Fiber-orientation independent component of R2* obtained from single-orientation MRI measurements in simulations and a post-mortem human optic chiasm.

The effective transverse relaxation rate (R2*) is sensitive to the microstructure of the human brain like the g-ratio which characterises the relative myelination of axons. However, the fibre-orientation dependence of R2* degrades its reproducibility and any microstructural derivative measure. To estimate its orientation-independent part (R2,iso*) from single multi-echo gradient-recalled-echo (meGRE) measurements at arbitrary orientations, a second-order polynomial in time model (hereafter M2) can be used. Its linear time-dependent parameter, ß1, can be biophysically related to R2,iso* when neglecting the myelin water (MW) signal in the hollow cylinder fibre model (HCFM). Here, we examined the performance of M2 using experimental and simulated data with variable g-ratio and fibre dispersion. We found that the fitted ß1 can estimate R2,iso* using meGRE with long maximum-echo time (TEmax ≈ 54 ms), but not accurately captures its microscopic dependence on the g-ratio (error 84%). We proposed a new heuristic expression for ß1 that reduced the error to 12% for ex vivo compartmental R2 values. Using the new expression, we could estimate an MW fraction of 0.14 for fibres with negligible dispersion in a fixed human optic chiasm for the ex vivo compartmental R2 values but not for the in vivo values. M2 and the HCFM-based simulations failed to explain the measured R2*-orientation-dependence around the magic angle for a typical in vivo meGRE protocol (with TEmax ≈ 18 ms). In conclusion, further validation and the development of movement-robust in vivo meGRE protocols with TEmax ≈ 54 ms are required before M2 can be used to estimate R2,iso* in subjects.

Simultaneous time of flight-MRA and T2* imaging for cerebrovascular MRI.

PURPOSE: 3D multi-echo gradient-recalled echo (ME-GRE) can simultaneously generate time-of-flight magnetic resonance angiography (pTOF) in addition to T2*-based susceptibility-weighted images (SWI). We assessed the clinical performance of pTOF generated from a 3D ME-GRE acquisition compared with conventional TOF-MRA (cTOF). METHODS: Eighty consecutive children were retrospectively identified who obtained 3D ME-GRE alongside cTOF. Two blinded readers independently assessed pTOF derived from 3D ME-GRE and compared them with cTOF. A 5-point Likert scale was used to rank lesion conspicuity and to assess for diagnostic confidence. RESULTS: Across 80 pediatric neurovascular pathologies, a similar number of lesions were reported on pTOF and cTOF (43-40%, respectively, p > 0.05). Rating of lesion conspicuity was higher with cTOF (4.5 ± 1.0) as compared with pTOF (4.0 ± 0.7), but this was not significantly different (p = 0.06). Diagnostic confidence was rated higher with cTOF (4.8 ± 0.5) than that of pTOF (3.7 ± 0.6; p < 0.001). Overall, the inter-rater agreement between two readers for lesion count on pTOF was classified as almost perfect (κ = 0.98, 96% CI 0.8-1.0). CONCLUSIONS: In this study, TOF-MRA simultaneously generated in addition to SWI from 3D MR-GRE can serve as a diagnostic adjunct, particularly for proximal vessel disease and when conventional TOF-MRA images are absent.

Robust water-fat separation based on deep learning model exploring multi-echo nature of mGRE.

PURPOSE: To design a new deep learning network for fast and accurate water-fat separation by exploring the correlations between multiple echoes in multi-echo gradient-recalled echo (mGRE) sequence and evaluate the generalization capabilities of the network for different echo times, field inhomogeneities, and imaging regions. METHODS: A new multi-echo bidirectional convolutional residual network (MEBCRN) was designed to separate water and fat images in a fast and accurate manner for the mGRE data. This new MEBCRN network contains 2 main modules, the first 1 is the feature extraction module, which learns the correlations between consecutive echoes, and the other one is the water-fat separation module that processes the feature information extracted from the feature extraction module. The multi-layer feature fusion (MLFF) mechanism and residual structure were adopted in the water-fat separation module to increase separation accuracy and robustness. Moreover, we trained the network using in vivo abdomen images and tested it on the abdomen, knee, and wrist images. RESULTS: The results showed that the proposed network could separate water and fat images accurately. The comparison of the proposed network and other deep learning methods shows the advantage in both quantitative metrics and robustness for different TEs, field inhomogeneities, and images acquired for various imaging regions. CONCLUSION: The proposed network could learn the correlations between consecutive echoes and separate water and fat images effectively. The deep learning method has certain generalization capabilities for TEs and field inhomogeneity. Although the network was trained only in vivo abdomen images, it could be applied for different imaging regions.

Robust water-fat separation for multi-echo gradient-recalled echo sequence using convolutional neural network.

PURPOSE: To accurately separate water and fat signals for bipolar multi-echo gradient-recalled echo sequence using a convolutional neural network (CNN). METHODS: A CNN architecture was designed and trained using the relationship between multi-echo images from the bipolar multi-echo gradient-recalled echo sequence and artifact-free water-fat-separated images. The artifact-free water-fat-separated images for training the CNN were obtained from multiple signals with different TEs by using iterative decomposition of water and fat with echo asymmetry and the least-squares estimation method, in which multiple signals at different TEs were acquired using a single-echo gradient-recalled echo sequence. We also proposed a data augmentation method using a synthetic field inhomogeneity to generate multi-echo signals, including various bipolar multi-echo gradient-recalled echo artifacts so that the CNN could prevent overfitting and increase the separation accuracy. We trained the CNN using in vivo knee images and tested it using in vivo knee, head, and ankle images. RESULTS: In vivo imaging results showed that the proposed CNN could separate water-fat images accurately. Although the proposed CNN was trained using only in vivo knee images, the proposed CNN could also separate water-fat images of different imaging regions. The proposed data augmentation method could prevent overfitting even with a limited number of training data sets and make the method robust to magnetic field inhomogeneities. CONCLUSION: The proposed CNN could obtain water-fat-separated images from the multi-echo images acquired from the bipolar multi-echo gradient-recalled echo sequence, which included artifacts from the bipolar gradients.

Multi-echo gradient recalled echo imaging of the pelvis for improved depiction of brachytherapy seeds and fiducial markers facilitating radiotherapy planning and treatment of prostatic carcinoma.

PURPOSE: MR localization of implanted devices for radiotherapy (RT) in prostatic carcinoma is critical for treatment planning. This clinical note studies the application of a multi-echo gradient recalled echo (GRE) pulse sequence with sum of squares echo combination (ME GRE) to enhance detection of seeds and fiducials. MATERIALS AND METHODS: Fifteen patients who underwent MRI using fast spin echo (FSE), single-echo and ME GRE over a 9-month period were retrospectively evaluated by two readers who assessed overall image quality, depiction of seeds/fiducials and image sharpness using a 5-point scale (1 = poor, 2 = suboptimal, 3 = adequate, 4 = above average, 5 = excellent). Image scores were compared using the Wilcoxon sign rank test. RESULTS: In all 15 patients, both readers rated the depiction of seeds/fiducials with ME GRE as excellent. In all 15 patients, overall image quality and image sharpness with ME GRE was rated as excellent by reader 1. In 12/15 patients, overall image quality and image sharpness with ME GRE was rated as excellent and in the other patients above average by reader 2. There was a difference in depiction of seeds/fiducials comparing GRE to FSE (P < 0.001) and ME to single echo GRE (P < 0.001). Overall image quality and sharpness was higher with ME compared with single echo GRE (P < 0.001) and similar to FSE (P = 0.26 and P = 0.16). CONCLUSION: Multi-echo GRE provides better detection of implanted seeds and fiducial markers when compared with both FSE and single-echo GRE potentially improving RT treatment planning for prostate carcinoma.

Reversible, irreversible and effective transverse relaxation rates in normal aging brain at 3T.

Quantitative transverse relaxation rates in normal aging brain are essential to investigate pathologies associated with iron accumulation and tissue degeneration. Since absolute values depend on imaging methods and magnetic field strengths, continuous evaluation of specific reference values remains requisite. Multi-echo turbo spin echo and multi-echo gradient recalled echo imaging sequences were applied to 66 healthy subjects (18-84years) at 3T to quantify the irreversible (R2), effective (R2*) and reversible (R2'=R2*-R2) transverse relaxation rates. Representative regions-of-interest (ROIs) were determined automatically in gray matter (GM) and white matter (WM) on T1-weighted scans. Phantom experiments of different sized iron-oxide particles were conducted to explore the correlation of R2' related to R2 for the evaluation of the size of iron deposits. R2 decreased with age for the majority of ROIs, but increased for putamen, head of caudate nucleus and nucleus accumbens. R2* and R2' increased with age in deep GM structures except for the thalamus. R2* and R2' showed a distinct dependency on fiber orientation in exemplary WM regions. R2', R2 and R2* were strongly linear proportional to age-related iron content in deep GM with slopes of 0.88, 0.18 and 1.08 in [1/s/mg Fe per 100g wet tissue] and intercepts of 1.69, 9.25 and 10.69 in [1/s], respectively. Linear and non-linear curve fitting of R2' vs. R2 in phantoms revealed increased slopes with increasing particle size. In vivo, averaged R2' vs. R2 data points of patients with Parkinson's disease and progressive supranuclear palsy were above the fitted curves of healthy subjects suggesting larger sized iron deposits in these neurodegenerative diseases. Decreased R2 with age may reflect physiological tissue degeneration, whereas increased R2* and R2' with age most likely denote physiological iron accumulation. The low intercept of R2' vs. iron content suggests a nearly sole sensitivity of R2' to iron in deep GM, potentially allowing a more specific estimation of the iron content than R2 or R2*. Since R2* and R2' depend on the fiber orientation, their feasibility to estimate iron content in WM is challenging. The analysis of R2' related to R2 may provide valuable information about the size of iron deposits.