Magnetic resonance elastography (MRE) provides detailed maps of tissue stiffness, helping to diagnose various health conditions, but requires the use of expensive clinical MRI scanners. Our approach utilizes compact, cost-effective portable MR sensors that offer bulk characterization of material properties in a region of interest close to the surface (within 1-2 cm). This accessible instrument could enable routine monitoring and prevention of diseases not readily evaluated with conventional tools.

The method was tested on tissue-mimicking phantoms with varying stiffness. The gels were excited with acoustic pulses (one cycle of a sinusoidal waveform) at a fixed distance from the MR sensor. A series of delays between acoustic excitation and MR signal detection allowed time for the pulse to travel to the sensitive region.

The "arrival time" of the shear wave, determined by the time-dependent MR signal response, was used to calculate the shear wave speed. MR measurements of shear wave speed were compared with optical sensor measurements and manufacturer-tabulated values, aligning with expected relative differences between samples.

A portable MR-based transient elastometry technique for measuring tissue elasticity was developed and demonstrated on tissue-mimicking phantoms. Future improvements include using a new portable magnet to investigate depth-dependent changes in elasticity in stratified samples and integrating MR relaxation and diffusion measurements for comprehensive tissue analysis. This approach can complement conventional MRE in applications where a portable, affordable, and localized assessment of tissue stiffness is required.

Quantitative T1 mapping has the potential to replace biopsy for noninvasive diagnosis and quantitative staging of chronic liver disease. Conventional T1 mapping methods are confounded by fat andB 1 + $$ {B}_1^{+} $$ inhomogeneities, resulting in unreliable T1 estimations. Furthermore, these methods trade off spatial resolution and volumetric coverage for shorter acquisitions with only a few images obtained within a breath-hold. This work proposes a novel, volumetric (3D), free-breathing T1 mapping method to account for multiple confounding factors in a single acquisition.

Free-breathing, confounder-corrected T1 mapping was achieved through the combination of non-Cartesian imaging, magnetization preparation, chemical shift encoding, and a variable flip angle acquisition. A subspace-constrained, locally low-rank image reconstruction algorithm was employed for image reconstruction. The accuracy of the proposed method was evaluated through numerical simulations and phantom experiments with a T1/proton density fat fraction phantom at 3.0 T. Further, the feasibility of the proposed method was investigated through contrast-enhanced imaging in healthy volunteers, also at 3.0 T.

The method showed excellent agreement with reference measurements in phantoms across a wide range of T1 values (200 to 1000 ms, slope = 0.998 (95% confidence interval (CI) [0.963 to 1.035]), intercept = 27.1 ms (95% CI [0.4 54.6]), r2 = 0.996), and a high level of repeatability. In vivo imaging studies demonstrated moderate agreement (slope = 1.099 (95% CI [1.067 to 1.132]), intercept = -96.3 ms (95% CI [-82.1 to -110.5]), r2 = 0.981) compared to saturation recovery-based T1 maps.

The proposed method produces whole-liver, confounder-corrected T1 maps through simultaneous estimation of T1, proton density fat fraction, andB 1 + $$ {B}_1^{+} $$ in a single, free-breathing acquisition and has excellent agreement with reference measurements in phantoms.

Fibrosis is the aberrant process of connective tissue deposition from abnormal tissue repair in response to sustained tissue injury caused by hypoxia, infection, or physical damage. It can affect almost all organs in the body causing dysfunction and ultimate organ failure. Tissue fibrosis also plays a vital role in carcinogenesis and cancer progression. The early and accurate diagnosis of organ fibrosis along with adequate surveillance are helpful to implement early disease-modifying interventions, important to reduce mortality and improve quality of life. While extensive research has already been carried out on the topic, a thorough understanding of how this relationship reveals itself using modern imaging techniques has yet to be established. This work outlines the ways in which fibrosis shows up in abdominal organs and has listed the most relevant imaging technologies employed for its detection. New imaging technologies and developments are discussed along with their promising applications in the early detection of organ fibrosis.

Evidence suggests that cardiometabolic index (CMI) has been identified as a novel obesity-related index associated with diabetes, hypertension, and cardiovascular disease. Current evidence suggests that the differences in sex hormones and regional fat distribution in both sexes are directly correlated with metabolic dysfunction-associated fatty liver disease (MAFLD) risk. This study aimed to investigate the diagnostic value of CMI in MAFLD in both sexes.

This retrospective study included 6107 subjects who underwent annual health check-ups from March 2021 to January 2022. CMI was calculated by multiplying the ratio of triglycerides and high-density lipoprotein cholesterol (TG/HDL-C) by waist-to-height ratio (WHtR). Multivariable logistic regression analysis and restricted cubic spline were used to investigate the association of CMI and MAFLD risk. Receiver operating characteristic curve analysis was conducted for the exploration of the diagnostic accuracies of obesity-related indicators. Areas under the curves (AUCs) with 95% CIs were calculated.

Prevalence of MAFLD increased with elevated quartiles of CMI in both sexes. The median (IQR) age was 46.00 (18.00) years. Multivariate logistic regression analyses showed that higher CMI was independently associated with MAFLD, in which every additional standard deviation (SD) of CMI increased the risk of MAFLD (OR=2.72, 95% CI:2.35-3.15 for males; OR=3.26, 95% CI:2.36-4.51 for females). Subjects in the fourth quartile of CMI had the highest odds of MAFLD for males (OR=15.82, 95% CI:11.84-21.14) and females (OR=22.60, 95% CI:9.52-53.65)(all P for trend<0.001). Besides, CMI had a non-linearity association with MAFLD (all P for non-linearity<0.001). Furthermore, CMI exhibited the largest AUC compared to other obesity-related indexes in terms of discriminating MAFLD in males (AUC=0.796, 95% CI:0.782-0.810) and females (AUC=0.853, 95% CI:0.834-0.872).

CMI was a convenient indicator for the screening of MAFLD among Chinese adults. Females with high CMI had a better diagnostic value for MAFLD than males.

Nonalcoholic fatty liver disease (NAFLD) is a common cause of morbidity and mortality. Nonfocal liver biopsy is the historical reference standard for evaluating NAFLD, but it is limited by invasiveness, high cost, and sampling error. Imaging methods are ideally situated to provide quantifiable results and rule out other anatomic diseases of the liver. MRI and US have shown great promise for the noninvasive evaluation of NAFLD. US is particularly well suited to address the population-level problem of NAFLD because it is lower-cost, more available, and more tolerable to a broader range of patients than MRI. Noninvasive US methods to evaluate liver fibrosis are widely available, and US-based tools to evaluate steatosis and inflammation are gaining traction. US techniques including shear-wave elastography, Doppler spectral imaging, attenuation coefficient, hepatorenal index, speed of sound, and backscatter-based estimation have regulatory clearance and are in clinical use. New methods based on channel and radiofrequency data analysis approaches have shown promise but are mostly experimental. This review discusses the advantages and limitations of clinically available and experimental approaches to sonographic liver tissue characterization for NAFLD diagnosis as well as future applications and strategies to overcome current limitations.

Chronic liver disease is a significant public health issue that can lead to considerable morbidity and mortality, imposing an enormous burden on healthcare resources. Understanding the mechanisms underlying chronic liver disease pathogenesis and developing effective treatment strategies are urgently needed. In this regard, the activation of liver resident macrophages, namely Kupffer cells, plays a vital role in liver inflammation and fibrosis. Macrophages display remarkable plasticity and can polarize into different phenotypes according to diverse microenvironmental stimuli. The polarization of macrophages into M1 pro-inflammatory or M2 anti-inflammatory phenotypes is regulated by complex signaling pathways such as the PI3K/Akt pathway. This review focuses on investigating the potential of using plant chemicals targeting the PI3K/Akt pathway for treating chronic liver disease while elucidating the polarization mechanism of macrophages under different microenvironments. Studies have demonstrated that inhibiting M1-type macrophage polarization or promoting M2-type polarization can effectively combat chronic liver diseases such as alcoholic liver disease, non-alcoholic fatty liver disease, and liver fibrosis. The PI3K/Akt pathway acts as a pivotal modulator of macrophage survival, migration, proliferation, and their responses to metabolism and inflammatory signals. Activating the PI3K/Akt pathway induces anti-inflammatory cytokine expression, resulting in the promotion of M2-like phenotype to facilitate tissue repair and resolution of inflammation. Conversely, inhibiting PI3K/Akt signaling could enhance the M1-like phenotype, which exacerbates liver damage. Targeting the PI3K/Akt pathway has tremendous potential as a therapeutic strategy for regulating macrophage polarization and activity to treat chronic liver diseases with plant chemicals, providing new avenues for liver disease treatment.

Chronic liver disease (CLD) is a common source of morbidity and mortality worldwide. Non-alcoholic fatty liver disease (NAFLD) serves as a major cause of CLD with a rising annual prevalence. Additionally, iron overload can be both a cause and effect of CLD with a negative synergistic effect when combined with NAFLD. The development of state-of-the-art multiparametric MR solutions has led to a change in the diagnostic paradigm in CLD, shifting from traditional liver biopsy to innovative non-invasive methods for providing accurate and reliable detection and quantification of the disease burden. Novel imaging biomarkers such as MRI-PDFF for fat, R2 and R2* for iron, and liver stiffness for fibrosis provide important information for diagnosis, surveillance, risk stratification, and treatment. In this article, we provide a concise overview of the MR concepts and techniques involved in the detection and quantification of liver fat, iron, and fibrosis including their relative strengths and limitations and discuss a practical abbreviated MR protocol for clinical use that integrates these three MR biomarkers into a single simplified MR assessment. Multiparametric MR techniques provide accurate and reliable non-invasive detection and quantification of liver fat, iron, and fibrosis. These techniques can be combined in a single abbreviated MR "Triple Screen" assessment to offer a more complete metabolic imaging profile of CLD.

Nonalcoholic fatty liver disease (NAFLD) is the leading cause of chronic liver disease worldwide. Liver biopsy remains the gold standard for diagnosis and staging of disease. There is a clinical need for noninvasive diagnostic tools for risk stratification, follow-up, and monitoring treatment response that are currently lacking, as well as preclinical models that recapitulate the etiology of the human condition. We have characterized the progression of NAFLD in eNOS^-/- mice fed a high fat diet (HFD) using noninvasive Dixon-based magnetic resonance imaging and single voxel STEAM spectroscopy-based protocols to measure liver fat fraction at 3 T. After 8 weeks of diet intervention, eNOS^-/- mice exhibited significant accumulation of intra-abdominal and liver fat compared with control mice. Liver fat fraction measured by ¹ H-MRS in vivo showed a good correlation with the NAFLD activity score measured by histology. Treatment of HFD-fed NOS3^-/- mice with metformin showed significantly reduced liver fat fraction and altered hepatic lipidomic profile compared with untreated mice. Our results show the potential of in vivo liver MRI and ¹ H-MRS to noninvasively diagnose and stage the progression of NAFLD and to monitor treatment response in an eNOS^-/- murine model that represents the classic NAFLD phenotype associated with metabolic syndrome.

Cardiometabolic index (CMI) is a well promising indicator for predicting obesity-related diseases, but its predictive value for metabolic associated fatty liver disease (MAFLD) is unclear. This study aimed to investigate the relationship between CMI and MAFLD and to evaluate the predictive value of CMI for MAFLD.

A total of 943 subjects were enrolled in this cross-sectional study. CMI was calculated by multiplying the ratio of triglycerides and high-density lipoprotein cholesterol (TG/HDL-C) by waist-to-height ratio (WHtR). Multivariate logistic regression analysis was used to systematically evaluate the relationship between CMI and MAFLD. Receiver operating characteristic (ROC) curves were used to assess the predictive power of CMI for MAFLD and to determine the optimal cutoff value. The diagnostic performance of high CMI for MAFLD was validated in 131 subjects with magnetic resonance imaging diagnosis.

Subjects with higher CMI exhibited a significantly increased risk of MAFLD. The odds ratio for a 1-standard-deviation increase in CMI was 3.180 (2.102-4.809) after adjusting for various confounding factors. Further subgroup analysis showed that there were significant additive interactions between CMI and MAFLD risk in gender, age, and BMI (P for interaction < 0.05), and the area under the ROC curve(AUC) of CMI for predicting MAFLD were significantly higher in female, young, and nonobese subgroups than that in male, middle-aged and elderly, and obese subgroups (all P < 0.05). Moreover, among nonobese subjects, the AUC of CMI was significantly higher than that of waist circumference, BMI, TG/HDL-C, and TG (all P < 0.05). The best cutoff values of CMI to diagnose MAFLD in males and females were 0.6085 and 0.4319, respectively, and the accuracy, sensitivity, and specificity of high CMI for diagnosing MAFLD in the validation set were 85.5%, 87.5%, and 80%, respectively.

CMI was strongly and positively associated with the risk of MAFLD and can be a reference predictor for MAFLD. High CMI had excellent diagnostic performance for MALFD, which can enable important clinical value for early identification and screening of MAFLD.

To evaluate performance of 3D magnetic resonance elastography (MRE) using spin-echo echo-planar imaging (seEPI) for assessment of hepatic stiffness compared with 2D gradient-recalled echo (GRE) and 2D seEPI sequences.

Fifty-seven liver MRE examinations including 2D GRE, 2D seEPI, and 3D seEPI sequences were retrospectively evaluated. Elastograms were analyzed by 2 radiologists and polygonal regions of interests (ROIs) were drawn in 2 different fashions: "curated" ROI (avoiding liver edge, major vessels, and areas of wave interferences) and "non-curated" ROI (including largest cross section of liver, to assess the contribution of artifacts). Liver stiffness measurement (LSM) was calculated as the arithmetic mean of individual stiffness values for each technique. For 3D MRE, LSMs were also calculated based on 4 slices ("abbreviated LSM"). Intra-patient variations in LSMs and different methods of ROI placement were assessed by univariate tests. A p-value of < 0.05 was set as a statistically significant difference.

Mean surface areas of the ROIs were 50,723 mm², 12,669 mm², 5814 mm², and 10,642 mm² for 3D MRE, abbreviated 3D MRE, 2D GRE, and 2D seEPI, respectively. 3D LSMs based on curated and non-curated ROIs showed no clinically significant difference, with a mean difference less than 0.1 kPa. Abbreviated 3D LSMs had excellent correlation with 3D LSMs based on all slices (r = 0.9; p < 0.001) and were not significantly different (p = 0.927).

3D MRE allows more reproducible measurements due to its lower susceptibility to artifacts and provides larger areas of parenchyma, enabling a more comprehensive evaluation of the liver.

Background: Histological fibrosis stage is the most important prognostic factor in chronic liver disease. MR elastography (MRE) is the most accurate noninvasive method for detecting and staging liver fibrosis. Although accurate, manual ROI-based MRE analysis is complex, time consuming, requires specialized readers, and prone to methodologic variability and suboptimal inter-reader agreement. Objectives: To develop an automated convolutional neural network (CNN)-based method for liver MRE analysis, evaluate its agreement with manual ROI-based analysis, and assess its discriminative performance for dichotomized fibrosis stages using histology as reference standard. Methods: In this retrospective cross-sectional study, 675 participants who underwent MRE using different MRI systems and field strengths at 28 imaging sites from five multicenter international clinical trials of nonalcoholic steatohepatitis were included for algorithm development and internal testing of agreement between automated CNN- and manual ROI-based analyses. Eighty-one patients (52 women, 29 men; mean age, 54 years) who underwent MRE using a single 3-Tesla system and liver biopsy for clinical purposes at a single institution were included for external testing of agreement and assessment of fibrosis stage discriminative performance. Agreement was evaluated using intra-class correlation coefficients (ICC). 95% CIs were computed using bootstrapping. Discriminative performance of each method for dichotomized histologic fibrosis stage was evaluated by AUC and compared using bootstrapping. Results: Mean CNN- and manual ROI-based stiffness measurements ranged from 3.21 to 3.34 kPa in trial participants and from 3.30 to 3.45 kPa in clinical patients. ICC for CNN- and manual ROI-based measurements was 0.98 (95% CI, 0.978-0.98) in trial participants and 0.99 (95% CI: 0.98-0.99) in clinical patients. AUC for classification of dichotomized fibrosis stage ranged from 0.89-0.93 for CNN- and 0.87-0.93 for manual ROI-based analyses (p=.23-.75). Conclusion: Stiffness measurements using the automated CNN-based method agreed strongly with manual ROIbased analysis across MRI systems and field strengths, with excellent discriminative performance for histology-determined dichotomized fibrosis stages in external testing. Clinical Impact: Given the high incidence of chronic liver disease worldwide, it is important that noninvasive tools to assess fibrosis are applied reliably across different settings. CNN-based analysis is feasible and may reduce reliance on expert image analysts.

Contrast-enhanced MR imaging plays an important role in the evaluation of patients with chronic liver disease, particularly for detection and characterization of liver lesions. The two most commonly used contrast agents for liver MR imaging are extracellular agents (ECAs) and hepatobiliary agents (HBAs). In patients with liver disease, the main advantage of ECA-enhanced MR imaging is its high specificity for the diagnosis of progressed HCCs. Conversely, HBAs have an additional contrast mechanism, which results in high liver-to-lesion contrast and highest sensitivity for lesion detection in the hepatobiliary phase. Emerging data suggest that features depicted on contrast-enhanced MR imaging scans are related to tumor biology and are predictive of patients' prognosis, likely to further expand the role of contrast-enhanced MR imaging in the clinical care of patients with chronic liver disease.