To develop a method for slice‐wise dynamic distortion correction for EPI using rapid spatiotemporal B0 field measurements from FID navigators (FIDnavs) and to evaluate the efficacy of this new approach relative to an established data‐driven technique.
A low‐resolution reference image was used to create a forward model of FIDnav signal changes to enable estimation of spatiotemporal B0 inhomogeneity variations up to second order from measured FIDnavs. Five volunteers were scanned at 3 T using a 64‐channel coil with FID‐navigated EPI. The accuracy of voxel shift measurements and geometric distortion correction was assessed for experimentally induced magnetic field perturbations. The temporal SNR was evaluated in EPI time‐series acquired at rest and with a continuous nose‐touching action, before and after image realignment.
Field inhomogeneity coefficients and voxel shift maps measured using FIDnavs were in excellent agreement with multi‐echo EPI measurements. The FID‐navigated distortion correction accurately corrected image geometry in the presence of induced magnetic field perturbations, outperforming the data‐driven approach in regions with large field offsets. In functional MRI scans with nose touching, FIDnav‐based correction yielded temporal SNR gains of 30% in gray matter. Following image realignment, which accounted for global image shifts, temporal SNR gains of 3% were achieved.
Our proposed application of FIDnavs enables slice‐wise dynamic distortion correction with high temporal efficiency. We achieved improved signal stability by leveraging the encoding information from multichannel coils. This approach can be easily adapted to other EPI‐based sequences to improve temporal SNR for a variety of clinical and research applications.
BACKGROUND: Geometric distortions resulting from large pose changes reduce the accuracy of motion measurements and interfere with the ability to generate artifact-free information. Our goal is to develop an algorithm and pulse sequence to enable motion-compensated, geometric distortion compensated diffusion-weighted MRI, and to evaluate its efficacy in correcting for the field inhomogeneity and position changes, induced by large and frequent head motions. METHODS: Dual echo planar imaging (EPI) with a blip-reversed phase encoding distortion correction technique was evaluated in five volunteers in two separate experiments and compared with static field map distortion correction. In the first experiment, dual-echo EPI images were acquired in two head positions designed to induce a large field inhomogeneity change. A field map and a distortion-free structural image were acquired at each position to assess the ability of dual-echo EPI to generate reliable field maps and enable geometric distortion correction in both positions. In the second experiment, volunteers were asked to move to multiple random positions during a diffusion scan. Images were reconstructed using the dual-echo correction and a slice-to-volume registration (SVR) registration algorithm. The accuracy of SVR motion estimates was compared to externally measured ground truth motion parameters. RESULTS: Our results show that dual-echo EPI can produce slice-level field maps with comparable quality to field maps generated by the reference gold standard method. We also show that slice-level distortion correction improves the accuracy of SVR algorithms as slices acquired at different orientations have different levels of distortion, which can create errors in the registration process. CONCLUSIONS: Dual-echo acquisitions with blip-reversed phase encoding can be used to generate slice-level distortion-free images, which is critical for motion-robust slice to volume registration. The distortion corrected images not only result in better motion estimates, but they also enable a more accurate final diffusion image reconstruction.
Quantitative parameter maps, as opposed to qualitative grayscale images, may represent the future of diagnostic MRI. A new quantitative MRI method is introduced here that requires a single 3D acquisition, allowing good spatial coverage to be achieved in relatively short scan times.
A multipathway multi‐echo sequence was developed, and at least 3 pathways with 2 TEs were needed to generate T1, T2, T2*, B1+, and B0 maps. The method required the central k‐space region to be sampled twice, with the same sequence but with 2 very different nominal flip angle settings. Consequently, scan time was only slightly longer than that of a single scan. The multipathway multi‐echo data were reconstructed into parameter maps, for phantom as well as brain acquisitions, in 5 healthy volunteers at 3 T. Spatial resolution, matrix size, and FOV were 1.2 × 1.0 × 1.2 mm3, 160 × 192 × 160, and 19.2 × 19.2 × 19.2 cm3 (whole brain), acquired in 11.5 minutes with minimal acceleration. Validation was performed against T1, T2, and T2* maps calculated from gradient‐echo and spin‐echo data.
In Bland‐Altman plots, bias and limits of agreement for T1 and T2 results in vivo and in phantom were −2.9/±125.5 ms (T1 in vivo), −4.8/±20.8 ms (T2 in vivo), −1.5/±18.1 ms (T1 in phantom), and −5.3/±7.4 ms (T2 in phantom), for regions of interest including given brain structures or phantom compartments. Due to relatively high noise levels, the current implementation of the approach may prove more useful for region of interest–based as opposed to pixel‐based interpretation.
We proposed a novel approach to quantitatively map MR parameters based on a multipathway multi‐echo acquisition.
Purpose: Quantitative parameter maps, as opposed to qualitative grayscale images, may represent the future of diagnostic MRI. A new quantitative MRI method is introduced here that requires little more than a single 3D acquisition, allowing good spatial coverage to be achieved in relatively short scan times.
Methods: A multi-pathway multi-echo (MPME) sequence was developed, and at least three pathways with two echo times were needed to generate T1, T2, T2*, B1 and B0 maps. The method required the central k-space region to be sampled twice, with the same sequence but with two very different nominal flip angle settings. Consequently, scan time was only slightly longer than that of a single scan. MPME data were reconstructed into parameter maps, for phantom as well as brain acquisitions, in five healthy volunteers at 3T. Spatial resolution, matrix size and FOV were 1.2×1.0×1.2 mm3, 160×192×160 and 19.2×19.2×19.2 cm3 (whole brain), acquired in 11.5 min with minimal acceleration. Validation was performed against T1, T2 and T2* maps calculated from gradient-echo and spin-echo data.
Results: In Bland-Altman plots, bias and limits of agreement for T1 and T2 results in vivo and in phantom were: -2.9/±125.5ms (T1 in vivo), -4.8/±20.8ms (T2 in vivo), -1.5/±18.1ms (T1 in phantom), and -5.3/±7.4ms (T2 in phantom), for ROIs including given brain structures or phantom compartments.
Conclusions: We proposed a novel approach to quantitatively map MR parameters based on an MPME acquisition.
Structured low-rank matrix models have previously been introduced to enable calibrationless MR image reconstruction from sub-Nyquist data, and such ideas have recently been extended to enable navigator-free echo-planar imaging (EPI) ghost correction. This paper presents novel theoretical analysis which shows that, because of uniform subsampling, the structured low-rank matrix optimization problems for EPI data will always have either undesirable or non-unique solutions in the absence of additional constraints. This theory leads us to recommend and investigate problem formulations for navigatorfree EPI that incorporate side information from either imagedomain or k-space domain parallel imaging methods. The importance of using nonconvex low-rank matrix regularization is also identified. We demonstrate using phantom and in vivo data that the proposed methods are able to eliminate ghost artifacts for several navigator-free EPI acquisition schemes, obtaining better performance in comparison to state-of-the-art methods across a range of different scenarios. Results are shown for both single-channel acquisition and highly accelerated multi-channel acquisition.
Purpose: A ghost correction strategy for Simultaneous Multi-Slice (SMS) EPI methods that provides improved ghosting artifact reduction compared to conventional methods is presented. Conventional Nyquist ghost correction methods for SMS-EPI rely on navigator data that contain phase errors from all slices in the simultaneously-acquired slice-group. These navigator data may contain spatially non-linear phase differences near regions of B0 inhomogeneity, which violates the linear model employed by most EPI ghost correction algorithms, resulting in poor reconstructions.
Methods: Dual-Polarity GRAPPA (DPG) was previously shown to accurately model and correct both spatially non-linear and 2D phase errors in conventional single-slice EPI data. Here, an extension we call Dual-Polarity slice-GRAPPA (DPsG) is adapted to the slice-GRAPPA method and applied to SMS-EPI data for slice separation and ghost correction concurrently---eliminating the need for a separate ghost correction step while also providing improved slice-specific EPI phase error correction.
Results: Images from in vivo SMS-EPI data reconstructed using DPsG in place of conventional Nyquist ghost correction and slice-GRAPPA are presented. DPsG is shown to reduce ghosting artifacts and provide improved temporal SNR compared to the conventional reconstruction.
Conclusion: The proposed use of DPsG for SMS-EPI reconstruction can provide images with lower artifact levels, higher image fidelity, and improved time-series stability compared to conventional reconstruction methods.
Objective: To examine neural mechanisms of action in behavioral weight loss treatment (BWL) and explore neural and genetic predictors of BWL. Methods: Neural activation to milkshake receipt and genetics were compared in 17 women with obesity who received 12 weeks of BWL and 17 women who received no intervention. Participants were scanned twice using functional magnetic resonance imaging at baseline and 12 weeks. Weight was assessed at baseline, 12, 36, and 60 weeks. Results: BWL participants lost more weight than controls at 12 weeks (−4.82% versus −0.70%). After 12 weeks, BWL had greater reduction in right caudate activation response to milk shake receipt than did controls. Among BWL participants, baseline to 12-week reduction in frontostriatal activation to milk shake predicted greater weight loss at 12, 36, and 60 weeks. Possessing the A/A or T/A genotype of the fat mass and obesity–associated (FTO) variant rs9939609 predicted greater weight loss at 12 and 36 weeks. Conclusions: These preliminary data reveal that reduction in right caudate activation may be a neural mechanism of weight loss in BWL, and baseline FTO variant and reduction in frontostriatal activation during BWL predict short- and long-term weight loss. These findings require replication in larger samples.
Purpose: To combine MRI, ultrasound, and computer science methodologies toward generating MRI contrast at the high frame rates of ultrasound, inside and even outside the MRI bore.
Methods: A small transducer, held onto the abdomen with an adhesive bandage, collected ultrasound signals during MRI. Based on these ultrasound signals and their correlations with MRI, a machine-learning algorithm created synthetic MR images at frame rates up to 100 per second. In one particular implementation, volunteers were taken out of the MRI bore with the ultrasound sensor still in place, and MR images were generated on the basis of ultrasound signal and learned correlations alone in a “scannerless” manner.
Results: Hybrid ultrasound-MRI data were acquired in eight separate imaging sessions. Locations of liver features, in synthetic images, were compared with those from acquired images: The mean error was 1.0 pixel (2.1 mm), with best case 0.4 and worst case 4.1 pixels (in the presence of heavy coughing). For results from outside the bore, qualitative validation involved optically tracked ultrasound imaging with/without coughing.
Conclusion: The proposed setup can generate an accurate stream of high-speed MR images, up to 100 frames per second, inside or even outside the MR bore.
Purpose: The purpose of this study was to seek improved image quality from accelerated echo planar imaging (EPI) data, particularly at ultrahigh fields. Certain artifacts in EPI reconstructions can be attributed to nonlinear phase differences between data acquired using frequency-encoding gradients of alternating polarity. These errors appear near regions of local susceptibility gradients and typically cannot be corrected with conventional Nyquist ghost correction (NGC) methods. Methods: We propose a new reconstruction method that integrates ghost correction into the parallel imaging data recovery process. This is achieved through a pair of generalized autocalibrating partially parallel acquisitions (GRAPPA) kernels that operate directly on the measured EPI data. The proposed dual-polarity GRAPPA (DPG) method estimates missing kspace data while simultaneously correcting inherent EPI phase errors. Results: Simulation results showed that standard NGC is incapable of correcting higher-order phase errors, whereas the DPG kernel approach successfully removed these errors. The presence of higher-order phase errors near regions of local susceptibility gradients was demonstrated with in vivo data. DPG reconstructions of in vivo 3T and 7T EPI data acquired near these regions showed a marked improvement over conventional methods. Conclusion: This new parallel imaging method for reconstructing accelerated EPI data shows better resilience to inherent EPI phase errors, resulting in higher image quality in regions where higher-order EPI phase errors commonly occur.
Magnetic Resonance (MR) imaging provides excellent image quality at a high cost and low frame rate. Ultrasound (US) provides poor image quality at a low cost and high frame rate. We propose an instance-based learning system to obtain the best of both worlds: high quality MR images at high frame rates from a low cost single-element US sensor. Concurrent US and MRI pairs are acquired during a relatively brief offline learning phase involving the US transducer and MR scanner. High frame rate, high quality MR imaging of respiratory organ motion is then predicted from US measurements, even after stopping MRI acquisition, using a probabilistic kernel regression framework. Experimental results show predicted MR images to be highly representative of actual MR images.
The quality of high-resolution Echo Planar images of the human brain has improved greatly in recent years, enabled by novel multi-channel receiver coil arrays and parallel imaging. However, in regions with local field inhomogeneity, EPI artifacts limit which parts of the brain can be imaged successfully. In this work, we present evidence that certain image artifacts can be attributed to nonlinear phase errors that are present in regions of local susceptibility gradients and certain coil array elements. Because these phase errors cannot be corrected with conventional Nyquist ghost correction, we propose a new method that integrates ghost correction with parallel imaging reconstruction. The proposed Dual-Polarity GRAPPA method operates directly on raw EPI data to estimate k-space data from the under-sampled acquisition while simultaneously correcting inherent EPI phase errors. We present examples of this method successfully removing strong phase-error artifacts in high-resolution 7T EPI data.