Single-shot echo planar time-resolved imaging for multi-echo functional MRI and distortion-free diffusion imaging

PURPOSE

To develop a single-shot SNR-efficient distortion-free multi-echo imaging technique for dynamic imaging applications.

METHODS

Echo planar time-resolved imaging (EPTI) was first introduced as a multi-shot technique for distortion-free multi-echo imaging. This work aims to develop single-shot EPTI (ss-EPTI) to achieve improved robustness to motion/physiological noise, increased temporal resolution, and higher SNR efficiency. A new spatiotemporal encoding that enables reduced phase-encoding blips and minimized echo spacing under the single-shot regime was developed, which improves sampling efficiency and enhances spatiotemporal correlation in the k-TE space for improved reconstruction. A continuous readout with minimized deadtime was employed to optimize SNR efficiency. Moreover, k-TE partial Fourier and simultaneous multi-slice acquisition were integrated for further acceleration.

RESULTS

ss-EPTI provided distortion-free imaging with densely sampled multi-echo images at standard resolutions (e.g., ˜1.25 to 3 mm) in a single-shot. Improved SNR efficiency was observed in ss-EPTI due to improved motion/physiological-noise robustness and efficient continuous readout. Its ability to eliminate dynamic distortions-geometric changes across dynamics due to field changes induced by physiological variations or eddy currents-further improved the data's temporal stability. For multi-echo fMRI, ss-EPTI's multi-echo images recovered signal dropout in short-T 2 * $$ {\mathrm{T}}_2^{\ast } $$ regions and provided TE-dependent functional information to distinguish non-BOLD noise for further tSNR improvement. For diffusion MRI, it achieved shortened TEs for improved SNR and provided images free from both B0-induced and diffusion-encoding-dependent eddy-current-induced distortions with multi-TE diffusion metrics.

CONCLUSION

ss-EPTI provides SNR-efficient distortion-free multi-echo imaging with comparable temporal resolutions to ss-EPI, offering a new acquisition tool for dynamic imaging.

Partial Fourier in the presence of respiratory motion in prostate diffusion-weighted echo planar imaging

PURPOSE

To investigate the effect of respiratory motion in terms of signal loss in prostate diffusion-weighted imaging (DWI), and to evaluate the usage of partial Fourier in a free-breathing protocol in a clinically relevant b-value range using both single-shot and multi-shot acquisitions.

METHODS

A controlled breathing DWI acquisition was first employed at 3 T to measure signal loss from deep breathing patterns. Single-shot and multi-shot (2-shot) acquisitions without partial Fourier (no pF) and with partial Fourier (pF) factors of 0.75 and 0.65 were employed in a free-breathing protocol. The apparent SNR and ADC values were evaluated in 10 healthy subjects to measure if low pF factors caused low apparent SNR or overestimated ADC.

RESULTS

Controlled breathing experiments showed a difference in signal coefficient of variation between shallow and deep breathing. In free-breathing single-shot acquisitions, the pF 0.65 scan showed a significantly (p < 0.05) higher apparent SNR than pF 0.75 and no pF in the peripheral zone (PZ) of the prostate. In the multi-shot acquisitions in the PZ, pF 0.75 had a significantly higher apparent SNR than 0.65 pF and no pF. The single-shot pF 0.65 scan had a significantly lower ADC than single-shot no pF.

CONCLUSION

Deep breathing patterns can cause intravoxel dephasing in prostate DWI. For single-shot acquisitions at a b-value of 800 s/mm2, any potential risks of motion-related artefacts at low pF factors (pF 0.65) were outweighed by the increase in signal from a lower TE, as shown by the increase in apparent SNR. In multi-shot acquisitions however, the minimum pF factor should be larger, as shown by the lower apparent SNR at low pF factors.

Romer-EPTI: rotating-view motion-robust super-resolution EPTI for SNR-efficient distortion-free in-vivo mesoscale dMRI and microstructure imaging

Purpose

To overcome the major challenges in dMRI acquisition, including low SNR, distortion/blurring, and motion vulnerability.

Methods

A novel Romer-EPTI technique is developed to provide distortion-free dMRI with significant SNR gain, high motion-robustness, sharp spatial resolution, and simultaneous multi-TE imaging. It introduces a ROtating-view Motion-robust supEr-Resolution technique (Romer) combined with a distortion/blurring-free EPTI encoding. Romer enhances SNR by a simultaneous multi-thick-slice acquisition with rotating-view encoding, while providing high motion-robustness through a motion-aware super-resolution reconstruction, which also incorporates slice-profile and real-value diffusion, to resolve high-isotropic-resolution volumes. The in-plane encoding is performed using distortion/blurring-free EPTI, which further improves effective spatial resolution and motion robustness by preventing not only T2/T2*-blurring but also additional blurring resulting from combining encoded volumes with inconsistent geometries caused by dynamic distortions. Self-navigation was incorporated to enable efficient phase correction. Additional developments include strategies to address slab-boundary artifacts, achieve minimal TE for SNR gain at 7T, and achieve high robustness to strong phase variations at high b-values.

Results

Using Romer-EPTI, we demonstrate distortion-free whole-brain mesoscale in-vivo dMRI at both 3T (500-μm-iso) and 7T (485-μm-iso) for the first time, with high SNR efficiency (e.g., 25 × ), and high image quality free from distortion and slab-boundary artifacts with minimal blurring. Motion experiments demonstrate Romer-EPTI's high motion-robustness and ability to recover sharp images in the presence of motion. Romer-EPTI also demonstrates significant SNR gain and robustness in high b-value (b=5000s/mm2) and time-dependent dMRI.

Conclusion

Romer-EPTI significantly improves SNR, motion-robustness, and image quality, providing a highly efficient acquisition for high-resolution dMRI and microstructure imaging.

Single-shot Echo Planar Time-resolved Imaging for multi-echo functional MRI and distortion-free diffusion imaging

Purpose

To develop EPTI, a multi-shot distortion-free multi-echo imaging technique, into a single-shot acquisition to achieve improved robustness to motion and physiological noise, increased temporal resolution, and high SNR efficiency for dynamic imaging applications.

Methods

A new spatiotemporal encoding was developed to achieve single-shot EPTI by enhancing spatiotemporal correlation in k-t space. The proposed single-shot encoding improves reconstruction conditioning and sampling efficiency, with additional optimization under various accelerations to achieve optimized performance. To achieve high SNR efficiency, continuous readout with minimized deadtime was employed that begins immediately after excitation and extends for an SNR-optimized length. Moreover, k-t partial Fourier and simultaneous multi-slice acquisition were integrated to further accelerate the acquisition and achieve high spatial and temporal resolution.

Results

We demonstrated that ss-EPTI achieves higher tSNR efficiency than multi-shot EPTI, and provides distortion-free imaging with densely-sampled multi-echo images at resolutions ~1.25-3 mm at 3T and 7T-with high SNR efficiency and with comparable temporal resolutions to ss-EPI. The ability of ss-EPTI to eliminate dynamic distortions common in EPI also further improves temporal stability. For fMRI, ss-EPTI also provides early-TE images (e.g., 2.9ms) to recover signal-intensity and functional-sensitivity dropout in challenging regions. The multi-echo images provide TE-dependent information about functional fluctuations, successfully distinguishing noise-components from BOLD signals and further improving tSNR. For diffusion MRI, ss-EPTI provides high-quality distortion-free diffusion images and multi-echo diffusion metrics.

Conclusion

ss-EPTI provides distortion-free imaging with high image quality, rich multi-echo information, and enhanced efficiency within comparable temporal resolution to ss-EPI, offering a robust and efficient acquisition for dynamic imaging.

Sampling strategies and integrated reconstruction for reducing distortion and boundary slice aliasing in high-resolution 3D diffusion MRI

PURPOSE

To develop a new method for high-fidelity, high-resolution 3D multi-slab diffusion MRI with minimal distortion and boundary slice aliasing.

METHODS

Our method modifies 3D multi-slab imaging to integrate blip-reversed acquisitions for distortion correction and oversampling in the slice direction (kz ) for reducing boundary slice aliasing. Our aim is to achieve robust acceleration to keep the scan time the same as conventional 3D multi-slab acquisitions, in which data are acquired with a single direction of blip traversal and without kz -oversampling. We employ a two-stage reconstruction. In the first stage, the blip-up/down images are respectively reconstructed and analyzed to produce a field map for each diffusion direction. In the second stage, the blip-reversed data and the field map are incorporated into a joint reconstruction to produce images that are corrected for distortion and boundary slice aliasing.

RESULTS

We conducted experiments at 7T in six healthy subjects. Stage 1 reconstruction produces images from highly under-sampled data (R = 7.2) with sufficient quality to provide accurate field map estimation. Stage 2 joint reconstruction substantially reduces distortion artifacts with comparable quality to fully-sampled blip-reversed results (2.4× scan time). Whole-brain in-vivo results acquired at 1.22 mm and 1.05 mm isotropic resolutions demonstrate improved anatomical fidelity compared to conventional 3D multi-slab imaging. Data demonstrate good reliability and reproducibility of the proposed method over multiple subjects.

CONCLUSION

The proposed acquisition and reconstruction framework provide major reductions in distortion and boundary slice aliasing for 3D multi-slab diffusion MRI without increasing the scan time, which can potentially produce high-quality, high-resolution diffusion MRI.

Multi-band multi-shot diffusion MRI reconstruction with joint usage of structured low-rank constraints and explicit phase mapping

PURPOSE

To develop a joint reconstruction method for multi-band multi-shot diffusion MRI.

THEORY AND METHODS

Multi-band multi-shot EPI acquisition is an effective approach for high-resolution diffusion MRI, but requires specific algorithms to correct the inter-shot phase variations. The phase correction can be done by first estimating the explicit phase map and then feeding it into the k-space signal formulation model. Alternatively, the phase information can be used indirectly as structured low-rank constraints in k-space. The 2 methods differ in reconstruction accuracy and efficiency. We aim to combine the 2 different approaches for improved image quality and reconstruction efficiency simultaneously, termed "joint usage of structured low-rank constraints and explicit phase mapping" (JULEP). The proposed JULEP reconstruction is tested on both single-band and multi-band, multi-shot diffusion data, with different resolutions and b values. The results of JULEP are compared with conventional methods with explicit phase mapping (i.e., multiplexed sensitivity-encoding [MUSE]) and structured low-rank constraints (i.e., MUSSELS), and another joint reconstruction method (i.e., network estimated artifacts for tempered reconstruction [NEATR]).

RESULTS

JULEP improves the quality of the navigator and subsequently facilitates the reconstruction of final diffusion images. Compared with all 3 other methods (MUSE, MUSSELS, and NEATR), JULEP mitigates residual structural bias and improves temporal SNRs in the final diffusion image, particularly at high multi-band factors. Compared with MUSSELS, JULEP also improves computational efficiency.

CONCLUSION

The proposed JULEP method significantly improves the image quality and reconstruction efficiency of multi-band multi-shot diffusion MRI, which can promote a broader application of high-resolution diffusion MRI.

Sheared two-dimensional radiofrequency excitation for off-resonance robustness and fat suppression in reduced field-of-view imaging

PURPOSE

Two-dimensional (2D) echo-planar radiofrequency (RF) pulses are widely used for reduced field-of-view (FOV) imaging in applications such as diffusion-weighted imaging. However, long pulse durations render the 2D RF pulses sensitive to off-resonance effects, causing local signal losses in reduced-FOV images. This work aims to achieve off-resonance robustness for 2D RF pulses via a sheared trajectory design.

THEORY AND METHODS

A sheared 2D RF pulse design is proposed to reduce pulse durations while covering identical excitation k-space extent as a standard 2D RF pulse. For a given shear angle, the number of sheared trajectory lines is minimized to obtain the shortest pulse duration, such that the excitation replicas are repositioned outside the slice stack to guarantee unlimited slice coverage. A target fat/water signal ratio of 5% is chosen to achieve robust fat suppression.

RESULTS

Simulations, imaging experiments on a custom head and neck phantom, and in vivo imaging experiments in the spinal cord at 3 T demonstrate that the sheared 2D RF design provides significant improvement in image quality while preserving profile sharpnesses. In regions with high off-resonance effects, the sheared 2D RF pulse improves the signal by more than 50% when compared to the standard 2D RF pulse.

CONCLUSION

The proposed sheared 2D RF design successfully reduces pulse durations, exhibiting significantly improved through-plane off-resonance robustness, while providing unlimited slice coverage and high fidelity fat suppression. This method will be especially beneficial in regions suffering from a variety of off-resonance effects, such as spinal cord and breast.

Simultaneous pure T2 and varying T2'-weighted BOLD fMRI using Echo Planar Time-resolved Imaging for mapping cortical-depth dependent responses

Spin-echo (SE) BOLD fMRI has high microvascular specificity, and thus provides a more reliable means to localize neural activity compared to conventional gradient-echo BOLD fMRI. However, the most common SE BOLD acquisition method, SE-EPI, is known to suffer from T2' contrast contamination with undesirable draining vein bias. To address this, in this study, we extended a recently developed distortion/blurring-free multi-shot EPI technique, Echo-Planar Time-resolved Imaging (EPTI), to cortical-depth dependent SE-fMRI at 7T to test whether it could provide purer SE BOLD contrast with minimal T2' contamination for improved neuronal specificity. From the same acquisition, the time-resolved feature of EPTI also provides a series of asymmetric SE (ASE) images with varying T2' weightings, and enables extraction of data equivalent to conventional SE EPI with different echo train lengths (ETLs). This allows us to systematically examine how T2'-contribution affects different SE acquisition strategies using a single dataset. A low-rank spatiotemporal subspace reconstruction was implemented for the SE-EPTI acquisition, which incorporates corrections for both shot-to-shot phase variations and dynamic B0 drifts. SE-EPTI was used in a visual task fMRI experiment to demonstrate that i) the pure SE image provided by EPTI results in the highest microvascular specificity; ii) the ASE EPTI series, with a graded introduction of T2' weightings at time points farther away from the pure SE, show a gradual sensitivity increase along with increasing draining vein bias; iii) the longer ETL seen in conventional SE EPI acquisitions will induce more draining vein bias. Consistent results were observed across multiple subjects, demonstrating the robustness of the proposed technique for SE-BOLD fMRI with high specificity.

Autocalibrating segmented diffusion-weighted acquisitions

PURPOSE

Segmented echo-planar imaging enables high-resolution diffusion-weighted imaging (DWI). However, phase differences between segments can lead to severe artifacts. This work investigates an algorithm to enable reconstruction of interleaved segmented acquisitions without the need of additional calibration or navigator measurements.

METHODS

A parallel imaging algorithm is presented that jointly reconstructs all segments of one DWI frame maintaining their phase information. Therefore, the algorithm allows for an iterative improvement of the phase estimates included in the joint reconstruction. Given a limited number of interleaves, the initial-phase estimates can be calculated by a traditional parallel-imaging reconstruction, using the unweighted scan of the DWI measurement as a reference.

RESULTS

Reconstruction of phantom data and g-factor simulations show substantial improvement (up to 93% reduction in root mean square error) compared with a generalized auto-calibrating partially parallel-acquisition reconstruction. In vivo experiments show robust reconstruction outcomes in critical imaging situations, including small numbers of receiver channels or low signal-to-noise ratio.

CONCLUSION

An algorithm for the robust reconstruction of segmented DWI data is presented. The method requires neither navigator nor calibration measurements; therefore, it can be applied to existing DWI data sets.

In vivo human whole-brain Connectom diffusion MRI dataset at 760 µm isotropic resolution

We present a whole-brain in vivo diffusion MRI (dMRI) dataset acquired at 760 μm isotropic resolution and sampled at 1260 q-space points across 9 two-hour sessions on a single healthy participant. The creation of this benchmark dataset is possible through the synergistic use of advanced acquisition hardware and software including the high-gradient-strength Connectom scanner, a custom-built 64-channel phased-array coil, a personalized motion-robust head stabilizer, a recently developed SNR-efficient dMRI acquisition method, and parallel imaging reconstruction with advanced ghost reduction algorithm. With its unprecedented resolution, SNR and image quality, we envision that this dataset will have a broad range of investigational, educational, and clinical applications that will advance the understanding of human brain structures and connectivity. This comprehensive dataset can also be used as a test bed for new modeling, sub-sampling strategies, denoising and processing algorithms, potentially providing a common testing platform for further development of in vivo high resolution dMRI techniques. Whole brain anatomical T1-weighted and T2-weighted images at submillimeter scale along with field maps are also made available.

Segmented diffusion imaging with iterative motion-corrected reconstruction (SEDIMENT) for brain echo-planar imaging

Multi-shot techniques offer improved resolution and signal-to-noise ratio for diffusion- weighted imaging, but make the acquisition vulnerable to shot-specific phase variations and inter-shot macroscopic motion. Several model-based reconstruction approaches with iterative phase correction have been proposed, but robust macroscopic motion estimation is still challenging. Segmented diffusion imaging with iterative motion-corrected reconstruction (SEDIMENT) uses iteratively refined data-driven shot navigators based on sensitivity encoding to cure phase and rigid in-plane motion artifacts. The iterative scheme is compared in simulations and in vivo with a non-iterative reference algorithm for echo-planar imaging with up to sixfold segmentation. The SEDIMENT framework supports partial Fourier acquisitions and furthermore includes options for data rejection and learning-based modules to improve robustness and convergence.

Echo planar time-resolved imaging with subspace reconstruction and optimized spatiotemporal encoding

PURPOSE

To develop new encoding and reconstruction techniques for fast multi-contrast/quantitative imaging.

METHODS

The recently proposed Echo Planar Time-resolved Imaging (EPTI) technique can achieve fast distortion- and blurring-free multi-contrast/quantitative imaging. In this work, a subspace reconstruction framework is developed to improve the reconstruction accuracy of EPTI at high encoding accelerations. The number of unknowns in the reconstruction is significantly reduced by modeling the temporal signal evolutions using low-rank subspace. As part of the proposed reconstruction approach, a B₀ -update algorithm and a shot-to-shot B₀ variation correction method are developed to enable the reconstruction of high-resolution tissue phase images and to mitigate artifacts from shot-to-shot phase variations. Moreover, the EPTI concept is extended to 3D k-space for 3D GE-EPTI, where a new "temporal-variant" of CAIPI encoding is proposed to further improve performance.

RESULTS

The effectiveness of the proposed subspace reconstruction was demonstrated first in 2D GESE EPTI, where the reconstruction achieved higher accuracy when compared to conventional B₀ -informed GRAPPA. For 3D GE-EPTI, a retrospective undersampling experiment demonstrates that the new temporal-variant CAIPI encoding can achieve up to 72× acceleration with close to 2× reduction in reconstruction error when compared to conventional spatiotemporal-CAIPI encoding. In a prospective undersampling experiment, high-quality whole-brain T 2 ∗ and tissue phase maps at 1 mm isotropic resolution were acquired in 52 seconds at 3T using 3D GE-EPTI with temporal-variant CAIPI encoding.

CONCLUSION

The proposed subspace reconstruction and optimized temporal-variant CAIPI encoding can further improve the performance of EPTI for fast quantitative mapping.

Highly accelerated multishot echo planar imaging through synergistic machine learning and joint reconstruction

PURPOSE

To introduce a combined machine learning (ML)- and physics-based image reconstruction framework that enables navigator-free, highly accelerated multishot echo planar imaging (msEPI) and demonstrate its application in high-resolution structural and diffusion imaging.

METHODS

Single-shot EPI is an efficient encoding technique, but does not lend itself well to high-resolution imaging because of severe distortion artifacts and blurring. Although msEPI can mitigate these artifacts, high-quality msEPI has been elusive because of phase mismatch arising from shot-to-shot variations which preclude the combination of the multiple-shot data into a single image. We utilize deep learning to obtain an interim image with minimal artifacts, which permits estimation of image phase variations attributed to shot-to-shot changes. These variations are then included in a joint virtual coil sensitivity encoding (JVC-SENSE) reconstruction to utilize data from all shots and improve upon the ML solution.

RESULTS

Our combined ML + physics approach enabled Rinplane × multiband (MB) = 8- × 2-fold acceleration using 2 EPI shots for multiecho imaging, so that whole-brain T2 and T2 * parameter maps could be derived from an 8.3-second acquisition at 1 × 1 × 3-mm3 resolution. This has also allowed high-resolution diffusion imaging with high geometrical fidelity using 5 shots at Rinplane × MB = 9- × 2-fold acceleration. To make these possible, we extended the state-of-the-art MUSSELS reconstruction technique to simultaneous multislice encoding and used it as an input to our ML network.

CONCLUSION

Combination of ML and JVC-SENSE enabled navigator-free msEPI at higher accelerations than previously possible while using fewer shots, with reduced vulnerability to poor generalizability and poor acceptance of end-to-end ML approaches.

Echo planar time-resolved imaging (EPTI)

PURPOSE

To develop an efficient distortion- and blurring-free multi-shot EPI technique for time-resolved multiple-contrast and/or quantitative imaging.

METHODS

EPI is a commonly used sequence but suffers from geometric distortions and blurring. Here, we introduce a new multi-shot EPI technique termed echo planar time-resolved imaging (EPTI), which has the ability to rapidly acquire distortion- and blurring-free multi-contrast data set. The EPTI approach performs encoding in ky -t space and uses a new highly accelerated spatio-temporal CAIPI sampling trajectory to take advantage of signal correlation along these dimensions. Through this acquisition and a B0 -informed parallel imaging reconstruction, hundreds of "time-resolved" distortion- and blurring-free images at different TEs across the EPI readout window can be created at sub-millisecond temporal increments using a small number of EPTI shots. Moreover, a method for self-estimation and correction of shot-to-shot B0 variations was developed. Simultaneous multi-slice acquisition was also incorporated to further improve the acquisition efficiency.

RESULTS

We evaluated EPTI under varying simulated acceleration factors, B0 -inhomogeneity, and shot-to-shot B0 variations to demonstrate its ability to provide distortion- and blurring-free images at multiple TEs. Two variants of EPTI were demonstrated in vivo at 3T: (1) a combined gradient- and spin-echo EPTI for quantitative mapping of T2 , T2* , proton density, and susceptibility at 1.1 × 1.1 × 3 mm3 whole-brain in 28 s (0.8 s/slice), and (2) a gradient-echo EPTI, for multi-echo and quantitative T2* fMRI at 2 × 2 × 3 mm3 whole-brain at a 3.3 s temporal resolution.

CONCLUSION

EPTI is a new approach for multi-contrast and/or quantitative imaging that can provide fast acquisition of distortion- and blurring-free images at multiple TEs.

The quest for high spatial resolution diffusion-weighted imaging of the human brain in vivo

Diffusion-weighted imaging, a contrast unique to MRI, is used for assessment of tissue microstructure in vivo. However, this exquisite sensitivity to finer scales far above imaging resolution comes at the cost of vulnerability to errors caused by sources of motion other than diffusion motion. Addressing the issue of motion has traditionally limited diffusion-weighted imaging to a few acquisition techniques and, as a consequence, to poorer spatial resolution than other MRI applications. Advances in MRI imaging methodology have allowed diffusion-weighted MRI to push to ever higher spatial resolution. In this review we focus on the pulse sequences and associated techniques under development that have pushed the limits of image quality and spatial resolution in diffusion-weighted MRI.

Technical Note: Clustering-based motion compensation scheme for multishot diffusion tensor imaging

PURPOSE

To extend image reconstruction using image-space sampling function (IRIS) to address large-scale motion in multishot diffusion-weighted imaging (DWI).

METHODS

A clustered IRIS (CIRIS) algorithm that would extend IRIS was proposed to correct for large-scale motion. For DWI, CIRIS initially groups the shots into clusters without intracluster large-scale motion and reconstructs each cluster by using IRIS. Then, CIRIS registers these cluster images and combines the registered images by using a weighted average to correct for voxel mismatch caused by intercluster large-scale motion. For diffusion tensor imaging (DTI), CIRIS further reduces the effect of motion on diffusion directions by treating motion-induced direction changes as additional diffusion directions. CIRIS also introduces the detection and rejection of motion-corrupted data to avoid corresponding image degradation. The proposed method was evaluated by simulation and in vivo diffusion datasets.

RESULTS

Experiments demonstrated that CIRIS can reduce motion-induced blurring and artifacts in DWI and provide more accurate DTI estimations in the presence of large-scale motion, compared with IRIS.

CONCLUSION

The proposed method presents a novel approach to correct for large-scale in-plane motion for multishot DWI and is expected to benefit the practical application of high-resolution diffusion imaging.

Tilted-CAIPI for highly accelerated distortion-free EPI with point spread function (PSF) encoding

PURPOSE

To develop a method for fast distortion- and blurring-free imaging.

THEORY

EPI with point-spread-function (PSF) mapping can achieve distortion- and blurring-free imaging at a cost of long acquisition time. In this study, an acquisition/reconstruction technique, termed "tilted-CAIPI," is proposed to achieve >20× acceleration for PSF-EPI. The proposed method systematically optimized the k-space sampling trajectory with B₀ -inhomogeneity-informed reconstruction, to exploit the inherent signal correlation in PSF-EPI and take full advantage of coil sensitivity. Susceptibility-induced phase accumulation is regarded as an additional encoding that is estimated by calibration data and integrated into reconstruction. Self-navigated phase correction was developed to correct shot-to-shot phase variation in diffusion imaging.

METHODS

Tilted-CAIPI was implemented at 3T, with incorporation of partial Fourier and simultaneous multislice to achieve further accelerations. T₂ -weighted, T₂ ^* -weighted, and diffusion-weighted imaging experiments were conducted to evaluate the proposed method.

RESULTS

The ability of tilted-CAIPI to provide highly accelerated imaging without distortion and blurring was demonstrated through in vivo brain experiments, where only 8 shots per simultaneous slice group were required to provide high-quality, high-SNR imaging at 0.8-1 mm resolution.

CONCLUSION

Tilted-CAIPI achieved fast distortion- and blurring-free imaging with high SNR. Whole-brain T₂ -weighted, T₂ ^* -weighted, and diffusion imaging can be obtained in just 15-60 s.

Kernel Principal Component Analysis of Coil Compression in Parallel Imaging

A phased array with many coil elements has been widely used in parallel MRI for imaging acceleration. On the other hand, it results in increased memory usage and large computational costs for reconstructing the missing data from such a large number of channels. A number of techniques have been developed to linearly combine physical channels to produce fewer compressed virtual channels for reconstruction. A new channel compression technique via kernel principal component analysis (KPCA) is proposed. The proposed KPCA method uses a nonlinear combination of all physical channels to produce a set of compressed virtual channels. This method not only reduces the computational time but also improves the reconstruction quality of all channels when used. Taking the traditional GRAPPA algorithm as an example, it is shown that the proposed KPCA method can achieve better quality than both PCA and all channels, and at the same time the calculation time is almost the same as the existing PCA method.

A diffusion-matched principal component analysis (DM-PCA) based two-channel denoising procedure for high-resolution diffusion-weighted MRI

Over the past several years, significant efforts have been made to improve the spatial resolution of diffusion-weighted imaging (DWI), aiming at better detecting subtle lesions and more reliably resolving white-matter fiber tracts. A major concern with high-resolution DWI is the limited signal-to-noise ratio (SNR), which may significantly offset the advantages of high spatial resolution. Although the SNR of DWI data can be improved by denoising in post-processing, existing denoising procedures may potentially reduce the anatomic resolvability of high-resolution imaging data. Additionally, non-Gaussian noise induced signal bias in low-SNR DWI data may not always be corrected with existing denoising approaches. Here we report an improved denoising procedure, termed diffusion-matched principal component analysis (DM-PCA), which comprises 1) identifying a group of (not necessarily neighboring) voxels that demonstrate very similar magnitude signal variation patterns along the diffusion dimension, 2) correcting low-frequency phase variations in complex-valued DWI data, 3) performing PCA along the diffusion dimension for real- and imaginary-components (in two separate channels) of phase-corrected DWI voxels with matched diffusion properties, 4) suppressing the noisy PCA components in real- and imaginary-components, separately, of phase-corrected DWI data, and 5) combining real- and imaginary-components of denoised DWI data. Our data show that the new two-channel (i.e., for real- and imaginary-components) DM-PCA denoising procedure performs reliably without noticeably compromising anatomic resolvability. Non-Gaussian noise induced signal bias could also be reduced with the new denoising method. The DM-PCA based denoising procedure should prove highly valuable for high-resolution DWI studies in research and clinical uses.

eIRIS: Eigen-analysis approach for improved spine multi-shot diffusion MRI

Image reconstruction using image-space sampling function (IRIS) corrects motion-induced inter-shot phase variations using phase maps from navigator-echo for multi-shot diffusion MRI. However, the bandwidth along the phase-encoding direction of navigator-echo is usually lower than that of image-echo, and thus their geometric distortions may be different. This geometric mismatch is corrected in IRIS by using the B₀ map from an additional scan. In this paper, we present an enhanced IRIS (eIRIS) method that remove the requirement of B₀ map. eIRIS treats shots as virtual coils, and then uses an eigen-analysis-based approach, which is insensitive to geometric mismatch, to estimates coil sensitivity maps containing the inter-shot phase variations. The final image is reconstructed under the framework of SENSE. Simulation, phantom, and cervical spine experiments were performed to evaluate the eIRIS method. The images generated by IRIS without B₀ correction contain severe artifacts. eIRIS obtains results without noticeable artifacts and comparable to those of IRIS with B₀ correction and GRAPPA with a compact kernel (GRAPPA-CK) method. eIRIS slightly outperforms GRAPPA-CK in the terms of normalized root-mean-square error and signal-to-noise ratio. eIRIS has the potential to obtain high-quality diffusion-weighted images and will benefit the research and clinical diagnosis of spinal cord.