Diagnostic potential of T1ρ and T2 relaxations in assessing the severity of liver fibrosis and necro-inflammation

Simultaneous whole-liver water T 1 $$ {\mathrm{T}}_1 $$ and T 2 $$ {\mathrm{T}}_2 $$ mapping with isotropic resolution during free-breathing

PURPOSE

To develop and validate a data acquisition scheme combined with a motion-resolved reconstruction and dictionary-matching-based parameter estimation to enable free-breathing isotropic resolution self-navigated whole-liver simultaneous water-specificT 1 $$ {\mathrm{T}}_1 $$ (wT 1 $$ {\mathrm{wT}}_1 $$ ) andT 2 $$ {\mathrm{T}}_2 $$ (wT 2 $$ {\mathrm{wT}}_2 $$ ) mapping for the characterization of diffuse and oncological liver diseases.

METHODS

The proposed data acquisition consists of a magnetization preparation pulse and a two-echo gradient echo readout with a radial stack-of-stars trajectory, repeated with different preparations to achieve differentT 1 $$ {\mathrm{T}}_1 $$ andT 2 $$ {\mathrm{T}}_2 $$ contrasts in a fixed acquisition time of 6 min. Regularized reconstruction was performed using self-navigation to account for motion during the free-breathing acquisition, followed by water-fat separation. Bloch simulations of the sequence were applied to optimize the sequence timing forB 1 $$ {B}_1 $$ insensitivity at 3 T, to correct for relaxation-induced blurring, and to mapT 1 $$ {\mathrm{T}}_1 $$ andT 2 $$ {\mathrm{T}}_2 $$ using a dictionary. The proposed method was validated on a water-fat phantom with varying relaxation properties and in 10 volunteers against imaging and spectroscopy reference values. The performance and robustness of the proposed method were evaluated in five patients with abdominal pathologies.

RESULTS

Simulations demonstrate goodB 1 $$ {B}_1 $$ insensitivity of the proposed method in measuringT 1 $$ {\mathrm{T}}_1 $$ andT 2 $$ {\mathrm{T}}_2 $$ values. The proposed method produces co-registeredwT 1 $$ {\mathrm{wT}}_1 $$ andwT 2 $$ {\mathrm{wT}}_2 $$ maps with a good agreement with reference methods (phantom:wT 1 = 1 . 02 wT 1,ref - 8 . 93 ms , R 2 = 0 . 991 $$ {\mathrm{wT}}_1=1.02\kern0.1em {\mathrm{wT}}_{1,\mathrm{ref}}-8.93\kern0.1em \mathrm{ms},{R}^2=0.991 $$ ;wT 2 = 1 . 03 wT 2,ref + 0 . 73 ms , R 2 = 0 . 995 $$ {\mathrm{wT}}_2=1.03\kern0.1em {\mathrm{wT}}_{2,\mathrm{ref}}+0.73\kern0.1em \mathrm{ms},{R}^2=0.995 $$ ). The proposedwT 1 $$ {\mathrm{wT}}_1 $$ andwT 2 $$ {\mathrm{wT}}_2 $$ mapping exhibits good repeatability and can be robustly performed in patients with pathologies.

CONCLUSIONS

The proposed method allows whole-liverwT 1 $$ {\mathrm{wT}}_1 $$ andwT 2 $$ {\mathrm{wT}}_2 $$ quantification with high accuracy at isotropic resolution in a fixed acquisition time during free-breathing.

The application of B1 inhomogeneity-corrected variable flip angle T1 mapping for assessing liver fibrosis

PURPOSE

The aim of this study was to evaluate the diagnostic accuracy of the B1 inhomogeneity-corrected variable flip angle (VFA) method using native T1 values in the staging of liver fibrosis.

METHODS

Eighty-three patients who presented for liver biopsy due to varying degrees of liver damage, underwent MR examinations and had T1-mapping images of the liver acquired using the B1 inhomogeneity-corrected VFA VIBE method. Among them, 65 patients underwent Fibroscan, and their results were used to evaluate the elasticity of liver tissue. Additionally, T1-mapping images were collected from 19 normal control patients. Independent sample t-tests were used to analyze the correlation between T1 mapping and Fibroscan. The diagnostic efficacy of T1 mapping in patients with different stages of liver fibrosis was evaluated using receiver operating characteristic (ROC) curves.

RESULTS

The consistency between different observer groups was intraclass correlation coefficient (ICC) =0.802. T1 mapping demonstrated significant differences between mid-stage liver fibrosis (S = 2) and late-stage liver fibrosis (S = 3), as well as moderate inflammation (G = 2) and severe inflammation (G = 3), P < 0.05. The Area Under Curve(AUC) values of T1 mapping for early liver fibrosis (S ≥ 1), significant liver fibrosis (S ≥ 2), advanced liver fibrosis (S ≥ 3), and end-stage liver fibrosis (S = 4) were 0.760, 0.709, 0.790, and 0.768, respectively. T1 mapping combined with Fibroscan had an AUC value of 0.860.

CONCLUSIONS

The B1 inhomogeneity-corrected VFA T1 mapping may be useful for the staging of liver fibrosis. It has a superior diagnostic efficiency for diagnosing advanced fibrosis (≥S3), while native T1 values combined with Fibroscan have potential value for the staging of liver fibrosis.

Combining T1rho and advanced diffusion MRI for noninvasively staging liver fibrosis: an experimental study in rats

PURPOSE

To investigate the value of imaging parameters derived from T1 relaxation times in the rotating frame (T1ρ or T1rho), diffusion kurtosis imaging (DKI) and intravoxel incoherent motion (IVIM) in assessment of liver fibrosis in rats and propose an optimal diagnostic model based on multiparametric MRI.

METHODS

Thirty rats were divided into one control group and four fibrosis experimental groups (n = 6 for each group). Liver fibrosis was induced by administering thioacetamide (TAA) for 2, 4, 6, and 8 weeks. T1ρ, mean kurtosis (MK), mean diffusivity (MD), perfusion fraction (f), true diffusion coefficient (D), and pseudo-diffusion coefficient (D*) were measured and compared among different fibrosis stages. An optimal diagnostic model was established and the diagnostic efficiency was evaluated by receiver operating characteristic (ROC) curve analysis.

RESULTS

The mean AUC values, sensitivity, and specificity of T1ρ and MD derived from DKI across all liver fibrosis stages were comparable but much higher than those of other imaging parameters (0.954, 92.46, 91.85 for T1ρ; 0.949, 92.52, 91.24 for MD). The model combining T1ρ and MD exhibited better diagnostic performance with higher AUC values than any individual method for staging liver fibrosis (≥ F1: 1.000 (0.884-1.000); ≥ F2: 0.935 (0.782-0.992); ≥ F3: 0.982 (0.852-1.000); F4: 0.986 (0.859-1.000)).

CONCLUSION

Among the evaluated imaging parameters, T1ρ and MD were superior for differentiating varying liver fibrosis stages. The model combining T1ρ and MD was promising to be a credible diagnostic biomarker to detect and accurately stage liver fibrosis.

An uncertainty aided framework for learning based liverT1ρmapping and analysis

Objective. QuantitativeT1ρimaging has potential for assessment of biochemical alterations of liver pathologies. Deep learning methods have been employed to accelerate quantitativeT1ρimaging. To employ artificial intelligence-based quantitative imaging methods in complicated clinical environment, it is valuable to estimate the uncertainty of the predicatedT1ρvalues to provide the confidence level of the quantification results. The uncertainty should also be utilized to aid the post-hoc quantitative analysis and model learning tasks.Approach. To address this need, we propose a parametric map refinement approach for learning-basedT1ρmapping and train the model in a probabilistic way to model the uncertainty. We also propose to utilize the uncertainty map to spatially weight the training of an improvedT1ρmapping network to further improve the mapping performance and to remove pixels with unreliableT1ρvalues in the region of interest. The framework was tested on a dataset of 51 patients with different liver fibrosis stages.Main results. Our results indicate that the learning-based map refinement method leads to a relative mapping error of less than 3% and provides uncertainty estimation simultaneously. The estimated uncertainty reflects the actual error level, and it can be used to further reduce relativeT1ρmapping error to 2.60% as well as removing unreliable pixels in the region of interest effectively.Significance. Our studies demonstrate the proposed approach has potential to provide a learning-based quantitative MRI system for trustworthyT1ρmapping of the liver.

Uncertainty-weighted Multi-tasking for T1ρ and T2 Mapping in the Liver with Self-supervised Learning

Multi-parametric mapping of MRI relaxations in liver has the potential of revealing pathological information of the liver. A self-supervised learning based multi-parametric mapping method is proposed to map T1ρ and T2 simultaneously, by utilising the relaxation constraint in the learning process. Data noise of different mapping tasks is utilised to make the model uncertainty-aware, which adaptively weight different mapping tasks during learning. The method was examined on a dataset of 51 patients with non-alcoholic fatter liver disease. Results showed that the proposed method can produce comparable parametric maps to the traditional multi-contrast pixel wise fitting method, with a reduced number of images and less computation time. The uncertainty weighting also improves the model performance. It has the potential of accelerating MRI quantitative imaging.

Conventional and artificial intelligence-based computed tomography and magnetic resonance imaging quantitative techniques for non-invasive liver fibrosis staging

Chronic liver disease (CLD) ultimately develops into liver fibrosis and cirrhosis and is a major public health problem globally. The assessment of liver fibrosis is important for patients with CLD for prognostication, treatment decisions, and surveillance. Liver biopsies are traditionally performed to determine the stage of liver fibrosis. However, the risks of complications and technical limitations restrict their application to screening and sequential monitoring in clinical practice. CT and MRI are essential for evaluating cirrhosis-associated complications in patients with CLD, and several non-invasive methods based on them have been proposed. Artificial intelligence (AI) techniques have also been applied to stage liver fibrosis. This review aimed to explore the values of conventional and AI-based CT and MRI quantitative techniques for non-invasive liver fibrosis staging and summarized their diagnostic performance, advantages, and limitations.

Detecting Early-Stage Liver Fibrosis Using Macromolecular Proton Fraction Mapping Based on Spin-Lock MRI: Preliminary Observations

BACKGROUND

Liver fibrosis is characterized by macromolecule depositions. Recently, a novel technology termed macromolecular proton fraction quantification based on spin-lock magnetic resonance imaging (MPF-SL) is reported to measure macromolecule levels.

HYPOTHESIS

MPF-SL can detect early-stage liver fibrosis by measuring macromolecule levels in the liver.

STUDY TYPE

Retrospective.

SUBJECTS

Fifty-five participants, including 22 with no fibrosis (F0) and 33 with early-stage fibrosis (F1-2), were recruited.

FIELD STRENGTH/SEQUENCE

3 T; two-dimensional (2D) MPF-SL turbo spin-echo sequence, 2D spin-lock T1rho turbo spin-echo sequence, and multi-slice 2D gradient echo sequence.

ASSESSMENT

Macromolecular proton fraction (MPF), T1rho, liver iron concentration (LIC), and fat fraction (FF) biomarkers were quantified within regions of interest.

STATISTICAL TESTS

Group comparison of the biomarkers using Mann-Whitney U tests; correlation between the biomarkers assessed using Spearman's rank correlation coefficient and linear regression with goodness-of-fit; fibrosis stage differentiation using receiver operating characteristic curve (ROC) analysis. P-value < 0.05 was considered statistically significant.

RESULTS

Average T1rho was 41.76 ± 2.94 msec for F0 and 41.15 ± 3.73 msec for F1-2 (P = 0.60). T1rho showed nonsignificant correlation with either liver fibrosis (ρ = -0.07; P = 0.61) or FF (ρ = -0.14; P = 0.35) but indicated a negative correlation with LIC (ρ = -0.66). MPF was 4.73 ± 0.45% and 5.65 ± 0.81% for F0 and F1-2 participants, respectively. MPF showed a positive correlation with liver fibrosis (ρ = 0.59), and no significant correlations with LIC (ρ = 0.02; P = 0.89) or FF (ρ = 0.05; P = 0.72). The area under the ROC curve was 0.85 (95% confidence interval [CI] 0.75-0.95) and 0.55 (95% CI 0.39-0.71; P = 0.55) for MPF and T1rho to discriminate between F0 and F1-2 fibrosis, respectively.

DATA CONCLUSION

MPF-SL has the potential to diagnose early-stage liver fibrosis and does not appear to be confounded by either LIC or FF.

LEVEL OF EVIDENCE

3 TECHNICAL EFFICACY STAGE: 3.

Uncertainty-aware self-supervised neural network for liverT1ρmapping with relaxation constraint

Objective.T1ρmapping is a promising quantitative MRI technique for the non-invasive assessment of tissue properties. Learning-based approaches can mapT1ρfrom a reduced number ofT1ρweighted images but requires significant amounts of high-quality training data. Moreover, existing methods do not provide the confidence level of theT1ρestimation. We aim to develop a learning-based liverT1ρmapping approach that can mapT1ρwith a reduced number of images and provide uncertainty estimation.Approach. We proposed a self-supervised neural network that learns aT1ρmapping using the relaxation constraint in the learning process. Epistemic uncertainty and aleatoric uncertainty are modelled for theT1ρquantification network to provide a Bayesian confidence estimation of theT1ρmapping. The uncertainty estimation can also regularize the model to prevent it from learning imperfect data. Main results. We conducted experiments onT1ρdata collected from 52 patients with non-alcoholic fatty liver disease. The results showed that when only collecting twoT1ρ-weighted images, our method outperformed the existing methods forT1ρquantification of the liver. Our uncertainty estimation can further regularize the model to improve the performance of the model and it is consistent with the confidence level of liverT1ρvalues.Significance. Our method demonstrates the potential for accelerating theT1ρmapping of the liver by using a reduced number of images. It simultaneously provides uncertainty ofT1ρquantification which is desirable in clinical applications.

Effect of Iron Deposition on Native T1 Mapping and Blood Oxygen Level Dependent for the Assessment of Liver Fibrosis in Rabbits With Carbon Tetrachloride Intoxication

RATIONALE AND OBJECTIVES

This study aimed to explore the effect of iron deposition on native T1 mapping and blood oxygen level-dependent (BOLD) imaging in detecting liver fibrosis (LF) in a rabbit model.

MATERIALS AND METHODS

An LF group (n = 100) was established by subcutaneously injecting 50% carbon tetrachloride (CCl₄) oil solution, and 20 normal rabbits composed a control group. Native T1 mapping and BOLD were performed, and the T1_native and R₂* quantitative parameters were analyzed by receiver operating characteristic (ROC) and multiple logistic regression analyses, with histopathological results and liver iron content (LIC) serving as reference standards.

RESULTS

In total, 18, 17, 16, 18, and 15 rabbits were histopathologically diagnosed with LF stages F0, F1, F2, F3, and F4, respectively. T1_native (r = 0.47), R₂* (r = 0.75) and LIC (r = 0.61) increased with LF stage progression (p < 0.001). Compared to T1_native values, R₂* performed better in diagnosing the LF stage, especially for distinguishing F1-F2 from F3-F4 (AUC = 0.66 vs. 0.91, p = 0.01). Combined with the LIC, both T1_native and R₂* showed improved diagnostic value in comparison to the individual imaging techniques, particularly for diagnosing F0 vs. F1-F2 and F0 vs. F1-F4, with AUC values of 0.90 vs. 0.70 (p = 0.01) and 0.93 vs. 0.77 (p = 0.01) for T1_native + LIC vs. LIC, respectively.

CONCLUSION

BOLD imaging performed better than native T1 mapping in predicting and diagnosing LF stage progression. The decrease in diagnostic accuracy caused by the deposition of liver iron is a potential pitfall in the assessment of LF with BOLD imaging and native T1 mapping.

Characterization and correction of the effects of hepatic iron on T1ρ relaxation in the liver at 3.0T

PURPOSE

Quantitative T_1ρ imaging is an emerging technique to assess the biochemical properties of tissues. In this paper, we report our observation that liver iron content (LIC) affects T_1ρ quantification of the liver at 3.0T field strength and develop a method to correct the effect of LIC.

THEORY AND METHODS

On-resonance R_1ρ (1/T_1ρ ) is mainly affected by the intrinsic R₂ (1/T₂ ), which is influenced by LIC. As on-resonance R_1ρ is closely related to the Carr-Purcell-Meiboom-Gill (CPMG) R₂ , and because the calibration between CPMG R₂ and LIC has been reported at 1.5T, a correction method was proposed to correct the R₂ contribution to the R_1ρ . The correction coefficient was obtained from the calibration results and related transformed factors. To compensate for the difference between CPMG R₂ and R_1ρ , a scaling factor was determined using the values of CPMG R₂ and R_1ρ , obtained simultaneously from a single breath-hold from volunteers. The livers of 110 subjects were scanned to validate the correction method.

RESULTS

LIC was significantly correlated with R_1ρ in the liver. However, when the proposed correction method was applied to R_1ρ , LIC and the iron-corrected R_1ρ were not significantly correlated.

CONCLUSION

LIC can affect T_1ρ in the liver. We developed an iron-correction method for the quantification of T_1ρ in the liver at 3.0T.