Multiphase Image Research Articles

A Multigrid Algorithm for Maxflow and Min-Cut Problems with Applications to Multiphase Image Segmentation

A Multigrid Algorithm for Maxflow and Min-Cut Problems with Applications to Multiphase Image Segmentation

Detection and Numerical Simulation of Potential Hazard in Oil Pipeline Areas Based on UAV Surveys

Western China is rich in oil and gas resources, and many oil and gas pipelines are under construction or have been completed. However, many water-related natural hazards, such as landslides, collapses, rockfalls, and debris flows, have developed in the areas passed through by oil and gas pipelines and seriously threaten the operational safety of these pipelines. Therefore, it is urgent to carry out large-scale identification and assessment of pipeline geological hazards. At present, conventional on-site investigation, evaluation, monitoring, and early warning methods are difficult to apply for rapid identification and evaluation of pipeline geological hazards across large-scale areas. Based on this, this study takes the pipeline of Sinopec Marketing South China Branch in Yunnan Province as the research area. In this research, unmanned aerial vehicle (UAV) and photogrammetry technology were used to quickly and accurately obtain multi-phase images of an oil pipeline passing through the study area, and the images were post-processed to obtain multi-phase high-resolution, high-precision digital orthophoto maps and digital terrain models (DTMs) to identify landform changes and deformation. The focus of this research is to propose a set of technical methods for UAV point cloud filtering. The DTMs obtained based on this method can effectively identify unstable areas of oil pipelines. In addition, we have carried out numerical simulations under different motion scenarios in unstable regions, providing scientific support for future geological hazard prevention and mitigation and engineering practices in oil pipeline areas.

Liver Imaging and Data System (LI-RADS) Version 2018 and Other Imaging Features in Intrahepatic Cholangiocarcinoma in Chinese Adults with vs. without Chronic Hepatitis B Viral Infection

Purpose To describe liver imaging reporting and data system (LI-RADS) version 2018 and other MRI imaging features in intrahepatic mass-forming cholangiocarcinoma (iCCA) in Chinese adults with vs. without chronic hepatitis B viral (HBV) infection. Methods We retrospectively enrolled 89 patients with pathologically proven iCCA after multiphase imaging performed between 2004 and 2017 at a tertiary medical center in southern China. Based on whether patients had chronic HBV, iCCA was divided into two subgroups: HBV-positive (n = 50 patients, including 9 with cirrhosis) vs. HBV-negative (n = 39 patients, including 14 with hepatolithiasis and 25 with no identifiable risk factor for iCCA; none had cirrhosis). Two independent abdominal radiologists in consensus reviewed the largest mass in each patient to assign LI-RADS v2018 features; they also scored each observation's shape and location. Imaging features were compared using chi-square or Fisher's exact tests. Results Most iCCAs in HBV-positive (88% (44/50)) and HBV-negative (97% (38/39)) patients had at least one LR-M feature. Compared to iCCAs in HBV-negative patients, iCCAs in HBV-positive patients were more likely to have at least one major feature of HCC (46% (23/50) vs. 8% (3/39), P < 0.001) and more likely to be smooth (42% (21/50) vs. 10% (4/39), P = 0.001). Six of 50 (12%) iCCAs in HBV-positive patients and 1/39 (3%) iCCAs in HBV-negative patients had at least one major feature of HCC without any LR-M feature. Conclusions In this retrospective single-center study in Chinese adults, iCCAs in HBV-positive patients were more likely to resemble HCCs than iCCAs in HBV-negative patients.

Photon-counting detectors in computed tomography: from quantum physics to clinical practice.

Over the last decade, afundamentally new type of computed tomography (CT) detectors has proved its superior capabilities in both physical and preclinical evaluations and is now approaching the stage of clinical practice. These detectors are able to discriminate single photons and quantify their energy and are hence called photon-counting detectors. Among the promising benefits of this technology are improved radiation dose efficiency, increased contrast-to-noise ratio, reduced metal artifacts, improved spatial resolution, simultaneous multi-energy acquisitions, and the prospect of multi-phase imaging within asingle acquisition using multiple contrast agents. Taking the conventional energy-integrating detectors as areference, the authors demonstrate the technical principles of this new technology and provide phantom and patient images acquired by awhole-body photon-counting CT. These images serve as abasis for discussing the potential future of clinical CT.

Study on the Segmentation Method of Multi-Phase CT Liver Tumor Based on Dual Channel U-Nets

As a common malignant tumor disease, hepatocellular carcinoma is the most common cancers in the world. The incidence of hepatocellular carcinoma in China is higher than that in the world. Therefore, it is very important for doctors to separate liver and tumor from CT images by means of computer-aided diagnosis and treatment. In this paper, a multiscale DC-CUNets network liver tumor segmentation method is proposed to enhance the fusion of multi-phase image features in CT, the scale of liver tumors, and the optimization of network training process. (1) A multistage CT liver tumor segmentation method based on two-channel cascaded U-Nets (DC-CUNets) is proposed. The liver was segmented using the first-order U-Net, and then the segmented area of interest of the liver was input into the second-order U-Net network to segment liver tumors. We designed two-channel U-Nets to learn the image characteristics of CT images in arterial and venous phases respectively, and to achieve two-channel feature fusion through feature cascade to improve the overall accuracy of liver tumor segmentation.(2) A multistage CT liver tumor segmentation method based on multiscale DC-CUNets was proposed. For the scale problem of liver tumors, we designed a two-layer multiscale void convolution module to obtain image features at different scales for large, medium and small tumors, and fuse the multiscale features at the output of the module. We have replaced the convolution layer of the fourth module in the second-order two-channel liver tumor segmentation U-Nets by the two-layer multiscale cavity convolution module to implement multiscale DC-CUNets.

Follicular Dendritic Cell Sarcomas: CT and MRI Findings in 20 Patients.

OBJECTIVE. The objective of this study was to assess the imaging features of follicular dendritic cell sarcoma (FDCS) on CT and MRI. MATERIALS AND METHODS. The clinical data and pretreatment findings of 20 patients with pathologically proven FDCS on CT (n = 15), MRI (n = 7), or both (n = 2) were analyzed retrospectively. Tumor location, number, size, morphology, attenuation or signal intensity, margin, presence of metastases, and contrast enhancement were evaluated. RESULTS. FDCS originated from lymph nodes (n = 6) or a variety of extranodal sites (n = 14). The tumors were typically solitary and well-circumscribed. Extranodal lesions (mostly in the abdomen or mediastinum with mean diameter, 11.8 cm) were larger than nodal lesions (mean diameter, 6.5 cm). Nodal-type cases presented with homogeneous masses on CT and MRI. However, on CT, all extranodal tumors (n = 12) showed heterogeneous attenuation, of which 91.7% (11/12) contained areas of lower attenuation because of internal necrosis and 50.0% (6/12) showed calcifications. On MRI, primary hepatic or splenic tumors (n = 3) also appeared as large heterogeneous masses. Seven patients (35.0%) had advanced-stage disease, and intraabdominal extranodal cases were more likely to have regional lymphadenopathy (n = 4) and distant metastases (n = 5). Hypervascularity was seen in 90.0% (18/20) of patients and progressive enhancement was seen in 11 (78.6%) of 14 tumors with multiphase imaging. CONCLUSION. FDCS is a rare, mostly solitary, well-delineated malignancy. A nodal-type FDCS typically presents as a small homogeneous mass, whereas an extranodal FDCS in the mediastinum or abdomen manifests as a large heterogeneous mass with internal necrosis and calcifications accompanied by regional lymphadenopathy. Hypervascularity and progressive enhancement can be seen in the majority of tumors.

Image Segmentation and Denoising Algorithm Based on Partial Differential Equations

The image denoising algorithm based on partial differential equation can selectively solve the problem of noise and image smoothing, which has become the focus of current image denoising algorithms. In the research of image segmentation, this paper proposes a new image segmentation model, which not only can segment multi-phase images, but also achieves simple implementation, which greatly saves the time required for segmentation. The total variation denoising model is analyzed to improve the fidelity of the model: (1) The total variation and the image denoising model, which constructs a trend fidelity term that can effectively suppress the “staircase effect”; 2) Wavelet transform image denoising model based on curvature variation, which uses a horizontal set of enhanced images to establish a curvature drive function based on the horizontal set curvature, and then introduces the curvature drive function as a correction factor into the variational model to establish the curvature change. The experimental results show that the two improved Partial differential equation image segmentation models (PDEISM) have obvious denoising effects, good visual effects and strong comprehensive performance. The simulation experiment was carried out with MATLAB software to verify the superiority of the improved algorithm. The conclusions drawn from the final simulation experiment are consistent with the conclusions of the theoretical analysis, which indicates that the improved algorithm is superior to the traditional algorithm in overall performance and has better practical application value.

Hepatic perfusional changes on CT and MRI: a radiology primer.

Hepatic perfusional changes are common in response to, or as a result of, a multitude of pathological processes. These can be neoplastic, inflammatory, fibrotic, or ischemic in origin, to name a few. The liver, having a dual blood supply, is a unique organ to study using contrast-enhanced CT and MRI imaging due to its varied appearance on multiphasic imaging. Knowledge of the CT and MRI appearance of hepatic perfusional changes, in addition to the clinical presentation, can often result in an accurate differential diagnosis. Many of the conditions that cause these changes in hepatic blood flow result in similar appearances on imaging. As a result, it is important that radiologists be aware of common pitfalls when dealing with hepatic perfusional changes to prevent misdiagnosis or delayed diagnosis. As such, this review will focus on some of the various causes of hepatic perfusional changes and how to accurately identify and diagnose them based on their CT and MRI appearance.

S1083 Assessing Predictors of Microvascular Invasion of HCC at Explant in Liver Transplantation

INTRODUCTION: Liver transplant (LT) is curative in patients with cirrhosis complicated by hepatocellular cancer (HCC) within T2/Milan Criteria. Microvascular invasion (MVI) of HCC portends poor prognosis and is associated with higher risk of recurrence post LT; however, is often undetectable with multiphasic imaging modalities during pre-LT staging. Factors associated with the presence of MVI at LT remain poorly defined. Our aim is to examine pre-LT clinical demographic and tumor related factors and their association with MVI on explant at LT. METHODS: Retrospective evaluation identifying 154 consecutive patients undergoing LT with HCC over a 5-year timeframe (2012-2017). HCC explant pathologic specimen reports reviewed identifying tumor focality, site, size (greatest dimension), histologic type and grade; and presence/absence of microvascular and perineural invasion. Review allowed for stratification into MVI on explant and no MVI on explant groups. Demographic and tumor variables were collected for the MVI and no MVI groups assessing for differences in: age, days on wait-list ( > or < 365) , gender, race, BMI, etiology of cirrhosis, biologic model for end-stage liver disease (MELD) score, number of presenting lesions, tumor volume, type of locoregional therapy (LRT), response to initial and number of bridging therapies; categorical variables analyzed using Fisher’s exact test; continuous variables compared using Levine’s t-test for equal variances and student’s t-test; P < 0.05, significant. RESULTS: 154 LT recipients for HCC reviewed with 16 (10.4%) demonstrating MVI on explant. No significant demographic differences between MVI and non MVI groups apart from age 59 +/- 6 years vs 60+/- 6 years (P < 0.0001) [Table 1]. No significant differences in LRT modality or number of presenting lesions. Although trends associated with presence of MVI were seen (shorter wait times for LT, larger total tumor volume, and increased number of LRT sessions); differences did not reach significance between cohorts. CONCLUSION: Demographic and current staging modalities for HCC do not reliably capture or predict the phenomenon of MVI on explant. Presence of MVI may be associated with larger tumor volumes and shorter wait times. Results support the current approach of 6 month wait times for MELD exception points and call further attention to the unmet need for tissue or serum-based biomarkers in HCC prognosis.Table 1.: Characteristics of liver transplant patients

A Coarse-to-Fine Registration on 3D Multi-Phase Abdominal CT Images

Registration is a technical support for the integration of nanomaterial imaging-aided diagnosis and treatment. In this paper, a coarse-to-fine three-dimensional (3D) multi-phase abdominal CT images registration method is proposed. Firstly, a linear model is used to coarsely register the paired multiphase images. Secondly, an intensity-based registration framework is proposed, which contains the data and spatial regularization terms and performs fine registration on the paired images obtained in the coarse registration step. The results illustrate that the proposed method is superior to some existing methods with the average MSE, PSNR, and SSIM values of 0.0082, 21.2695, and 0.8956, respectively. Therefore, the proposed method provides an efficient and robust framework for 3D multi-phase abdominal CT images registration.

Acute kidney injury after multiphase imaging for lesions detected on hepatocellular carcinoma surveillance in patients with cirrhosis

ObjectiveThe risk difference between multiphase multidetector contrast-enhanced CT and MRI for developing acute kidney injury (AKI) has not been previously evaluated in patients with cirrhosis undergoing hepatocellular carcinoma (HCC) surveillance....

Diagnostic Accuracy of Single-Phase Computed Tomography Texture Analysis for Prediction of LI-RADS v2018 Category.

The aim of this study was to determine if texture analysis can classify liver observations likely to be hepatocellular carcinoma based on the Liver Imaging Reporting and Data System (LI-RADS) using single portal venous phase computed tomography. This research ethics board-approved retrospective cohort study included 64 consecutive LI-RADS observations. Individual observation texture analysis features were compared using Kruskal-Wallis and 2 sample t tests. Logistic regression was used for prediction of LI-RADS group. Diagnostic accuracy was assessed using receiver operating characteristic curves and Youden method. Multiple texture features were associated with LI-RADS including the mean HU (P = 0.003), median (P = 0.002), minimum (P = 0.010), maximum (P = 0.013), standard deviation (P = 0.009), skewness (P = 0.007), and entropy (P < 0.001). On logistic regression, LI-RADS group could be predicted with area under the curve, sensitivity, and specificity of 0.98, 96%, and 100%, respectively. Texture analysis features on portal venous phase computed tomography can identify liver observations likely to be hepatocellular carcinoma, which may preclude the need to recall some patients for additional multiphase imaging.

The Vese-Chan model without redundant parameter estimation for multiphase image segmentation

The Vese-Chan model for multiphase image segmentation uses m binary label functions to construct 2m characteristic functions for different phases/regions systematically; the terms in this model have moderate degrees comparing with other schemes of multiphase segmentation. However, if the number of desired regions is less than 2m, there exist some empty phases which need costly parameter estimation for segmentation purpose. In this paper, we propose an automatic construction method for characteristic functions via transformation between a natural number and its binary expression, and thus, the characteristic functions of empty phases can be written and recognized naturally. In order to avoid the redundant parameter estimations of these regions, we add area constraints in the original model to replace the corresponding region terms to preserve its systematic form and achieve high efficiency. Additionally, we design the alternating direction method of multipliers (ADMM) for the proposed modified model to decompose it into some simple sub-problems of optimization, which can be solved using Gauss-Seidel iterative method or generalized soft thresholding formulas. Some numerical examples for gray images and color images are presented finally to demonstrate that the proposed model has the same or better segmentation effects as the original one, and it reduces the estimation of redundant parameters and improves the segmentation efficiency.

Imaging and Management of Liver Cancer

Imaging of primary hepatic neoplasms in patients at risk for hepatocellular carcinoma (HCC) and in patients with otherwise normal livers relies on proper multiphase image acquisition technique, with emphasis on a high-quality, late arterial phase, using either CT or MRI for accurate image interpretation. The introduction of liver imaging reporting and data system in 2011, with subsequent multiple updates, the most recent in 2018, has provided standardization of image interpretation, reporting and management recommendations for liver observations in patients at risk for HCC. This review article will emphasize key points of imaging primary liver tumors with emphasis on liver imaging reporting and data system, including strengths of this system. We will also review imaging of less common primary liver tumors such as cholangiocarcinoma and angiosarcomas. Imaging pitfall associated with primary liver malignancies will be demonstrated as well as ways to mitigate them. Finally, imaging and reporting of findings following locoregional treatment of HCC will be reviewed.

P3734Feasibility of percutaneous pulmonary valve implantation in the native right ventricle outflow tract from in vivo dynamic regional strain analysis

Abstract Background Patients surgically treated for congenital heart disease (e.g., Tetralogy of Fallot) frequently report long-term dysfunctions (i.e., pulmonary stenosis and/or incompetence) of the native right ventricle outflow tract (RVOT). The efficacy of percutaneous pulmonary valve implantation (PPVI) is recognized worldwide; however, the procedure is only feasible in RVOTs with appropriate size and functional substrate. Accordingly, a three-dimensional (3D) and dynamic assessment of the native RVOT function can be crucial to identify patients who can effectively benefit from PPVI, thus avoiding the risk of device embolization or fracture. Purpose We herein exploited an optical flow-based approach to develop a novel 3D framework for the quantitative in vivo assessment of dimension and dynamic changes of the native RVOT throughout the cardiac cycle. PPVI candidates (n=15) with previous surgery of the native RVOT were enrolled to demonstrate the additional contribution of our 3D patient-tailored analysis to the decision-making process. Methods Contrast-enhanced computed tomography (CT) was performed on a 64-slice dual-source multidetector CT system with retrospective ECG-gating. Multi-phase images of the RVOT were acquired at each 10% increment of the cardiac cycle (slice thickness = 1.5mm, increment = 1mm, pixel spacing = 0.35mm). We implemented a dedicated in-house framework, based on the optical flow tracking algorithm, to dynamically follow three anatomical cross-sections (i.e., proximal, mid and distal) of the native RVOT. The time course of area, perimeter and other relevant parameters (e.g., equivalent radius, elliptical ratio) were extracted and both areal (εA) and longitudinal strain (εlong) were computed on each the RVOT tracked cross-section. Maximum regional strain were calculated between the maximum and minimum value over the cardiac cycle. Dynamic changes in CT-derived variables were assessed using analysis of variance (p&lt;0.05, statistically significant). Results All the enrolled anatomies were successfully analysed, locally pinpointing the in vivo pattern of deformation within each 3D RVOT anatomy (a). Anatomical regional RVOT dimensions (p&lt;0.0001) and changes proved to be significantly different (p≤0.0002) throughout the cardiac cycle. In addition, the dysfunctional RVOT anatomy exhibited an irregular pattern of contraction and dilation: maximum regional strains markedly differed between RVOT regions, e.g., comparing the εA between RVOT mid (22.6%) and distal (46.0%) regions. In vivo tracking of RVOT dynamic changes Conclusions The combination of patient-specific in vivo imaging and bioengineering strategies can improve our understanding of RVOT dysfunctions in congenital patients referred to PPVI. Our optical flow-based and clinically-oriented framework can support the patient selection process and the planning of the percutaneous procedure in order to enhance its efficacy and shorten the operating time while improving the patient safety. Acknowledgement/Funding IRCCS Policlinico San Donato is a clinical research hospital partially funded by the Italian Ministry of Health

Spectral Photon-Counting Computed Tomography (SPCCT): in-vivo single-acquisition multi-phase liver imaging with a dual contrast agent protocol

Diagnostic imaging of hepatocellular carcinoma (HCC) requires a liver CT or MRI multiphase acquisition protocol. Patients would benefit from a high-resolution imaging method capable of performing multi-phase imaging in a single acquisition without an increase in radiation dose. Spectral Photon-Counting Computed Tomography (SPCCT) has recently emerged as a novel and promising imaging modality in the field of diagnostic radiology. SPCCT is able to distinguish between two contrast agents referred to as multicolor imaging because, when measuring in three or more energy regimes, it can detect and quantify elements with a K-edge in the diagnostic energy range. Based on this capability, we tested the feasibility of a dual-contrast multi-phase liver imaging protocol via the use of iodinated and gadolinated contrast agents on four healthy New Zealand White (NZW) rabbits. To perform a dual-contrast protocol, we injected the agents at different times so that the first contrast agent visualized the portal phase and the second the arterial phase, both of which are mandatory for liver lesion characterization. We demonstrated a sensitive discrimination and quantification of gadolinium within the arteries and iodine within the liver parenchyma. In the hepatic artery, the concentration of gadolinium was much higher than iodine (8.5 ± 3.9 mg/mL versus 0.7 ± 0.1 mg/mL) contrary to the concentrations found in the liver parenchyma (0.5 ± 0.3 mg/mL versus 4.2 ± 0.3 mg/mL). In conclusion, our results confirm that SPCCT allows in-vivo dual contrast qualitative and quantitative multi-phase liver imaging in a single acquisition.

Dynamic contrast-enhanced computed tomography diagnosis of primary liver cancers using transfer learning of pretrained convolutional neural networks: Is registration of multiphasic images necessary?

To evaluate the effect of image registration on the diagnostic performance of transfer learning (TL) using pretrained convolutional neural networks (CNNs) and three-phasic dynamic contrast-enhanced computed tomography (DCE-CT) for primary liver cancers. We retrospectively evaluated 215 consecutive patients with histologically proven primary liver cancers, including six early, 58 well-differentiated, 109 moderately differentiated, 29 poorly differentiated hepatocellular carcinomas (HCCs), and 13 non-HCC malignant lesions containing cholangiocellular components. We performed TL using various pretrained CNNs and preoperative three-phasic DCE-CT images. Three-phasic DCE-CT images were manually registered to correct respiratory motion. The registered DCE-CT images were then assigned to the three color channels of an input image for TL: pre-contrast, early phase, and delayed phase images for the blue, red, and green channels, respectively. To evaluate the effects of image registration, the registered input image was intentionally misaligned in the three color channels by pixel shifts, rotations, and skews with various degrees. The diagnostic performances (DP) of the pretrained CNNs after TL in the test set were compared by three general radiologists (GRs) and two experienced abdominal radiologists (ARs). The effects of misalignment in the input image and the type of pretrained CNN on the DP were statistically evaluated. The mean DPs for histological subtype classification and differentiation in primary malignant liver tumors on DCE-CT for GR and AR were 39.1%, and 47.9%, respectively. The highest mean DPs for CNNs after TL with pixel shifts, rotations, and skew misalignments were 44.1%, 44.2%, and 43.7%, respectively. Two-way analysis of variance revealed that the DP is significantly affected by the type of pretrained CNN (P = 0.0001), but not by misalignments in input images other than skew deformations. TL using pretrained CNNs is robust against misregistration of multiphasic images and comparable to experienced ARs in classifying primary liver cancers using three-phasic DCE-CT.

Advanced CT Techniques for Decreasing Radiation Dose, Reducing Sedation Requirements, and Optimizing Image Quality in Children.

CT is an invaluable diagnostic tool for pediatric patients; however, concerns have arisen about the potential risks of ionizing radiation associated with diagnostic imaging in young patients, particularly for pediatric populations that may require serial CT examinations. Recent attention has also been focused on the immediate and long-term risks of administration of anesthetic medications to infants and young children who require sedation to undergo imaging examinations. These concerns can be mitigated with use of advanced CT techniques that can decrease scan time and radiation dose while preserving image quality. In this article, current state-of-the-art CT acquisition techniques are reviewed as part of a comprehensive strategy to reduce radiation dose, decrease sedation needs, and optimize image quality in infants and young children. Three imaging strategies are discussed, including (a) dual-energy CT (DECT), (b) imaging with a low tube potential, and (c) rapid scanning. Consolidating multiphase imaging protocols into a single phase with virtual nonenhanced imaging on DECT scanners, as well as use of low tube voltage, can reduce the radiation dose while increasing the conspicuity of contrast material-enhanced structures with a reduced volume of iodinated contrast material and a reduced rate of injection. Rapid scanning techniques with either ultrahigh pitch at dual-source CT or with wide-area detector single-source CT facilitate scanning without the need for sedation in many children. ©RSNA, 2019 See discussion on this article by Szczykutowicz .

Real-Time Artifact Compensation for Depth Images of Multi-Frequency ToF

During the last few years, Time-of-Flight(TOF) sensor achieved a significant impact onto research and industrial fields due to that it can capture depth easily. For dynamic scenes and phase fusion, ToF sensor's working principles can lead to significant artifacts, therefore an efficient method to combine motion compensation and kernel density estimate multi-frequency unwrapping is proposed. Firstly, the raw multi-phase images are captured, then calculate the optical flow between each frequency. Secondly, by generating multiple depth hypotheses, uses a spatial kernel density estimation is used to rank them with wrapped phase images. Finally, the accurate depth from fused phase image is gotten. The algorithm on Kinect V2 is validated and the pixel-wise part is optimized using GPU. The method shows its real time superior performance on real datasets.

The effects of a transjugular intrahepatic portosystemic shunt on the diagnosis of hepatocellular cancer.

Background and aimsTransjugular intrahepatic portosystemic shunt (TIPS) may be placed to treat complications of portal hypertension by creating a conduit between the hepatic and portal vein. The diagnosis of hepatocellular carcinoma (HCC) is typically made by multiphasic imaging studies demonstrating arterial enhancement with washout on arterial, portal venous, and delayed phase imaging. The aim of our study was to determine how the presence of TIPS would affect the imaging diagnosis of HCC.MethodsThis was a single-center electronic database review of all patients who underwent multiphasic imaging with MRI or CT scan for HCC screening between January 2000 and July 2017 and who were subsequently diagnosed with HCC. Data collected included patient demographics, liver disease characteristics including CPT score, MELD-Na, AFP, type of imaging, tumor stage, and lab values at the time of HCC diagnosis. The diagnosis of HCC was made using LI-RADS criteria on contrast-enhanced CT or MR imaging and confirmed by chart abstraction as documented by the treating clinician. Demographic and imaging characteristics for HCC patients with and without TIPS were compared.ResultsA total of 279 patients met eligibility criteria for the study, 37 (13.2%) of whom had TIPS placed prior to diagnosis of HCC. There was no significant difference in demographics or liver disease characteristics between patients with and without TIPS. Compared to cirrhotic patients with no TIPS prior to HCC diagnosis, patients with TIPS had significantly more scans with a longer duration of surveillance until HCC diagnosis. However, LI-RADS criteria and stage of HCC at diagnosis were not significantly different between both groups. There were no differences in outcomes including liver transplant and survival.ConclusionThe presence of TIPS does not lead to a delayed diagnosis of HCC. It is associated, however, with greater duration of time from first scan to diagnosis of HCC.