Simultaneous assessment of liver volume and whole liver fat content: a step towards one-stop shop preoperative MRI protocol

Measuring liver fat fraction with complex-based chemical shift MRI: the effect of simplified sampling protocols on accuracy

BACKGROUND

The assessment of liver percentage fat fraction (%FF) using proton density fat fraction sequences is becoming increasingly accessible. Previous studies have tended to use multiple small ROIs that focus on Couinaud segments. In an effort to simplify day-to-day analysis, this study assesses the impact of using larger, elliptical ROIs focused on a single hepatic lobe. Additionally, we assess the impact of sampling fewer transhepatic slices when measuring %FF.

METHODS

Retrospective analysis of prospectively obtained images from 34 volunteers using an IDEAL IQ sequence. Two observers independently measured %FF using three different protocols: freehand whole-liver ROI (fh-ROI), elliptical-ROI on the right lobe (rt-ROI) and elliptical-ROI on the left lobe (lt-ROI).

RESULTS

Inter-observer reliability for all measurements techniques was 'excellent' (Spearman's rank correlation coefficients 0.81-0.98). There was a significant difference (Paired Wilcoxon Test: p < 0.001) between the median %FF obtained using fh-ROI when compared to the rt-ROI method, the maximum mean difference between the two techniques was 2.79% (95% CI). For all sampling methods a Kruskall-Wallis analysis demonstrated no significant difference in mean %FF when the number of slices sampled was reduced from 11 to 1. The mean coefficient of variance increased when more slices were sampled (3 slices = 0.1, 11 slices = 0.17, p < 0.001).

CONCLUSION

Simplified ROIs focused on one hepatic lobe provide %FF measurements that are unlikely to be sufficiently accurate for use in clinical practice. Freehand whole-liver ROIs should be used in preference. A single freehand ROI measurement taken at the level of the hepatic hilum yields a %FF that is representative of the mean whole liver % FF. Multiple slices are needed to measure heterogeneity.

Accuracy of Automated Liver Contouring, Fat Fraction, and R2* Measurement on Gradient Multiecho Magnetic Resonance Images

OBJECTIVE

This study aimed to evaluate the performance of an automated workflow of volumetric liver proton density fat fraction (PDFFvol) and R2* quantification with automated inline liver volume (LV) segmentation.

METHODS

Dual-echo and multiecho Dixon magnetic resonance images were evaluated in 74 consecutive patients (group A, PDFF < 10%; B, PDFF ≥ 10%; C, R2* ≥ 100 s; D, post-hemihepatectomy). The values of PDFFvol and R2*vol measurements across the LV were generated on multiecho images in an automated fashion based on inline liver segmentation on dual-echo images. Similar measurements were performed manually.

RESULTS

Using the inline algorithm, the mis-segmented LV was highest in group D (80%). There were no significant differences between automated and manual measurements of PDFFvol. Automated R2*vol was significantly lower than manual R2*vol in group A (P = 0.004).

CONCLUSIONS

Inline LV segmentation performed well in patients without and with hepatic steatosis. In cases with iron overload and post-hemihepatectomy, extrahepatic areas were erroneously included to a greater extent, with a tendency toward overestimation of PDFFvol.

Liver segmentation: indications, techniques and future directions

OBJECTIVES

Liver 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.

METHODS

Using 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.

RESULTS

Liver 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.

CONCLUSIONS

Liver 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.

Comparison of MRI- and CT-based semiautomated liver segmentation: a validation study

PURPOSE

To compare the repeatability, agreement, and efficiency of MRI- and CT-based semiautomated liver segmentation for the assessment of total and subsegmental liver volume.

METHODS

This retrospective study was conducted in 31 subjects who underwent contemporaneous liver MRI and CT. Total and subsegmental liver volumes were segmented from contrast-enhanced 3D gradient-recalled echo MRI sequences and CT images. Semiautomated segmentation was based on variational interpolation and Laplacian mesh optimization. All segmentations were repeated after 2 weeks. Manual segmentation of CT images using an active contour tool was used as the reference standard. Repeatability and agreement of the methods were evaluated with intra-class correlation coefficients (ICC) and Bland-Altman analysis. Total interaction time was recorded.

RESULTS

Intra-reader ICC were ≥0.987 for MRI and ≥0.995 for CT. Intra-reader repeatability was 30 ± 217 ml (bias ± 1.96 SD) (95% limits of agreement: -187 to 247 ml) for MRI and -10 ± 143 ml (-153 to 133 ml) for CT. Inter-method ICC between semiautomated and manual volumetry were ≥0.995 for MRI and ≥0.986 for CT. Inter-method segmental ICC varied between 0.584 and 0.865 for MRI and between 0.596 and 0.890 for CT. Inter-method agreement was -14 ± 136 ml (-150 to 122 ml) for MRI and 50 ± 226 ml (-176 to 276 ml) for CT. Inter-method segmental agreement ranged from 10 ± 47 ml (-37 to 57 ml) to 2 ± 214 ml (-212 to 216 ml) for MRI and 9 ± 45 ml (-36 to 54 ml) to -46 ± 183 ml (-229 to 137 ml) for CT. Interaction time (mean ± SD) was significantly shorter for MRI-based semiautomated segmentation (7.2 ± 0.1 min, p < 0.001) and for CT-based semiautomated segmentation (6.5 ± 0.2 min, p < 0.001) than for CT-based manual segmentation (14.5 ± 0.4 min).

CONCLUSION

MRI-based semiautomated segmentation provides similar repeatability and agreement to CT-based segmentation for total liver volume.

Liver Segmentation on CT and MR Using Laplacian Mesh Optimization

OBJECTIVE

The purpose of this paper is to describe a semiautomated segmentation method for the liver and evaluate its performance on CT-scan and MR images.

METHODS

First, an approximate 3-D model of the liver is initialized from a few user-generated contours to globally outline the liver shape. The model is then automatically deformed by a Laplacian mesh optimization scheme until it precisely delineates the patient's liver. A correction tool was implemented to allow the user to improve the segmentation until satisfaction.

RESULTS

The proposed method was tested against 30 CT-scans from the SLIVER07 challenge repository and 20 MR studies from the Montreal University Hospital Center, covering a wide spectrum of liver morphologies and pathologies. The average volumetric overlap error was 5.1% for CT and 7.6% for MRI and the average segmentation time was 6 min.

CONCLUSION

The obtained results show that the proposed method is efficient, reliable, and could effectively be used routinely in the clinical setting.

SIGNIFICANCE

The proposed approach can alleviate the cumbersome and tedious process of slice-wise segmentation required for precise hepatic volumetry, virtual surgery, and treatment planning.

Stereological Analysis of Liver Biopsy Histology Sections as a Reference Standard for Validating Non-Invasive Liver Fat Fraction Measurements by MRI

Background and Aims

Validation of non-invasive methods of liver fat quantification requires a reference standard. However, using standard histopathology assessment of liver biopsies is problematical because of poor repeatability. We aimed to assess a stereological method of measuring volumetric liver fat fraction (VLFF) in liver biopsies and to use the method to validate a magnetic resonance imaging method for measurement of VLFF.

Methods

VLFFs were measured in 59 subjects (1) by three independent analysts using a stereological point counting technique combined with the Delesse principle on liver biopsy histological sections and (2) by three independent analysts using the HepaFat-Scan^® technique on magnetic resonance images of the liver. Bland Altman statistics and intraclass correlation (IC) were used to assess the repeatability of each method and the bias between the methods of liver fat fraction measurement.

Results

Inter-analyst repeatability coefficients for the stereology and HepaFat-Scan^® methods were 8.2 (95% CI 7.7–8.8)% and 2.4 (95% CI 2.2–2.5)% VLFF respectively. IC coefficients were 0.86 (95% CI 0.69–0.93) and 0.990 (95% CI 0.985–0.994) respectively. Small biases (≤3.4%) were observable between two pairs of analysts using stereology while no significant biases were observable between any of the three pairs of analysts using HepaFat-Scan^®. A bias of 1.4±0.5% VLFF was observed between the HepaFat-Scan^® method and the stereological method.

Conclusions

Repeatability of the stereological method is superior to the previously reported performance of assessment of hepatic steatosis by histopathologists and is a suitable reference standard for validating non-invasive methods of measurement of VLFF.

Quantification of liver fat: A comprehensive review

Fat accumulation in the liver causes metabolic diseases such as obesity, hypertension, diabetes or dyslipidemia by affecting insulin resistance, and increasing the risk of cardiac complications and cardiovascular disease mortality. Fatty liver diseases are often reversible in their early stage; therefore, there is a recognized need to detect their presence and to assess its severity to recognize fat-related functional abnormalities in the liver. This is crucial in evaluating living liver donors prior to transplantation because fat content in the liver can change liver regeneration in the recipient and donor. There are several methods to diagnose fatty liver, measure the amount of fat, and to classify and stage liver diseases (e.g. hepatic steatosis, steatohepatitis, fibrosis and cirrhosis): biopsy (the gold-standard procedure), clinical (medical physics based) and image analysis (semi or fully automated approaches). Liver biopsy has many drawbacks: it is invasive, inappropriate for monitoring (i.e., repeated evaluation), and assessment of steatosis is somewhat subjective. Qualitative biomarkers are mostly insufficient for accurate detection since fat has to be quantified by a varying threshold to measure disease severity. Therefore, a quantitative biomarker is required for detection of steatosis, accurate measurement of severity of diseases, clinical decision-making, prognosis and longitudinal monitoring of therapy. This study presents a comprehensive review of both clinical and automated image analysis based approaches to quantify liver fat and evaluate fatty liver diseases from different medical imaging modalities.

MRI-determined liver proton density fat fraction, with MRS validation: Comparison of regions of interest sampling methods in patients with type 2 diabetes

PURPOSE

To assess the agreement between published magnetic resonance imaging (MRI)-based regions of interest (ROI) sampling methods using liver mean proton density fat fraction (PDFF) as the reference standard.

MATERIALS AND METHODS

This retrospective, internal review board-approved study was conducted in 35 patients with type 2 diabetes. Liver PDFF was measured by magnetic resonance spectroscopy (MRS) using a stimulated-echo acquisition mode sequence and MRI using a multiecho spoiled gradient-recalled echo sequence at 3.0T. ROI sampling methods reported in the literature were reproduced and liver mean PDFF obtained by whole-liver segmentation was used as the reference standard. Intraclass correlation coefficients (ICCs), Bland-Altman analysis, repeated-measures analysis of variance (ANOVA), and paired t-tests were performed.

RESULTS

ICC between MRS and MRI-PDFF was 0.916. Bland-Altman analysis showed excellent intermethod agreement with a bias of -1.5 ± 2.8%. The repeated-measures ANOVA found no systematic variation of PDFF among the nine liver segments. The correlation between liver mean PDFF and ROI sampling methods was very good to excellent (0.873 to 0.975). Paired t-tests revealed significant differences (P < 0.05) with ROI sampling methods that exclusively or predominantly sampled the right lobe. Significant correlations with mean PDFF were found with sampling methods that included higher number of segments, total area equal or larger than 5 cm(2) , or sampled both lobes (P = 0.001, 0.023, and 0.002, respectively).

CONCLUSION

MRI-PDFF quantification methods should sample each liver segment in both lobes and include a total surface area equal or larger than 5 cm(2) to provide a close estimate of the liver mean PDFF.

Cross-sectional and longitudinal evaluation of liver volume and total liver fat burden in adults with nonalcoholic steatohepatitis

PURPOSE

To explore the cross-sectional and longitudinal relationships between fractional liver fat content, liver volume, and total liver fat burden.

METHODS

In 43 adults with non-alcoholic steatohepatitis participating in a clinical trial, liver volume was estimated by segmentation of magnitude-based low-flip-angle multiecho GRE images. The liver mean proton density fat fraction (PDFF) was calculated. The total liver fat index (TLFI) was estimated as the product of liver mean PDFF and liver volume. Linear regression analyses were performed.

RESULTS

Cross-sectional analyses revealed statistically significant relationships between TLFI and liver mean PDFF (R 2 = 0.740 baseline/0.791 follow-up, P < 0.001 baseline/P < 0.001 follow-up), and between TLFI and liver volume (R 2 = 0.352/0.452, P < 0.001/< 0.001). Longitudinal analyses revealed statistically significant relationships between liver volume change and liver mean PDFF change (R 2 = 0.556, P < 0.001), between TLFI change and liver mean PDFF change (R 2 = 0.920, P < 0.001), and between TLFI change and liver volume change (R 2 = 0.735, P < 0.001).

CONCLUSION

Liver segmentation in combination with MRI-based PDFF estimation may be used to monitor liver volume, liver mean PDFF, and TLFI in a clinical trial.

Comparison between modified Dixon MRI techniques, MR spectroscopic relaxometry, and different histologic quantification methods in the assessment of hepatic steatosis

OBJECTIVES

To compare systematically quantitative MRI, MR spectroscopy (MRS), and different histological methods for liver fat quantification in order to identify possible incongruities.

METHODS

Fifty-nine consecutive patients with liver disorders were examined on a 3 T MRI system. Quantitative MRI was performed using a dual- and a six-echo variant of the modified Dixon (mDixon) sequence, calculating proton density fat fraction (PDFF) maps, in addition to single-voxel MRS. Histological fat quantification included estimation of the percentage of hepatocytes containing fat vesicles as well as semi-automatic quantification (qHisto) using tissue quantification software.

RESULTS

In 33 of 59 patients, the hepatic fat fraction was >5% as determined by MRS (maximum 45%, mean 17%). Dual-echo mDixon yielded systematically lower PDFF values than six-echo mDixon (mean difference 1.0%; P < 0.001). Six-echo mDixon correlated excellently with MRS, qHisto, and the estimated percentage of hepatocytes containing fat vesicles (R = 0.984, 0.967, 0.941, respectively, all P < 0.001). Mean values obtained by the estimated percentage of hepatocytes containing fat were higher by a factor of 2.5 in comparison to qHisto. Six-echo mDixon and MRS showed the best agreement with values obtained by qHisto.

CONCLUSIONS

Six-echo mDixon, MRS, and qHisto provide the most robust and congruent results and are therefore most appropriate for reliable quantification of liver fat.

KEY POINTS

• Six-echo mDixon correlates excellently with MRS, qHisto, and the estimated percentage of fat-containing hepatocytes. • Six-echo mDixon, MRS, and qHisto provide the most robust and congruent results. • Dual-echo mDixon yields systematically lower PDFF values than six-echo mDixon. • The percentage of fat-containing hepatocytes is 2.5-fold higher than fat fraction determined by qHisto. • Performance characteristics and systematic differences of the various methods should be considered.

Liver segmentation in MRI: A fully automatic method based on stochastic partitions

There are few fully automated methods for liver segmentation in magnetic resonance images (MRI) despite the benefits of this type of acquisition in comparison to other radiology techniques such as computed tomography (CT). Motivated by medical requirements, liver segmentation in MRI has been carried out. For this purpose, we present a new method for liver segmentation based on the watershed transform and stochastic partitions. The classical watershed over-segmentation is reduced using a marker-controlled algorithm. To improve accuracy of selected contours, the gradient of the original image is successfully enhanced by applying a new variant of stochastic watershed. Moreover, a final classifier is performed in order to obtain the final liver mask. Optimal parameters of the method are tuned using a training dataset and then they are applied to the rest of studies (17 datasets). The obtained results (a Jaccard coefficient of 0.91 ± 0.02) in comparison to other methods demonstrate that the new variant of stochastic watershed is a robust tool for automatic segmentation of the liver in MRI.

Diagnostic performance of a rapid magnetic resonance imaging method of measuring hepatic steatosis

OBJECTIVES

Hepatic steatosis is associated with an increased risk of developing serious liver disease and other clinical sequelae of the metabolic syndrome. However, visual estimates of steatosis from histological sections of biopsy samples are subjective and reliant on an invasive procedure with associated risks. The aim of this study was to test the ability of a rapid, routinely available, magnetic resonance imaging (MRI) method to diagnose clinically relevant grades of hepatic steatosis in a cohort of patients with diverse liver diseases.

MATERIALS AND METHODS

Fifty-nine patients with a range of liver diseases underwent liver biopsy and MRI. Hepatic steatosis was quantified firstly using an opposed-phase, in-phase gradient echo, single breath-hold MRI methodology and secondly, using liver biopsy with visual estimation by a histopathologist and by computer-assisted morphometric image analysis. The area under the receiver operating characteristic (ROC) curve was used to assess the diagnostic performance of the MRI method against the biopsy observations.

RESULTS

The MRI approach had high sensitivity and specificity at all hepatic steatosis thresholds. Areas under ROC curves were 0.962, 0.993, and 0.972 at thresholds of 5%, 33%, and 66% liver fat, respectively. MRI measurements were strongly associated with visual (r(2) = 0.83) and computer-assisted morphometric (r(2) = 0.84) estimates of hepatic steatosis from histological specimens.

CONCLUSIONS

This MRI approach, using a conventional, rapid, gradient echo method, has high sensitivity and specificity for diagnosing liver fat at all grades of steatosis in a cohort with a range of liver diseases.

Liver fat quantification by dual-echo MR imaging outperforms traditional histopathological analysis

RATIONALE AND OBJECTIVES

The aim of this study was to evaluate the accuracy of dual-echo (DE) magnetic resonance imaging (MRI) with and without fat and water separation for the quantification of liver fat content (LFC) in vitro and in patients undergoing liver surgery, with comparison to histopathologic analysis.

MATERIALS AND METHODS

MRI was performed on a 1.5-T scanner using a three-dimensional DE MRI sequence with automated reconstruction of in-phase (IP) and out-of-phase (OP) and fat-signal-only and water-signal-only images. LFC was estimated by fat fractions from IP and OP images (MRI(IP/OP)) and from Dixon-based fat-only and water-only images (MRI(DIxON)). Seven phantoms containing a titrated mixture of liver and fat from 0% to 50% were examined. Forty-three biopsies in 22 patients undergoing liver surgery were prospectively evaluated by a pathologist by traditional determination of the cell-count fraction and by a computer-based algorithm, the latter serving as the reference standard.

RESULTS

In vitro, both MRI(IP/OP) and MRI(DIxON) were significantly correlated with titrated LFC (r = 0.993, P < .001), with a smaller measurement bias for MRI(IP/OP) (+2.6%) than for MRI(DIxON) (+4.5%). In vivo, both MRI(IP/OP) and MRI(DIxON) from DE MRI were correlated significantly better with computer-based histologic results (P < .001) and showed significantly smaller measurement bias (4.8% vs 21.1%) compared to histologic cell-count fraction (P < .001). Measurement bias was significantly smaller for MRI(IP/OP) than for MRI(DIxON) (P < .001).

CONCLUSIONS

DE MRI allows the accurate quantification of LFC in a surgical population, outperforming traditional histopathologic analysis. DE MRI without fat and water separation shows the highest accuracy and smallest measurement bias for the quantification of LFC.

Two- versus three-dimensional dual gradient-echo MRI of the liver: a technical comparison

OBJECTIVE

To compare 2D spoiled dual gradient-echo (SPGR-DE) and 3D SPGR-DE with fat and water separation for the assessment of focal and diffuse fatty infiltration of the liver.

METHODS

A total of 227 consecutive patients (141 men; 56 ± 14 years) underwent clinically indicated liver MRI at 1.5 T including multiple-breath-hold 2D SPGR-DE and single-breath-hold 3D SPGR-DE with automatic reconstruction of fat-only images. Two readers assessed the image quality and number of fat-containing liver lesions on 2D and 3D in- and opposed-phase (IP/OP) images. Liver fat content (LFC) was quantified in 138 patients without chronic liver disease from 2D, 3D IP/OP, and 3D fat-only images.

RESULTS

Mean durations of 3D and 2D SPGR-DE acquisitions were 23.7 ± 2.9 and 97.2 ± 9.1 s respectively. The quality of all 2D and 3D images was rated diagnostically. Three-dimensional SPGR-DE revealed significantly more breathing artefacts resulting in lower image quality (P < 0.001); 2D and 3D IP/OP showed a similar detection rate of fat-containing lesions (P = 0.334) and similar LFC estimations (mean: +0.4 %; P = 0.048). LFC estimations based on 3D fat-only images showed significantly higher values (mean: 2.7 % + 3.5 %) than those from 2D and 3D IP/OP images (P < 0.001).

CONCLUSION

Three dimensional SPGR-DE performs as well as 2D SPGR-DE for the assessment of focal and diffuse fatty infiltration of liver parenchyma. The 3D SPGR-DE sequence used was quicker but more susceptible to breathing artefacts. Significantly higher LFC values are derived from 3D fat-only images than from 2D or 3D IP/OP images.

Omega-3 supplementation and non-alcoholic fatty liver disease: a systematic review and meta-analysis

Non-alcoholic fatty liver disease (NAFLD) is a frequent accompaniment of obesity and insulin resistance. With the prevalence approaching 85% in obese populations, new therapeutic approaches to manage NAFLD are warranted. A systematic search of the literature was conducted for studies pertaining to the effect of omega-3 polyunsaturated fatty acid (PUFA) supplementation on NAFLD in humans. Primary outcome measures were liver fat and liver function tests: alanine aminotransferase (ALT) and aspartate aminotransferase [1]. Data were pooled and meta-analyses conducted using a random effects model. Nine eligible studies, involving 355 individuals given either omega-3 PUFA or control treatment were included. Beneficial changes in liver fat favoured PUFA treatment (effect size=-0.97, 95% CI: -0.58 to -1.35, p<0.001). A benefit of PUFA vs. control was also observed for AST (effect size=-0.97, 95% CI: -0.13 to -1.82, p=0.02). There was a trend towards favouring PUFA treatment on ALT but this was not significant (effect size=-0.56, 95% CI: -1.16 to 0.03, p=0.06). Sub-analyses of only randomised control trials (RCTs) showed a significant benefit for PUFA vs. control on liver fat (effect size=-0.96, 95% CI: -0.43 to -1.48, p<0.001), but not for ALT (p=0.74) or AST (p=0.28). There was significant heterogeneity between studies. The pooled data suggest that omega-3 PUFA supplementation may decrease liver fat, however, the optimal dose is currently not known. Well designed RCTs which quantify the magnitude of effect of omega-3 PUFA supplementation on liver fat are needed.