Adopting an appropriate noninvasive radiological method is crucial for periodic surveillance of liver metastases in colorectal cancer (CRC) patients after surgery, which is closely related to clinical management and prognosis. This study aimed to prospectively enroll stage II-III CRC patients for the surveillance of liver metastases and compare the diagnostic performance of contrast-enhanced CT (CE-CT) and non-enhanced abbreviated MRI (NE-AMRI) during this process.

587 CRC patients undergoing radical resection of the primary tumor were evaluated by 1 to 3 rounds of surveillance tests, consisting of abdominal CE-CT and contrast-enhanced MRI (CE-MRI) within 7 days at 6-month intervals. Subsequently, images of NE-AMRI were extracted from the CE-MRI examination, and paired CE-CT and NE-AMRI analysis were performed. The lesion-based detection rates between two protocols were compared, and a subgroup analysis was performed in lesions with a size of ≤10 mm. The patient-based sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, and the areas under the curves (AUCs) of CE-CT and NE-AMRI in each round were evaluated. Finally, the relationship between the diagnostic accuracy of two protocols and characteristics of patients was explored.

The lesion-based detection rates of NE-AMRI in three rounds were all significantly higher than those of CE-CT ( P < 0.001, P < 0.001, P = 0.003, respectively). In the subgroup analysis of lesions ≤ 10 mm, NE-AMRI also performed better than CE-CT ( P < 0.001, P = 0.002, P = 0.005, respectively). The patient-based sensitivities, specificities, NPVs, and PPVs of NE-AMRI were higher than those of CE-CT in three rounds of surveillance. The AUCs for NE-AMRI were all significantly better than those for CE-CT in each round ( P = 0.015, P = 0.045, P = 0.009, respectively). Furthermore, patient BMI and fatty liver disease had impacts on the diagnostic accuracy of the CE-CT protocol, but not on the NE-AMRI protocol.

NE-AMRI may be a promising periodic surveillance tool for CRC patients after surgery to increase diagnostic accuracy of liver metastases, developing personalized clinical management and improving prognosis, simultaneously avoiding side effects associated with ionizing radiation and contrast agents.

Chronic diffuse liver disease continues to increase in prevalence and represents a global health concern. Noninvasive detection and quantification of hepatic steatosis, iron overload, and fibrosis are critical, especially given the many relative disadvantages and potential risks of invasive liver biopsy. Although MRI techniques have emerged as the preferred reference standard for quantification of liver fat, iron, and fibrosis, CT can play an important role in opportunistic detection of unsuspected disease and is performed at much higher volumes. For hepatic steatosis, noncontrast CT provides a close approximation to MRI-based proton-density fat fraction (PDFF) quantification, with liver attenuation values less than or equal to 40 HU signifying at least moderate steatosis. Liver fat quantification with postcontrast CT is less precise but can generally provide categorical assessment (eg, mild vs moderate steatosis). Noncontrast CT can also trigger appropriate assessment for iron overload when increased parenchymal attenuation values are observed (eg, >75 HU). A variety of morphologic and functional CT features indicate the presence of underlying hepatic fibrosis and cirrhosis. Beyond subjective assessment, quantitative CT methods for staging fibrosis can provide comparable performance to that of elastography. Furthermore, quantitative CT assessment can be performed retrospectively, since prospective techniques are not required. Many of these CT quantitative measures are now fully automated via artificial intelligence (AI) deep learning algorithms. These retrospective and automated advantages have important implications for longitudinal clinical care and research. Ultimately, regardless of the indication for CT, opportunistic detection of steatosis, iron overload, and fibrosis can result in appropriate clinical awareness and management. ©RSNA, 2024 See the invited commentary by Yeh in this issue.

Hypoattenuation of the liver, consistent with hepatic steatosis or lipidosis, has been reported in veterinary patients. In people, measuring CT hepatic attenuation is diagnostic for hepatic steatosis, and hypoattenuation of the liver is defined as absolute if less than 40 HU or relative if the liver is 10 HU less than the spleen. The purpose of this study is to describe hepatic parenchymal attenuation in dogs with diabetes mellitus with or without diabetic ketosis (DK) or diabetic ketoacidosis (DKA), using the above categorization for absolute and relative hypoattenuation, as with humans. We hypothesized dogs with DK or DKA were more likely to have hypoattenuating livers. Twenty-seven diabetic dogs were included; fifteen were categorized in Group 1 as without DK or DKA, six in Group 2 as DK, and six in Group 3 as DKA. In Group 3, four of six dogs had absolute and relative hypoattenuating livers. Three of these were visually hypoattenuating to the vasculature, with one having negative attenuation and a histopathologic diagnosis of severe hepatic lipidosis. In Group 2, four of six dogs had relative hypoattenuating livers. In Group 1, only one of 15 dogs had a relatively hypoattenuating liver. Groups 2 and 3 had significantly lower absolute liver attenuation than Group 1. Presumed hepatic steatosis was present on CT and was more common with DK or DKA. These findings may help provide hepatic sampling recommendations and alter patient prognosis. Further research is needed to establish absolute and relative liver attenuation in dogs with correlation to histopathology and patient outcome.

Gut microbiota status after pancreaticoduodenectomy (PD) is unclear, and postoperative fatty liver is an important complication after PD. This study evaluated the relationship between postoperative fatty liver and gut microbiota after PD.

Fecal samples were collected from patients who had undergone PD and remained stable after 6 months of follow-up. A comprehensive bacterial analysis using 16S rRNA gene amplicon sequencing was performed. The results were compared with those of 85 healthy volunteers. The association between perioperative factors, gut microbiota, and development of fatty liver was investigated.

Twenty-four patients after PD, including 10 in the fatty liver (FL) group and 14 in the normal liver (NL) group were investigated. The β-diversity of the gut microbiota was significantly different between the healthy volunteers and patients after PD, with more Escherichia coli and Streptococcus gallolyticus and less Bifidobacterium catenulatum and Faecalibacterium prausnitzii in the patients with PD. Lactobacillus gasseri was significantly less abundant in the FL group than in the healthy volunteers, although this change was not observed in the NL group.

The gut microbiota of patients after PD was in dysbiosis at postoperative ≥6 months. Development of fatty liver might be associated with significant differences in gut microbiota.

To assess the diagnostic performance of post-contrast CT for predicting moderate hepatic steatosis in an older adult cohort undergoing a uniform CT protocol, utilizing hepatic and splenic attenuation values.

A total of 1676 adults (mean age, 68.4 ± 10.2 years; 1045M/631F) underwent a CT urothelial protocol that included unenhanced, portal venous, and 10-min delayed phases through the liver and spleen. Automated hepatosplenic segmentation for attenuation values (in HU) was performed using a validated deep-learning tool. Unenhanced liver attenuation < 40.0 HU, corresponding to > 15% MRI-based proton density fat, served as the reference standard for moderate steatosis.

The prevalence of moderate or severe steatosis was 12.9% (216/1676). The diagnostic performance of portal venous liver HU in predicting moderate hepatic steatosis (AUROC = 0.943) was significantly better than the liver-spleen HU difference (AUROC = 0.814) (p < 0.001). Portal venous phase liver thresholds of 80 and 90 HU had a sensitivity/specificity for moderate steatosis of 85.6%/89.6%, and 94.9%/74.7%, respectively, whereas a liver-spleen difference of -40 HU and -10 HU had a sensitivity/specificity of 43.5%/90.0% and 92.1%/52.5%, respectively. Furthermore, livers with moderate-severe steatosis demonstrated significantly less post-contrast enhancement (mean, 35.7 HU vs 47.3 HU; p < 0.001).

Moderate steatosis can be reliably diagnosed on standard portal venous phase CT using liver attenuation values alone. Consideration of splenic attenuation appears to add little value. Moderate steatosis not only has intrinsically lower pre-contrast liver attenuation values (< 40 HU), but also enhances less, typically resulting in post-contrast liver attenuation values of 80 HU or less.

Moderate steatosis can be reliably diagnosed on post-contrast CT using liver attenuation values alone. Livers with at least moderate steatosis enhance less than those with mild or no steatosis, which combines with the lower intrinsic attenuation to improve detection.

The liver-spleen attenuation difference is frequently utilized in routine practice but appears to have performance limitations. The liver-spleen attenuation difference is less effective than liver attenuation for moderate steatosis. Moderate and severe steatosis can be identified on standard portal venous phase CT using liver attenuation alone.

Background Traditional energy-integrating detector CT has limited utility in accurately quantifying liver fat due to protocol-induced CT value shifts, but this limitation can be addressed by using photon-counting detector (PCD) CT, which allows for a standardized CT value. Purpose To develop and validate a universal CT to MRI fat conversion formula to enhance fat quantification accuracy across various PCD CT protocols relative to MRI proton density fat fraction (PDFF). Materials and Methods In this prospective study, the feasibility of fat quantification was evaluated in phantoms with various nominal fat fractions. Five hundred asymptomatic participants and 157 participants with suspected metabolic dysfunction-associated steatotic liver disease (MASLD) were enrolled between September 2023 and March 2024. Participants were randomly assigned to six groups with different CT protocols regarding tube voltage (90, 120, or 140 kVp) and radiation dose (standard or low). Of the participants in the 120-kVp standard-dose asymptomatic group, 51% (53 of 104) were designated as the training cohort, with the rest of the asymptomatic group serving as the validation cohort. A CT to MRI fat quantification formula was derived from the training cohort to estimate the CT-derived fat fraction (CTFF). CTFF agreement with PDFF and its error were evaluated in the asymptomatic validation cohort and subcohorts stratified by tube voltage, radiation dose, and body mass index, and in the MASLD cohort. The factors influencing CTFF error were further evaluated. Results In the phantoms, CTFF showed excellent agreement with nominal fat fraction (intraclass correlation coefficient, 0.98; mean bias, 0.2%). A total of 412 asymptomatic participants and 122 participants with MASLD were included. A CT to MRI fat conversion formula was derived as follows: MRI PDFF (%) = -0.58 · CT (HU) + 43.1. Across all comparisons, CTFF demonstrated excellent agreement with PDFF (mean bias values < 1%). CTFF error was not influenced by tube voltage, radiation dose, body mass index, or PDFF. Agreement between CTFF and PDFF was also found in the MASLD cohort (mean bias, -0.2%). Conclusion Standardized CT value from PCD CT showed a robust and remarkable agreement with MRI PDFF across various protocols and may serve as a precise alternative for liver fat quantification. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Wildman-Tobriner in this issue.

Metabolic dysfunction-associated fatty liver disease (MAFLD), previously called metabolic nonalcoholic fatty liver disease, is the most prevalent chronic liver disease worldwide. The multi-factorial nature of MAFLD severity is delineated through an intricate composite analysis of the grade of activity in concert with the stage of fibrosis. Despite the preeminence of liver biopsy as the diagnostic and staging reference standard, its invasive nature, pronounced interobserver variability, and potential for deleterious effects (encompassing pain, infection, and even fatality) underscore the need for viable alternatives. We reviewed computed tomography (CT)-based methods for hepatic steatosis quantification (liver-to-spleen ratio; single-energy "quantitative" CT; dual-energy CT; deep learning-based methods; photon-counting CT) and hepatic fibrosis staging (morphology-based CT methods; contrast-enhanced CT biomarkers; dedicated postprocessing methods including liver surface nodularity, liver segmental volume ratio, texture analysis, deep learning methods, and radiomics). For dual-energy and photon-counting CT, the role of virtual non-contrast images and material decomposition is illustrated. For contrast-enhanced CT, normalized iodine concentration and extracellular volume fraction are explained. The applicability and salience of these approaches for clinical diagnosis and quantification of MAFLD are discussed.Relevance statementCT offers a variety of methods for the assessment of metabolic dysfunction-associated fatty liver disease by quantifying steatosis and staging fibrosis.Key points• MAFLD is the most prevalent chronic liver disease worldwide and is rapidly increasing.• Both hardware and software CT advances with high potential for MAFLD assessment have been observed in the last two decades.• Effective estimate of liver steatosis and staging of liver fibrosis can be possible through CT.

Metabolic associated fatty liver disease is a novel concept defined as fatty liver associated with metabolic disorders. We investigated the effect of metabolic associated fatty liver disease on hepatocellular carcinoma patient mortality.

A total of 624 patients with hepatocellular carcinoma between 2012 and 2020 were enrolled in this retrospective study. Hepatic steatosis was diagnosed using computed tomography or magnetic resonance imaging. Metabolic associated fatty liver disease was defined based on the proposed criteria in 2020. Propensity score matching was performed for patients with metabolic associated fatty liver disease and those without the condition. A Cox proportional hazards regression model was used to evaluate the association between metabolic associated fatty liver disease and hepatocellular carcinoma patient outcomes.

Patients with hepatocellular carcinoma and metabolic associated fatty liver disease tended to achieve better outcomes than did those without metabolic associated fatty liver disease after matching (p<0.001). Metabolic associated fatty liver disease was significantly associated with better prognosis in patients with concurrent hepatitis B infection (p<0.001). Moreover, high levels of hepatitis B viral DNA in serum samples was associated with a significantly increased risk of death in patients without non-metabolic associated fatty liver disease (p=0.045). Additionally, the association between metabolic associated fatty liver disease and survival in hepatitis B virus-related hepatocellular carcinoma was similar in all subgroups based on metabolic traits.

Metabolic associated fatty liver disease increases the survival rate of patients with hepatocellular carcinoma and hepatitis B virus infection. The potential interaction of steatosis and virus replication should be considered for future research and clinical treatment strategies.

Demonstration of a very dense or hyper-attenuated liver on the pre-contrast CT images of the abdomen can be an unexpected finding. It may present as a diagnostic challenge if the underlying cause of it is not apparent from the provided clinical history. There are about 12 different pathologic conditions that are associated with deposition of radiopaque elements within the hepatic parenchyma, resulting in diffuse or multi-lobar hyperdense appearance of the liver on abdominal radiographs and CT. Most of them are drug-induced or iatrogenic in nature, while others are the sequelae of genetic disorders like thalassemia, Wilson's disease, and primary hemochromatosis. This pictorial essay will present the CT appearance and etiology of hyper-attenuated liver in various clinical entities.

Early quantitative assessment of liver fat content is essential for patients with fatty liver disease. Mounting evidence has shown that magnetic resonance (MR) technique has high accuracy in the quantitative analysis of fatty liver, and is suitable for monitoring the therapeutic effect on fatty liver. However, many packaging methods and postprocessing functions have puzzled radiologists in clinical applications. Therefore, selecting a quantitative MR imaging technique for patients with fatty liver disease remains challenging.

To provide information for the proper selection of commonly used quantitative MR techniques to quantify fatty liver.

We completed a systematic literature review of quantitative MR techniques for detecting fatty liver, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses protocol. Studies were retrieved from PubMed, Embase, and Cochrane Library databases, and their quality was assessed using the Quality Assessment of Diagnostic Studies criteria. The Reference Citation Analysis database (https:// www.referencecitationanalysis.com) was used to analyze citation of articles which were included in this review.

Forty studies were included for spectroscopy, two-point Dixon imaging, and multiple-point Dixon imaging comparing liver biopsy to other imaging methods. The advantages and disadvantages of each of the three techniques and their clinical diagnostic performances were analyzed.

The proton density fat fraction derived from multiple-point Dixon imaging is a noninvasive method for accurate quantitative measurement of hepatic fat content in the diagnosis and monitoring of fatty liver progression.

Virtual (in silico) imaging trials (VITs), involving computerized phantoms and models of the imaging process, provide a modern alternative to clinical imaging trials. VITs are faster, safer, and enable otherwise-impossible investigations. Current phantoms used in VITs are limited in their ability to model functional behavior such as contrast perfusion which is an important determinant of dose and image quality in CT imaging. In our prior work with the XCAT computational phantoms, we determined and modeled inter-organ (organ to organ) intravenous contrast concentration as a function of time from injection. However, intra-organ concentration, heterogeneous distribution within a given organ, was not pursued. We extend our methods in this work to model intra-organ concentration within the XCAT phantom with a specific focus on the liver.

Intra-organ contrast perfusion depends on the organ's vessel network. We modeled the intricate vascular structures of the liver, informed by empirical and theoretical observations of anatomy and physiology. The developed vessel generation algorithm modeled a dual-input-single-output vascular network as a series of bifurcating surfaces to optimally deliver flow within the bounding surface of a given XCAT liver. Using this network, contrast perfusion was simulated within voxelized versions of the phantom by using knowledge of the blood velocities in each vascular structure, vessel diameters and length, and the time since the contrast entered the hepatic artery. The utility of the enhanced phantom was demonstrated through a simulation study with the phantom voxelized prior to CT simulation with the relevant liver vasculature prepared to represent blood and iodinated contrast media. The spatial extent of the blood-contrast mixture was compared to clinical data.

The vascular structures of the liver were generated with size and orientation which resulted in minimal energy expenditure required to maintain blood flow. Intravenous contrast was simulated as having known concentration and known total volume in the liver as calibrated from time-concentration curves. Measurements of simulated CT ROIs were found to agree with clinically observed values of early arterial phase contrast enhancement of the parenchyma (∼ 5 $ \sim 5$ HU). Similarly, early enhancement in the hepatic artery was found to agree with average clinical enhancement( 180 $(180$ HU).

The computational methods presented here furthered the development of the XCAT phantoms allowing for multi-timepoint contrast perfusion simulations, enabling more anthropomorphic virtual clinical trials intended for optimization of current clinical imaging technologies and applications.

Opportunistic CT screening leverages robust imaging data embedded within abdominal and thoracic scans that are generally unrelated to the specific clinical indication and have heretofore gone largely unused. This incidental imaging information may prove beneficial to patients in terms of wellness, prevention, risk profiling, and presymptomatic detection of relevant disease. The growing interest in CT-based opportunistic screening relates to a confluence of factors: the objective and generalizable nature of CT-based body composition measures, the emergence of fully automated explainable AI solutions, the sheer volume of body CT scans performed, and the increasing emphasis on precision medicine and value-added initiatives. With a systematic approach to body composition and other useful CT markers, initial evidence suggests that their ability to help radiologists assess biologic age and predict future adverse cardiometabolic events rivals even the best available clinical reference standards. Emerging data suggest that standalone "intended" CT screening over an unorganized opportunistic approach may be justified, especially when combined with established cancer screening. This review will discuss the current status of opportunistic CT screening, including specific body composition markers and the various disease processes that may be impacted. The remaining hurdles to widespread clinical adoption include generalization to more diverse patient populations, disparate technical settings, and reimbursement.

Nonalcoholic steatohepatitis (NASH) is a severe form of nonalcoholic fatty liver disease characterized by lobular inflammation and hepatocyte injury and is a key determinant of clinical outcome.¹ Liver biopsy remains the gold standard for diagnosis but is limited by risks of the procedure and interobserver variability. Although magnetic resonance imaging (MRI)-based technology may provide novel means to identify NASH,² there remains a significant need for other modalities to diagnose NASH noninvasively. Glucose transport, an integral tissue process altered in NASH,³ is measurable with ¹⁸F-fluorodeoxyglucose positron emission tomography (FDG PET). Because unenhanced computed tomography (CT) scan can detect hepatic steatosis quite reliably,⁴ and PET combines unenhanced CT for attenuation correction, we hypothesized that measurement of the combination of glucose transport by PET and steatosis by CT could yield a reliable radiologic correlate of NASH.

BACKGROUND. Hepatic attenuation at unenhanced CT is linearly correlated with the MRI proton density fat fraction (PDFF). Liver fat quantification at contrast-enhanced CT is more challenging. OBJECTIVE. The purpose of this article is to evaluate liver steatosis categorization on contrast-enhanced CT using a fully automated deep learning volumetric hepatosplenic segmentation algorithm and unenhanced CT as the reference standard. METHODS. A fully automated volumetric hepatosplenic segmentation algorithm using 3D convolutional neural networks was applied to unenhanced and contrast-enhanced series from a sample of 1204 healthy adults (mean age, 45.2 years; 726 women, 478 men) undergoing CT evaluation for renal donation. The mean volumetric attenuation was computed from all designated liver and spleen voxels. PDFF was estimated from unenhanced CT attenuation and served as the reference standard. Contrast-enhanced attenuations were evaluated for detecting PDFF thresholds of 5% (mild steatosis, 10% and 15% (moderate steatosis); PDFF less than 5% was considered normal. RESULTS. Using unenhanced CT as reference, estimated PDFF was ≥ 5% (mild steatosis), ≥ 10%, and ≥ 15% (moderate steatosis) in 50.1% (n = 603), 12.5% (n = 151) and 4.8% (n = 58) of patients, respectively. ROC AUC values for predicting PDFF thresholds of 5%, 10%, and 15% using contrast-enhanced liver attenuation were 0.669, 0.854, and 0.962, respectively, and using contrast-enhanced liver-spleen attenuation difference were 0.662, 0.866, and 0.986, respectively. A total of 96.8% (90/93) of patients with contrast-enhanced liver attenuation less than 90 HU had steatosis (PDFF ≥ 5%); this threshold of less than 90 HU achieved sensitivity of 75.9% and specificity of 95.7% for moderate steatosis (PDFF ≥ 15%). Liver attenuation less than 100 HU achieved sensitivity of 34.0% and specificity of 94.2% for any steatosis (PDFF ≥ 5%). A total of 93.8% (30/32) of patients with contrast-enhanced liver-spleen attenuation difference 10 HU or less had moderate steatosis (PDFF ≥ 15%); a liver-spleen difference less than 5 HU achieved sensitivity of 91.4% and specificity of 95.0% for moderate steatosis. Liver-spleen difference less than 10 HU achieved sensitivity of 29.5% and specificity of 95.5% for any steatosis (PDFF ≥ 5%). CONCLUSION. Contrast-enhanced volumetric hepatosplenic attenuation derived using a fully automated deep learning CT tool may allow objective categoric assessment of hepatic steatosis. Accuracy was better for moderate than mild steatosis. Further confirmation using different scanning protocols and vendors is warranted. CLINICAL IMPACT. If these results are confirmed in independent patient samples, this automated approach could prove useful for both individualized and population-based steatosis assessment.

Chemical shift-encoded magnetic resonance imaging enables accurate quantification of liver fat content though estimation of proton density fat-fraction (PDFF). Computed tomography (CT) is capable of quantifying fat, based on decreased attenuation with increased fat concentration. Current quantitative fat phantoms do not accurately mimic the CT number of human liver. The purpose of this work was to develop and validate an optimized phantom that simultaneously mimics the MRI and CT signals of fatty liver.

An agar-based phantom containing 12 vials doped with iodinated contrast, and with a granular range of fat fractions was designed and constructed within a novel CT and MR compatible spherical housing design. A four-site, three-vendor validation study was performed. MRI (1.5T and 3T) and CT images were obtained using each vendor's PDFF and CT reconstruction, respectively. An ROI centered in each vial was placed to measure MRI-PDFF (%) and CT number (HU). Mixed-effects model, linear regression, and Bland-Altman analysis were used for statistical analysis.

MRI-PDFF agreed closely with nominal PDFF values across both field strengths and all MRI vendors. A linear relationship (slope = -0.54 ± 0.01%/HU, intercept = 37.15 ± 0.03%) with an R² of 0.999 was observed between MRI-PDFF and CT number, replicating established in vivo signal behavior. Excellent test-retest repeatability across vendors (MRI: mean = -0.04%, 95% limits of agreement = [-0.24%, 0.16%]; CT: mean = 0.16 HU, 95% limits of agreement = [-0.15HU, 0.47HU]) and good reproducibility using GE scanners (MRI: mean = -0.21%, 95% limits of agreement = [-1.47%, 1.06%]; CT: mean = -0.18HU, 95% limits of agreement = [-1.96HU, 1.6HU]) were demonstrated.

The proposed fat phantom successfully mimicked quantitative liver signal for both MRI and CT. The proposed fat phantom in this study may facilitate broader application and harmonization of liver fat quantification techniques using MRI and CT across institutions, vendors and imaging platforms.

Abdominal CT is a frequently performed imaging examination for a wide variety of clinical indications. In addition to the immediate reason for scanning, each CT examination contains robust additional data on body composition that generally go unused in routine clinical practice. There is now growing interest in harnessing this additional information. Prime examples of cardiometabolic information include measurement of bone mineral density for osteoporosis screening, quantification of aortic calcium for assessment of cardiovascular risk, quantification of visceral fat for evaluation of metabolic syndrome, assessment of muscle bulk and density for diagnosis of sarcopenia, and quantification of liver fat for assessment of hepatic steatosis. All of these relevant biometric measures can now be fully automated through the use of artificial intelligence algorithms, which provide rapid and objective assessment and allow large-scale population-based screening. Initial investigations into these measures of body composition have demonstrated promising performance for prediction of future adverse events that matches or exceeds the best available clinical prediction models, particularly when these CT-based measures are used in combination. In this review, the concept of CT-based opportunistic screening is discussed, and an overview of the various automated biomarkers that can be derived from essentially all abdominal CT examinations is provided, drawing heavily on the authors' experience. As radiology transitions from a volume-based to a value-based practice, opportunistic screening represents a promising example of adding value to services that are already provided. If the potentially high added value of these objective CT-based automated measures is ultimately confirmed in subsequent investigations, this opportunistic screening approach could be considered for intentional CT-based screening. ^©RSNA, 2021.

OBJECTIVE. The purpose of this study was to evaluate the utility of laboratory and CT metrics in identifying patients with high-risk nonalcoholic fatty liver disease (NAFLD). MATERIALS AND METHODS. Patients with biopsy-proven NAFLD who underwent CT within 1 year of biopsy were included. Histopathologic review was performed by an experienced gastrointestinal pathologist to determine steatosis, inflammation, and fibrosis. The presence of any lobular inflammation and hepatocyte ballooning was categorized as nonalcoholic steatohepatitis (NASH). Patients with NAFLD and advanced fibrosis (stage F3 or higher) were categorized as having high-risk NAFLD. Aspartate transaminase to platelet ratio index and Fibrosis-4 (FIB-4) laboratory scores were calculated. CT metrics included hepatic attenuation, liver segmental volume ratio (LSVR), splenic volume, liver surface nodularity score, and selected texture features. In addition, two readers subjectively assessed the presence of NASH (present or not present) and fibrosis (stages F0-F4). RESULTS. A total of 186 patients with NAFLD (mean age, 49 years; 74 men and 112 women) were included. Of these, 87 (47%) had NASH and 112 (60%) had moderate to severe steatosis. A total of 51 patients were classified as fibrosis stage F0, 42 as F1, 23 as F2, 37 as F3, and 33 as F4. Additionally, 70 (38%) had advanced fibrosis (stage F3 or F4) and were considered to have high-risk NAFLD. FIB-4 score correlated with fibrosis (ROC AUC of 0.75 for identifying high-risk NAFLD). Of the individual CT parameters, LSVR and splenic volume performed best (AUC of 0.69 for both for detecting high-risk NAFLD). Subjective reader assessment performed best among all parameters (AUCs of 0.78 for reader 1 and 0.79 for reader 2 for detecting high-risk NAFLD). FIB-4 and subjective scores were complementary (combined AUC of 0.82 for detecting high-risk NAFLD). For NASH assessment, FIB-4 performed best (AUC of 0.68), whereas the AUCs were less than 0.60 for all individual CT features and subjective assessments. CONCLUSION. FIB-4 and multiple CT findings can identify patients with high-risk NAFLD (advanced fibrosis or cirrhosis). However, the presence of NASH is elusive on CT.

Metabolic syndrome describes a constellation of reversible cardiometabolic abnormalities associated with cardiovascular risk and diabetes. The present study investigates the use of fully automated abdominal CT-based biometric measures for opportunistic identification of metabolic syndrome in adults without symptoms.

International Diabetes Federation criteria were applied to a cohort of 9223 adults without symptoms who underwent unenhanced abdominal CT. After patients with insufficient clinical data for diagnosis were excluded, the final cohort consisted of 7785 adults (mean age, 57.0 years; 4361 women and 3424 men). Previously validated and fully automated CT-based algorithms for quantifying muscle, visceral and subcutaneous fat, liver fat, and abdominal aortic calcification were applied to this final cohort.

A total of 738 subjects (9.5% of all subjects; mean age, 56.7 years; 372 women and 366 men) met the clinical criteria for metabolic syndrome. Subsequent major cardiovascular events occurred more frequently in the cohort with metabolic syndrome (p < 0.001). Significant differences were observed between the two groups for all CT-based biomarkers (p < 0.001). Univariate L1-level total abdominal fat (area under the ROC curve [AUROC] = 0.909; odds ratio [OR] = 27.2), L3-level skeletal muscle index (AUROC = 0.776; OR = 5.8), and volumetric liver attenuation (AUROC = 0.738; OR = 5.1) performed well when compared with abdominal aortic calcification scoring (AUROC = 0.578; OR = 1.6). An L1-level total abdominal fat threshold of 460.6 cm2 was 80.1% sensitive and 85.4% specific for metabolic syndrome. For women, the AUROC was 0.930 when fat and muscle measures were combined.

Fully automated quantitative tissue measures of fat, muscle, and liver derived from abdominal CT scans can help identify individuals who are at risk for metabolic syndrome. These visceral measures can be opportunistically applied to CT scans obtained for other clinical indications, and they may ultimately provide a more direct and useful definition of metabolic syndrome.

Excessive intracellular accumulation of triglycerides in the liver, or hepatic steatosis, is a highly prevalent condition affecting approximately one billion people worldwide. In the absence of secondary cause, the term nonalcoholic fatty liver disease (NAFLD) is used. Hepatic steatosis may progress into nonalcoholic steatohepatitis, the more aggressive form of NAFLD, associated with hepatic complications such as fibrosis, liver failure and hepatocellular carcinoma. Hepatic steatosis is associated with metabolic syndrome, cardiovascular disease and represents an independent risk factor for type 2 diabetes, cardiovascular disease and malignancy. Percutaneous liver biopsy is the current reference standard for NAFLD assessment; however, it is an invasive procedure associated with complications and suffers from high sampling variability, impractical for clinical routine and drug efficiency studies. Therefore, noninvasive imaging methods are increasingly used for the diagnosis and monitoring of NAFLD. Among the methods quantifying liver fat, chemical-shift-encoded MRI (CSE-MRI)-based proton density fat-fraction (PDFF) has shown the most promise. MRI-PDFF is increasingly accepted as quantitative imaging biomarker of liver fat that is transforming daily clinical practice and influencing the development of new treatments for NAFLD. Furthermore, CT is an important imaging method for detection of incidental steatosis, and the practical advantages of quantitative ultrasound hold great promise for the future. Understanding the disease burden of NAFLD and the role of imaging may initiate important interventions aimed at avoiding the hepatic and extrahepatic complications of NAFLD. This article reviews clinical burden of NAFLD, and the role of noninvasive imaging techniques for quantification of liver fat.

Indole is a microbiota metabolite that exerts anti-inflammatory responses. However, the relevance of indole to human non-alcoholic fatty liver disease (NAFLD) is not clear. It also remains largely unknown whether and how indole acts to protect against NAFLD. The present study sought to examine the association between the circulating levels of indole and liver fat content in human subjects and explore the mechanisms underlying indole actions in mice with diet-induced NAFLD.

In a cohort of 137 subjects, the circulating levels of indole were reversely correlated with body mass index. In addition, the circulating levels of indole in obese subjects were significantly lower than those in lean subjects and were accompanied with increased liver fat content. At the whole-animal level, treatment of high-fat diet (HFD)-fed C57BL/6J mice with indole caused significant decreases in the severity of hepatic steatosis and inflammation. In cultured cells, indole treatment stimulated the expression of 6-phosphofructo-2-kinase/fructose-2,6-biphosphatase 3 (PFKFB3), a master regulatory gene of glycolysis, and suppressed macrophage proinflammatory activation in a PFKFB3-dependent manner. Moreover, myeloid cell-specific PFKFB3 disruption exacerbated the severity of HFD-induced hepatic steatosis and inflammation and blunted the effect of indole on alleviating diet-induced NAFLD phenotype.

Taken together, our results demonstrate that indole is relevant to human NAFLD and capable of alleviating diet-induced NAFLD phenotypes in mice in a myeloid cell PFKFB3-dependent manner. Therefore, indole mimetic and/or macrophage-specific PFKFB3 activation may be the viable preventive and/or therapeutic approaches for inflammation-associated diseases including NAFLD.

Background: Adipose tissue (AT) around and within non-AT organs (i.e., ectopic adiposity) is emerging as a strong risk factor for type 2 diabetes (T2D). Not known is whether major ectopic adiposity depots, such as hepatic, skeletal muscle, and pericardial adiposity (PAT), are associated with T2D independent of visceral adiposity (VAT). More data are particularly needed among high-risk nonobese minority populations, as the race/ethnic gap in T2D risk is greatest among nonobese. Methods: Thus, we measured several ectopic adiposity depots by computed tomography in 718 (mean age = 64 years) African-Caribbean men on the Island of Tobago overall, and stratified by obesity (obese N = 187 and nonobese N = 532). Results: In age, lifestyle risk factors, health status, lipid-lowering medication intake, body mass index and all other adiposity-adjusted regression analyses, and hepatic and skeletal muscle adiposity were associated with T2D among nonobese men only (all P < 0.05), despite no association between VAT and PAT and T2D. Conclusions: Our results support the "ectopic fat syndrome" theory in the pathogenesis of T2D among nonobese African-Caribbean men. Longitudinal studies are needed to clarify the independent role of ectopic adiposity in T2D, and to identify possible biological mechanisms underlying this relationship, particularly in high-risk African ancestry and other nonwhite populations.

Background Although chemical shift-encoded (CSE) MRI proton density fat fraction (PDFF) is the current noninvasive reference standard for liver fat quantification, the liver is more frequently imaged with CT. Purpose To validate quantitative CT measurements of liver fat against the MRI PDFF reference standard. Materials and Methods In this prospective study, 400 healthy participants were recruited between August 2015 and July 2016. Each participant underwent same-day abdominal unenhanced quantitative CT with a calibration phantom and CSE 3.0-T MRI. CSE MRI liver fat measurements were used to calibrate an equation to adjust CT fat measurements and put them on the PDFF measurement scale. CT and PDFF liver fat measurements were plotted as histograms, medians, and interquartile ranges compared; scatterplots and Bland-Altman plots obtained; and Pearson correlation coefficients calculated. Receiver operating characteristic curves including areas under the curve were evaluated for mild (PDFF, 5%) and moderate (PDFF, 14%) steatosis thresholds for both raw and adjusted CT measurements. Sensitivity, specificity, positive predictive value, and negative predictive value were calculated. Results Four hundred volunteers (mean age, 52.6 years ± 15.2; 227 women) were evaluated. MRI PDFF measurements of liver fat ranged between 0% and 28%, with 41.5% (166 of 400) of participants with PDFF greater than 5%. Both raw and adjusted quantitative CT values correlated well with MRI PDFF (r2 = 0.79; P < .001). Bland-Altman analysis of adjusted CT values showed no slope or bias. Both raw and adjusted CT had areas under the receiver operating characteristic curve of 0.87 and 0.99, respectively, to identify participants with mild (PDFF, >5%) and moderate (PDFF, >14%) steatosis, respectively. The sensitivity, specificity, positive predictive value, and negative predictive value for unadjusted CT was 75.9% (126 of 166), 85.0% (199 of 234), 78.3% (126 of 161), and 83.3% (199 of 239), respectively, for PDFF greater than 5%; and 84.8% (28 of 33), 98.4% (361 of 367), 82.4% (28 of 34), and 98.6% (361 of 366), respectively, for PDFF greater than 14%. Results for adjusted CT were mostly identical. Conclusion Quantitative CT liver fat exhibited good correlation and accuracy with proton density fat fraction measured with chemical shift-encoded MRI. © RSNA, 2019 Online supplemental material is available for this article.

Background Nonalcoholic fatty liver disease and its consequences are a growing public health concern requiring cross-sectional imaging for noninvasive diagnosis and quantification of liver fat. Purpose To investigate a deep learning-based automated liver fat quantification tool at nonenhanced CT for establishing the prevalence of steatosis in a large screening cohort. Materials and Methods In this retrospective study, a fully automated liver segmentation algorithm was applied to noncontrast abdominal CT examinations from consecutive asymptomatic adults by using three-dimensional convolutional neural networks, including a subcohort with follow-up scans. Automated volume-based liver attenuation was analyzed, including conversion to CT fat fraction, and compared with manual measurement in a large subset of scans. Results A total of 11 669 CT scans in 9552 adults (mean age ± standard deviation, 57.2 years ± 7.9; 5314 women and 4238 men; median body mass index [BMI], 27.8 kg/m²) were evaluated, including 2117 follow-up scans in 1862 adults (mean age, 59.2 years; 971 women and 891 men; mean interval, 5.5 years). Algorithm failure occurred in seven scans. Mean CT liver attenuation was 55 HU ± 10, corresponding to CT fat fraction of 6.4% (slightly fattier in men than in women [7.4% ± 6.0 vs 5.8% ± 5.7%; P < .001]). Mean liver Hounsfield unit varied little by age (<4 HU difference among all age groups) and only weak correlation was seen with BMI (r² = 0.14). By category, 47.9% (5584 of 11 669) had negligible or no liver fat (CT fat fraction <5%), 42.4% (4948 of 11 669) had mild steatosis (CT fat fraction of 5%-14%), 8.8% (1025 of 11 669) had moderate steatosis (CT fat fraction of 14%-28%), and 1% (112 of 11 669) had severe steatosis (CT fat fraction >28%). Excellent agreement was seen between automated and manual measurements, with a mean difference of 2.7 HU (median, 3 HU) and r² of 0.92. Among the subcohort with longitudinal follow-up, mean change was only -3 HU ± 9, but 43.3% (806 of 1861) of patients changed steatosis category between first and last scans. Conclusion This fully automated CT-based liver fat quantification tool allows for population-based assessment of hepatic steatosis and nonalcoholic fatty liver disease, with objective data that match well with manual measurement. The prevalence of at least mild steatosis was greater than 50% in this asymptomatic screening cohort. © RSNA, 2019.

To evaluate the feasibility of computed tomography (CT) in the assessment of the change in hepatic steatosis (HS) in longitudinal follow-up by employing pathological HS as the reference standard.We retrospectively evaluated 38 living liver donor candidates (27 men and 11 women; mean age, 29.5 years) who underwent liver biopsy twice and had liver CT scans within 1 week of each biopsy. Four readers independently calculated CTL-S index by subtracting spleen attenuation from liver attenuation on non-enhanced CT images. The changes in pathological HS (ΔHS) and CTL-S (ΔCTL-S) between the 1st and 2nd examinations were assessed. The correlation between ΔHS and ΔCTL-S was assessed using the linear regression analysis. Inter-observer measurement error for ΔCTL-S among the 4 readers was assessed using the repeatability coefficient.ΔCTL-S showed a significant correlation with ΔHS in all readers (r = 0.571-0.65, P < .001). The inter-observer measurement error for ΔCTL-S was ±8.9. The ΔCTL-S values beyond the measurement error were associated with a consistent change in HS in 83.3% (13/15) to 100% (15/15), with sensitivities of 47.8 to 79.9% and specificities of 86.7 to 100% for detecting an absolute change of ≥10% in HS among the 4 readers. However, ΔCTL-S values within the measurement error were associated with a consistent change in HS in 43.5% (8/19) to 61.5% (16/26).The change in CTL-S roughly reflects the change in HS during longitudinal follow-up. A small change in CTL-S should not be considered meaningful, while a larger change in CTL-S beyond the measurement error strongly indicates a true change in HS.

To compare the performances of CT indices for diagnosing hepatic steatosis (HS) and to determine and validate the CT index cut-off values.

Three indices were measured on non-enhanced CT images of 4413 living liver donor candidates (2939 men, 1474 women; mean age, 31.4 years): hepatic attenuation (CT_L), hepatic attenuation minus splenic attenuation (CT_L-S), and hepatic attenuation divided by splenic attenuation (CT_L/S). The performances of these CT indices in diagnosing HS, relative to pathologic diagnosis, were compared in the development cohort of 3312 subjects by receiver operating characteristic (ROC) analysis. The cut-off values for diagnosing HS > 33% in the development cohort were determined at 95% specificity and 95% sensitivity using bootstrap ROC analysis, and the diagnostic performance of these cut-off values was validated in the test cohort of 1101 subjects.

CT_L-S showed the highest performance for diagnosing HS ≥ 5% and HS > 33% (areas under the curve (AUCs) = 0.737 and 0.926, respectively), followed by CT_L/S (AUCs = 0.732 and 0.925, respectively) and CT_L (AUCs = 0.707 and 0.880, respectively). For CT scans using 120 kVp, the CT_L-S cut-off values for highly specific (i.e., - 2.1) and highly sensitive (i.e., 7.6) diagnosis of HS > 33% resulted in a specificity of 96.4% with a sensitivity of 64.0% and a sensitivity of 97.3% with a specificity of 54.9%, respectively, in the test cohort.

CT indices using liver and spleen attenuations have higher performance for diagnosing HS than indices using liver attenuation alone. The CT_L-S cut-off values in this study may have utility for diagnosing HS in clinical practice and research.

• CT indices based on both liver attenuation and spleen attenuation (CT_L-Sand CT_L/S) have higher diagnostic performance than CT_Lbased on liver attenuation alone in diagnosing HS using various CT techniques. • The CT index cut-off values determined in this study can be utilized for reliable diagnosis or to rule out subjects with moderate to severe HS in clinical practice and research, including the selection of living liver donors and the development of cohorts with HS or healthy controls.

Hepatic steatosis is a frequently encountered imaging finding that may indicate chronic liver disease, the most common of which is non-alcoholic fatty liver disease. Non-alcoholic fatty liver disease is implicated in the development of systemic diseases and its progressive phenotype, non-alcoholic steatohepatitis, leads to increased liver-specific morbidity and mortality. With the rising obesity epidemic and advent of novel therapeutics aimed at altering metabolism, there is a growing need to quantify and monitor liver steatosis. Imaging methods for assessing steatosis range from simple and qualitative to complex and highly accurate metrics. Ultrasound may be appropriate in some clinical instances as a screening modality to identify the presence of abnormal liver morphology. However, it lacks sufficient specificity and sensitivity to constitute a diagnostic modality for instigating and monitoring therapy. Newer ultrasound techniques such as quantitative ultrasound show promise in turning qualitative assessment of steatosis on conventional ultrasound into quantitative measurements. Conventional unenhanced CT is capable of detecting and quantifying moderate to severe steatosis but is inaccurate at diagnosing mild steatosis and involves the use of radiation. Newer CT techniques, like dual energy CT, show potential in expanding the role of CT in quantifying steatosis. MRI proton-density fat fraction is currently the most accurate and precise imaging biomarker to quantify liver steatosis. As such, proton-density fat fraction is the most appropriate noninvasive end point for steatosis reduction in clinical trials and therapy response assessment.

The purpose of this study was to evaluate the relation between unenhanced CT liver attenuation values and MRI-derived proton density fat fraction (PDFF) for estimation of liver fat content at CT.

A CT-MRI phantom was constructed and imaged containing 12 vials with lipid fractions ranging from 0% to 100%. For the retrospective clinical arm, 221 patients (120 men, 101 women; mean age, 54 years) underwent both unenhanced CT and chemical shift-encoded MRI of the liver between 2007 and 2017. Among these patients, 92 had more than one 120-kV CT scan for comparison. CT attenuation and MRI PDFF were derived with coregistered ROI measurements in the right hepatic lobe. The 120-kV subgroup of CT examinations performed within 1 month of MRI PDFF examinations (n = 72) served as the primary cohort for linear correlation. The effects of different tube voltage settings, time intervals between CT and MRI, and iron overload were assessed. Linear least squares regression analysis was performed.

Phantom results showed excellent linear fit between CT attenuation and MRI PDFF (r² = 0.986). In patients, 120-kV CT performed within 1 month of MRI PDFF exhibited strong linear correlation (r² = 0.828) that closely matched the phantom data, yielding the following clinical CT-MRI conversion formula: MRI PDFF (%) = -0.58 × CT attenuation (HU) + 38.2. Correlation worsened for CT-to-MRI intervals longer than 1 month (r² = 0.565), and this specific relationship did not apply as well to non-120-kV settings (r² = 0.554). For patients with multiple scans, correlation progressively worsened over time. CT-based liver fat content was underestimated in several patients with iron overload.

The linear correlation between unenhanced CT attenuation and MRI PDFF allows quantification of liver fat content by means of unenhanced CT in clinical practice. As expected, correlation worsened with increasing CT-MRI time interval, variable tube voltage settings, and iron overload.

Studies of soft tissue composition using computed tomography (CT) scans are often semi-quantitative and based on Hounsfield units (HU) measurements that have not been calibrated with a quantitative CT (QCT) phantom. We describe a study to establish the water (H₂O) and dipotassium hydrogen phosphate (K₂HPO₄) basis set equivalent densities of fat and fat-free liver tissue. With this information liver fat can be accurately measured from any abdominal CT scan calibrated with a suitable phantom.

Liver fat content was measured by comparing single-energy QCT (SEQCT) HU measurements of the liver with predicted HU values for fat and fat-free liver tissue calculated from their H₂O and K₂HPO₄ equivalent densities and calibration data from a QCT phantom. The equivalent densities of fat were derived from a listing of its constituent fatty acids, and those of fat-free liver tissue from a dual-energy QCT (DEQCT) study performed in 14 healthy Chinese subjects. This information was used to calculate liver fat from abdominal SEQCT scans performed in a further 541 healthy Chinese subjects (mean age 62 years; range, 31-95 years) enrolled in the Prospective Urban Rural Epidemiology (PURE) Study.

The equivalent densities of fat were 941.75 mg/cm³ H₂O and -43.72 mg/cm³ K₂HPO₄, and for fat-free liver tissue 1,040.13 mg/cm³ H₂O and 21.34 mg/cm³ K₂HPO₄. Liver fat in the 14 subjects in the DEQCT study varied from 0-17.9% [median: 4.5%; interquartile range (IQR): 3.0-7.9%]. Liver fat in the 541 PURE study subjects varied from -0.3-29.9% (median: 4.9%; IQR: 3.4-6.9%).

We have established H₂O and K₂HPO₄ equivalent densities for fat and fat-free liver tissue that allow a measurement of liver fat to be obtained from any abdominal CT scan acquired with a QCT phantom. Although radiation dose considerations preclude the routine use of QCT to measure liver fat, the method described here facilitates its measurement in patients having CT scans performed for other purposes. Further studies comparing the results with magnetic resonance (MR) measurements of liver fat are required to validate the method as a useful clinical tool.

The aim of the study was to assess whether hepatic steatosis predicts muscle fat content independent of body mass index (BMI).

Regions of interest were drawn over several trunk muscles and over the right lobe of the liver to obtain the computed tomography (CT) density in 100 subjects with unenhanced CT studies of the abdomen and pelvis. Univariate and multivariate linear regression were used to examine the associations between BMI and hepatic steatosis and between BMI and trunk muscle density.

Body mass index was associated with trunk muscle fat (P < 0.05) and hepatic steatosis (P < 0.05). Computed tomography density of the liver correlated with that of each trunk muscle (P < 0.05). After adjusting for age, sex, and BMI, hepatic steatosis was associated with increased trunk muscle fat content in the psoas only.

The association between muscle fat in most trunk muscles and hepatic steatosis is due to underlying BMI. However, hepatic steatosis predicted psoas muscle fat content independent of BMI (P < 0.05).

This study aimed to assess short- and long-term reproducibility of intrahepatic lipid (IHL) quantification by proton magnetic resonance spectroscopy (H-MRS) and computed tomography (CT).

Sixteen obese subjects underwent H-MRS using a single-voxel point-resolved single-voxel spectroscopy sequence at 3 T and noncontrast single-slice CT of the liver. Measurements were repeated after 6 weeks and 6 months. Clinical parameters (weight, activity, serum lipids) were collected. Short-term (baseline to 6 weeks) and long-term (baseline to 6 months) reproducibility of IHL was assessed by coefficient of variance (CV), SD, and intraclass correlation coefficient (ICC).

Short-term reproducibility and long-term reproducibility of H-MRS were as follows: CV, 5.9% to 18.8%; SD, 0.7 to 1.9; and ICC, 0.998 to 0.995 (95% confidence interval, 0.942-0.999). Short-term reproducibility and long-term reproducibility of CT were as follows: CV, 4.4% to 14.2%; SD, 2.4 to 8.7; and ICC, 0.766 to 0.982 (95% confidence interval, 0.271-0.994). There was no significant change in clinical parameters (P > 0.3).

Proton magnetic resonance spectroscopy and CT are reproducible methods for short- and long-term quantification of IHL content. Our results can guide sample size calculations for interventional and longitudinal studies.

Trends in obesity have continued to increase in the developed world over the past few decades, along with related conditions such as metabolic syndrome, which is strongly associated with this epidemic. Novel and innovative methods to assess relevant obesity-related biomarkers are needed to determine the clinical significance, allow for surveillance and intervene if appropriate. Aggregations of specific types of fat, specifically hepatic and visceral adiposity, are now known to be correlated with these conditions, and there are a variety of imaging techniques to identify and quantify their distributions and provide diagnostic information. These methods are particularly salient for metabolic syndrome, which is related to both hepatic and visceral adiposity but currently not defined by it. Simpler non-specific fat measurements, such as body weight, abdominal circumference and body mass index are more frequently used but lack the ability to characterize fat location. In addition, non-alcoholic fatty liver disease (NAFLD) is a related condition that carries relevance not only for obesity-related diseases but also for the progression of the liver-specific disease, including non-alcoholic steatohepatitis and cirrhosis, albeit at a much lower frequency. Recent CT and MRI techniques have emerged to potentially optimize diagnosing metabolic syndrome and NAFLD through non-invasive quantification of visceral fat and hepatic steatosis with high accuracy. These imaging modalities should aid us in further understanding the relationship of hepatic and visceral fat to the obesity-related conditions such as metabolic syndrome, NAFLD and cardiovascular disease.