The purpose was to evaluate the ability of three magnetic resonance (MR) techniques to detect liver steatosis and to determine which noninvasive technique (MR, bioassays) or combination of techniques is optimal for the quantification of hepatic fat using histopathology as a reference. Twenty patients with histopathologically proven steatosis and 24 control subjects underwent single-voxel proton MR spectroscopy (MRS; 3 voxels), dual-echo in phase/out of phase MR imaging (DEI) and diffusion-weighted MR imaging (DWI) examinations of the liver. Blood or urine bioassays were also performed for steatosis patients. Both MRS and DEI data allowed to detect steatosis with a high sensitivity (0.95 for MRS; 1 for DEI) and specificity (1 for MRS; 0.875 for DEI) but not DWI. Strong correlations were found between fat fraction (FF) measured by MRS, DEI and histopathology segmentation as well as with low density lipoprotein (LDL) and cholesterol concentrations. A Bland-Altman analysis showed a good agreement between the FF measured by MRS and DEI. Partial correlation analyses failed to improve the correlation with segmentation FF when MRS or DEI data were combined with bioassay results. Therefore, FF from MRS or DEI appear to be the best parameters to both detect steatosis and accurately quantify fat liver noninvasively.

ObjectivesLiver volumetry has emerged as an important tool in clinical practice. Liver volume is assessed primarily via organ segmentation of computed tomography (CT) and magnetic resonance imaging (MRI) images. The goal of this paper is to provide an accessible overview of liver segmentation targeted at radiologists and other healthcare professionals.MethodsUsing images from CT and MRI, this paper reviews the indications for liver segmentation, technical approaches used in segmentation software and the developing roles of liver segmentation in clinical practice.ResultsLiver segmentation for volumetric assessment is indicated prior to major hepatectomy, portal vein embolisation, associating liver partition and portal vein ligation for staged hepatectomy (ALPPS) and transplant. Segmentation software can be categorised according to amount of user input involved: manual, semi-automated and fully automated. Manual segmentation is considered the “gold standard” in clinical practice and research, but is tedious and time-consuming. Increasingly automated segmentation approaches are more robust, but may suffer from certain segmentation pitfalls. Emerging applications of segmentation include surgical planning and integration with MRI-based biomarkers.ConclusionsLiver segmentation has multiple clinical applications and is expanding in scope. Clinicians can employ semi-automated or fully automated segmentation options to more efficiently integrate volumetry into clinical practice.Teaching points• Liver volume is assessed via organ segmentation on CT and MRI examinations. • Liver segmentation is used for volume assessment prior to major hepatic procedures. • Segmentation approaches may be categorised according to the amount of user input involved. • Emerging applications include surgical planning and integration with MRI-based biomarkers. Electronic supplementary materialThe online version of this article (doi:10.1007/s13244-017-0558-1) contains supplementary material, which is available to authorised users.

ObjectiveFatty liver deposition is a very common finding, but it has many atypical patterns of distribution that can represent diagnostic pitfalls. The purpose of this pictorial essay is to review different patterns of fatty liver deposition and sparing.MethodsWe searched our archive retrospectively, reviewed the literature, and identified six patterns of liver steatosis.ResultsSteatosis may be diffuse, geographic, focal, subcapsular, multifocal or perivascular.ConclusionsPrevious knowledge of atypical patterns of steatosis distribution may prevent misdiagnosis of infiltrative disease or focal liver lesions. When an unusual form of fatty liver deposition is suspected on ultrasound or computed tomography, magnetic resonance imaging may be used to confirm the diagnosis.

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The rates of growth of medically treated abdominal aortic aneurysms (AAA) are difficult to determine, and relationships with parietal inflammation and with metabolic parameters from 18 F-FDG PET remain unclear. This 18 F-FDG PET sequential observational study was aimed at analyzing the metabolic changes accompanying the growth phases of medically treated AAA. Methods: Thirty-nine patients (37 men; age [mean ± SD], 71 ± 12 y) exhibiting small and medically treated AAA (maximal diameter, 46 ± 3 mm) underwent 18 F-FDG PET and CT angiography at baseline and 9 mo later. Clinical and imaging parameter correlates of the 9-mo increase in maximal diameter were investigated; these included 18 F-FDG maximal standardized uptake values (SUV max ) averaged for slices encompassing the AAA volume. Results: Of the 39 patients, 9 (23%) had a significant ($2.5 mm) increase in maximal diameter at 9 mo, whereas the remaining 30 did not. The patients with an increase in maximal diameter at 9 mo exhibited lower SUV max within the AAA at baseline than patients who did not have such an increase (1.80 ± 0.45 vs. 2.21 ± 0.52; P 5 0.04); they also displayed a trend toward greater changes in SUV max at 9 mo (difference between 9 mo and baseline: 10.40 ± 0.85 vs. −0.06 ± 0.57; P 5 0.07). Similar levels were ultimately reached in both groups at 9 mo (2.20 ± 0.83 and 2.15 ± 0.66). SUV max was a significant, yet modest, baseline predictor of the absolute change in maximal diameter during follow-up (P 5 0.049). Conclusion: The enhancement in the maximal diameter of small AAA was preceded by a stage with a low level of 18 F-FDG uptake, but this low level of uptake was no longer documented after the growth phases, suggesting a pattern of cyclic metabolic changes.

The aim of this study is to interactively assess reendothelialization of stents at an accuracy of down to a few micrometer by analyzing endovascular optical coherence tomography (OCT) sequences. Vessel wall and stent struts are automatically detected by using morphological, gradient, and symmetry operators coupled with active contour models; alerts are issued to ask for user supervision over some extreme irregular geometries caused by thrombotic lesions or dissections. A complete distance map is then computed from sparse distances measured between wall and struts. Missing values are interpolated by thin-plate spline (TPS) functions. Accuracy and robustness are increased by taking into account the inhomogeneity of data points and integrating in the same framework orthogonalized forward selection of support points, optimal selection of regularization parameters by generalized cross-validation, and rejection of detection outliers. Validation is performed on simulated data, phantom acquisitions and 11 typical in vivo OCT sequences. The comparison against manual expert measurements demonstrates a bias of the order of OCT resolution (less than 10 microm) and a standard deviation of the order of the strut width (less than 150 microm).