsulitis. Data analysis included McNemar’s test, t test, and U test (p < .05).ResultsUsing non-enhanced MRI, readers achieved a mean sensitivity of 63.9% and a mean specificity of 86.4%. By additional use of CE sequences, the mean sensitivity (85.5%) and the sensitivity for each reader increased significantly (p = .046, p < .01, p < .001, p = .045) while the improvement in mean specificity was not significant. Reader’s experience had a positive effect on sensitivity and specificity, which was in part but not consistently significant.ConclusionThe addition of CE sequences can significantly increase the sensitivity of MRI in the diagnosis of ACS. Reader’s experience has shown to be another important factor for the diagnostic outcome.

Strain measurement for 2D echo with ultrasound enhancing agents can be performed with software available for non-enhanced recordings

Abstract Funding Acknowledgements Type of funding sources: Public Institution(s). Main funding source(s): ABACUS Cardiovascular Research Centre, Mazankowski Alberta Heart Institute Background Global longitudinal strain (GLS) measurement are less reliable when 2D image quality is reduced. While ultrasound enhancing agents (UEA; also known as "echo contrast") have been shown to enhance the reliability of EF measurements, there has been sparse evidence on performing GLS measurements after UEA injection. The aim of this study was to assess the feasibility of GLS analysis using the same software validated for non-enhanced 2D recordings. Methods GLS measurements were performed before and after injection of an approved UEA in 131 patients with acceptable image quality for GLS measurements. These patients were referred prior to chemotherapy initiation or were being monitored for potential cardiotoxic effects. As all these patients undergo echocardiography with UEA in order to achieve the best reproducibility of EF measurements, comparison of GLS measurements with and without UEA was possible. A commercially available ultrasound system was used and the same analysis software was applied. On end-diastolic and end-systolic frames, the inner border of the region of interest was manually adjusted to align with interface between the compact and trabeculated myocardium on non-enhanced images and the LV blood pool on the recordings with UEA. Results GLS measurements on recordings with UEA were performed in 131 patients. A strong positive correlation (r 0.67, p &amp;lt; 0.001) was found between measurements on non-enhanced recordings with mean bias 0.58% (see figure). Mean GLS was 19.5 +/- 2.1% from non-enhanced recordings and 20.1 +/- 2.2% from UEA recordings. Differences in GLS &amp;gt; 2% between methods were related to foreshortening or suboptimal delineation of segments on non-contrast recordings. Conclusion On a commercially available echocardiography scanner, software developed for GLS measurements on non-enhanced 2D recordings can be also applied to recordings that use UEA. Comparable results are obtained, provided the LV cavity is well delineated and not foreshortened. Abstract Figure 1

Comparison between subtraction and dynamic MRI in assessing treatment response following radiofrequency ablation in patients with hepatocellular carcinoma

BackgroundHepatocellular carcinoma (HCC) is one of the most prevalent cancers worldwide, and if left untreated, one of the most lethal. Ablative therapies including radiofrequency ablation (RFA) play increasingly important role for patients with liver tumors who are not surgical candidates. Monitoring treatment response following ablation is crucial in oncologic imaging. Dynamic contrast-enhanced MRI can assess changes in tumor vascularity and perfusion while subtraction imaging is useful in differentiating residual tumor from post-ablation parenchymal changes. The aim of this study is to compare the role of subtraction MRI and conventional dynamic MRI in assessing treatment response following RFA in patients with HCC.ResultsThe study included 48 patients with 62 HCC lesions who underwent RFA from May to October 2020, followed by MRI evaluation with 1-month interval. Two readers with experience in hepatic imaging interpreted the dynamic and subtraction dynamic MRI. The hepatic focal lesions were classified into “well-ablated” and “residual” groups according to MRI findings, and the agreement between the two readers was evaluated. Using dynamic MRI, the first reader reported 38 well-ablated lesions, and the second reader agreed in 34 of them (89.5%). Residual disease was reported by the first reader in 22 lesions and the second reader disagreed in 10 of them (45.5%) where complete ablation was reported. Thirty-eight out 44 well-ablated lesions (86.4%) showed high signal intensity on non-enhanced T1 images, and 28 lesion (63.6%) showed intermediate T2 signal. All the mis-matched readings occurred in lesions with a high signal intensity in pre-contrast T1 images. Moderate agreement between the two readers was found with Kappa value of 0.467. Significant additive value of subtraction technique to dynamic MRI was detected with a P value of 0.009. No major complications recorded except for a single case of major portal vein branch occlusion.ConclusionMRI is a powerful imaging tool in assessing tumor viability and complications after RFA in patients with HCC. Dynamic MRI study is the gold standard in detecting recurrent lesions while subtraction technique is crucial in differentiating between arterial enhancement due to residual disease and normal hyperintense T1 signal of the ablation zone.