Motion corrected silent <scp>ZTE</scp> neuroimaging

Ljungberg, Emil; Wood, Tobias C.; Solana, Ana Beatriz; Williams, Steven; Barker, Gareth J.

doi:10.1002/mrm.29201

Cited by 7 publications

(2 citation statements)

References 85 publications

(158 reference statements)

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“…Long scan times intensify issues related to subject motion and may require motion correction or compensation strategies beyond the relatively basic approach used here. Common approaches for motion correction 35 are based on intrinsic information, 36 navigator acquisitions, or external sensors. Integration of such methods with HYFI and/or the employed short‐ T 2 imaging system is feasible, particularly because the requirements for precision and accuracy are moderate at the practicable image resolution.…”

Section: Discussionmentioning

confidence: 99%

Myelin bilayer mapping in the human brain in vivo

Baadsvik,

Weiger,

Froidevaux

et al. 2024

Magnetic Resonance in Med

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PurposeTo quantitatively map the myelin lipid‐protein bilayer in the live human brain.MethodsThis goal was pursued by integrating a multi‐TE acquisition approach targeting ultrashort T2 signals with voxel‐wise fitting to a three‐component signal model. Imaging was performed at 3 T in two healthy volunteers using high‐performance RF and gradient hardware and the HYFI sequence. The design of a suitable imaging protocol faced substantial constraints concerning SNR, imaging volume, scan time, and RF power deposition. Model fitting to data acquired using the proposed protocol was made feasible through simulation‐based optimization, and filtering was used to condition noise presentation and overall depiction fidelity.ResultsA multi‐TE protocol (11 TEs of 20–780 μs) for in vivo brain imaging was developed in adherence with applicable safety regulations and practical scan time limits. Data acquired using this protocol produced accurate model fitting results, validating the suitability of the protocol for this purpose. Structured, grainy texture of myelin bilayer maps was observed and determined to be a manifestation of correlated image noise resulting from the employed acquisition strategy. Map quality was significantly improved by filtering to uniformize the k‐space noise distribution and simultaneously extending the k‐space support. The final myelin bilayer maps provided selective depiction of myelin, reconciling competitive resolution (1.4 mm) with adequate SNR and benign noise texture.ConclusionUsing the proposed technique, quantitative maps of the myelin bilayer can be obtained in vivo. These maps offer unique information content with potential applications in basic research, diagnosis, disease monitoring, and drug development.

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Section: Discussionmentioning

confidence: 99%

Myelin bilayer mapping in the human brain in vivo

Baadsvik,

Weiger,

Froidevaux

et al. 2024

Magnetic Resonance in Med

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“…Furthermore, ASL-MRA allows dynamic signal collection with appropriate timing delays after labeling to achieve 4D MRA with temporal information, which is not possible with TOF and was previously only possible with conventional angiography [14,[19][20][21][22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Synthesizing 4D Magnetic Resonance Angiography From 3D Time-of-Flight Using Deep Learning: A Feasibility Study

Wada,

Akatsu,

Ikenouchi

et al. 2024

Cureus

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Objective and background This study aimed to develop a deep convolutional neural network (DCNN) model capable of generating synthetic 4D magnetic resonance angiography (MRA) from 3D time-of-flight (TOF) images, allowing estimation of temporal changes in arterial flow. TOF MRA provides static information about arterial structures through maximum intensity projection (MIP) processing, but it does not capture the dynamic information of contrast agent circulation, which is lost during MIP processing. Considering the principles of TOF, it is hypothesized that dynamic information about arterial blood flow is latent within TOF signals. Although arterial spin labeling (ASL) can extract dynamic arterial information, ASL MRA has drawbacks, such as longer imaging times and lower spatial resolution than TOF MRA. This study's primary aim is to extend the utility of TOF MRA by training a machine-learning model on paired TOF and ASL data to extract latent dynamic information from TOF signals. Methods A DCNN combining a modified U-Net and a long-short-term memory (LSTM) network was trained on a dataset of 13 subjects (11 men and two women, aged 42-77 years) using paired 3D TOF MRA and 4D ASL MRA images. Subjects had no history of cerebral vessel occlusion or significant stenosis. The dataset was acquired using a 3T MRI system with a 32-channel head coil. Preprocessing involved resampling and intensity normalization of TOF and ASL images, followed by data augmentation and arterial mask generation. The model learned to extract flow information from TOF images and generate 8-phase 4D MRA images. The precision of flow estimation was evaluated using the coefficient of determination (R²) and Bland-Altman analysis. A board-certified neuroradiologist validated the quality of the images and the absence of significant stenosis in the major cerebral arteries. Results The generated 4D MRA images closely resembled the ground-truth ASL MRA data, with R² values of 0.92, 0.85, and 0.84 for the internal carotid artery (ICA), proximal middle cerebral artery (MCA), and distal MCA, respectively. Bland-Altman analysis revealed a systematic error of -0.06, with 95% agreement limits ranging from -0.18 to 0.12. Additionally, the model successfully identified flow abnormalities in a subject with left MCA stenosis, displaying a delayed peak and subsequent flattening distal to the stenosis, indicative of reduced blood flow. Visualization of the predicted arterial flow overlaid on the original TOF MRA images highlighted the spatial progression and dynamics of the flow. Conclusions The DCNN model effectively generated synthetic 4D MRA images from TOF images, demonstrating its potential to estimate temporal changes in arterial flow accurately. This non-invasive technique offers a promising alternative to conventional methods for visualizing and evaluating healthy and pathological flow dynamics. It has significant potential to improve the diagnosis and treatment of cerebrovascu...

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Motion corrected silent ZTE neuroimaging

Ljungberg

Wood

Solana

et al. 2022

Magnetic Resonance in Med

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To develop self-navigated motion correction for 3D silent zero echo time (ZTE) based neuroimaging and characterize its performance for different types of head motion. Methods:The proposed method termed MERLIN (Motion Estimation & Retrospective correction Leveraging Interleaved Navigators) achieves self-navigation by using interleaved 3D phyllotaxis k-space sampling. Low resolution navigator images are reconstructed continuously throughout the ZTE acquisition using a sliding window and co-registered in image space relative to a fixed reference position. Rigid body motion corrections are then applied retrospectively to the k-space trajectory and raw data and reconstructed into a final, high-resolution ZTE image. Results: MERLIN demonstrated successful and consistent motion correction for magnetization prepared ZTE images for a range of different instructed motion paradigms. The acoustic noise response of the self-navigated phyllotaxis trajectory was found to be only slightly above ambient noise levels (<4 dBA). Conclusion:Silent ZTE imaging combined with MERLIN addresses two major challenges intrinsic to MRI (i.e., subject motion and acoustic noise) in a synergistic and integrated manner without increase in scan time and thereby forms a versatile and powerful framework for clinical and research MR neuroimaging applications.

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Motion corrected silent ZTE neuroimaging

Cited by 7 publications

References 85 publications

Myelin bilayer mapping in the human brain in vivo

Myelin bilayer mapping in the human brain in vivo

Synthesizing 4D Magnetic Resonance Angiography From 3D Time-of-Flight Using Deep Learning: A Feasibility Study

Motion corrected silent ZTE neuroimaging

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