Quantitative susceptibility mapping in combination with water-fat separation for simultaneous liver iron and fat fraction quantification

Water/fat separate reconstruction for body quantitative susceptibility mapping in MRI

This study aimed to investigate the cause of susceptibility underestimation in body quantitative susceptibility mapping (QSM) and propose a water/fat separate reconstruction to address this issue. A numerical simulation was conducted using conventional QSM with/without body masking. The conventional method with body masking underestimated the susceptibility across all regions, whereas the method without body masking estimated an equivalent value to the ground truth. Additional numerical simulations and human experiments were conducted to compare the water/fat separate reconstruction, which separately reconstructs water and fat susceptibility maps based on the water/fat separation, with conventional QSM with body masking. The proposed method improved susceptibility estimation specifically in only the water tissue. The results of the human experiments were consistent with those of the numerical simulations. The lack of phase information outside the body contributed to susceptibility underestimation in conventional QSM. The developed method addressed susceptibility underestimation only in water tissue in body QSM.

Using Dual-source Photon-counting Detector CT to Simultaneously Quantify Fat and Iron Content: A Phantom Study

RATIONALE AND OBJECTIVES

To evaluate the feasibility of using photon-counting detector computed tomography (PCD CT) to simultaneously quantify fat and iron content MATERIALS AND METHODS: Phantoms with pure fat, pure iron and fat-iron deposition were scanned by two tube voltages (120 and 140 kV) and two image quality (IQ) settings (80 and 145). Using an iron-specific three-material decomposition algorithm, virtual noniron (VNI) and virtual iron content (VIC) images were generated at quantum iterative reconstruction (QIR) strength levels 1-4.

RESULTS

Significant linear correlations were observed between known fat content (FC) and VNI for pure fat phantoms (r = 0.981-0.999, p < 0.001) and between known iron content (IC) and VIC for pure iron phantoms (r = 0.897-0.975, p < 0.001). In fat-iron phantoms, the measurement for fat content of 5-30% demonstrated good linearity between FC and VNI (r = 0.919-0.990, p < 0.001), and VNI were not affected by 75, 150, and 225 µmol/g iron overload (p = 0.174-0.519). The measurement for iron demonstrated a linear range of 75-225 µmol/g between IC and VIC (r = 0.961-0.994, p < 0.001) and VIC was not confounded by the coexisting 5%, 20%, and 30% fat deposition (p = 0.943-0.999). The Bland-Altman of fat and iron measurements were not significantly different at varying tube voltages and IQ settings (all p > 0.05). No significant difference in VNI and VIC at QIR 1-4.

CONCLUSION

PCD CT can accurately and simultaneously quantify fat and iron, including scan parameters with lower radiation dose.

Magnetic susceptibility and R2*-based texture analysis for evaluating liver fibrosis in chronic liver disease

PURPOSE

To explore potential feasibility of texture features in magnetic susceptibility and R2* maps for evaluating liver fibrosis.

METHODS

Thirty-one patients (median age 46 years; 22 male) with chronic liver disease were prospectively recruited and underwent magnetic resonance imaging (MRI), blood tests, and liver biopsy. Susceptibility and R2* maps were obtained using a 3-dimensional volumetric interpolated breath-hold examination sequence with a 3T MRI scanner. Texture features, including histogram, gray-level co-occurrence matrix (GLCM), gray-level dependence matrix (GLDM), gray-level run length matrix (GLRLM), gray-level size zone matrix (GLSZM), and neighboring gray tone difference matrix (NGTDM) features, were extracted. Texture features and blood test results of non-significant (Ishak-F < 3) and significant fibrosis patients (Ishak-F ≥ 3) were compared, and correlations with Ishak-F stages were analyzed. Areas under the curve (AUCs) were calculated to determine the efficacy for evaluating liver fibrosis.

RESULTS

Nine texture features of susceptibility maps and 19 features of R2* maps were significantly different between non-significant and significant fibrosis groups (all P < 0.05). Large dependence high gray-level emphasis (LDHGLE) of GLDM and long run high gray-level emphasis (LRHGLE) of GLRLM in R2* maps showed significantly negative and good correlations with Ishak-F stages (r = -0.616, P < 0.001; r = -0.637, P < 0.001). Busyness (NGTDM) in susceptibility maps, LDHGLE of GLDM and LRHGLE of GLRLM in R2* maps yield the highest AUCs (AUC = 0.786, P = 0.007; AUC = 0.807, P = 0.004; AUC = 0.819, P = 0.003).

CONCLUSION

Texture characteristics of susceptibility and R2* maps revealed possible staging values for liver fibrosis. Susceptibility and R2*-based texture analysis may be a useful and noninvasive method for staging liver fibrosis.

Liver PDFF estimation using a multi-decoder water-fat separation neural network with a reduced number of echoes

OBJECTIVE

To accurately estimate liver PDFF from chemical shift-encoded (CSE) MRI using a deep learning (DL)-based Multi-Decoder Water-Fat separation Network (MDWF-Net), that operates over complex-valued CSE-MR images with only 3 echoes.

METHODS

The proposed MDWF-Net and a U-Net model were independently trained using the first 3 echoes of MRI data from 134 subjects, acquired with conventional 6-echoes abdomen protocol at 1.5 T. Resulting models were then evaluated using unseen CSE-MR images obtained from 14 subjects that were acquired with a 3-echoes CSE-MR pulse sequence with a shorter duration compared to the standard protocol. Resulting PDFF maps were qualitatively assessed by two radiologists, and quantitatively assessed at two corresponding liver ROIs, using Bland Altman and regression analysis for mean values, and ANOVA testing for standard deviation (STD) (significance level: .05). A 6-echo graph cut was considered ground truth.

RESULTS

Assessment of radiologists demonstrated that, unlike U-Net, MDWF-Net had a similar quality to the ground truth, despite it considered half of the information. Regarding PDFF mean values at ROIs, MDWF-Net showed a better agreement with ground truth (regression slope = 0.94, R2 = 0.97) than U-Net (regression slope = 0.86, R2 = 0.93). Moreover, ANOVA post hoc analysis of STDs showed a statistical difference between graph cuts and U-Net (p < .05), unlike MDWF-Net (p = .53).

CONCLUSION

MDWF-Net showed a liver PDFF accuracy comparable to the reference graph cut method, using only 3 echoes and thus allowing a reduction in the acquisition times.

CLINICAL RELEVANCE STATEMENT

We have prospectively validated that the use of a multi-decoder convolutional neural network to estimate liver proton density fat fraction allows a significant reduction in MR scan time by reducing the number of echoes required by 50%.

KEY POINTS

• Novel water-fat separation neural network allows for liver PDFF estimation by using multi-echo MR images with a reduced number of echoes. • Prospective single-center validation demonstrated that echo reduction leads to a significant shortening of the scan time, compared to standard 6-echo acquisition. • Qualitative and quantitative performance of the proposed method showed no significant differences in PDFF estimation with respect to the reference technique.

Quantitative MRI of diffuse liver diseases: techniques and tissue-mimicking phantoms

Quantitative magnetic resonance imaging (MRI) techniques are emerging as non-invasive alternatives to biopsy for assessment of diffuse liver diseases of iron overload, steatosis and fibrosis. For testing and validating the accuracy of these techniques, phantoms are often used as stand-ins to human tissue to mimic diffuse liver pathologies. However, currently, there is no standardization in the preparation of MRI-based liver phantoms for mimicking iron overload, steatosis, fibrosis or a combination of these pathologies as various sizes and types of materials are used to mimic the same liver disease. Liver phantoms that mimic specific MR features of diffuse liver diseases observed in vivo are important for testing and calibrating new MRI techniques and for evaluating signal models to accurately quantify these features. In this study, we review the liver morphology associated with these diffuse diseases, discuss the quantitative MR techniques for assessing these liver pathologies, and comprehensively examine published liver phantom studies and discuss their benefits and limitations.

QSM Throughout the Body

Magnetic materials in tissue, such as iron, calcium, or collagen, can be studied using quantitative susceptibility mapping (QSM). To date, QSM has been overwhelmingly applied in the brain, but is increasingly utilized outside the brain. QSM relies on the effect of tissue magnetic susceptibility sources on the MR signal phase obtained with gradient echo sequence. However, in the body, the chemical shift of fat present within the region of interest contributes to the MR signal phase as well. Therefore, correcting for the chemical shift effect by means of water-fat separation is essential for body QSM. By employing techniques to compensate for cardiac and respiratory motion artifacts, body QSM has been applied to study liver iron and fibrosis, heart chamber blood and placenta oxygenation, myocardial hemorrhage, atherosclerotic plaque, cartilage, bone, prostate, breast calcification, and kidney stone.

Toward a realistic in silico abdominal phantom for QSM

PURPOSE

QSM outside the brain has recently gained interest, particularly in the abdominal region. However, the absence of reliable ground truths makes difficult to assess reconstruction algorithms, whose quality is already compromised by additional signal contributions from fat, gases, and different kinds of motion. This work presents a realistic in silico phantom for the development, evaluation and comparison of abdominal QSM reconstruction algorithms.

METHODS

Synthetic susceptibility andR 2 * $$ {R}_2^{\ast } $$ maps were generated by segmenting and postprocessing the abdominal 3T MRI data from a healthy volunteer. Susceptibility andR 2 * $$ {R}_2^{\ast } $$ values in different tissues/organs were assigned according to literature and experimental values and were also provided with realistic textures. The signal was simulated using as input the synthetic QSM andR 2 * $$ {R}_2^{\ast } $$ maps and fat contributions. Three susceptibility scenarios and two acquisition protocols were simulated to compare different reconstruction algorithms.

RESULTS

QSM reconstructions show that the phantom allows to identify the main strengths and limitations of the acquisition approaches and reconstruction algorithms, such as in-phase acquisitions, water-fat separation methods, and QSM dipole inversion algorithms.

CONCLUSION

The phantom showed its potential as a ground truth to evaluate and compare reconstruction pipelines and algorithms. The publicly available source code, designed in a modular framework, allows users to easily modify the susceptibility,R 2 * $$ {R}_2^{\ast } $$ and TEs, and thus creates different abdominal scenarios.

Identification of ovarian endometriotic cysts in cystic lesions of the ovary by amide proton transfer-weighted imaging and R2∗ mapping

AIM

To investigate the value of amide proton transfer weighted (APTw) imaging and R2∗ mapping of cystic fluid in differentiating ovarian endometriotic cysts (OE) from other ovarian cystic (OOC) lesions.

MATERIALS AND METHODS

A total of 42 patients who underwent 3 T pelvic magnetic resonance imaging (MRI) were enrolled. Nineteen lesions were OE and 27 lesions were OOC. The APTw imaging and R2∗ values of the cystic fluid were measured and compared between the two groups using the independent sample t-test or Mann-Whitney U-test. Receiver operating characteristic (ROC) curves were used to evaluate the diagnostic efficacy of different parameters. The area under ROC curves (AUCs) was compared using the Delong test. Spearman's correlation analysis was used to assess the correlation between APTw imaging and R2∗ values.

RESULTS

APTw imaging values of OE were lower, while R2∗ values were higher in OE than those in OOC (p=0.001 and < 0.001). The AUCs of APTw imaging and R2∗ values to identify OE from OOC were 0.910 and 0.975. The AUC increased to 0.990 when combining APTw imaging and R2∗ values, yet without a significant difference to the APTw imaging or R2∗ value alone (p=0.229 and 0.082, respectively). APTw imaging values were negatively correlated with R2∗ values (r=-0.522, p<0.001).

CONCLUSION

Both APTw imaging and R2∗ values of OE are significantly different from other ovarian cystic lesions. APTw imaging combined with R2∗ values show excellent diagnostic efficacy to differentiate between OE and OOC.

Simultaneous liver steatosis, fibrosis and iron deposition quantification with mDixon quant based on radiomics analysis in a rabbit model

PURPOSE

To evaluate the feasibility of simultaneous quantification of liver fibrosis, liver steatosis and abnormal iron deposition using mDixon Quant based on radiomics analysis, and to eliminate the interference among different histopathologic features.

METHODS

One hundred and twenty rabbits that were administered CCl4 for 4-16 weeks and a cholesterol rich diet for the initial 4 weeks in the experimental group and 20 rabbits in the control group were examined using mDixon. Radiomics features of the whole liver were extracted from PDFF and R2* and radiomics models for discriminating steatosis: S0-S1 vs. S2-S4, fibrosis: F0-F2 vs. F3-F4 and iron deposition: normal vs. abnormal were constructed respectively and evaluated using receiver operating characteristic (ROC) curves with the histopathological results as reference standard. Combined corrected models merging the radscore and the other two histopathologic features were evaluated using multiple logistic regression analyses and compared with radiomics models.

RESULTS

The area under the ROC curve (AUC) of the radiomics model with PDFF features was 0.886 and 0.843 in the training and the test set, respectively, for the diagnosis of liver steatosis grade S0-1 and S2-S4. The radiomics model based on R2* features were 0.815 and 0.801 for distinguishing F0-F2 and F3-F4 and 0.831 and 0.738 for discriminating abnormal iron deposition in the training and test set, respectively. The corrected model for liver steatosis and fibrosis (0.944 and 0.912 in the test set) outperformed the radiomics models by eliminating the interference of histopathologic features(P < 0.05), but had comparable diagnostic performance for abnormal iron deposition(P > 0.05).

CONCLUSIONS

It is feasible for mDixon to simultaneously quantify whole liver steatosis, fibrosis and iron deposition based on radiomics analysis. It is valuable to minimize the interference of different pathological features for the assessment of liver steatosis and fibrosis.

Preconditioned water-fat total field inversion: Application to spine quantitative susceptibility mapping

PURPOSE

To (a) develop a preconditioned water-fat total field inversion (wfTFI) algorithm that directly estimates the susceptibility map from complex multi-echo gradient echo data for water-fat regions and to (b) evaluate the performance of the proposed wfTFI quantitative susceptibility mapping (QSM) method in comparison with a local field inversion (LFI) method and a linear total field inversion (TFI) method in the spine.

METHODS

Numerical simulations and in vivo spine multi-echo gradient echo measurements were performed to compare wfTFI to an algorithm based on disjoint background field removal (BFR) and LFI and to a formerly proposed TFI algorithm. The data from 1 healthy volunteer and 10 patients with metastatic bone disease were included in the analysis. Clinical routine computed tomography (CT) images were used as a reference standard to distinguish osteoblastic from osteolytic changes. The ability of the QSM methods to distinguish osteoblastic from osteolytic changes was evaluated.

RESULTS

The proposed wfTFI method was able to decrease the normalized root mean square error compared to the LFI and TFI methods in the simulation. The in vivo wfTFI susceptibility maps showed reduced BFR artifacts, noise amplification, and streaking artifacts compared to the LFI and TFI maps. wfTFI provided a significantly higher diagnostic confidence in differentiating osteolytic and osteoblastic lesions in the spine compared to the LFI method (p = .012).

CONCLUSION

The proposed wfTFI method can minimize BFR artifacts, noise amplification, and streaking artifacts in water-fat regions and can thus better differentiate between osteoblastic and osteolytic changes in patients with metastatic disease compared to LFI and the original TFI method.

Magnetic resonance quantitative susceptibility mapping in the evaluation of hepatic fibrosis in chronic liver disease: a feasibility study

Background

Noninvasive methods for the early diagnosis and staging of hepatic fibrosis are needed. The present study aimed to investigate the alteration of magnetic susceptibility in the liver of patients with various fibrosis stages and to evaluate the feasibility of using susceptibility to stage hepatic fibrosis.

Methods

A total of 30 consecutive patients with chronic liver diseases (CLDs) underwent magnetic resonance imaging (MRI) and liver biopsy evaluation of hepatic fibrosis, necroinflammatory activity, iron load, and steatosis. Quantitative susceptibility mapping (QSM), R2* and proton density fat fraction (PDFF) images were postprocessed from the same gradient-echo data for quantitative tissue characterization using region of interest (ROI) analysis. The differences for MRI measurements between cohorts of non-significant (Ishak-F <3) and significant fibrosis (Ishak-F ≥3) and the correlation of MRI measurements with fibrosis stages and necroinflammatory activity grades were tested. Receiver operating characteristic (ROC) analysis was also performed.

Results

There was a significant difference in liver susceptibility between the cohorts of significant and non-significant fibrosis (Z=-2.880, P=0.004). A moderate negative correlation between the stages of liver fibrosis and liver susceptibility was observed (r=-0.471, P=0.015). Liver magnetic susceptibility differentiated non-significant from significant hepatic fibrosis with an area under the receiver operating curve (AUC) of 0.836 (P=0.004). A highly sensitive diagnostic performance with an AUC of 0.933 was obtained using magnetic susceptibility and PDFF together (P<0.001).

Conclusions

A noninvasive liver QSM-based evaluation promises an accurate assessment of significant fibrosis in patients with CLDs.

Quantitative Susceptibility Mapping Using a Multispectral Autoregressive Moving Average Model to Assess Hepatic Iron Overload

BACKGROUND

R2*-MRI is clinically used to noninvasively assess hepatic iron content (HIC) to guide potential iron chelation therapy. However, coexisting pathologies, such as fibrosis and steatosis, affect R2* measurements and may thus confound HIC estimations.

PURPOSE

To evaluate whether a multispectral auto regressive moving average (ARMA) model can be used in conjunction with quantitative susceptibility mapping (QSM) to measure magnetic susceptibility as a confounder-free predictor of HIC.

STUDY TYPE

Phantom study and in vivo cohort.

SUBJECTS

Nine iron phantoms covering clinically relevant R2* range (20-1200/second) and 48 patients (22 male, 26 female, median age 18 years).

FIELD STRENGTH/SEQUENCE

Three-dimensional (3D) and two-dimensional (2D) multi-echo gradient echo (GRE) at 1.5 T.

ASSESSMENT

ARMA-QSM modeling was performed on the complex 3D GRE signal to estimate R2*, fat fraction (FF), and susceptibility measurements. R2*-based dry clinical HIC values were calculated from the 2D GRE acquisition using a published R2*-HIC calibration curve as reference standard.

STATISTICAL TESTS

Linear regression analysis was performed to compare ARMA R2* and susceptibility-based estimates to iron concentrations and dry clinical HIC values in phantoms and patients, respectively.

RESULTS

In phantoms, the ARMA R2* and susceptibility values strongly correlated with iron concentrations (R²  ≥ 0.9). In patients, the ARMA R2* values highly correlated (R²  = 0.97) with clinical HIC values with slope = 0.026, and the susceptibility values showed good correlation (R²  = 0.82) with clinical dry HIC values with slope = 3.3 and produced a dry-to-wet HIC ratio of 4.8.

DATA CONCLUSION

This study shows the feasibility that ARMA-QSM can simultaneously estimate susceptibility-based wet HIC, R2*-based dry HIC and FFs from a single multi-echo GRE acquisition. Our results demonstrate that both, R2* and susceptibility-based wet HIC values estimated with ARMA-QSM showed good association with clinical dry HIC values with slopes similar to published R2*-biopsy HIC calibration and dry-to-wet tissue weight ratio, respectively. Hence, our study shows that ARMA-QSM can provide potentially confounder-free assessment of hepatic iron overload.

LEVEL OF EVIDENCE

3 TECHNICAL EFFICACY: Stage 2.

Feasibility of quantitative susceptibility mapping (QSM) of the human kidney

Objective

To evaluate the feasibility of in-vivo quantitative susceptibility mapping (QSM) of the human kidney.

Methods

An axial single-breath-hold 3D multi-echo sequence (acquisition time 33 s) was completed on a 3 T-MRI-scanner (Magnetom Prisma, Siemens Healthineers, Erlangen, Germany) in 19 healthy volunteers. Graph-cut-based unwrapping combined with the T₂*-IDEAL approach was performed to remove the chemical shift of fat and to quantify QSM of the upper abdomen. Mean susceptibility values of the entire, renal cortex and medulla in both kidneys and the liver were determined and compared. Five subjects were measured twice to examine the reproducibility. One patient with severe renal fibrosis was included in the study to evaluate the potential clinical relevance of QSM.

Results

QSM was successful in 17 volunteers and the patient with renal fibrosis. Anatomical structures in the abdomen were clearly distinguishable by QSM and the susceptibility values obtained in the liver were comparable to those found in the literature. The results showed a good reproducibility. Besides, the mean renal QSM values obtained in healthy volunteers (0.04 ± 0.07 ppm for the right and − 0.06 ± 0.19 ppm for the left kidney) were substantially higher than that measured in the investigated fibrotic kidney (− 0.43 ± − 0.02 ppm).

Conclusion

QSM of the human kidney could be a promising approach for the assessment of information about microscopic renal tissue structure. Therefore, it might further improve functional renal MR imaging.

Electronic supplementary material

The online version of this article (10.1007/s10334-020-00895-9) contains supplementary material, which is available to authorized users.

Improved body quantitative susceptibility mapping by using a variable-layer single-min-cut graph-cut for field-mapping

PURPOSE

To develop a robust algorithm for field-mapping in the presence of water-fat components, large B 0 field inhomogeneities and MR signal voids and to apply the developed method in body applications of quantitative susceptibility mapping (QSM).

METHODS

A framework solving the cost-function of the water-fat separation problem in a single-min-cut graph-cut based on the variable-layer graph construction concept was developed. The developed framework was applied to a numerical phantom enclosing an MR signal void, an air bubble experimental phantom, 14 large field of view (FOV) head/neck region in vivo scans and to 6 lumbar spine in vivo scans. Field-mapping and subsequent QSM results using the proposed algorithm were compared to results using an iterative graph-cut algorithm and a formerly proposed single-min-cut graph-cut.

RESULTS

The proposed method was shown to yield accurate field-map and susceptibility values in all simulation and in vivo datasets when compared to reference values (simulation) or literature values (in vivo). The proposed method showed improved field-map and susceptibility results compared to iterative graph-cut field-mapping especially in regions with low SNR, strong field-map variations and high R 2 ∗ values.

CONCLUSIONS

A single-min-cut graph-cut field-mapping method with a variable-layer construction was developed for field-mapping in body water-fat regions, improving quantitative susceptibility mapping particularly in areas close to MR signal voids.

R1ρ at high spin-lock frequency could be a complementary imaging biomarker for liver iron overload quantification

PURPOSE

To compare the correlations among the R1ρ, R2, and R2* relaxation rates with liver iron concentration (LIC) in the assessment of rat liver iron content and explore the application potential of R1ρ in assessing liver iron content.

METHODS

Iron dextran (dosage of 0, 25, 50, 100, and 200 mg/kg body weight) was injected into 35 male rats to increase the amount of iron storage in the liver. After one week, all rats were euthanized with isoflurane. A portion of the largest hepatic lobe was extracted to quantify the LIC by inductively coupled plasma, and the remaining liver tissue was stored in 4% buffered paraformaldehyde for 24 h before MRI. Spin-lock preparation with a RARE (rapid acquisition with relaxation enhancement) readout (9 different spin-lock times and 7 different spin-lock frequencies (FSLs)) and multi-echo UTE (ultrashort TE) pulses were developed to quantify R1ρ and R2 * on a Bruker 11.7 T MR system. For comparisons with R1ρ and R2*, R2 was acquired using the CPMG sequence.

RESULTS

Mean R1ρ values displayed dispersion, with decrease in R1ρ at higher FSLs. Spearman's correlation analysis (two-tailed) indicated that the R1ρ values were significantly associated with LIC at FSL = 2000, 2500, and 3000 Hz (r = 0.365 and P = 0.031, r = 0.608 and P < 0.001, and r = 0.764 and P < 0.001, respectively), and were not significantly associated with LIC at FSL = 500, 1000, 1250, and 1500 Hz (all P > 0.05). R2 and R2* showed significant linear correlations with LIC (r = 0.787 and P < 0.001, and r = 0.859 and P < 0.001, respectively). Correlation analysis across R1ρ, R2, and R* also suggested that the correlation strength between R1ρ and R2 and between R1ρ and R* showed an increasing trend with increase in FSL.

CONCLUSION

In this study, a strong association was observed between R1ρ and LIC at high FSLs further confirming previous findings. The results demonstrated that R1ρ at high FSL might serve as a complementary imaging biomarker for liver iron overload quantification.

Decreasing iron susceptibility with temperature in quantitative susceptibility mapping: A phantom study

To clarify the temperature dependence of susceptibility estimated by quantitative susceptibility mapping (QSM) analysis, we investigated the relationship between temperature and susceptibility using a cylinder phantom with varying temperatures. Six solutions with various concentrations of superparamagnetic iron oxide (SPIO) nanoparticles were employed. These tubes were placed in a cylinder phantom and surrounded with water. The temperature of the circulated water was adjusted to change the temperature in the cylinder phantom from 25.8 °C to 42.5 °C. The cylinder phantom was scanned via a three-dimensional multiple spoiled gradient-echo sequence for R2* and QSM analyses with varying temperatures. The relationships between temperature, susceptibility, and R2* values were determined. Moreover, the temperature coefficients of susceptibility (χ-Tc) and (R2*-Tc) were calculated at each concentration and the linearities in these indices against each SPIO concentration were validated. Significant inverse correlations were found between temperature, susceptibility, and R2* values at each SPIO concentration due to the decrease in paramagnetic iron susceptibility that occurred with increasing temperature based on Curie's law. Moreover, although there were significant correlations between the susceptibility and R2* values at any temperature, the slopes of the regression lines grew in height with greater temperatures. The percentage of difference per Celsius degree in susceptibility in any SPIO concentration was lower than the corresponding finding among the R2* results. There were strong linearities between the SPIO concentration, χ-Tc (r = -0.994; p < 0.001), and R2*-Tc (r = -0.998; p < 0.001). The χ-Tc and R2*-Tc outcomes in a particular voxel varied considerably with the iron contents. Although there was an inverse correlation noted between temperature and susceptibility, the susceptibility analysis showed smaller temperature dependence relative to the R2* analysis. QSM analysis might be a more suitable option for magnetic resonance-based iron quantification in comparison with R2* relaxometry.

Evaluation of liver iron overload with R2* relaxometry with versus without fat suppression: both are clinically accurate but there are differences

OBJECTIVES

To assess clinically relevant difference in hepatic iron quantification using R2* relaxometry with (FS) and without (non-FS) fat saturation for the evaluation of patients with suspected hepatic iron overload.

METHODS

We prospectively enrolled 134 patients who underwent 1.5-T MRI R2* relaxometry with FS and non-FS gradient echo sequences (12 echoes, initial TE = 0.99 ms). Proton density fat fraction for the quantification of steatosis was assessed. Linear regression analyses and Bland-Altman plots including Lin's concordance correlation coefficient were performed for correlation of FS R2* with non-FS R2*. Patients were grouped into 4 severity classes of iron overload (EASL based), and agreement was evaluated by contingency tables and the proportion of overall agreement.

RESULTS

A total of 41.8% of patients showed hepatic iron overload; 67.9% had concomitant steatosis; and 58.2% revealed no iron overload of whom 60.3% had steatosis. The mean R2* value for all FS data was 102.86 1/s, for non-FS 108.16 1/s. Linear regression resulted in an R-squared value of 0.99 (p < 0.001); Bland-Altman plot showed a mean R2* difference of 5.26 1/s (SD 17.82). The concordance correlation coefficient was only slightly lower for patients with steatosis compared with non-steatosis (0.988 vs. 0.993). The overall agreement between FS and non-FS R2* measurements was 94.8% using either method to classify patients according to severity of iron storage. No correlation between R2* and proton density fat fraction was found for both methods.

CONCLUSION

R2* relaxometry showed an excellent overall agreement between FS and non-FS acquisition. Both variants can therefore be used in daily routine. However, clinically relevant differences might result when switching between the two methods or during patient follow-up, when fat content changes over time. We therefore recommend choosing a method and keeping it straight in the context of follow-up examinations.

KEY POINTS

• Both variants of R2* relaxometry (FS and non-FS) may be used in daily routine. • Clinically relevant differences might result when switching between the two methods or during patient follow-up, when fat content changes over time. • It seems advisable choosing one method and keeping it straight in the context of follow-up examinations.

To measure T1 of short T2 species using an inversion recovery prepared three-dimensional ultrashort echo time (3D IR-UTE) method: A phantom study

PURPOSE

To demonstrate the feasibility of a new method for measuring T₁ of short T₂ species based on an adiabatic inversion recovery-prepared three-dimensional ultrashort echo time Cones (3D IR-UTE-Cones) sequence.

METHODS

T₁ values for short T₂ species were quantified using 3D IR-UTE-Cones data acquired with different repetition times (TRs) and inversion times (TIs). An inversion efficiency factor Q was introduced into the fitting model to accurately calculate T₁ values for short T₂ species. Experiments were performed on twelve MnCl₂ aqueous solution phantoms with a wide range of T₁ values and T₂* values on a 3 T clinical MR system to verify the efficacy of the proposed method. For comparison, a variable flip angle UTE (VFA-UTE) sequence, a variable TR UTE (VTR-UTE) sequence, and a conventional 2D IR fast spin echo (IR-FSE) sequence were also used to quantify T₁ values of those phantoms. T₁ values were compared between all performed sequences.

RESULTS

The proposed 3D IR-UTE-Cones method provided higher contrast images of short T₂ phantoms and measured much shorter T₁ values than the VFA-UTE, VTR-UTE and 2D IR-FSE methods. T₁ values as short as 2.95 ms could be measured by the 3D IR-UTE-Cones sequence. The 3D IR-UTE-Cones methods with different TRs were applied to different ranges of T₁ measurement, and the scan time was significantly decreased by using 5 TIs along the recovery curves to perform fitting with comparable accuracy.

CONCLUSION

The 3D IR-UTE-Cones sequence could accurately measure short T₁ values while providing high contrast images of short T₂ species.

Quantification of liver fat content in liver and primary liver lesions using triple-echo-gradient-echo MRI

OBJECTIVES

To quantify and compare the fat fraction of background liver and primary liver lesions using a triple-echo-gradient-echo sequence. M&M: This IRB-approved study included 128 consecutive patients who underwent a liver MRI for lesion characterization. Fat fraction from the whole lesion volume and the normal liver parenchyma were computed from triple-echo (consecutive in-phase, opposed-phase, in-phase echo times) sequence.

RESULTS

Forty-seven hepatocellular carcinoma (HCCs), 25 hepatocellular adenomas (HCAs), and 56 focal nodular hyperplasia (FNH) were included. The mean intralesional fat fraction for various lesions was 7.1% (range, 0.5-23.6; SD, 5.6) for HCAs, 5.7% (range, 0.8-14; SD, 2.9) for HCCs, and 2.3% (range, 0.8-10.3; SD, 1.9) for FNHs (p = 0.6 for HCCs vs HCA, p < 0.001 for FNH vs HCCs or HCA). A fat fraction threshold of 2.7% enabled distinction between HCA and FNH with a sensitivity of 80% and a specificity of 77%. The mean normal liver parenchyma fat fraction was lower than the intralesional fat fraction in the HCC group (p = 0.04) and higher in the FNH group (p = 0.001), but not significantly different in the HCA group (p = 0.51).

CONCLUSION

Triple-echo-gradient-echo is a feasible technique to quantify fat fraction of background liver and primary liver lesions. Intralesional fat fraction obtained from lesion whole volume is greater for HCCs and HCA compared to FNH. When trying to distinguish FNH and HCA, an intralesional fat fraction < 2.7% may orient toward the diagnosis of FNH.

KEY POINTS

• Triple-echo technique is feasible to quantify intralesional fat fraction of primary liver lesions. • Whole volume intralesional fat fraction is greater for HCCs and HCA compared to FNH. • An intralesional fat fraction < 2.7% may orient toward the diagnosis of FNH.

Topics on quantitative liver magnetic resonance imaging

Liver magnetic resonance imaging (MRI) is subject to continuous technical innovations through advances in hardware, sequence and novel contrast agent development. In order to utilize the abilities of liver MR to its full extent and perform high-quality efficient exams, it is mandatory to use the best imaging protocol, to minimize artifacts and to select the most adequate type of contrast agent. In this article, we review the routine clinical MR techniques applied currently and some latest developments of liver imaging techniques to help radiologists and technologists to better understand how to choose and optimize liver MRI protocols that can be used in clinical practice. This article covers topics on (I) fat signal suppression; (II) diffusion weighted imaging (DWI) and intravoxel incoherent motion (IVIM) analysis; (III) dynamic contrast-enhanced (DCE) MR imaging; (IV) liver fat quantification; (V) liver iron quantification; and (VI) scan speed acceleration.

Imaging diamagnetic susceptibility of collagen in hepatic fibrosis using susceptibility tensor imaging

PURPOSE

To characterize the magnetic susceptibility changes of liver fibrosis using susceptibility tensor imaging.

METHODS

Liver biopsy tissue samples of patients with liver fibrosis were obtained. Three-dimensional gradient-echo and diffusion-weighted images were acquired at 9.4 T. Susceptibility tensors of the samples were calculated using the gradient-echo phase signal acquired at 12 different orientations relative to the B₀ field. Susceptibility anisotropy of the liver collagen fibers was quantified and compared with diffusion anisotropy, measured by DTI. For validation, a comparison was made to histology including hematoxylin and eosin staining, iron staining, and Masson's trichrome staining.

RESULTS

Areas with strong diamagnetic susceptibility were observed in the tissue samples forming fibrous patterns. This diamagnetic susceptibility was highly anisotropic. Both the mean magnetic susceptibility and susceptibility anisotropy of collagen fibers exhibited a strong contrast against the surrounding nonfibrotic tissues. The same regions also showed an elevated diffusion anisotropy but with much lower tissue contrast. Masson's trichrome staining identified concentrated collagens in the fibrous regions with high susceptibility anisotropy, and a linear correlation was found between the susceptibility anisotropy and the histology-based staging.

CONCLUSION

Diamagnetic susceptibility indicates the presence of collagen in the fibrotic liver tissues. Mapping magnetic susceptibility anisotropy may serve as a potential marker to quantify collagen fiber changes in patients with liver fibrosis.

Detection of hepatic steatosis and iron content at 3 Tesla: comparison of two-point Dixon, quantitative multi-echo Dixon, and MR spectroscopy

PURPOSE

To compare qualitative results obtained from computer-aided dual-ratio analysis on T1-weighted two-point Dixon, with T2*-corrected multi-echo Dixon and T2-corrected multi-echo single-voxel MR spectroscopy sequence (MRS) for evaluation of liver fat and iron at 3T.

METHODS AND MATERIALS

This retrospective, HIPAA-compliant, IRB-approved study included 479 patients with known or suspected liver disease. Two-point Dixon, multi-echo Dixon, and MR spectroscopy sequences were performed for each patient at 3T. A receiver-operating characteristic analysis was performed to compare the diagnostic performance in 80 patients using biopsy as the standard. Sensitivity, specificity, PPV, and NPV of qualitative two-point Dixon results, multi-echo Dixon (PDFF and R2*), and MRS (fat fraction and R2 water) for detection of hepatic steatosis and siderosis were assessed.

RESULTS

Fat fractions obtained from MRS and multi-echo Dixon have equivalent accuracy for detection of hepatic steatosis (AUC, sensitivity and specificity: 0.90 vs 0.88, 0.77 vs. 0.82, and 0.90 vs. 0.82), but the optimal cutoff value is higher for MRS (6.05% vs. 3.4%). The dual-ratio Dixon discrimination technique showed high negative predictive value for detection of hepatic steatosis and siderosis (0.90 and 0.94, respectively). R2* from multi-echo Dixon and R2_water from MRS have equivalent accuracy for detection of iron overload at 3T (AUC 0.89 vs. 0.88). The optimal cutoff for R2* and R2_water are 60.5 s^-1 and 40.85 s^-1, respectively.

CONCLUSION

The computer-aided dual-ratio discrimination with two-point Dixon is a useful qualitative screening tool with high negative predictive value for hepatic steatosis and iron overload. Multi-echo Dixon and MRS have similar accuracy for detection of hepatic steatosis and iron overload at 3 Tesla.

Patents on Quantitative Susceptibility Mapping (QSM) of Tissue Magnetism

BACKGROUND

Quantitative susceptibility mapping (QSM) depicts biodistributions of tissue magnetic susceptibility sources, including endogenous iron and calcifications, as well as exogenous paramagnetic contrast agents and probes. When comparing QSM with simple susceptibility weighted MRI, QSM eliminates blooming artifacts and shows reproducible tissue susceptibility maps independent of field strength and scanner manufacturer over a broad range of image acquisition parameters. For patient care, QSM promises to inform diagnosis, guide surgery, gauge medication, and monitor drug delivery. The Bayesian framework using MRI phase data and structural prior knowledge has made QSM sufficiently robust and accurate for routine clinical practice.

OBJECTIVE

To address the lack of a summary of US patents that is valuable for QSM product development and dissemination into the MRI community.

METHOD

We searched the USPTO Full-Text and Image Database for patents relevant to QSM technology innovation. We analyzed the claims of each patent to characterize the main invented method and we investigated data on clinical utility.

RESULTS

We identified 17 QSM patents; 13 were implemented clinically, covering various aspects of QSM technology, including the Bayesian framework, background field removal, numerical optimization solver, zero filling, and zero-TE phase.

CONCLUSION

Our patent search identified patents that enable QSM technology for imaging the brain and other tissues. QSM can be applied to study a wide range of diseases including neurological diseases, liver iron disorders, tissue ischemia, and osteoporosis. MRI manufacturers can develop QSM products for more seamless integration into existing MRI scanners to improve medical care.

Fat spectral modeling on triglyceride composition quantification using chemical shift encoded magnetic resonance imaging

PURPOSE

To explore, at a high field strength of 7T, the performance of various fat spectral models on the quantification of triglyceride composition and proton density fat fraction (PDFF) using chemical-shift encoded MRI (CSE-MRI).

METHODS

MR data was acquired from CSE-MRI experiments for various fatty materials, including oil and butter samples and in vivo brown and white adipose mouse tissues. Triglyceride composition and PDFF were estimated using various a priori 6- or 9-peak fat spectral models. To serve as references, NMR spectroscopy experiments were conducted to obtain material specific fat spectral models and triglyceride composition estimates for the same fatty materials. Results obtained using the spectroscopy derived material specific models were compared to results obtained using various published fat spectral models.

RESULTS

Using a 6-peak fat spectral model to quantify triglyceride composition may lead to large biases at high field strengths. When using a 9-peak model, triglyceride composition estimations vary greatly depending on the relative amplitudes of the chosen a priori spectral model, while PDFF estimations show small variations across spectral models. Material specific spectroscopy derived spectral models produce estimations that better correlate with NMR spectroscopy estimations in comparison to those obtained using non-material specific models.

CONCLUSION

At a high field strength of 7T, a material specific 9-peak fat spectral model, opposed to a widely accepted or generic human liver model, is necessary to accurately quantify triglyceride composition when using CSE-MRI estimation methods that assume the spectral model to be known as a priori information. CSE-MRI allows for the quantification of the spatial distribution of triglyceride composition for certain in vivo applications. Additionally, PDFF quantification is shown to be independent of the chosen a priori spectral model, which agrees with previously reported results obtained at lower field strengths (e.g. 3T).

Iron deposition quantification: Applications in the brain and liver

Iron has long been implicated in many neurological and other organ diseases. It is known that over and above the normal increases in iron with age, in certain diseases there is an excessive iron accumulation in the brain and liver. MRI is a noninvasive means by which to image the various structures in the brain in three dimensions and quantify iron over the volume of the object of interest. The quantification of iron can provide information about the severity of iron-related diseases as well as quantify changes in iron for patient follow-up and treatment monitoring. This article provides an overview of current MRI-based methods for iron quantification, specifically for the brain and liver, including: signal intensity ratio, R₂ , R2*, R2', phase, susceptibility weighted imaging and quantitative susceptibility mapping (QSM). Although there are numerous approaches to measuring iron, R₂ and R2* are currently preferred methods in imaging the liver and QSM has become the preferred approach for imaging iron in the brain.

LEVEL OF EVIDENCE

5 Technical Efficacy: Stage 5 J. Magn. Reson. Imaging 2018. J. MAGN. RESON. IMAGING 2018;48:301-317.