Prospective motion correction and automatic segmentation of penetrating arteries in phase contrast MRI at 7 T

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PurposeTo develop a 3D MR fingerprinting (MRF) method in combination with fat navigators to improve its motion robustness for neuroimaging.MethodsA rapid fat navigator was developed using the stack‐of‐spirals acquisition and non‐Cartesian spiral GRAPPA. The fat navigator module was implemented in the 3D MRF sequence with high scan efficiency. The developed method was first validated in phantoms and five healthy subjects with intentional head motion. The method was further applied to infants with neonatal opioid withdrawal symptoms. The 3D MRF scans with fat navigators acquired with and without acceleration along the partition‐encoding direction were both examined in the study.ResultsBoth phantom and in vivo results demonstrated that the added fat navigator modules did not influence the quantification accuracy in MRF. In combination with non‐Cartesian spiral GRAPPA, a rapid fat navigator sampling with whole‐brain coverage was achieved in ˜0.5 s at 3T, reducing its sensitivity to potential motion. Based on the motion waveforms extracted from fat navigators, the motion robustness of the 3D MRF was largely improved. With the proposed method, the motion‐corrupted MRF datasets yielded T1 and T2 maps with significantly reduced artifacts and high correlations with measurements from the reference motion‐free MRF scans.ConclusionWe developed a 3D MRF method coupled with rapid fat navigators to improve its motion robustness for quantitative neuroimaging. Our results demonstrate that (1) accurate tissue quantification was preserved with the fat navigator modules and (2) the motion robustness for quantitative tissue mapping was largely improved with the developed method.

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Improving motion robustness of 3D MR fingerprinting with a fat navigator

Chen

Zong

et al. 2023

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“…In either case, potential negative impacts on the quality of image or navigator data will have to be assessed and may benefit from decoupling using fat-selective excitation. 8,10 It is also worth noting that the rotation estimation approach used here assumes stable contrast between the navigators themselves and the corresponding baseline data, increasing the length of the reference scan as TR increases. Such applications will especially benefit from reducing the range of lookup table with update of the navigator trajectory and perhaps imposing prior knowledge of typical motion (e.g., further limiting y-rotation 27 ).…”

Section: Rms Motionmentioning

confidence: 99%

“…For example, low-resolution images acquired throughout the scan, denoted volumetric navigators (VNAVs), can provide this motion information using image registration 7,8 ; however, relatively long acquisition and processing times (hundreds of milliseconds) make them more appropriate for application in long TR sequences. 9,10 Using the properties of the Fourier transform, k-space navigators enable shorter acquisition and processing times. Orbital navigators (ONAVs) use a circular trajectory in k-space to measure in-plane rotation and translation, 11 and can fully characterize rigid motion using three orthogonal trajectories.…”

Section: Introductionmentioning

confidence: 99%

“…Imaging or k‐space data obtained with the scanner itself can also inform motion estimates for PMC. For example, low‐resolution images acquired throughout the scan, denoted volumetric navigators (VNAVs), can provide this motion information using image registration 7,8 ; however, relatively long acquisition and processing times (hundreds of milliseconds) make them more appropriate for application in long TR sequences 9,10 …”

Section: Introductionmentioning

confidence: 99%

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Prospective motion correction for brain MRI using spherical navigators

Hewlett,

Oran,

Liu

et al. 2024

PurposeTo demonstrate for the first time the feasibility of performing prospective motion correction using spherical navigators (SNAVs).MethodsSNAVs were interleaved in a 3D FLASH sequence with an additional short baseline scan (6.8 s) for fast rotation estimation. Assessment of SNAV‐based prospective motion correction was performed in six volunteers. Participant motion was guided using randomly generated stepwise prompts as well as prompts derived from real motion cases. Experiments were performed on a 3 T MRI scanner using a 32‐channel head coil.ResultsWhen optimized for real‐time application, SNAV‐based motion estimates were computed in 25.8 ± 1.3 ms. Phantom‐based quantification of rotation and translation accuracy indicated mean absolute errors of 0.10 ± 0.09° and 0.25 ± 0.14 mm, respectively. Implementing SNAV‐based motion estimates for prospective motion correction led to a clear improvement in image quality with minimal increase in scan time (<5%).ConclusionOptimization of SNAV processing for real‐time application enables prospective motion correction with low latency and minimal scan time requirements.

Effects of prospective motion correction on perivascular spaces at 7T MRI evaluated using motion artifact simulation

Zhao,

Zhou,

Zong

2024

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PurposeThe effectiveness of prospective motion correction (PMC) is often evaluated by comparing artifacts in images acquired with and without PMC (NoPMC). However, such an approach is not applicable in clinical setting due to unavailability of NoPMC images. We aim to develop a simulation approach for demonstrating the ability of fat‐navigator‐based PMC in improving perivascular space (PVS) visibility in T2‐weighted MRI.MethodsMRI datasets from two earlier studies were used for motion artifact simulation and evaluating PMC, including T2‐weighted NoPMC and PMC images. To simulate motion artifacts, k‐space data at motion‐perturbed positions were calculated from artifact‐free images using nonuniform Fourier transform and misplaced onto the Cartesian grid before inverse Fourier transform. The simulation's ability to reproduce motion‐induced blurring, ringing, and ghosting artifacts was evaluated using sharpness at lateral ventricle/white matter boundary, ringing artifact magnitude in the Fourier spectrum, and background noise, respectively. PVS volume fraction in white matter was employed to reflect its visibility.ResultsIn simulation, sharpness, PVS volume fraction, and background noise exhibited significant negative correlations with motion score. Significant correlations were found in sharpness, ringing artifact magnitude, and PVS volume fraction between simulated and real NoPMC images (p ≤ 0.006). In contrast, such correlations were reduced and nonsignificant between simulated and real PMC images (p ≥ 0.48), suggesting reduction of motion effects with PMC.ConclusionsThe proposed simulation approach is an effective tool to study the effects of motion and PMC on PVS visibility. PMC may reduce the systematic bias of PVS volume fraction caused by motion artifacts.