A simulation-based comparison of two methods for determining relaxation rates from relaxometry images

A fully automatic parenchyma extraction method for MRI T2* relaxometry of iron loaded liver in transfusion-dependent patients

PURPOSE

To develop a fully automatic parenchyma extraction method for the T2* relaxometry of iron overload liver.

METHODS

A retrospective multicenter collection of liver MR examinations from 177 transfusion-dependent patients was conducted. The proposed method extended a semiautomatic parenchyma extraction algorithm to a fully automatic approach by introducing a modified TransUNet on the R2* (1/T2*) map for liver segmentation. Axial liver slices from 129 patients at 1.5 T were allocated to training (85%) and internal test (15%) sets. Two external test sets separately included 1.5 T data from 20 patients and 3.0 T data from 28 patients. The final T2* measurement was obtained by fitting the average signal of the extracted liver parenchyma. The agreement between T2* measurements using fully and semiautomatic parenchyma extraction methods was assessed using coefficient of variation (CoV) and Bland-Altman plots.

RESULTS

Dice of the deep network-based liver segmentation was 0.970 ± 0.019 on the internal dataset, 0.960 ± 0.035 on the external 1.5 T dataset, and 0.958 ± 0.014 on the external 3.0 T dataset. The mean difference bias between T2* measurements of the fully and semiautomatic methods were separately 0.12 (95% CI: -0.37, 0.61) ms, 0.04 (95% CI: -1.0, 1.1) ms, and 0.01 (95% CI: -0.25, 0.23) ms on the three test datasets. The CoVs between the two methods were 4.2%, 4.8% and 2.0% on the internal test set and two external test sets.

CONCLUSIONS

The developed fully automatic parenchyma extraction approach provides an efficient and operator-independent T2* measurement for assessing hepatic iron content in clinical practice.

Fast, Accurate, and Robust T2 Mapping of Articular Cartilage by Neural Networks

For T2 mapping, the underlying mono-exponential signal decay is traditionally quantified by non-linear Least-Squares Estimation (LSE) curve fitting, which is prone to outliers and computationally expensive. This study aimed to validate a fully connected neural network (NN) to estimate T2 relaxation times and to assess its performance versus LSE fitting methods. To this end, the NN was trained and tested in silico on a synthetic dataset of 75 million signal decays. Its quantification error was comparatively evaluated against three LSE methods, i.e., traditional methods without any modification, with an offset, and one with noise correction. Following in-situ acquisition of T2 maps in seven human cadaveric knee joint specimens at high and low signal-to-noise ratios, the NN and LSE methods were used to estimate the T2 relaxation times of the manually segmented patellofemoral cartilage. In-silico modeling at low signal-to-noise ratio indicated significantly lower quantification error for the NN (by medians of 6−33%) than for the LSE methods (p < 0.001). These results were confirmed by the in-situ measurements (medians of 10−35%). T2 quantification by the NN took only 4 s, which was faster than the LSE methods (28−43 s). In conclusion, NNs provide fast, accurate, and robust quantification of T2 relaxation times.

Improved Liver R2* Mapping by Averaging Decay Curves

Liver R2* mapping is often degraded by the low signal-to-noise ratio (SNR) especially in the presence of severe iron. This study aims to improve liver R2* mapping at low SNRs by averaging decay curves before the process of curve-fitting. Independently filtering echo images by nonlocal means (NLM) demonstrated improved quality of R2* mapping, but may introduce new errors due to the nonlinear nature of the NLM filter, during which the averaging weights may vary with different image contents at multiple echo times. In addition, the image denoising effect of the NLM may decline when no sufficient similar patches are available. To overcome these drawbacks, we proposed to filter decay curves instead of images. In this novel scheme, decay curves were averaged in a local window, each with a weight assigned according to the curve-similarity measured by the distance between one of the neighboring curves and the targeted one. The proposed method was tested on simulated, phantom and patient data. The results demonstrate that the proposed method can provide more accurate R2* mapping compared with the NLM algorithm, and hence has the potential to improve diagnosis and therapy in patients with liver iron.

On the R2⁎ relaxometry in complex multi-peak multi-Echo chemical shift-based water-fat quantification: Applications to the neuromuscular diseases

PURPOSE

Investigation of the feasibility of the R₂^⁎ mapping techniques by using latest theoretical models corrected for confounding factors and optimized for signal to noise ratio.

THEORY AND METHODS

The improvement of the performance of state of the art magnetic resonance imaging (MRI) relaxometry algorithms is challenging because of a non-negligible bias and still unresolved numerical instabilities. Here, R₂^⁎ mapping reconstructions, including complex fitting with multi-spectral fat-correction by using single-decay and double-decay formulation, are deeply studied in order to investigate and identify optimal configuration parameters and minimize the occurrence of numerical artifacts. The effects of echo number, echo spacing, and fat/water relaxation model type are evaluated through both simulated and in-vivo data. We also explore the stability and feasibility of the fat/water relaxation model by analyzing the impact of high percentage of fat infiltrations and local transverse relaxation differences among biological species.

RESULTS

The main limits of the MRI relaxometry are the presence of bias and the occurrence of artifacts, which significantly affect its accuracy. Chemical-shift complex R₂^⁎-correct single-decay reconstructions exhibit a large bias in presence of a significant difference in the relaxation rates of fat and water and with fat concentration larger than 30%. We find that for fat-dominated tissues or in patients affected by extensive iron deposition, MRI reconstructions accounting for multi-exponential relaxation time provide accurate R₂^⁎ measurements and are less prone to numerical artifacts.

CONCLUSIONS

Complex fitting and fat-correction with multi-exponential decay formulation outperforms the conventional single-decay approximation in various diagnostic scenarios. Although it still lacks of numerical stability, which requires model enhancement and support from spectroscopy, it offers promising perspectives for the development of relaxometry as a reliable tool to improve tissue characterization and monitoring of neuromuscular disorders.

Validation of a new T2* algorithm and its uncertainty value for cardiac and liver iron load determination from MRI magnitude images

Purpose

To validate an automatic algorithm for offline T2* measurements, providing robust, vendor‐independent T2*, and uncertainty estimates for iron load quantification in the heart and liver using clinically available imaging sequences.

Methods

A T2* region of interest (ROI)‐based algorithm was developed for robustness in an offline setting. Phantom imaging was performed on a 1.5 Tesla system, with clinically available multiecho gradient‐recalled‐echo (GRE) sequences for cardiac and liver imaging. A T2* single‐echo GRE sequence was used as reference. Simulations were performed to assess accuracy and precision from 2000 measurements. Inter‐ and intraobserver variability was obtained in a patient study (n = 23).

Results

Simulations: Accuracy, in terms of the mean differences between the proposed method and true T2* ranged from 0–0.73 ms. Precision, in terms of confidence intervals of repeated measurements, was 0.06–4.74 ms showing agreement between the proposed uncertainty estimate and simulations. Phantom study: Bias and variability were 0.26 ± 4.23 ms (cardiac sequence) and −0.23 ± 1.69 ms (liver sequence). Patient study: Intraobserver variability was similar for experienced and inexperienced observers (0.03 ± 1.44 ms versus 0.16 ± 2.33 ms). Interobserver variability was 1.0 ± 3.77 ms for the heart and −0.52 ± 2.75 ms for the liver.

Conclusion

The proposed algorithm was shown to provide robust T2* measurements and uncertainty estimates over the range of clinically relevant T2* values. Magn Reson Med, 2015. © 2015 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. Magn Reson Med 75:1717–1729, 2016. © 2015 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance.

Fat and iron quantification in the liver: past, present, and future

Liver fat, iron, and combined overload are common manifestations of diffuse liver disease and may cause lipotoxicity and iron toxicity via oxidative hepatocellular injury, leading to progressive fibrosis, cirrhosis, and eventually, liver failure. Intracellular fat and iron cause characteristic changes in the tissue magnetic properties in predictable dose-dependent manners. Using dedicated magnetic resonance pulse sequences and postprocessing algorithms, fat and iron can be objectively quantified on a continuous scale. In this article, we will describe the basic physical principles of magnetic resonance fat and iron quantification and review the imaging techniques of the "past, present, and future." Standardized radiological metrics of fat and iron are introduced for numerical reporting of overload severity, which can be used toward objective diagnosis, grading, and longitudinal disease monitoring. These noninvasive imaging techniques serve an alternative or complimentary role to invasive liver biopsy. Commercial solutions are increasingly available, and liver fat and iron quantitative imaging is now within reach for routine clinical use and may soon become standard of care.

Optimal region-of-interest MRI R2* measurements for the assessment of hepatic iron content in thalassaemia major

OBJECTIVES

To evaluate the performance of region-of-interest (ROI)-based MRI R2* measurements by using the first-moment noise-corrected model (M(1)NCM) to correct the non-central Chi noise in magnitude images from phased arrays for hepatic iron content (HIC) assessment.

METHODS

R2* values were quantified using the M(1)NCM model. Three approaches were employed to determine the representative R2*: fitting of the ROI-averaged signal (average-then-fit, ATF); outputting the median and mean of R2*s from the pixel-wise fitting of decay signals within the ROI (denoted as PWFmed and PWFmea, respectively). The accuracy and precision of the three approaches were evaluated on synthesized data. The agreement among these approaches and their intra- and inter-observer reproducibility were assessed on 105 thalassaemia major patients.

RESULTS

Simulations showed that ATF consistently yielded the highest accuracy and precision at varying noise levels. By contrast, PWFmed and PWFmea slightly and significantly overestimated high R2* at poor signal-to-noise ratios, respectively. Patient study showed that ATF agreed well with PWFmed, whereas PWFmea produced high R2* measurements for patients with severe HIC. No significant difference was observed in the reproducibility of the three approaches.

CONCLUSIONS

PWFmea tends to overestimate high R2*, whereas ATF and PWFmed can produce more accurate R2* measurements for HIC assessment.

A novel semiautomatic parenchyma extraction method for improved MRI R2* relaxometry of iron loaded liver

PURPOSE

To propose and evaluate an automatic method of extracting parenchyma from a manually delineated whole liver for the R2* measurement of iron load.

MATERIALS AND METHODS

In all, 108 transfusion-dependent patients with a wide range of hepatic iron content were scanned with a multiecho gradient-echo sequence. The R2* was measured by fitting the average signal of liver parenchyma, extracted by the proposed semiautomatic parenchyma extraction (SAPE), traditional manually delineated multiple regions-of-interest (mROIs), and T2* thresholding methods to the noise-corrected monoexponential model. The R2* measurement accuracy of the SAPE method was evaluated through simulation; the intra- and interobserver reproducibility of SAPE, mROI, and T2* thresholding were assessed from the in vivo data using coefficient of variation (CoV).

RESULTS

In the simulation, the mean absolute percentage error of R2* measurement using SAPE was 0.23% (range 0.01%-1.09%). In vivo study, the CoVs of intra- and interobserver reproducibility were 0.83%, 1.39% for SAPE, 3.63%, 6.28% for mROI, and 1.62%, 2.66% for T2* thresholding, respectively.

CONCLUSION

The SAPE method provides an accurate and reliable approach to assessing the overall hepatic iron content. The improved magnetic resonance imaging (MRI) R2* reproducibility using the SAPE method may lead to more accurate tissue characterization and increased diagnostic confidence.

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.

Liver and heart MR relaxometry in iron loading: reproducibility of three methods

PURPOSE

To compare the derived T2* values and reproducibility of three methods used to assess iron-loading in heart and liver.

MATERIALS AND METHODS

In 23 pediatric patients, liver and cardiac gradient-echo imaging datasets (within-exam repeated sequence pairs) were evaluated. Data analyses compared derived relaxation values (average of pairs) and coefficient of variation (reproducibility of pairs).

RESULTS

T2* values showed differences across methods, with pixel-wise mean > average fit > pixel-wise median. Coefficient of variation was found to be lower (better) with pixel-wise median and average fit methods compared to the pixel-wise mean technique. Maximum coefficient of variation values were lowest for the pixel-wise median approach in both the heart and liver.

CONCLUSION

Differences in derived T2* values between methods must be considered when comparing values to established magnetic resonance imaging (MRI)-biopsy formulas. The pixel-wise median and average fit methods demonstrate substantial benefits in reproducibility compared to the pixel-wise mean method. Since minimal variation in measurement is critical for patient care, median processing of relaxometry data may be preferable in both tissue types.

Quantification of superparamagnetic iron oxide with large dynamic range using TurboSPI

This work proposes the use of TurboSPI, a multi-echo single point imaging sequence, for the quantification of labeled cells containing moderate to high concentrations of iron oxide contrast agent. At each k-space location, TurboSPI acquires several hundred time points during a spin echo, permitting reliable relaxation rate mapping of large-R(2)(∗) materials. An automatic calibration routine optimizes image quality by promoting coherent alignment of spin and stimulated echoes throughout the multi-echo train, and this calibration is sufficiently robust for in vivo applications. In vitro relaxation rate measurements of SPIO-loaded cervical cancer cells exhibit behavior consistent with theoretical predictions of the static dephasing regime in the spin echo case; the relaxivity measured with TurboSPI was 10.47±2.3 s(-1)/mG, comparable to the theoretical value of 10.78 s(-1)/mG. Similar measurements of micron-sized iron oxide particles (0.96 μm and 1.63 μm diameter) show a reduced relaxivity of 8.06±0.68 s(-1)/mG and 7.13±0.31 s(-1)/mG respectively, indicating that the static dephasing criterion was not met. Nonetheless, accurate quantification of such particles is demonstrated up to R(2)(∗)=900 s(-1), with a potentially higher upper limit for loaded cells having a more favorable R(2)('):R(2) ratio. Based on the cells used in this study, reliable quantification of cells loaded with 10 pg of iron per cell should be possible up to a density of 27 million cells/mL. Such quantification will be of crucial importance to the development of longitudinal monitoring for cellular therapy and other procedures using iron-labeled cells.