Deep learning-assisted preclinical MR fingerprinting for sub-millimeter T1 and T2 mapping of entire macaque brain

PURPOSE

Preclinical MR fingerprinting (MRF) suffers from long acquisition time for organ-level coverage due to demanding image resolution and limited undersampling capacity. This study aims to develop a deep learning-assisted fast MRF framework for sub-millimeter T1 and T2 mapping of entire macaque brain on a preclinical 9.4 T MR system.

METHODS

Three dimensional MRF images were reconstructed by singular value decomposition (SVD) compressed reconstruction. T1 and T2 mapping for each axial slice exploited a self-attention assisted residual U-Net to suppress aliasing-induced quantification errors, and the transmit-field (B1 + ) measurements for robustness against B1 + inhomogeneity. Supervised network training used MRF images simulated via virtual parametric maps and a desired undersampling scheme. This strategy bypassed the difficulties of acquiring fully sampled preclinical MRF data to guide network training. The proposed fast MRF framework was tested on experimental data acquired from ex vivo and in vivo macaque brains.

RESULTS

The trained network showed reasonable adaptability to experimental MRF images, enabling robust delineation of various T1 and T2 distributions in the brain tissues. Further, the proposed MRF framework outperformed several existing fast MRF methods in handling the aliasing artifacts and capturing detailed cerebral structures in the mapping results. Parametric mapping of entire macaque brain at nominal resolution of 0.35× $$ \times $$ 0.35× $$ \times $$ 1 mm3 can be realized via a 20-min 3D MRF scan, which was sixfold faster than the baseline protocol.

CONCLUSION

Introducing deep learning to MRF framework paves the way for efficient organ-level high-resolution quantitative MRI in preclinical applications.

A systematic review demonstrating correlation of MRI compositional parameters with clinical outcomes following articular cartilage repair interventions in the knee

OBJECTIVE

Compositional-MRI parameters enable the assessment of cartilage ultrastructure. Correlation of these parameters with clinical outcomes is unclear. This systematic review investigated the correlation of various compositional- MRI parameters with clinical outcome measures following cartilage repair or regeneration interventions in the knee.

DESIGN

This study was registered with PROSPERO and reported in accordance with PRISMA. PubMed, Institute of Science Index, Scopus, Cochrane Central Register of Controlled Trials, and Embase databases were searched. All studies, regardless of type, that presented correlation of compositional- MRI parameters with clinical outcome measures were included. Two researchers independently performed data extraction and QUADAS-2 analysis. Compositional-MRI parameter change following intervention and correlation with clinical outcome measures were evaluated.

RESULTS

19 studies were included. Risk of bias was generally low. 5 different compositional parameters were observed from the included studies. However, due to the significant variability in the reporting of compositional-MRI parameters across studies, meta-analyses were possible only for T2 values and T2 index values (T2 value of repair cartilage relative to normal cartilage). Correlation of T2 values of repair cartilage with clinical outcome score was r ​= ​0.33 [0.15, 0.52]. Correlation of T2 index with clinical outcome score was r ​= ​0.52 [0.32, 0.77].

CONCLUSIONS

Correlation between T2 values and clinical outcome scores following knee cartilage repair were found. The heterogeneity of the correlations extracted from the included studies limited the scope for the meta-analysis. Thus, standardised, high-quality studies are required for better assessment of correlation between compositional MRI parameters and clinical outcome measures after cartilage repair.

REGISTRATION NUMBER

PROSPERO CRD42021287364.Study protocol available on PROSPERO website.

Cascade of Denoising and Mapping Neural Networks for MRI R2* Relaxometry of Iron-Loaded Liver

MRI of effective transverse relaxation rate (R2*) measurement is a reliable method for liver iron concentration quantification. However, R2* mapping can be degraded by noise, especially in the case of iron overload. This study aimed to develop a deep learning method for MRI R2* relaxometry of an iron-loaded liver using a two-stage cascaded neural network. The proposed method, named CadamNet, combines two convolutional neural networks separately designed for image denoising and parameter mapping into a cascade framework, and the physics-based R2* decay model was incorporated in training the mapping network to enforce data consistency further. CadamNet was trained using simulated liver data with Rician noise, which was constructed from clinical liver data. The performance of CadamNet was quantitatively evaluated on simulated data with varying noise levels as well as clinical liver data and compared with the single-stage parameter mapping network (MappingNet) and two conventional model-based R2* mapping methods. CadamNet consistently achieved high-quality R2* maps and outperformed MappingNet at varying noise levels. Compared with conventional R2* mapping methods, CadamNet yielded R2* maps with lower errors, higher quality, and substantially increased efficiency. In conclusion, the proposed CadamNet enables accurate and efficient iron-loaded liver R2* mapping, especially in the presence of severe noise.

The Endometriotic Neoplasm Algorithm for Risk Assessment (e-NARA) Index Sheds Light on the Discrimination of Endometriosis-Associated Ovarian Cancer from Ovarian Endometrioma

BACKGROUND

Magnetic resonance (MR) relaxometry provides a noninvasive tool to discriminate endometriosis-associated ovarian cancer (EAOC) from ovarian endometrioma (OE) with high accuracy. However, this method has a limitation in discriminating malignancy in clinical use because the R2 value depends on the device manufacturer and repeated imaging is unrealistic. The current study aimed to reassess the diagnostic accuracy of MR relaxometry and investigate a more powerful tool to distinguish EAOC from OE.

METHODS

This retrospective study was conducted at our institution from December, 2012, to May, 2022. A total of 150 patients were included in this study. Patients with benign ovarian tumors (n = 108) mainly received laparoscopic surgery, and cases with suspected malignancy (n = 42) underwent laparotomy. Information from a chart review of the patients' medical records was collected.

RESULTS

A multiple regression analysis revealed that the age, the tumor diameter, and the R2 value were independent malignant predicting factors. The endometriotic neoplasm algorithm for risk assessment (e-NARA) index provided high accuracy (sensitivity, 85.7%; specificity, 87.0%) to discriminate EAOC from OE.

CONCLUSIONS

The e-NARA index is a reliable tool to assess the probability of malignant transformation of endometrioma.

Rapid MR relaxometry using deep learning: An overview of current techniques and emerging trends

Quantitative mapping of MR tissue parameters such as the spin-lattice relaxation time (T₁ ), the spin-spin relaxation time (T₂ ), and the spin-lattice relaxation in the rotating frame (T_1ρ ), referred to as MR relaxometry in general, has demonstrated improved assessment in a wide range of clinical applications. Compared with conventional contrast-weighted (eg T₁ -, T₂ -, or T_1ρ -weighted) MRI, MR relaxometry provides increased sensitivity to pathologies and delivers important information that can be more specific to tissue composition and microenvironment. The rise of deep learning in the past several years has been revolutionizing many aspects of MRI research, including image reconstruction, image analysis, and disease diagnosis and prognosis. Although deep learning has also shown great potential for MR relaxometry and quantitative MRI in general, this research direction has been much less explored to date. The goal of this paper is to discuss the applications of deep learning for rapid MR relaxometry and to review emerging deep-learning-based techniques that can be applied to improve MR relaxometry in terms of imaging speed, image quality, and quantification robustness. The paper is comprised of an introduction and four more sections. Section 2 describes a summary of the imaging models of quantitative MR relaxometry. In Section 3, we review existing "classical" methods for accelerating MR relaxometry, including state-of-the-art spatiotemporal acceleration techniques, model-based reconstruction methods, and efficient parameter generation approaches. Section 4 then presents how deep learning can be used to improve MR relaxometry and how it is linked to conventional techniques. The final section concludes the review by discussing the promise and existing challenges of deep learning for rapid MR relaxometry and potential solutions to address these challenges.

Elevated magnetic resonance imaging measures of adipose tissue deposition in women with breast cancer treatment-related lymphedema

PURPOSE

Breast cancer treatment-related lymphedema (BCRL) is a common co-morbidity of breast cancer therapies, yet factors that contribute to BCRL progression remain incompletely characterized. We investigated whether magnetic resonance imaging (MRI) measures of subcutaneous adipose tissue were uniquely elevated in women with BCRL.

METHODS

MRI at 3.0 T of upper extremity and torso anatomy, fat and muscle tissue composition, and T₂ relaxometry were applied in left and right axillae of healthy control (n = 24) and symptomatic BCRL (n = 22) participants to test the primary hypothesis that fat-to-muscle volume fraction is elevated in symptomatic BCRL relative to healthy participants, and the secondary hypothesis that fat-to-muscle volume fraction is correlated with MR relaxometry of affected tissues and BCRL stage (significance criterion: two-sided p < 0.05).

RESULTS

Fat-to-muscle volume fraction in healthy participants was symmetric in the right and left sides (p = 0.51); in BCRL participants matched for age, sex, and BMI, fat-to-muscle volume fraction was elevated on the affected side (fraction = 0.732 ± 0.184) versus right and left side in controls (fraction = 0.545 ± 0.221, p < 0.001). Fat-to-muscle volume fraction directly correlated with muscle T₂ (p = 0.046) and increased with increasing level of BCRL stage (p = 0.041).

CONCLUSION

Adiposity quantified by MRI is elevated in the affected upper extremity of women with BCRL and may provide a surrogate marker of condition onset or severity.

CLINICAL TRIAL

NCT02611557.

A Novel Predictive Tool for Discriminating Endometriosis Associated Ovarian Cancer from Ovarian Endometrioma: The R2 Predictive Index

Simple Summary

Ovarian Endometrioma (OE) is a precancerous condition for endometriosis-associated ovarian cancer (EAOC). For many clinicians observing OE outpatients, setting the appropriate time for surgery can be a challenge because there is no suggestive milestone. Out of the fear of malignant transformation, many patients have surgery conducted according to respective faculty standards. This study aims to investigate a novel, noninvasive method not requiring an MRI device. This study partly helps to lift the above restrictions, and has the potential to suggest intervention-appropriate timing to the physician.

Abstract

Background: Magnetic resonance (MR) relaxometry provides a noninvasive tool to discriminate between ovarian endometrioma (OE) and endometriosis-associated ovarian cancer (EAOC), with a sensitivity and specificity of 86% and 94%, respectively. MRI models that can measure R2 values are limited, and the R2 values differ between MRI models. This study aims to extract the factors contributing to the R2 value, and to make a formula for estimating the R2 values, and to assess whether the R2 predictive index calculated by the formula could discriminate EAOC from OE. Methods: This retrospective study was conducted at our institution from November 2012 to February 2019. A total of 247 patients were included in this study. Patients with benign ovarian tumors mainly received laparoscopic surgery, and the patients suspected of having malignant tumors underwent laparotomy. Information from a chart review of the patients’ medical records was collected. Results: In the investigative cohort, among potential factors correlated with the R2 value, multiple regression analyses revealed that tumor diameter and CEA could predict the R2 value. In the validation cohort, multivariate analysis confirmed that age, CRP, and the R2 predictive index were the independent factors. Conclusions: The R2 predictive index is useful and valuable to the detection of the malignant transformation of endometrioma.

Correlation of Native Liver Parenchyma T1 and T2 Relaxation Times and Liver Synthetic Function Tests: A Pilot Study

MR relaxometry increasingly contributes to liver imaging. Studies on native relaxation times mainly describe relation to the presence of fibrosis. The hypothesis was that relaxation times are also influenced by other inherent factors, including changes in liver synthesis function. With the approval of the local ethics committee and written informed consent, data from 94 patients referred for liver MR imaging, of which 20 patients had cirrhosis, were included. Additionally to standard sequences, both native T1 and T2 parametric maps and T1 maps in the hepatobiliary phase of gadoxetate disodium were acquired. Associations with laboratory variables were assessed. Altogether, there was a negative correlation between albumin and all acquired relaxation times in cirrhotic patients. In non-cirrhotic patients, only T1 values exhibited a negative correlation with albumin. In all patients, bilirubin correlated significantly with post-contrast T1 relaxation times, whereas native relaxation times correlated only in cirrhotic patients. Evaluating patients with pathological INR values, post-contrast relaxation times were significantly higher, whereas native relaxation times did not correlate. In conclusion, apart from confirming the value of hepatobiliary phase T1 mapping, our results show a correlation of native T1 with serum albumin even in non-cirrhotic liver parenchyma, suggesting a direct influence of liver’s synthesis capacity on T1 relaxation times.

Reproducibility of liver iron concentration estimates in MRI through R2* measurement determined by least-squares curve fitting

Measuring transverse relaxation rate (R2* = 1/T2*) via MRI allows for noninvasive evaluation of multiple clinical parameters, including liver iron concentration (LIC) and fat fraction. Both fat and iron contribute to diffuse liver disease when stored in excess in the liver. This liver damage leads to fibrosis and cirrhosis with an increased risk of developing hepatocellular carcinoma. Liver iron concentration is linearly related to R2* measurements using MRI. A phantom was constructed to assess R2* quantification variability on 1.5 and 3 T MRI systems. Quantification was executed using least-squares curve fitting techniques. The phantom was created using readily available, low-cost materials. It contains four vials with R2* values that cover a clinically relevant range (100 to 420 Hz at 1.5 T). Iron content was achieved using ferric chloride solutions contained in glass vials, each affixed in a three-dimensional (3D)-printed polylactide (PLA) structure, surrounded by distilled water, all housed in a sealed acrylic cylinder. Multiple phantom stands were also 3D-printed using PLA for precise orientation of the phantom with respect to the direction of the static magnetic field. Acquisitions at different phantom angles, across multiple MRI systems, and with different pulse sequence parameters were evaluated. The variability between any two R2* measurements, taken in the same vial under these various acquisition conditions, on a 1.5 T MRI system, was <7% for each of the four vials. For 3 T MRI systems, variability was less than 14% in all cases. Variability was <6% for both 1.5 and 3 T acquisitions when unchanged pulse sequence parameters were used. The phantom can be used to mimic a range of clinically relevant levels of R2* relaxation rates, as measured using MRI. These measurements were found to be reproducible relative to the gold-standard method, liver biopsy, across several different image acquisition conditions.

Toward quantitative neuroimaging biomarkers for Friedreich's ataxia at 7 Tesla: Susceptibility mapping, diffusion imaging, R2 and R1 relaxometry

Friedreich's ataxia (FRDA) is a rare genetic disorder leading to degenerative processes. So far, no effective treatment has been found. Therefore, it is important to assist the development of medication with imaging biomarkers reflecting disease status and progress. Ten FRDA patients (mean age 37 ± 14 years; four female) and 10 age- and sex-matched controls were included. Acquisition of magnetic resonance imaging (MRI) data for quantitative susceptibility mapping, R₁ , R₂ relaxometry and diffusion imaging was performed at 7 Tesla. Results of volume of interest (VOI)-based analyses of the quantitative data were compared with a voxel-based morphometry (VBM) evaluation. Differences between patients and controls were assessed using the analysis of covariance (ANCOVA; p < 0.01) with age and sex as covariates, effect size of group differences, and correlations with disease characteristics with Spearman correlation coefficient. For the VBM analysis, a statistical threshold of 0.001 for uncorrected and 0.05 for corrected p-values was used. Statistically significant differences between FRDA patients and controls were found in five out of twelve investigated structures, and statistically significant correlations with disease characteristics were revealed. Moreover, VBM revealed significant white matter atrophy within regions of the brainstem, and the cerebellum. These regions overlapped partially with brain regions for which significant differences between healthy controls and patients were found in the VOI-based quantitative MRI evaluation. It was shown that two independent analyses provided overlapping results. Moreover, positive results on correlations with disease characteristics were found, indicating that these quantitative MRI parameters could provide more detailed information and assist the search for effective treatments.

Mapping the human brainstem: Brain nuclei and fiber tracts at 3 T and 7 T

Structural high-resolution imaging of the brainstem can be of high importance in clinical practice. However, ultra-high field magnetic resonance imaging (MRI) is still restricted in use due to limited availability. Therefore, quantitative MRI techniques (quantitative susceptibility mapping [QSM], relaxation measurements [ R2* , R₁ ], diffusion tensor imaging [DTI]) and T₂ - and proton density (PD)-weighted imaging in the human brainstem at 3 T and 7 T are compared. Five healthy volunteers (mean age: 21.5 ± 1.9 years) were measured at 3 T and 7 T using multi-echo gradient echo sequences for susceptibility mapping and R2* relaxometry, magnetization-prepared 2 rapid acquisition gradient echo sequences for R₁ relaxometry, turbo-spin echo sequences for PD- and T₂ -weighted imaging and readout-segmented echo planar sequences for DTI. Susceptibility maps were computed using Laplacian-based phase unwrapping, V-SHARP for background field removal and the streaking artifact reduction for QSM algorithm for dipole inversion. Contrast-to-noise ratios (CNRs) were determined at 3 T and 7 T in ten volumes of interest (VOIs). Data acquired at 7 T showed higher CNR. However, in four VOIs, lower CNR was observed for R2* at 7 T. QSM was shown to be the contrast with which the highest number of structures could be identified. The depiction of very fine tracts such as the medial longitudinal fasciculus throughout the brainstem was only possible in susceptibility maps acquired at 7 T. DTI effectively showed the main tracts (crus cerebri, transverse pontine fibers, corticospinal tract, middle and superior cerebellar peduncle, pontocerebellar tract, and pyramid) at both field strengths. Assessing the brainstem with quantitative MRI methods such as QSM, R2* , as well as PD- and T₂ -weighted imaging with great detail, is also possible at 3 T, especially when using susceptibility mapping calculated from a gradient echo sequence with a wide range of echo times from 10.5 to 52.5 ms. However, tracing smallest structures strongly benefits from imaging at ultra-high field.

Magnetic resonance relaxometry improves the accuracy of conventional MRI in the diagnosis of endometriosis-associated ovarian cancer: A case report

Endometriosis is a precancerous condition for endometriosis-associated ovarian cancer (EAOC). In the present study, conventional magnetic resonance imaging (MRI) and MR relaxometry were used to examine a case of clear cell carcinoma that arose in a pre-existing right-sided benign ovarian endometrioma (OE). The 42-year-old nulliparous woman suspected of EOAC, as assessed by conventional MRI, requested fertility-sparing surgery such as laparoscopic endometrioma cystectomy. Furthermore, the MR transverse relaxation rate (R2) was determined using a single-voxel, multi-echo MR sequence using a 3 Tesla-MR system. An R2 value <12.1 s^-1 was indicative of malignancy, as described in previous studies. In the present study, MR relaxometry identified an R2 value of 7.98 s^-1 in the right cyst, which suggested the malignant transformation of benign OE. Based on these findings, fertility-sparing surgery was contraindicated. In conclusion, MR relaxometry may represent a new clinical approach as an adjunctive modality for the diagnosis of EAOC. When patients exhibiting a pelvic mass suspected of EAOC desire fertility-sparing treatment options, MR relaxometry can facilitate the selection of conservative management.

Measuring non-parametric distributions of intravoxel mean diffusivities using a clinical MRI scanner

We measure spectra of water mobilities (i.e., mean diffusivities) from intravoxel pools in brain tissues of healthy subjects with a non-parametric approach. Using a single-shot isotropic diffusion encoding (IDE) preparation, we eliminate signal confounds caused by anisotropic diffusion, including microscopic anisotropy, and acquire in vivo diffusion-weighted images (DWIs) over a wide range of diffusion sensitizations. We analyze the measured IDE signal decays using a regularized inverse laplace transform (ILT) to derive a probability distribution of mean diffusivities of tissue water in each voxel. Based on numerical simulations we assess the sensitivity and accuracy of our ILT analysis and optimize an experimental protocol for use with clinical MRI scanners. In vivo spectra of intravoxel mean diffusivities measured in healthy subjects generally show single-peak distributions throughout the brain parenchyma, with small differences in peak location and shape among white matter, cortical and subcortical gray matter, and cerebrospinal fluid. Mean diffusivity distributions (MDDs) with multiple peaks are observed primarily in voxels at tissue interfaces and are likely due to partial volume contributions. To quantify tissue-specific MDDs with improved statistical power, we average voxel-wise normalized MDDs in corresponding regions-of-interest (ROIs). This non-parametric, rotation-invariant assessment of isotropic diffusivities of tissue water may reflect important microstructural information, such as cell packing and cell size, and active physiological processes, such as water transport and exchange, which may enhance biological specificity in the clinical diagnosis and characterization of ischemic stroke, cancer, neuroinflammation, and neurodegenerative disorders and diseases.

A diffusion model-free framework with echo time dependence for free-water elimination and brain tissue microstructure characterization

Purpose

The compartmental nature of brain tissue microstructure is typically studied by diffusion MRI, MR relaxometry or their correlation. Diffusion MRI relies on signal representations or biophysical models, while MR relaxometry and correlation studies are based on regularized inverse Laplace transforms (ILTs). Here we introduce a general framework for characterizing microstructure that does not depend on diffusion modeling and replaces ill‐posed ILTs with blind source separation (BSS). This framework yields proton density, relaxation times, volume fractions, and signal disentanglement, allowing for separation of the free‐water component.

Theory and Methods

Diffusion experiments repeated for several different echo times, contain entangled diffusion and relaxation compartmental information. These can be disentangled by BSS using a physically constrained nonnegative matrix factorization.

Results

Computer simulations, phantom studies, together with repeatability and reproducibility experiments demonstrated that BSS is capable of estimating proton density, compartmental volume fractions and transversal relaxations. In vivo results proved its potential to correct for free‐water contamination and to estimate tissue parameters.

Conclusion

Formulation of the diffusion‐relaxation dependence as a BSS problem introduces a new framework for studying microstructure compartmentalization, and a novel tool for free‐water elimination.

Quantitative MR Evaluation of Chronic Pancreatitis: Extracellular Volume Fraction and MR Relaxometry

OBJECTIVE

The purpose of this study was to determine if extracellular volume fraction and T1 mapping can be used to diagnose chronic pancreatitis (CP).

MATERIALS AND METHODS

This HIPAA-compliant study analyzed 143 consecutive patients with and without CP who underwent MR imaging between May 2016 and February 2017. Patients were selected for the study according to inclusion and exclusion criteria that considered history and clinical and laboratory findings. Eligible patients (n = 119) were grouped as normal (n = 60) or with mild (n = 22), moderate (n = 27), or severe (n = 10) CP on the basis of MRCP findings using the Cambridge classification as the reference standard. T1 maps were acquired in unenhanced and late contrast-enhanced phases using a 3D dual flip-angle gradient-echo sequence. All patients were imaged on the same 3-T scanner using the same imaging parameters, contrast agent, and dosage.

RESULTS

Mean extracellular volume fractions and T1 relaxation times were significantly different within the study groups (one-way ANOVA, p < 0.001). Using the AUC curve analysis, extracellular volume fraction of > 0.27 showed 92% sensitivity (54/59) and 77% specificity (46/60) for the diagnosis of CP (AUC = 0.90). A T1 relaxation time of > 950 ms revealed 64% sensitivity (38/59) and 88% specificity (53/60) (AUC = 0.80). Combining extracellular volume fraction and T1 mapping yielded sensitivity of 85% (50/59) and specificity of 92% (55/60) (AUC = 0.94).

CONCLUSION

Extracellular volume fraction and T1 mapping may provide quantitative metrics for determining the presence and severity of acinar cell loss and aid in the diagnosis of CP.

Volumetric, relaxometric and diffusometric correlates of psychotic experiences in a non-clinical sample of young adults

BACKGROUND

Grey matter (GM) abnormalities are robust features of schizophrenia and of people at ultra high-risk for psychosis. However the extent to which neuroanatomical alterations are evident in non-clinical subjects with isolated psychotic experiences is less clear.

METHODS

Individuals (mean age 20 years) with (n = 123) or without (n = 125) psychotic experiences (PEs) were identified from a population-based cohort. All underwent T1-weighted structural, diffusion and quantitative T1 relaxometry MRI, to characterise GM macrostructure, microstructure and myelination respectively. Differences in quantitative GM structure were assessed using voxel-based morphometry (VBM). Binary and ordinal models of PEs were tested. Correlations between socioeconomic and other risk factors for psychosis with cortical GM measures were also computed.

RESULTS

GM volume in the left supra-marginal gyrus was reduced in individuals with PEs relative to those with no PEs. The greater the severity of PEs, the greater the reduction in T1 relaxation rate (R1) across left temporoparietal and right pre-frontal cortices. In these regions, R1 was positively correlated with maternal education and inversely correlated with general psychopathology.

CONCLUSIONS

PEs in non-clinical subjects were associated with regional reductions in grey-matter volume reduction and T1 relaxation rate. The alterations in T1 relaxation rate were also linked to the level of general psychopathology. Follow up of these subjects should clarify whether these alterations predict the later development of an ultra high-risk state or a psychotic disorder.

Myocardial T1 mapping at 3.0 tesla using an inversion recovery spoiled gradient echo readout and bloch equation simulation with slice profile correction (BLESSPC) T1 estimation algorithm

BACKGROUND

To develop an accurate and precise myocardial T1 mapping technique using an inversion recovery spoiled gradient echo readout at 3.0 Tesla (T).

THEORY AND METHODS

The modified Look-Locker inversion-recovery (MOLLI) sequence was modified to use fast low angle shot (FLASH) readout, incorporating a BLESSPC (Bloch Equation Simulation with Slice Profile Correction) T1 estimation algorithm, for accurate myocardial T1 mapping. The FLASH-MOLLI with BLESSPC fitting was compared with different approaches and sequences with regards to T1 estimation accuracy, precision and image artifact based on simulation, phantom studies, and in vivo studies of 10 healthy volunteers and three patients at 3.0 Tesla.

RESULTS

The FLASH-MOLLI with BLESSPC fitting yields accurate T1 estimation (average error = -5.4 ± 15.1 ms, percentage error = -0.5% ± 1.2%) for T1 from 236-1852 ms and heart rate from 40-100 bpm in phantom studies. The FLASH-MOLLI sequence prevented off-resonance artifacts in all 10 healthy volunteers at 3.0T. In vivo, there was no significant difference between FLASH-MOLLI-derived myocardial T1 values and "ShMOLLI+IE" derived values (1458.9 ± 20.9 ms versus 1464.1 ± 6.8 ms, P = 0.50); However, the average precision by FLASH-MOLLI was significantly better than that generated by "ShMOLLI+IE" (1.84 ± 0.36% variance versus 3.57 ± 0.94%, P < 0.001).

CONCLUSION

The FLASH-MOLLI with BLESSPC fitting yields accurate and precise T1 estimation, and eliminates banding artifacts associated with bSSFP at 3.0T.

MR relaxometry in chronic liver diseases: Comparison of T1 mapping, T2 mapping, and diffusion-weighted imaging for assessing cirrhosis diagnosis and severity

BACKGROUND

MR relaxometry has been extensively studied in the field of cardiac diseases, but its contribution to liver imaging is unclear. We aimed to compare liver and spleen T1 mapping, T2 mapping, and diffusion-weighted MR imaging (DWI) for assessing the diagnosis and severity of cirrhosis.

METHODS

We prospectively included 129 patients with normal (n=40) and cirrhotic livers (n=89) from May to September 2014. Non-enhanced liver T1 mapping, splenic T2 mapping, and liver and splenic DWI were measured and compared for assessing cirrhosis severity using Child-Pugh score, MELD score, and presence or not of large esophageal varices (EVs) and liver stiffness measurements using Fibroscan(®) as reference.

RESULTS

Liver T1 mapping was the only variable demonstrating significant differences between normal patients (500±79ms), Child-Pugh A patients (574±84ms) and Child-Pugh B/C patients (690±147ms; all p-values <0.00001). Liver T1 mapping had a significant correlation with Child-Pugh score (Pearson's correlation coefficient of 0.46), MEDL score (0.30), and liver stiffness measurement (0.52). Areas under the receiver operating characteristic curves of liver T1 mapping for the diagnosis of cirrhosis (O.85; 95% confidence intervals (CI), 0.77-0.91), Child-Pugh B/C cirrhosis (0.87; 95%CI, 0.76-0.93), and large EVs (0.75; 95%CI, 0.63-0.83) were greater than that of spleen T2 mapping, liver and spleen DWI (all p-values<0.01).

CONCLUSION

Liver T1 mapping is a promising new diagnostic tool for assessing cirrhosis diagnosis and severity, showing higher diagnostic accuracy than liver and spleen DWI, while T2 mapping is not reliable.

Assessment of T1, T1ρ, and T2 values of the ulnocarpal disc in healthy subjects at 3 tesla

OBJECTIVE

The purpose of this study was to implement clinically feasible imaging techniques for determination of T1, T1ρ, and T2 values of the ulnocarpal disc and to assess those values in a cohort of asymptomatic subjects at 3 tesla. Resulting values were compared between different age groups, since former histological findings of the ulnocarpal disc indicated frequent early degenerative changes of this tissue starting in the third decade of life, even in asymptomatic subjects.

MATERIALS AND METHODS

Twenty-seven healthy subjects were included in this study. T1 measurements were performed using 3D spoiled gradient-echo (GRE) sequence with variable flip angle. A series of T1ρ and T2-weighted images was acquired by a 3D GRE sequence after suitable magnetization preparation. T1,T1ρ, and T2 maps of the ulnocarpal disc were calculated pixel-wise. Representative mean values from extended regions were analysed.

RESULTS

Mean T1 values of the ulnocarpal disc ranged from 722 ms in a 39 year-old subject to 1264 ms in a 65 year-old subject, T1ρ ranged from 9.2 ms (26 year-old subject) to 25.9 ms (65 year-old subject). Calculated T2 values showed a large range from 4.1 ms to 22.3 ms. T1ρ and T1 values tended to increase with age (p<0.05), whereas T2 did not.

CONCLUSIONS

MR relaxometry of the ulnocarpal disc is feasible, and T1,T1ρ, and T2 values show modest variance in asymptomatic subjects. The potential of relaxation mapping to reveal relevant structural changes in patients has to be investigated in further studies.

Rapid look-up table method for noise-corrected curve fitting in the R2* mapping of iron loaded liver

PURPOSE

Fitting the measured decay signal to the first moment in the presence of noncentral chi noise (M(1) NCM) can correctly address the effect of noise on the effective transverse relaxation rate (R2*) relaxometry of iron loaded liver. However, this method requires intensive computation, which restricts its application to R2* mapping. This work aims to develop a rapid implementation of the M(1) NCM method for R2* mapping.

METHODS

The computation of the confluent hypergeometric function in the M(1) NCM model was approximated using cubic spline interpolation with breakpoints and coefficients precalculated and stored in a look-up table (M(1) NCM-LUT). The performance of the proposed M(1) NCM-LUT method was evaluated through simulation and based on in vivo liver R2* relaxometry data.

RESULTS

In both simulation and in vivo studies, the maximum absolute difference between R2* maps generated by the M(1) NCM and M(1) NCM-LUT methods was nearly 10(-3) s(-1) or less, and the M(1) NCM-LUT method obtained a R2* map in approximately 1 s and achieved an acceleration of approximately five orders of magnitude.

CONCLUSION

The proposed M(1) NCM-LUT method can significantly increase the speed of the liver R2* mapping using the M(1) NCM model. This development is important in promoting application of this R2* mapping technique for tissue iron quantification.

Quantification of liver iron with MRI: state of the art and remaining challenges

Liver iron overload is the histological hallmark of hereditary hemochromatosis and transfusional hemosiderosis, and can also occur in chronic hepatopathies. Iron overload can result in liver damage, with the eventual development of cirrhosis, liver failure, and hepatocellular carcinoma. Assessment of liver iron levels is necessary for detection and quantitative staging of iron overload and monitoring of iron-reducing treatments. This article discusses the need for noninvasive assessment of liver iron and reviews qualitative and quantitative methods with a particular emphasis on magnetic resonance imaging (MRI). Specific MRI methods for liver iron quantification include signal intensity ratio as well as R2 and R2* relaxometry techniques. Methods that are in clinical use, as well as their limitations, are described. Remaining challenges, unsolved problems, and emerging techniques to provide improved characterization of liver iron deposition are discussed.

Instantaneous signal loss simulation (InSiL): an improved algorithm for myocardial T₁ mapping using the MOLLI sequence

PURPOSE

To propose a T1 mapping algorithm for the modified Look-Locker inversion-recovery (MOLLI) sequence that can improve T1 estimation accuracy.

MATERIALS AND METHODS

The modified T1 mapping algorithm (InSiL) is based on the simulation of MOLLI signal evolution and simulates the longitudinal magnetization signal perturbation by each single-shot image acquisition in MOLLI as an instantaneous signal loss. InSiL was evaluated against original MOLLI using Bloch simulations, phantom studies, and in 15 healthy volunteers at 1.5T.

RESULTS

In phantom studies, the maximum absolute error by InSiL is less than 2%, while that by MOLLI is more than 20% for T1 values from 221 msec to 1539 msec. The benefit of InSiL is greatest at heart rate (HR) >80 bpm and T1 >1000 msec, and InSiL reduced MOLLI T1 error from 14.9 ± 4.5% to 0.4 ± 0.3%. Average InSiL-derived native myocardial T1 values at 1.5T in healthy volunteers were significantly higher than MOLLI-derived values by 236.9 ± 11.7 msec (1160.3 ± 25.1 msec vs. 923.4 ± 22.3 msec, P < 0.001) at an average HR of 65.1 ± 14.7 bpm.

CONCLUSION

The proposed InSiL approach yields better T1 mapping accuracy than MOLLI, and is less sensitive to HR variation in tissues with longer T1 values.

Improved MRI R2 * relaxometry of iron-loaded liver with noise correction

Accurate and reproducible MRI R2 * relaxometry for tissue iron quantification is important in managing transfusion-dependent patients. MRI data are often acquired using array coils and reconstructed by the root-sum-square algorithm, and as such, measured signals follow the noncentral chi distribution. In this study, two noise-corrected models were proposed for the liver R2 * quantification: fitting the signal to the first moment and fitting the squared signal to the second moment in the presence of the noncentral chi noise. These two models were compared with the widely implemented offset and truncation models on both simulation and in vivo data. The results demonstrated that the "slow decay component" of the liver R2 * was mainly caused by the noise. The offset model considerably overestimated R2 * values by incorrectly adding a constant to account for the slow decay component. The truncation model generally produced accurate R2 * measurements by only fitting the initial data well above the noise level to remove the major source of errors, but underestimated very high R2 * values due to the sequence limit of obtaining very short echo time images. Both the first and second-moment noise-corrected models constantly produced accurate and precise R2 * measurements by correctly addressing the noise problem.