Detection Of Liver Lesions Research Articles

Effects of Gadolinium Chelate Administration Timing on T2-weighted and Diffusion-weighted Abdominal MRI Examination: A Prospective Study.

Magnetic Resonance Imaging (MRI) data acquisition includes several sequences that might be optimized to reduce the scan time. This study aimed to investigate the impact of gadolinium chelate administration timing on scan duration and image quality in Diffusion-weighted Imaging (DWI) and T2-weighted Imaging (T2WI) during abdominal MRI examinations. A prospective study was conducted from October 2018 to May 2020. Study participants were assigned into a conventional group, undergoing MRI with DWI and T2WI sequences pre and post-gadolinium injection, or an optimized group, receiving MRI with DWI and T2WI sequences after gadolinium injection. Quantitative image quality, measured by the Signal-to-noise Ratio (SNR), Contrast-to-noise Ratio (CNR), and Apparent Diffusion Coefficient (ADC), was analyzed. Kappa statistics were employed for the inter-observer agreement on liver lesion detection. Our study has included 341 patients, with 168 and 173 in the conventional and optimized groups, respectively. Mean scan durations were 1,304 (±143) and 1,015 (±129) s for the conventional and optimized groups, respectively (p<0.05). For the liver, spleen, and pancreas, SNR and ADC remained statistically unchanged in post-enhanced DWI and T2WI (p>0.05). Significant decreases in the SNR and ADC of the kidney were observed in post-contrast DWI and T2WI (p<0.05). Hepatic lesion detectability did not show significant differences between pre and post-contrast DWI and T2WI images (p>0.05). DWI and T2WI sequences assessed post-gadolinium administration exhibited shortened scan time without compromising the image quality for liver, spleen, and pancreas evaluations. However, these sequences should be examined before gadolinium administration when assessing the kidneys.

Photon-counting computed tomography versus energy-integrating computed tomography for detection of small liver lesions: comparison using a virtual framework imaging.

Photon-counting computed tomography (PCCT) has the potential to provide superior image quality to energy-integrating CT (EICT). We objectively compare PCCT to EICT for liver lesion detection. Fifty anthropomorphic, computational phantoms with inserted liver lesions were generated. Contrast-enhanced scans of each phantom were simulated at the portal venous phase. The acquisitions were done using DukeSim, a validated CT simulation platform. Scans were simulated at two dose levels ( 1.5 to 6.0mGy) modeling PCCT (NAEOTOM Alpha, Siemens, Erlangen, Germany) and EICT (SOMATOM Flash, Siemens). Images were reconstructed with varying levels of kernel sharpness (soft, medium, sharp). To provide a quantitative estimate of image quality, the modulation transfer function (MTF), frequency at 50% of the MTF ( ), noise magnitude, contrast-to-noise ratio (CNR, per lesion), and detectability index ( , per lesion) were measured. Across all studied conditions, the best detection performance, measured by , was for PCCT images with the highest dose level and softest kernel. With soft kernel reconstruction, PCCT demonstrated improved lesion CNR and compared with EICT, with a mean increase in CNR of 35.0% ( ) and 21% ( ) and a mean increase in of 41.0% ( ) and 23.3% ( ) for the 1.5 and 6.0mGy acquisitions, respectively. The improvements were greatest for larger phantoms, low-contrast lesions, and low-dose scans. PCCT demonstrated objective improvement in liver lesion detection and image quality metrics compared with EICT. These advances may lead to earlier and more accurate liver lesion detection, thus improving patient care.

Comparison of diagnostic accuracy of radiomics parameter maps and standard reconstruction for the detection of liver lesions in computed tomography.

The liver is a frequent location of metastatic disease in various malignant tumor entities. Computed tomography (CT) is the most frequently employed modality for initial diagnosis. However, liver metastases may only be delineated vaguely on CT. Calculating radiomics features in feature maps can unravel textures not visible to the human eye on a standard CT reconstruction (SCTR). This study aimed to investigate the comparative diagnostic accuracy of radiomics feature maps and SCTR for liver metastases. Forty-seven patients with hepatic metastatic colorectal cancer were retrospectively enrolled. Whole-liver maps of original radiomics features were generated. A representative feature was selected for each feature class based on the visualization of example lesions from five patients. These maps and the conventional CT image data were viewed and evaluated by four readers in terms of liver parenchyma, number of lesions, visual contrast of lesions and diagnostic confidence. T-tests and chi²-tests were performed with a significance cut off of p<0.05 to compare the feature maps with SCRT, and the data were visualized as boxplots. Regarding the number of lesions detected, SCTR showed superior performance compared to radiomics maps. However, the feature map for firstorder RootMeanSquared was ranked superior in terms of very high visual contrast in 57.4% of cases, compared to 41.0% in standard reconstructions (p < 0.001). All other radiomics maps ranked significantly lower in visual contrast when compared to SCTR. For diagnostic confidence, firstorder RootMeanSquared reached very high ratings in 47.9% of cases, compared to 62.8% for SCTR (p < 0.001). The conventional CT images showed superior results in all categories for the other features investigated. The application of firstorder RootMeanSquared feature maps may help visualize faintly demarcated liver lesions by increasing visual contrast. However, reading of SCTR remains necessary for diagnostic confidence.

Detectability of Hypoattenuating Liver Lesions with Deep Learning CT Reconstruction: A Phantom and Patient Study.

Background CT deep learning image reconstruction (DLIR) improves image quality by reducing noise compared with adaptive statistical iterative reconstruction-V (ASIR-V). However, objective assessment of low-contrast lesion detectability is lacking. Purpose To investigate low-contrast detectability of hypoattenuating liver lesions on CT scans reconstructed with DLIR compared with CT scans reconstructed with ASIR-V in a patient and a phantom study. Materials and Methods This single-center retrospective study included patients undergoing portal venous phase abdominal CT between February and May 2021 and a low-contrast-resolution phantom scanned with the same protocol. Four reconstructions (ASIR-V at 40% strength [ASIR-V 40] and DLIR at three strengths) were generated. Five radiologists qualitatively assessed the images using the five-point Likert scale for image quality, lesion diagnostic confidence, conspicuity, and small lesion (≤1 cm) visibility. Up to two key lesions per patient, confirmed at histopathologic testing or at prior or follow-up imaging studies, were included. Lesion-to-background contrast-to-noise ratio was calculated. Interreader variability was analyzed. Intergroup qualitative and quantitative metrics were compared between DLIR and ASIR-V 40 using proportional odds logistic regression models. Results Eighty-six liver lesions (mean size, 15 mm ± 9.5 [SD]) in 50 patients (median age, 62 years [IQR, 57-73 years]; 27 [54%] female patients) were included. Differences were not detected for various qualitative low-contrast detectability metrics between ASIR-V 40 and DLIR (P > .05). Quantitatively, medium-strength DLIR and high-strength DLIR yielded higher lesion-to-background contrast-to-noise ratios than ASIR-V 40 (medium-strength DLIR vs ASIR-V 40: odds ratio [OR], 1.96 [95% CI: 1.65, 2.33]; high-strength DLIR vs ASIR-V 40: OR, 5.36 [95% CI: 3.68, 7.82]; P < .001). Low-contrast lesion attenuation was reduced by 2.8-3.6 HU with DLIR. Interreader agreement was moderate to very good for the qualitative metrics. Subgroup analysis based on lesion size of larger than 1 cm and 1 cm or smaller yielded similar results (P > .05). Qualitatively, phantom study results were similar to those in patients (P > .05). Conclusion The detectability of low-contrast liver lesions was similar on CT scans reconstructed with low-, medium-, and high-strength DLIR and ASIR-V 40 in both patient and phantom studies. Lesion-to-background contrast-to-noise ratios were higher for DLIR medium- and high-strength reconstructions compared with ASIR-V 40. © RSNA, 2024 Supplemental material is available for this article.

Relevance of lesion size in navigator-triggered and free-breathing diffusion-weighted liver MRI.

The purpose of this study was to investigate the relevance of focal liver lesions (FLL) size for lesion detection comparing navigator triggering (TRIG) to free breathing (FB) liver Diffusion-weighted magnetic resonance imaging (DWI). Patients with known or suspected FLL were prospectively (registry number 276_19 B) included from October to December 2019 in this study, out of which 32 had liver lesions. Echo planar spin-echo DWI data both with TRIG and FB were with approximately constant acquisition times acquired at 1.5 T. Lesions were segmented in the b = 800 s/mm² images in both the TRIG and FB images. The lesion size, location (liver segment), liver lesion visibility, as well as contrast-to-noise ratio (CNR) were recorded. The CNR was assessed with the Wilcoxon-Mann-Whitney test and the number of visible lesions with the Fisher test. Data from 43 patients (22 female) were analyzed. The mean patient age was 58 ± 14 years. A total of 885 FLL (Ntotal) were segmented. Among these, 811 lesions (Nboth) were detected with TRIG and FB, 65 lesions exclusively with TRIG (NTRIG_Only), and nine exclusively in FB (NFB_Only). The largest additional lesion in TRIG/FB had a diameter of 10.4 mm/7.6 mm. The number of additional lesions detected with TRIG decreased with size. Among all lesions ≤ 4.7 mm, the relative number of additional lesions was 15.6%. Additional lesions were found in all liver segments with TRIG. In the left liver lobe, the relative proportion was 9.2%, and in the right liver lobe 5.4%. CNR and visibility were significantly higher in TRIG than in FB (p < 0.001). In relation to size, the difference is significant in terms of visibility and CNR for lesion diameters ≤ 8 mm. Respiration triggering can improve the detection of small liver lesions with diameters up to approx. 1 cm in the whole liver. Question Can respiration triggering (TRIG) improve the detection of small FLL compared to FB diffusion-weighted imaging? Findings Among 885 segmented FLL, TRIG was superior to FB for lesions smaller than 8 mm and had improved CNR and visibility. Clinical relevance Diffusion-weighted magnetic resonance imaging is used for the detection of focal liver lesions and image quality is influenced by breathing motion. Navigator triggering becomes more important for smaller lesions, and seems recommendable for the detection of small focal liver lesions.

Cytomorphological Spectrum of Hepatic Lesions on Ultrasonography (USG) or CT-Guided Fine-Needle Aspiration Cytology (FNAC) in a Tertiary Care Centre, Rajendra Institute of Medical Sciences (RIMS), Ranchi, Jharkhand, India.

Background Ultrasonography (USG)-guided fine-needle aspiration cytology (FNAC) of the liver is a primary diagnostic procedure for primary and metastatic hepatic lesions. Despite histopathology being the gold standard, the outcomes of ultrasound-guided fine needle aspiration cytology are encouraging. Aims and objective The purpose of the study is to determine the diagnostic utility of ultrasonography (USG) or computerized tomography (CT)-guided FNAC in the detection of liver lesions, to investigate cytomorphological patterns of liver lesions identified by guided FNAC, and, wherever feasible, to correlate FNAC diagnosis with histopathology and imaging modalities. Materials and methods This was a hospital-based observational study of 62 patients carried out in the Department of Pathology, Rajendra Institute of Medical Sciences (RIMS) Ranchi during a period of 1.5 years from August 2020 to February 2022. The patients with suspected hepatic lesions were subjected to ultrasound-guided or CT-guided fine needle aspiration cytology (FNAC) following clinical and radiological evaluation and cytomorphological features were analyzed. Results Cyto-morphological diagnosis of 62 cases was categorized into 19 (30.64%) non-neoplastic lesions and 43 (69.35%) malignant neoplastic lesions. The different neoplastic lesions were 17 (27.41%) hepatocellular carcinoma, 1 (1.61%) hepatoblastoma, 1 (1.61%) cholangiocarcinoma, 23 (37.09%) metastatic adenocarcinoma and one case (1.61%) of unclassified malignancy. Histopathological correlation for confirming the diagnosis could be done in 33 malignant neoplastic lesions and the concordance rate of FNAC with respect to histopathological examination (HPE) was 92.11%. Overall diagnostic accuracy of the FNAC of liver to detect malignant lesions was 98.39%. Conclusion Compared to ultrasonography alone, ultrasound or CT-guided fine needle aspiration of the liver has a more promising role in the diagnosis and classification of hepatic lesions as it demands a higher level of precision to achieve diagnostic accuracy.

Comparison of adaptive imaging receiver coil and traditional coil for multiplexed sensitivity encoding diffusion-weighted imaging of the liver.

To compare the image quality and efficacy of the adaptive imaging receiver (AIR) coil (GE Healthcare) and the traditional coil for multiplexed sensitivity encoding diffusion-weighted imaging (MUSE-DWI) in the detection of focal liver lesions (FLLs). Two groups of MUSE-DWI were obtained. Image quality was qualitatively evaluated by 3 independent blinded radiologists on a 5-point scale, and quantitative parameters were calculated by measurements of the region of interest in the liver and FLLs. McNemar's test were used to compare the characteristics and detectability. Less image noise, sharper contours, milder susceptibility artefacts, and better liver lesion conspicuity were found by all radiologists in 60 livers with 140 FLLs with the AIR coil than with the traditional coil (reader average mean, 4.3-4.4 vs. 3.7-4.0, P < .001). The signal-to-noise ratio (SNR) of the liver was significantly higher with the AIR coil than with the traditional coil (right lobe: mean, 8.89 vs.7.76, P < .05; left lobe: mean, 7.14 vs.6.19, P < .001), and the SNR of FLLs (mean, 24.62 vs. 21.01, P < .001) and lesion-to-liver CNR (mean, 16.61 vs. 14.02, P < .001) exhibited significant differences between the AIR coil and the traditional coil. Besides, superior detection of FLLs was observed with the AIR coil compared to the traditional coil (95.7% [134/140] vs. 85.7% [120/140], P < .001). The AIR coil yields less noise, fewer distortions, better lesion detectability, higher SNR of the liver and FLLs, and improved lesion-to-liver CNR during liver MUSE-DWI. Thus, it is a feasible and effective scanning scheme in liver MRI. The AIR coil improves SNR and the quality of liver MR imaging compared with the traditional coil.

Assessing the performance of AI-assisted technicians in liver segmentation, Couinaud division, and lesion detection: a pilot study

BackgroundIn patients with primary and secondary liver cancer, the number and sizes of lesions, their locations within the Couinaud segments, and the volume and health status of the future liver remnant are key for informing treatment planning. Currently this is performed manually, generally by trained radiologists, who are seeing an inexorable growth in their workload. Integrating artificial intelligence (AI) and non-radiologist personnel into the workflow potentially addresses the increasing workload without sacrificing accuracy. This study evaluated the accuracy of non-radiologist technicians in liver cancer imaging compared with radiologists, both assisted by AI.MethodsNon-contrast T1-weighted MRI data from 18 colorectal liver metastasis patients were analyzed using an AI-enabled decision support tool that enables non-radiology trained technicians to perform key liver measurements. Three non-radiologist, experienced operators and three radiologists performed whole liver segmentation, Couinaud segment segmentation, and the detection and measurements of lesions aided by AI-generated delineations. Agreement between radiologists and non-radiologists was assessed using the intraclass correlation coefficient (ICC). Two additional radiologists adjudicated any lesion detection discrepancies.ResultsWhole liver volume showed high levels of agreement between the non-radiologist and radiologist groups (ICC = 0.99). The Couinaud segment volumetry ICC range was 0.77–0.96. Both groups identified the same 41 lesions. As well, the non-radiologist group identified seven more structures which were also confirmed as lesions by the adjudicators. Lesion diameter categorization agreement was 90%, Couinaud localization 91.9%. Within-group variability was comparable for lesion measurements.ConclusionWith AI assistance, non-radiologist experienced operators showed good agreement with radiologists for quantifying whole liver volume, Couinaud segment volume, and the detection and measurement of lesions in patients with known liver cancer. This AI-assisted non-radiologist approach has potential to reduce the stress on radiologists without compromising accuracy.Graphical abstract

Dual-Split CT to Simulate Multiple Radiation Doses From a Single Scan-Liver Lesion Detection Compared With Dose-Matched Single-Energy CT.

The aim of this study was to evaluate the potential use of simulated radiation doses from a dual-split CT scan for dose optimization by comparing their lesion detectability to dose-matched single-energy CT acquisitions at different radiation dose levels using a mathematical model observer. An anthropomorphic abdominal phantom with liver lesions (5-10 mm, both hyperattenuating and hypoattenuating) was imaged using a third-generation dual-source CT in single-energy dual-source mode at 100 kVp and 3 radiation doses (5, 2.5, 1.25 mGy). The tube current was 67% for tube A and 33% for tube B. For each dose, 5 simulated radiation doses (100%, 67%, 55%, 45%, 39%, and 33%) were generated through linear image blending. The phantom was also imaged using traditional single-source single-energy mode at equivalent doses. Each setup was repeated 10 times. Image noise texture was evaluated by the average spatial frequency (fav) of the noise power spectrum. Liver lesion detection was measured by the area under the receiver operating curve (AUC), using a channelized Hotelling model observer with 10 dense Gaussian channels. Fav decreased at lower radiation doses and differed between simulated and single-energy images (eg, 0.16 mm-1 vs 0.14 mm-1 for simulated and single-energy images at 1.25 mGy), indicating slightly blotchier noise texture for dual-split CT. For hyperattenuating lesions, the mean AUC ranged between 0.92-0.99, 0.81-0.96, and 0.68-0.89 for single-energy, and between 0.91-0.99, 0.78-0.91, and 0.70-0.85 for dual-split at 5 mGy, 2.5 mGy, and 1.25 mGy, respectively. For hypoattenuating lesions, the AUC ranged between 0.90-0.98, 0.75-0.93, and 0.69-0.86 for the single-energy, and between 0.92-0.99, 0.76-0.87, and 0.67-0.81 for dual-split at 5 mGy, 2.5 mGy, and 1.25 mGy, respectively. AUC values were similar between both modes at 5 mGy, and slightly lower, albeit not significantly, for the dual-split mode at 2.5 and 1.25 mGy. Lesion detectability was comparable between multiple simulated radiation doses from a dual-split CT scan and dose-matched single-energy CT. Noise texture was slightly blotchier in the simulated images. Simulated doses using dual-split CT can be used to assess the impact of radiation dose reduction on lesion detectability without the need for repeated patient scans.

The Current Role of Contrast-Enhanced Ultrasound (CEUS) in the Diagnosis and Staging of Bladder Cancer: A Review of the Available Literature.

Contrast-enhanced ultrasound (CEUS) is an advanced imaging technique that integrates conventional US with the intravenous injection of specific US contrast agents (UCAs), combining the non-invasiveness of US with the higher accuracy of contrast-enhanced imaging. In contrast with magnetic resonance imaging (MRI), computed tomography (CT) and cystoscopy, CEUS has few contraindications, and UCAs are non-nephrotoxic agents that can be safely used in patients with kidney failure. CEUS is a well-established method for the detection of liver lesions and for echocardiography, and its indications are expanding. The updated 2018 WFUMB-EFSUMB guidelines have added the urinary bladder under non-hepatic applications of CEUS. The technique is able to distinguish between benign tissue, such as clots or hematoma, and malignant lesions by perfusing the mass with contrast agent. Thanks to the different perfusion rates of the various layers of the bladder wall, CEUS is also able to predict tumor invasion depth and stage. Despite that, current urological guidelines do not include CEUS as a plausible imaging technique for bladder urothelial carcinoma. The main reason for this omission might be the presence of scarce randomized evidence and the absence of large validated series. In this review, we describe the rationale behind the use of CEUS in bladder cancer and the added value of this imaging technique in the detection and staging of bladder lesions. In addition, we researched the available literature on the topic and then described the results of randomized clinical trials and a meta-analysis investigating the accuracy of CEUS in bladder cancer diagnosis and staging. The reported studies show that CEUS is a highly accurate diagnostic and staging tool for BC, reaching levels of specificity and sensitivity in differentiating between Ta-T1, or low-grade BC, and T2, or high-grade BC, that are comparable to those shown by the reference standard methods. Nonetheless, several limitations were found and are highlighted in this review. The aim of this study is to further validate and promote the use of CEUS as a quick, economic and effective diagnostic tool for this high-impact disease.

Leveraging radiomics and AI for precision diagnosis and prognostication of liver malignancies.

Liver tumors, whether primary or metastatic, have emerged as a growing concern with substantial global health implications. Timely identification and characterization of liver tumors are pivotal factors in order to provide optimum treatment. Imaging is a crucial part of the detection of liver tumors; however, conventional imaging has shortcomings in the proper characterization of these tumors which leads to the need for tissue biopsy. Artificial intelligence (AI) and radiomics have recently emerged as investigational opportunities with the potential to enhance the detection and characterization of liver lesions. These advancements offer opportunities for better diagnostic accuracy, prognostication, and thereby improving patient care. In particular, these techniques have the potential to predict the histopathology, genotype, and immunophenotype of tumors based on imaging data, hence providing guidance for personalized treatment of such tumors. In this review, we outline the progression and potential of AI in the field of liver oncology imaging, specifically emphasizing manual radiomic techniques and deep learning-based representations. We discuss how these tools can aid in clinical decision-making challenges. These challenges encompass a broad range of tasks, from prognosticating patient outcomes, differentiating benign treatment-related factors and actual disease progression, recognizing uncommon response patterns, and even predicting the genetic and molecular characteristics of the tumors. Lastly, we discuss the pitfalls, technical limitations and future direction of these AI-based techniques.

An Overview of Current Research on Computer-Aided Liver Diagnosis Based on Computed Tomography Employing Deep Learning Methods

Abstract: Compared to traditional X-rays, computed tomography (CT) scanning is a non-invasive diagnostic imaging method that yields more precise information about the liver. In contrast to ultrasonography (US) exams, the quality of a CT scan is not heavily operator dependent. Many studies have been conducted with improved outcomes utilizing traditional machine learning methods for computer-aided diagnosis (CAD) of the liver. Recent developments, particularly in the field of deep learning technology, have made it possible to identify, categorize, and segment patterns in medical images. These developments have also been used to other fields outside of medicine. The ability of deep learning to automatically learn feature representations from data, as opposed to feeding in manually created features based on application, is one of its fundamental capabilities. The fundamentals of deep learning are presented in this paper, along with their achievements in liver segmentation and lesion detection, classification utilizing CT imaging modalities, and discussion of their various network topologies. Another fascinating deep learning strategy that is covered is transfer learning. Thus, deep learning and the CAD system have had a significant influence and improved the performance of the healthcare sector.

Comments and illustrations of the WFUMB CEUS liver guidelines: Rare benign focal liver lesion, part I.

Improved detection and characterization of common focal liver lesions (FLL) are the main topics of the World Federation for Ultrasound in Medicine and Biology (WFUMB) guidelines on the use of contrast-enhanced ultrasound (CEUS). On stateof-the-art CEUS imaging, to create a library of rare FLL, especially concerning their atypical imaging characteristics, might be helpful for improving clinical diagnostic efficiency. In this review, we aim to summarize the ultrasound and CEUS features of rare benign FLL. Currently there are limited reports and images published.

A multicenter clinical AI system study for detection and diagnosis of focal liver lesions

Early and accurate diagnosis of focal liver lesions is crucial for effective treatment and prognosis. We developed and validated a fully automated diagnostic system named Liver Artificial Intelligence Diagnosis System (LiAIDS) based on a diverse sample of 12,610 patients from 18 hospitals, both retrospectively and prospectively. In this study, LiAIDS achieved an F1-score of 0.940 for benign and 0.692 for malignant lesions, outperforming junior radiologists (benign: 0.830-0.890, malignant: 0.230-0.360) and being on par with senior radiologists (benign: 0.920-0.950, malignant: 0.550-0.650). Furthermore, with the assistance of LiAIDS, the diagnostic accuracy of all radiologists improved. For benign and malignant lesions, junior radiologists’ F1-scores improved to 0.936-0.946 and 0.667-0.680 respectively, while seniors improved to 0.950-0.961 and 0.679-0.753. Additionally, in a triage study of 13,192 consecutive patients, LiAIDS automatically classified 76.46% of patients as low risk with a high NPV of 99.0%. The evidence suggests that LiAIDS can serve as a routine diagnostic tool and enhance the diagnostic capabilities of radiologists for liver lesions.

Compressed sensing with deep learning reconstruction: Improving capability of gadolinium-EOB-enhanced 3D T1WI

PurposeThe purpose of this study was to determine the utility of compressed sensing (CS) with deep learning reconstruction (DLR) for improving spatial resolution, image quality and focal liver lesion detection on high-resolution contrast-enhanced T1-weighted imaging (HR-CE-T1WI) obtained by CS with DLR as compared with conventional CE-T1WI with parallel imaging (PI). MethodsSeventy-seven participants with focal liver lesions underwent conventional CE-T1WI with PI and HR-CE-T1WI, surgical resection, transarterial chemoembolization, and radiofrequency ablation, followed by histopathological or >2-year follow-up examinations in our hospital. Signal-to-noise ratios (SNRs) of liver, spleen and kidney were calculated for each patient, after which each SNR was compared by means of paired t-test. To compare focal lesion detection capabilities of the two methods, a 5-point visual scoring system was adopted for a per lesion basis analysis. Jackknife free-response receiver operating characteristic (JAFROC) analysis was then performed, while sensitivity and false positive rates (/data set) for consensus assessment of the two methods were also compared by using McNemar's test or the signed rank test. ResultsEach SNR of HR-CE-T1WI was significantly higher than that of conventional CE-T1WI with PI (p < 0.05). Sensitivities for consensus assessment showed that HR-CE-MRI had significantly higher sensitivity than conventional CE-T1WI with PI (p = 0.004). Moreover, there were significantly fewer FP/cases for HR-CE-T1WI than for conventional CE-T1WI with PI (p = 0.04). ConclusionCS with DLR are useful for improving spatial resolution, image quality and focal liver lesion detection capability of Gd-EOB-DTPA enhanced 3D T1WI without any need for longer breath-holding time.

MSPA-DLA++: A Multi-Scale Phase Attention Deep Layer Aggregation for Lesion Detection in Multi-Phase CT Images.

Object detection using convolutional neural networks (CNNs) has achieved high performance and achieved state-of-the-art results with natural images. Compared to natural images, medical images present several challenges for lesion detection. First, the sizes of lesions vary tremendously, from several millimeters to several centimeters. Scale variations significantly affect lesion detection accuracy, especially for the detection of small lesions. Moreover, the effective extraction of temporal and spatial features from multi-phase CT images is also an important issue. In this paper, we propose a group-based deep layer aggregation method with multiphase attention for liver lesion detection in multi-phase CT images. The method, which is called MSPA-DLA++, is a backbone feature extraction network for anchor-free liver lesion detection in multi-phase CT images that addresses scale variations and extracts hidden features from such images. The effectiveness of the proposed method is demonstrated on public datasets (LiTS2017) and our private multiphase dataset. The results of the experiments show that MSPA-DLA++ can improve upon the performance of state-of-the-art networks by approximately 3.7%.

Contrast-enhanced ultrasound of the liver: basics and interpretation of common focal lesions.

Over the past 20 years, contrast-enhanced ultrasound (CEUS) has become established as a procedure that is complementary to B-mode ultrasound and color Doppler sonography.The aim of this review is to provide the fundamental knowledge required for examining the liver with CEUS. Additionally, the characteristic CEUS patterns of frequent focal liver lesions are described.Considering the limitations of ultrasound, CEUS offers an equivalent alternative to other imaging modalities, such as computed tomography and magnetic resonance imaging, for evaluating focal liver lesions. It should be utilized as a primary modality due to its lack of radiation exposure and rapid availability. KEY POINTS:: · CEUS plays an important role particularly in the detection and evaluation of incidentally detected liver lesions.. · Considering the limitations of ultrasound, CEUS offers an equivalent alternative to other imaging modalities, such as CT and MRI, for evaluating focal liver lesions.. CITATION FORMAT: · Safai Zadeh E, Prosch H, Ba-Ssalamah A et al. Contrast-enhanced ultrasound of the liver: basics and interpretation of common focal lesions. Fortschr Röntgenstr 2024; DOI: 10.1055/a-2219-4726.

Photon-Counting Detector CT for Liver Lesion Detection-Optimal Virtual Monoenergetic Energy for Different Simulated Patient Sizes and Radiation Doses.

The aim of this study was to evaluate the optimal energy level of virtual monoenergetic images (VMIs) from photon-counting detector computed tomography (CT) for the detection of liver lesions as a function of phantom size and radiation dose. An anthropomorphic abdominal phantom with liver parenchyma and lesions was imaged on a dual-source photon-counting detector CT at 120 kVp. Five hypoattenuating lesions with a lesion-to-background contrast difference of -30 HU and -45 HU and 3 hyperattenuating lesions with +30 HU and +90 HU were used. The lesion diameter was 5-10 mm. Rings of fat-equivalent material were added to emulate medium- or large-sized patients. The medium size was imaged at a volume CT dose index of 5, 2.5, and 1.25 mGy and the large size at 5 and 2.5 mGy, respectively. Each setup was imaged 10 times. For each setup, VMIs from 40 to 80 keV at 5 keV increments were reconstructed with quantum iterative reconstruction at a strength level of 4 (QIR-4). Lesion detectability was measured as area under the receiver operating curve (AUC) using a channelized Hotelling model observer with 10 dense differences of Gaussian channels. Overall, highest detectability was found at 65 and 70 keV for both hypoattenuating and hyperattenuating lesions in the medium and large phantom independent of radiation dose (AUC range, 0.91-1.0 for the medium and 0.94-0.99 for the large phantom, respectively). The lowest detectability was found at 40 keV irrespective of the radiation dose and phantom size (AUC range, 0.78-0.99). A more pronounced reduction in detectability was apparent at 40-50 keV as compared with 65-75 keV when radiation dose was decreased. At equal radiation dose, detection as a function of VMI energy differed stronger for the large size as compared with the medium-sized phantom (12% vs 6%). Detectability of hypoattenuating and hyperattenuating liver lesions differed between VMI energies for different phantom sizes and radiation doses. Virtual monoenergetic images at 65 and 70 keV yielded highest detectability independent of phantom size and radiation dose.

Advancements in Molecular Imaging for the Diagnosis and Management of Hepatocellular Carcinoma

Hepatocellular Carcinoma (HCC) is a growing global health burden with high incidence and mortality rates. Despite advances in surgical techniques and perioperative care, outcomes after surgical treatment have not improved over the past three decades. Molecular imaging is an emerging field that enables researchers to study diseases at the molecular and cellular levels, enabling the detection of elevated serum α-fetoprotein (AFP) and abnormal expressions of various HCC-specific and nonspecific cell surface antigens and intracellular targets. Molecular imaging techniques detect liver lesions at the molecular and cellular level, allowing early detection and accurate staging of HCC. Positron emission tomography (PET) imaging offers greater sensitivity and specificity, while hepatobiliary-specific radiotracers with SPECT imaging provide insights into benign and malignant lesion differentiation. Radiomics and artificial intelligence are vital in deciphering molecular imaging data, with machine learning algorithms boosting diagnostic gains and predicting treatment response. Theranostics, a state-of-the-art application, provides diagnostic and therapeutic leverage following a single imaging agent. By understanding tumor biology in real time, radiopharmaceuticals can be transformed into personalized radiotherapies, enabling clinicians to make science-driven decisions throughout the illness. Future directions include developing novel radiotracers and integrating AI into clinical decision-making. Collaboration between academic researchers, clinicians, and industry colleagues is crucial to converting exciting advances into improved clinical outcomes for HCC patients.

Comments and illustrations of the WFUMB CEUS liver guidelines: Rare focal liver lesion - infectious parasitic, fungus.

In this series of papers on comments and illustrations of the World Federation for Medicine and Biology (WFUMB) guidelines on contrast enhanced ultrasound (CEUS) the topics of parasitic and fungus infections are discussed. Improved detection and characterization of common focal liver lesions (FLL) are the main topics of these guidelines but detailed and illustrating information is missing. The focus in this paper on infectious (parasitic and fungus) focal liver lesions is on their appearance on B-mode and Doppler ultrasound and CEUS features. Knowledge of these data should help to raise awareness of these rarer findings, to think of these clinical pictures in the corresponding clinical situation, to interpret the ultrasound images correctly and thus to initiate the appropriate diagnostic and therapeutic steps in time.