Rapid B1 field mapping at 3 T using the 180° signal null method with extended flip angle

B1 mapping using an EPI-based double angle approach: A practical guide for correcting slice profile and B0 distortion effects

PURPOSE

Aim of this study was to develop a reliable B₁ mapping method for brain imaging based on vendor MR sequences available on clinical scanners. Correction procedures for B₀ distortions and slice profile imperfections are proposed, together with a phantom experiment for deriving the approximate time-bandwidth-product (TBP) of the excitation pulse, which is usually not known for vendor sequences.

METHODS

The double angle method was used, acquiring two gradient echo echo-planar imaging data sets with different excitation angles. A correction factor C (B₁ , TBP, B₀ ) was derived from simulations for converting double angle method signal quotients into bias-free B₁ maps. In vitro and in vivo tests compare results with reference B₁ maps based on an established in-house sequence.

RESULTS

The simulation shows that C has a negligible B₁ dependence, allowing for a polynomial approximation of C (TBP, B₀ ). Signal quotients measured in a phantom experiment with known TBP reconfirm the simulation results. In vitro and in vivo B₁ maps based on the proposed method, assuming TBP = 5.8 as derived from a phantom experiment, match closely the reference B₁ maps. Analysis without B₀ correction shows marked deviations in areas of distorted B₀ , highlighting the importance of this correction.

CONCLUSION

Double angle method-based B₁ mapping was set up for vendor gradient echo-echo-planar imaging sequences, using a correction procedure for slice profile imperfections and B₀ distortions. This will help to set up quantitative MRI studies on clinical scanners with release sequences, as the method does not require knowledge of the exact RF-pulse profiles or the use of in-house sequences.

Four-angle method for practical ultra-high-resolution magnetic resonance mapping of brain longitudinal relaxation time and apparent proton density

Changes in longitudinal relaxation time (T₁) and proton density (PD) are sensitive indicators of microstructural alterations associated with various central nervous system diseases as well as brain maturation and aging. In this work, we introduce a new approach for rapid and accurate high-resolution (HR) or ultra HR (UHR) mapping of T₁ and apparent PD (APD) of the brain with correction of radiofrequency field, B₁, inhomogeneities. The four-angle method (FAM) uses four spoiled-gradient recalled-echo (SPGR) images acquired at different flip angles (FA) and short repetition times (TRs). The first two SPGR images are acquired at low-spatial resolution and used to accurately map the active B₁⁺ field with the recently introduced steady-state double angle method (SS-DAM). The estimated B₁⁺ map is used in conjunction with the two other SPGR images, acquired at HR or UHR, to map T₁ and APD. The method is evaluated with numerical, phantom, and in-vivo imaging measurements. Furthermore, we investigated imaging acceleration methods to further shorten the acquisition time. Our results indicate that FAM provides an accurate method for simultaneous HR or UHR mapping of T₁ and APD in human brain in clinical high-field MRI. Derived parameter maps without B₁⁺correction suffer from large inaccuracies, but this issue is well-corrected through use of the SS-DAM. Furthermore, the use of SPGR imaging with short TR and phased-array coil acquisition permits substantial imaging acceleration and enables robust HR or UHR T₁ and APD mapping in a clinically acceptable time frame, with whole brain coverage obtained in less than 2 min or 5 min, respectively. The method exhibits high reproducibility and benefits from the use of the conventional SPGR sequence, available in all preclinical and clinical MRI machines, and very simple modeling to address a critical outstanding issue in neuroimaging.

Steady-state double-angle method for rapid B1 mapping

PURPOSE

To introduce an accurate, rapid, and practical method for active B₁ field mapping based on the double-angle method (DAM) in the steady-state (SS) signal regime.

METHODS

We introduced and evaluated the performance of the SS-DAM approach to map the B₁ field and compared the results to those calculated from the conventional DAM approach. Similar to DAM, SS-DAM uses the signal intensity ratio of 2 magnitude images acquired with different flip angles using the spoiled gradient recalled echo sequence. However, unlike DAM, in SS-DAM, these 2 spoiled gradient recalled echo images are acquired with very short TR, which allows substantially reduced acquisition time. Numerical, phantom, and in vivo brain imaging analyses, representing a wide range of T₁ s and large B₁ variation, were conducted. Methods for further accelerating acquisition were also investigated.

RESULTS

Our results demonstrate the potential of the SS-DAM approach to be applied widely in the clinical setting. B₁ maps derived from SS-DAM were demonstrated to be quantitatively comparable to those derived from DAM but were derived much more rapidly. Large-volume B₁ maps were obtained at a field strength of 3 tesla within clinically acceptable acquisition times.

CONCLUSION

SS-DAM permits accurate B₁ mapping in the clinical setting, with whole-brain coverage in less than 1 min.

A simple and fast adaptive nonlocal multispectral filtering algorithm for efficient noise reduction in magnetic resonance imaging

PURPOSE

We recently introduced a multispectral (MS) nonlocal (NL) filter based on maximum likelihood estimation (MLE) of voxel intensities, termed MS-NLML. While MS-NLML provides excellent noise reduction and improved image feature preservation as compared to other NL or MS filters, it requires considerable processing time, limiting its application in routine analyses. In this work, we introduced a fast, simple, and robust filter, termed nonlocal estimation of multispectral magnitudes (NESMA), for noise reduction in multispectral (MS) magnetic resonance imaging (MRI).

METHODS

Through extensive simulation and in-vivo analyses, we compared the performance of NESMA and MS-NLML in terms of noise reduction and processing efficiency. Further, we introduce two simple adaptive methods that permit spatial variation of similar voxels, R, used in the filtering. The first method is semi-adaptive and permits variation of R across the image by using a relative Euclidean distance (RED) similarity threshold. The second method is fully adaptive and filters the raw data with several RED similarity thresholds to spatially determine the optimal threshold value using an unbiased criterion.

RESULTS

NESMA shows very similar filtering performance as compared to MS-NLML, however, with much simple implementation and very fast processing time. Further, for both filters, the adaptive methods were shown to further reduce noise in comparison with the conventional non-adaptive method in which R is set to a constant value throughout the image.

CONCLUSIONS

NESMA is fast, robust, and straightforward to implement filter. These features render it suitable for routine clinical use and analysis of large MRI datasets.

Spatially adaptive unsupervised multispectral nonlocal filtering for improved cerebral blood flow mapping using arterial spin labeling magnetic resonance imaging

BACKGROUND

Cerebral blood flow (CBF) is an emerging biomarker for normal aging and neurodegenerative diseases. Arterial spin labeling (ASL) perfusion MRI permits noninvasive quantification of CBF. However, high-quality mapping of CBF from ASL imaging is challenging, largely due to noise.

NEW METHOD

We demonstrate the ability of the recently introduced nonlocal estimation of multispectral magnitudes (NESMA) filter to greatly improve determination of CBF estimates from ASL imaging data. We evaluated the results of NESMA-ASL for CBF mapping from data obtained on human brain (n = 10) across a wide age range (21-74 years) using a standard clinical protocol. Results were compared to those obtained from unfiltered images or filtered images using conventional and advanced filters. Quantitative analyses for different spatial image resolutions and signal-to-noise ratios, SNRs, were also conducted.

RESULTS

Our results demonstrate the potential of NESMA-ASL to permit high-quality high-resolution CBF mapping. NESMA-ASL substantially reduces random variation in derived CBF estimates while preserving edges and small structures, with minimal bias and dispersion in derived CBF estimates.

COMPARISON WITH EXISTING METHODS

NESMA-ASL outperforms all evaluated filters in terms of noise reduction and detail preservation. Further, unlike other filters, NESMA-ASL is straightforward to implement requiring only one user-defined parameter, which is relatively insensitive to SNR or local image structure.

CONCLUSIONS

In-vivo estimation of CBF in the human brain from ASL imaging data was markedly improved through use of the NESMA-ASL filter. The use of NESMA-ASL may contribute significantly to the goal of high-quality high-resolution CBF mapping within a clinically feasible acquisition time.