Multiphase Image Research Articles

One novel transfer learning-based CLIP model combined with self-attention mechanism for differentiating the tumor-stroma ratio in pancreatic ductal adenocarcinoma.

To develop a contrastive language-image pretraining (CLIP) model based on transfer learning and combined with self-attention mechanism to predict the tumor-stroma ratio (TSR) in pancreatic ductal adenocarcinoma on preoperative enhanced CT images, in order to understand the biological characteristics of tumors for risk stratification and guiding feature fusion during artificial intelligence-based model representation. This retrospective study collected a total of 207 PDAC patients from three hospitals. TSR assessments were performed on surgical specimens by pathologists and divided into high TSR and low TSR groups. This study developed one novel CLIP-adapter model that integrates the CLIP paradigm with a self-attention mechanism for better utilizing features from multi-phase imaging, thereby enhancing the accuracy and reliability of tumor-stroma ratio predictions. Additionally, clinical variables, traditional radiomics model and deep learning models (ResNet50, ResNet101, ViT_Base_32, ViT_Base_16) were constructed for comparison. The models showed significant efficacy in predicting TSR in PDAC. The performance of the CLIP-adapter model based on multi-phase feature fusion was superior to that based on any single phase (arterial or venous phase). The CLIP-adapter model outperformed traditional radiomics models and deep learning models, with CLIP-adapter_ViT_Base_32 performing the best, achieving the highest AUC (0.978) and accuracy (0.921) in the test set. Kaplan-Meier survival analysis showed longer overall survival in patients with low TSR compared to those with high TSR. The CLIP-adapter model designed in this study provides a safe and accurate method for predicting the TSR in PDAC. The feature fusion module based on multi-modal (image and text) and multi-phase (arterial and venous phase) significantly improves model performance.

Predictive Value of Multi-Phase CT Radiomics in Delineating Early and Advanced T Staging of Colon Cancer

This study leveraged the power of multi-phase CT radiomics, which extracts detailed features from multi-phase images, to explore the distinction between early (T1-T2) and advanced (T3-T4) colon cancer stages in each of multiple phases, providing valuable insights to healthcare professionals and enhancing their decision-making capabilities. A total of 191 patients with surgically confirmed primary colon cancer were retrospectively included, and multi-phase CT scans (non-enhanced contrast phase, arterial phase, portal venous phase, and delayed phase) were conducted within one week before surgery. Three-dimensional segmentation of colonic tumors was performed on the images of the four phases, and radiomics features of each colonic tumor were automatically extracted. Minimum redundancy maximum relevance (mRMR) was applied to features selection in each of the four phases. The least absolute shrinkage and selection operator logistic regression was conducted to determine the association between radiomics features and early (T1-T2) and advanced (T3-T4) stages of colon cancer. Additionally, diagnostic performance comparison was carried out in four phases. The mean (±SD) age of the 191 individuals was 61.87±13.137 years, with females comprising 43.5% of the cohort. The selected features for the non-enhanced, arterial, portal venous, and delayed phases numbered 7, 11, 6, and 10, respectively. In the test set, the AUC values for the on-enhanced, arterial, portal venous, and delayed phases were 0.86 (0.76-0.96), 0.84 (0.73-0.94), 0.82 (0.71-0.93), and 0.86 (0.75-0.97), with corresponding accuracies of 0.84, 0.80, 0.75, and 0.88, sensitivities of 0.73, 0.64, 0.86, and 0.68, and specificities of 0.91, 0.91, 0.68, and 1.00, respectively. DeLong's test revealed no statistically significant differences in the AUC values between the four phases within the test sets (P = 0.3233~ 0.9912). Multi-phase CT radiomics demonstrated substantial value in differentiating between early (T1-T2) and advanced (T3-T4) stages of patients with colon cancer.

Clinical application of single-shot fast spin-echo sequence for cerebrospinal fluid flow MR imaging.

In normal-pressure hydrocephalus, disturbances in cerebrospinal fluid (CSF) circulation occur; therefore, understanding CSF dynamics is crucial. The two-dimensional phase-contrast (2D-PC) method, a common approach for visualizing CSF flow on MRI, often presents challenges owing to prominent vein signals and excessively high contrast, hindering the interpretation of morphological information. Therefore, we devised a new imaging method that utilizes T2-weighted high-signal intensification of the CSF and saturation pulses, without requiring specialized imaging sequences. This sequence utilized a T2-weighted single-shot fast spin-echo combined with multi-phase imaging synchronized with a pulse wave. Optimal imaging conditions (repetition time, presence/absence of fast recovery, and echo time) were determined using self-made contrast and single-plate phantoms to evaluate signal-to-noise ratio, contrast ratio, and spatial resolution. In certain clinical cases of hydrocephalus, confirming CSF flow using 2D-PC was challenging. However, our method enabled the visualization of CSF flow, proving to be useful in understanding the pathophysiology of hydrocephalus.

Comparison of clinical and MRI features of hepatic angiosarcoma and epithelioid hemangioendothelioma.

Differentiating hepatic epithelioid hemangioendothelioma (EHE) and angiosarcoma (AS), the two most common vascular tumors in the liver, is important due to disparities in their prognosis and treatment. We aimed to compare clinical and MRI features of the two tumors. This retrospective study included patients with pathologically-confirmed AS or EHE who underwent MRI using gadoxetate disodium between 2008 and 2023. Two radiologists independently reviewed MR images. Wilcoxon rank sum and Fisher's exact tests were used to compare clinical and imaging features. Overall survival was compared using restricted mean survival time at 3years. 32 patients with AS (18 women [56.3%]; median age, 68years) and 38 with EHE (24 women [63.2%]; 51years) were included. Patients with AS were generally older (81.3% ≥ 60years; P < 0.001), had more frequent laboratory abnormalities (P ≤ 0.018), and poorer overall survival (11.2 vs. 31.8months; P < 0.001) than those with EHE. On MRI, a large dominant mass accompanied by smaller nodules (14/32, 43.8%), often with ill-defined margins (15/32, 46.9%) was prevalent in AS; compared with nodules of similar sizes (24/38, 63.2%; P = 0.015) with well-defined margin (30/38, 78.9%; P = 0.002) in EHE. Cirrhotic appearance of the liver was more frequent in AS (62.5%, P < 0.001), along with decreased parenchymal enhancement on hepatobiliary phase (31.3%, P < 0.001) and ascites (37.5%, P = 0.010). AS frequently presented with avid enhancement of bizarrely-shaped foci, with a centrifugal enhancement pattern. In comparison, targetoid appearance was characteristic of EHE (78.9% on T2-weighted, 54.1% on diffusion-weighted, 65.8% on multiphase images) (P ≤ 0.002), with enhancement degree typically lower than that of the aorta. On hepatobiliary phase, all the AS exhibited hypointensity, while 39.5% of EHE showed targetoid appearance (P < 0.001). Patients aged ≥ 60years presenting with laboratory abnormalities, typically with a large dominant mass accompanied by smaller nodules, exhibiting avid, bizarre, and centrifugal enhancement-particularly in the cirrhotic-appearing liver-suggests the likelihood of AS over EHE.

Radiogenic cancer risk from contrast enhanced computed tomography during pediatric abdomen and pelvis examinations in Saudi Arabia

The sensitivity to ionizing radiation is increasing by age of development for some malignant tumors. Therefore, children have higher risk to radiation induced tumors due the high cellular rate of proliferation and long lifespan probability. The risk is also increase with increase the effective and organ doses. Computed tomography (CT) exposed pediatric patients to higher radiation dose during multiphase image acquisition, repeated exams, for follow-up procedures. This research intended to estimate the radiogenic risks and effective radiation doses resulted from CT enhanced contrast for abdomen and pelvis. 126 (66 (62.3%) males, 60 (47.7%) females) pediatric patients underwent CT enhanced abdominal examination at Medical Imaging Department at King Khalid Hospital and Prince Sultan Center for Health Services, Alkharj, Saudi Arabia. The average and range of pediatric age (years) is 11.6 ± 5.0 (0.1–17). The mean, standard deviation, and range of the volume CT air kerma index (CVOL (mGy) and the air kerma length product (PKL, mGy.cm) were 9.8 ± 9.4 (2.1–45.8) and 1795 (221–3150) per abdominopelvic procedure, respectively. The mean and range of the effective dose (mSv) per procedure are 26.9 (2.4–59.1). The effective dose is higher compared to the most previously published studies. The effective dose per pediatric abdomen and pelvis with contrast procedure suggest that the patient dose is not optimized yet. Because the chest and pelvis region contain sensitive organs that are irradiated repeatedly, dose optimization is crucial.

A new method for evaluate the cold island effect in cities

Building urban parks is one of the important ways to alleviate the Urban Heat Island Effect, and the cold island effect is a commonly used indicator to characterize the cooling effect of urban parks on surrounding areas. This study takes the Olympic Park in Beijing as an example and combines remote sensing data with measured data from meteorological stations to propose a new method for evaluating the urban cold island effect based on the superposition of multiple remote sensing images. The research results show that: The magnitude of PCI(Park Cold Island)values has a highly significant correlation with the highest temperature of the day, and the two show a linear correlation. The higher the temperature, the greater the PCI; From 2013 to 2021, the difference between the highest temperature and the lowest temperature in the study area is 23 times, and the difference between the maximum and minimum PCI value is 19 times. The PCImix assessment method can better contain the extreme value, reduce the instability of temperature with seasonal changes and the impact of temperature variation differences between years, and reduce the contingency of assessment research; This new method is systematic, continuous, and statistically significant. The method proposed in this study for evaluating the urban cold island effect based on multi-phase image overlay can provide guidance for the future construction of urban parks and open up new ideas for evaluating the cooling effect of urban parks.

TPA: Two-stage progressive attention segmentation framework for hepatocellular carcinoma on multi-modality MRI.

Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) plays a crucial role in the diagnosis and measurement of hepatocellular carcinoma (HCC). The multi-modality information contained in the multi-phase images of DCE-MRI is important for improving segmentation. However, this remains a challenging task due to the heterogeneity of HCC, which may cause one HCC lesion to have varied imaging appearance in each phase of DCE-MRI. In particular, some phases exhibit inconsistent sizes and boundaries will result in a lack of correlation between modalities, and it may pose inaccurate segmentation results. We aim to design a multi-modality segmentation model that can learn meaningful inter-phase correlation for achieving HCC segmentation. In this study, we propose a two-stage progressive attention segmentation framework (TPA) for HCC based on the transformer and the decision-making process of radiologists. Specifically, the first stage aims to fuse features from multi-phase images to identify HCC and provide localization region. In the second stage, a multi-modality attention transformer module (MAT) is designed to focus on the features that can represent the actual size. We conduct training, validation, and test in a single-center dataset (386 cases), followed by external test on a batch of multi-center datasets (83 cases). Furthermore, we analyze a subgroup of data with weak inter-phase correlation in the test set. The proposed model achieves Dice coefficient of 0.822 and 0.772 in the internal and external test sets, respectively, and 0.829, 0.791 in the subgroup. The experimental results demonstrate that our model outperforms state-of-the-art models, particularly within subgroup. The proposed TPA provides best segmentation results, and utilizing clinical prior knowledge for network design is practical and feasible.

ECG-free cine MRI with data-driven clustering of cardiac motion for quantification of ventricular function.

Despite the widespread use of cine MRI for evaluation of cardiac function, existing real-time methods do not easily enable quantification of ventricular function. Moreover, segmented cine MRI assumes periodicity of cardiac motion. We aim to develop a self-gated, cine MRI acquisition scheme with data-driven cluster-based binning of cardiac motion. A Cartesian golden-step balanced steady-state free precession sequence with sorted k-space ordering was designed. Image data were acquired with breath-holding. Principal component analysis and k-means clustering were used for binning of cardiac phases. Cluster compactness in the time dimension was assessed using temporal variability, and dispersion in the spatial dimension was assessed using the Caliński-Harabasz index. The proposed and the reference electrocardiogram (ECG)-gated cine methods were compared using a four-point image quality score, SNR and CNR values, and Bland-Altman analyses of ventricular function. A total of 10 subjects with sinus rhythm and 8 subjects with arrhythmias underwent cardiac MRI at 3.0 T. The temporal variability was 45.6 ms (cluster) versus 24.6 ms (ECG-based) (p< 0.001), and the Caliński-Harabasz index was 59.1 ± 9.1 (cluster) versus 22.0 ± 7.1 (ECG based) (p< 0.001). In subjects with sinus rhythm, 100% of the end-systolic and end-diastolic images from both the cluster and reference approach received the highest image quality score of 4. Relative to the reference cine images, the cluster-based multiphase (cine) image quality consistently received a one-point lower score (p< 0.05), whereas the SNR and CNR values were not significantly different (p= 0.20). In cases with arrhythmias, 97.9% of the end-systolic and end-diastolic images from the cluster approach received an image quality score of 3 or more. The mean bias values for biventricular ejection fraction and volumes derived from the cluster approach versus reference cine were negligible. ECG-free cine cardiac MRI with data-driven clustering for binning of cardiac motion is feasible and enables quantification of cardiac function.

PA-Net: A phase attention network fusing venous and arterial phase features of CT images for liver tumor segmentation

Background and ObjectiveLiver cancer seriously threatens human health. In clinical diagnosis, contrast-enhanced computed tomography (CECT) images provide important supplementary information for accurate liver tumor segmentation. However, most of the existing methods of liver tumor automatic segmentation focus only on single-phase image features. And the existing multi-modal methods have limited segmentation effect due to the redundancy of fusion features. In addition, the spatial misalignment of multi-phase images causes feature interference. MethodsIn this paper, we propose a phase attention network (PA-Net) to adequately aggregate multi-phase information of CT images and improve segmentation performance for liver tumors. Specifically, we design a PA module to generate attention weight maps voxel by voxel to efficiently fuse multi-phase CT images features to avoid feature redundancy. In order to solve the problem of feature interference in the multi-phase image segmentation task, we design a new learning strategy and prove its effectiveness experimentally. ResultsWe conduct comparative experiments on the in-house clinical dataset and achieve the SOTA segmentation performance on multi-phase methods. In addition, our method has improved the mean dice score by 3.3% compared with the single-phase method based on nnUNet, and our learning strategy has improved the mean dice score by 1.51% compared with the ML strategy. ConclusionThe experimental results show that our method is superior to the existing multi-phase liver tumor segmentation method, and provides a scheme for dealing with missing modalities in multi-modal tasks. In addition, our proposed learning strategy makes more effective use of arterial phase image information and is proven to be the most effective in liver tumor segmentation tasks using thick-layer CT images. The source code is released on (https://github.com/Houjunfeng203934/PA-Net).

Incidental Indeterminate Renal Lesions: Distinguishing Non-Enhancing from Potential Enhancing Renal Lesions Using Iodine Quantification on Portal Venous Dual-Layer Spectral CT.

The purpose of our study is to determine a threshold for iodine quantification to distinguish definitely non-enhancing benign renal lesions from potential enhancing masses on portal venous dual-layer spectral computed tomography (CT) to reduce the need for additional multiphase CT. In this single-center retrospective study, patients (≥18 years) scanned between April 2021 and January 2023 following the local renal CT protocol were included. Exclusion criteria were patients without renal lesions, lesions smaller than 10 mm, only fat-containing lesions, abscesses or infarction, follow-up after radiofrequent ablation, wrong scan protocol, or artefacts. Scans were performed on a dual layer detector-based spectral CT (CT 7500, Philips Healthcare, Best, The Netherlands). Iodine concentration (mgI/mL) in renal lesions was determined using spectral data. Analyses were performed for all lesions and for lesions of >30 HU on portal venous CT. Enhancement on multiphase CT (≥20 ΔHU from true unenhanced (TUE) to portal venous phase (PVP) CT) was used as reference standard. To determine thresholds for iodine concentration receiver operating characteristic (ROC) curves, area under the curve (AUC) and 95% confidence intervals were calculated. To obtain thresholds for definite (non-)enhancement, 100% sensitivity with maximum specificity and 100% specificity with maximum sensitivity were noted. Data were measured using one reader. To assess interobserver agreement, a second reader performed measurements on the PVP CT scans. A total of 103 patients (62 years ± 14, 68 men) were included. We measured 328 renal lesions, 56 enhancing lesions (17%) in 38 patients and 272 non-enhancing lesions (83%) in 86 patients. The threshold for non-enhancing lesions was 0.76 mgI/mL or lower (100% sensitivity, 76% specificity). The threshold for a definite enhancing mass was 1.69 mgI/mL or higher (100% specificity, 78% sensitivity). A total of 77% of indeterminate lesions (>30 HU on PVP CT) in our study could be definitely characterized. Renal lesions can be definitively classified as non-enhancing or enhancing on PVP spectral CT using thresholds of 0.76 mgI/mL or 1.69 mgI/mL, respectively, eliminating the need for multiphase imaging.

Reciprocal cross-modal guidance for liver lesion segmentation from multiple phases under incomplete overlap

Objective:Liver lesion segmentation is beneficial for computer-aided disease diagnosis, which can be further enhanced by incorporating multi-phase images. Nevertheless, existing segmentation models still need improvement as they build on the unsolid assumption that lesions are well-aligned between different phases. Our goal was to develop a robust model that achieves high accuracy in lesion segmentation, even in cases where incomplete overlap occurs across multiple phases. Methods:To overcome the strong assumption of precise alignment between phases, we introduce a novel deep neural network featuring multi-scale feature fusion and cross-phase feature guidance. The former enables the retrieval of effective information from each phase that is helpful in lesion segmentation, while the latter ensures exchanging of guidance maps for interphase segmentation. Results:Comprehensive experiments were carried out on an in-house multi-phase dataset having liver lesions, as well as a multi-modal dataset from the CHAOS challenge. Results show that our model surpasses existing state-of-the-art methods in segmenting liver lesions and abdominal organs from multi-phase images, even if the registration between phases is inaccurate. Conclusion:A competent solution was proposed for accurate liver lesion segmentation from multi-phase images, considering the imperfect registration between phases. The model was proved to be effective in extracting pertinent information from each phase and sharing useful information between phases. Significance:The accommodation of incomplete overlap in multi-phase images provides a new paradigm of accurate and robust lesion segmentation, which is particularly helpful in real-world scenarios where registration accuracy may be compromised.

Automatic Evaluating of Multi-Phase Cranial CTA Collateral Circulation Based on Feature Fusion Attention Network Model.

Stroke is one of the main causes of disability and death, and it can be divided into hemorrhagic stroke and ischemic stroke. Ischemic stroke is more common, and about 8 out of 10 stroke patients suffer from ischemic stroke. In clinical practice, doctors diagnose stroke by using computed tomography angiography (CTA) image to accurately evaluate the collateral circulation in stroke patients. This imaging information is of great significance in assisting doctors to determine the patient's treatment plan and prognosis. Currently, great progress has been made in the field of computer-aided diagnosis technology in medicine by using artificial intelligence. However, in related research based on deep learning algorithms, researchers usually only use single-phase data for training, lacking the temporal dimension information of multi-phase image data. This makes it difficult for the model to learn more comprehensive and effective collateral circulation feature representation, thereby limiting its performance. Therefore, combining data for training is expected to improve the accuracy and reliability of collateral circulation evaluation. In this study, we propose an effective hybrid mechanism to assist the feature encoding network in evaluating the degree of collateral circulation in the brain. By using a hybrid attention mechanism, additional guidance and regularization are provided to enhance the collateral circulation feature representation across multiple stages. Time dimension information is added to the input, and multiple feature-level fusion modules are designed in the multi-branch network. The first fusion module in the single-stage feature extraction network completes the fusion of deep and shallow vessel features in the single-branch network, followed by the multi-stage network feature fusion module, which achieves feature fusion for four stages. Tested on a dataset of multi-phase cranial CTA images, the accuracy rate exceeding 90.43%. The experimental results demonstrate that the addition of these modules can fully explore collateral vessel features, improve feature expression capabilities, and optimize the performance of deep learning network model.

Detector-trigger-based cardiac multiphase micro-CT imaging for small animals.

Micro-computed tomography is important in cardiac imaging for preclinical small animal models, but motion artifacts may appear due to the rapid heart rates. To avoid influence of motion artifacts, the prospective ECG gating schemes based on an X-ray source trigger have been investigated. However, due to the lack of pulsed X-ray exposure modes, high-resolution micro-focus X-ray sources do not support source triggering in most cases. To develop a fast-cardiac multiphase acquisition strategy using prospective ECG gating for micro-focus X-ray tubes with a continuous emission mode. The proposed detector-trigger-based prospective ECG gating acquisition scheme (DTB-PG) triggers the X-ray detector at the R peak of ECG, and then collects multiple phase projections of the heart in one ECG cycle by sequence acquisition. Cardiac multiphase images are reconstructed after performing the same acquisition in all views. The feasibility of this strategy was verified in multiphase imaging experiments of a phantom with 150 ms motion period and a mouse heart on a micro-focus micro-CT system with continuous emission mode. Using a high frame-rate CMOS detector, DTB-PG discriminates the positions of the motion phantom well in 10 different phases and enables to distinguish the changes in the cardiac volume of the mouse in different phases. The acquisition rate of DTB-PG is much faster than other prospective gating schemes as demonstrated by theoretical analysis. DTB-PG combines the advantages of prospective ECG gating strategies and X-ray detector-trigger mode to suppress motion artifacts, achieve ultra-fast acquisition rates, and relax hardware limitations.

A Boundary-Enhanced Liver Segmentation Network for Multi-Phase CT Images with Unsupervised Domain Adaptation.

Multi-phase computed tomography (CT) images have gained significant popularity in the diagnosis of hepatic disease. There are several challenges in the liver segmentation of multi-phase CT images. (1) Annotation: due to the distinct contrast enhancements observed in different phases (i.e., each phase is considered a different domain), annotating all phase images in multi-phase CT images for liver or tumor segmentation is a task that consumes substantial time and labor resources. (2) Poor contrast: some phase images may have poor contrast, making it difficult to distinguish the liver boundary. In this paper, we propose a boundary-enhanced liver segmentation network for multi-phase CT images with unsupervised domain adaptation. The first contribution is that we propose DD-UDA, a dual discriminator-based unsupervised domain adaptation, for liver segmentation on multi-phase images without multi-phase annotations, effectively tackling the annotation problem. To improve accuracy by reducing distribution differences between the source and target domains, we perform domain adaptation at two levels by employing two discriminators, one at the feature level and the other at the output level. The second contribution is that we introduce an additional boundary-enhanced decoder to the encoder-decoder backbone segmentation network to effectively recognize the boundary region, thereby addressing the problem of poor contrast. In our study, we employ the public LiTS dataset as the source domain and our private MPCT-FLLs dataset as the target domain. The experimental findings validate the efficacy of our proposed methods, producing substantially improved results when tested on each phase of the multi-phase CT image even without the multi-phase annotations. As evaluated on the MPCT-FLLs dataset, the existing baseline (UDA) method achieved IoU scores of 0.785, 0.796, and 0.772 for the PV, ART, and NC phases, respectively, while our proposed approach exhibited superior performance, surpassing both the baseline and other state-of-the-art methods. Notably, our method achieved remarkable IoU scores of 0.823, 0.811, and 0.800 for the PV, ART, and NC phases, respectively, emphasizing its effectiveness in achieving accurate image segmentation.

Segmentation of Portal Vein in Multiphase CTA Image Based on Unsupervised Domain Transfer and Pseudo Label.

Clinically, physicians diagnose portal vein diseases on abdominal CT angiography (CTA) images scanned in the hepatic arterial phase (H-phase), portal vein phase (P-phase) and equilibrium phase (E-phase) simultaneously. However, existing studies typically segment the portal vein on P-phase images without considering other phase images. We propose a method for segmenting portal veins on multiphase images based on unsupervised domain transfer and pseudo labels by using annotated P-phase images. Firstly, unsupervised domain transfer is performed to make the H-phase and E-phase images of the same patient approach the P-phase image in style, reducing the image differences caused by contrast media. Secondly, the H-phase (or E-phase) image and its style transferred image are input into the segmentation module together with the P-phase image. Under the constraints of pseudo labels, accurate prediction results are obtained. This method was evaluated on the multiphase CTA images of 169 patients. The portal vein segmented from the H-phase and E-phase images achieved DSC values of 0.76 and 0.86 and Jaccard values of 0.61 and 0.76, respectively. The method can automatically segment the portal vein on H-phase and E-phase images when only the portal vein on the P-phase CTA image is annotated, which greatly assists in clinical diagnosis.

CT technology: photon-counting detector computed tomography

Photon-counting detector computed tomography (PCD-CT) is aCT technology that overcomes many limitations of conventional detectors. Direct conversion of photons hitting the detector into electrical signals combined with more sensitive and accurate photon detection simultaneously allows spectral evaluation and also potential reduction in radiation exposure to the patient. The combination of energy thresholds and elimination of detector septa allows for areduction of electronic noise, an increase of spatial resolution, and an improvement of dose efficiency. Recent research has confirmed significantly reduced image noise, reduced radiation dose, increased spatial resolution, improved iodine signal, and areduction in artifacts. Spectral imaging potentiates these effects and also allows retrospective calculation of virtual monoenergetic images, virtual noncontrast images or iodine maps. Thus, the photon-counting technique offers the possibility of using various contrast agents, with the prospect of single-scan multiphase imaging or visualization of specific metabolic processes. Therefore, further research and complementary approval processes are necessary for clinical application. Likewise, further research is needed to develop and validate optimal settings and reconstructions for awide variety of situations, as well as to test new application possibilities. The only photon-counting detector CT device available on the market to date received clinical approval in 2021. It remains to be seen which other applications will become possible through improvements in hardware and software. This technology has already demonstrated an impressive superiority compared with the current standard of CT imaging, especially regarding high-resolution imaging of detailed structures and examinations with high radiation exposure.

Multi-phase image segmentation by the Allen–Cahn Chan–Vese model

Multi-phase image segmentation by the Allen–Cahn Chan–Vese model

Standardized Follow-Up Recommendations Improve Reporting of Incidental Renal Lesions in a Community Setting.

Introduction The objective of this quality improvement study was to assess radiology report follow-up recommendation trends upon detection of incidental renal lesions before and after instituting standardized follow-up macros. Materials and methods A retrospective review was performed in 2019 of multiphase imaging workups on renal lesions (n = 396), including the following imaging modalities: ultrasound, CT with and without contrast, and spine MRI. Utilizing the same collection methods, a similar retrospective set of cases was collected in 2021, 12 months following the creation of the renal follow-up macros (n = 501). After exclusions, the second set was left with 98 cases of newly characterized incidental renal lesions.For both sets, we assessed the reports of the exams that initially detected the incidental renal lesion. We evaluated the incident reports for the presence of a follow-up recommendation, recommendation completeness, and alignment with the American College of Radiology (ACR) white paper suggestions for renal lesion follow-up. Results Before the implementation of the standardized renal follow-up macros, initial follow-up recommendations were in concordance with the ACR white paper recommendations in 33 of 98 cases (33.7%), incomplete or discordant in 49 of 98 (50.0%), and absent in 16 of 98 cases (16.3%). Following the institution of our macros, there was an improvement in concordant follow-up recommendations (51/98; 52.0%) (p = 0.009), a decrease in the number of incomplete or discordant recommendations (37/98; 37.8%), and a decrease in the number of reports lacking a follow-up recommendation (10/98; 10.2%). Conclusion Utilization of standard language renal lesion follow-up macros improves the rate of appropriate follow-up recommendations in radiology reports when encountering a previously unknown incidental renal lesion.

A Convergent Iterative Support Shrinking Algorithm for Non-Lipschitz Multi-phase Image Labeling Model

A Convergent Iterative Support Shrinking Algorithm for Non-Lipschitz Multi-phase Image Labeling Model

Robust End-to-End Focal Liver Lesion Detection Using Unregistered Multiphase Computed Tomography Images

The computer-aided diagnosis of focal liver lesions (FLLs) can help improve workflow and enable correct diagnoses; FLL detection is the first step in such a computer-aided diagnosis. Despite the recent success of deep-learning-based approaches in detecting FLLs, current methods are not sufficiently robust for assessing misaligned multiphase data. By introducing an attention-guided multiphase alignment in feature space, this study presents a fully automated, end-to-end learning framework for detecting FLLs from multiphase computed tomography (CT) images. Our method is robust to misaligned multiphase images owing to its complete learning-based approach, which reduces the sensitivity of the model’s performance to the quality of registration and enables a standalone deployment of the model in clinical practice. Evaluation on a large-scale dataset with 280 patients confirmed that our method outperformed previous state-of-the-art methods and significantly reduced the performance degradation for detecting FLLs using misaligned multiphase CT images. The robustness of the proposed method can enhance the clinical adoption of the deep-learning-based computer-aided detection system.