Synthesized b0 for diffusion distortion correction (Synb0-DisCo)

Schilling, Kurt G.; Blaber, Justin A.; Huo, Yuankai; Newton, Allen T.; Hansen, Colin B.; Nath, Vishwesh; Shafer, Andrea T.; Williams, Owen A.; Resnick, Susan M.; Rogers, Baxter P.; Anderson, Adam W.; Landman, Bennett A.

doi:10.1016/j.mri.2019.05.008

Cited by 117 publications

(86 citation statements)

References 46 publications

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“…However, to achieve robust performance, 3D model needs more training data to learn more parameters. A compromise is to use multiple adjacent slices that allow the model to capture more 3D context, or to train different networks for all three combinations of orthogonal 2D planes to produce pseudo‐3D information 133 …”

Section: Discussionmentioning

confidence: 99%

“…different networks for all three combinations of orthogonal 2D planes to produce pseudo-3D information. 133 The reviewed studies illustrate the advantages of learning-based methods over conventional methods in performance as well as clinical application. Learning-based methods generally outperform conventional methods in generating more realistic synthetic images with higher similarity to real images and better quantitative metrics.…”

Section: B3 | Petmentioning

confidence: 99%

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A review on medical imaging synthesis using deep learning and its clinical applications

Wang

Lei

et al. 2020

J Applied Clin Med Phys

192

131

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This paper reviewed the deep learning‐based studies for medical imaging synthesis and its clinical application. Specifically, we summarized the recent developments of deep learning‐based methods in inter‐ and intra‐modality image synthesis by listing and highlighting the proposed methods, study designs, and reported performances with related clinical applications on representative studies. The challenges among the reviewed studies were then summarized with discussion.

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Section: Discussionmentioning

confidence: 99%

Section: B3 | Petmentioning

confidence: 99%

A review on medical imaging synthesis using deep learning and its clinical applications

Wang

Lei

et al. 2020

J Applied Clin Med Phys

192

131

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“…It is clear that all correction methods significantly improve brain geometry. We point out that the legacy distortion correction [33] (S.D.) appears to show improvements mainly for the BLSA datasets (which it was trained on) and did not well generalize to additional contrasts and geometries-limitations which the currently proposed approach specifically intend to address.…”

Section: Plos Onementioning

confidence: 91%

“…), the legacy synthetic distortion (S.D.) [33], and the proposed synthetic-correction (Syn. C.), where a higher value serves as an indicator of a closer match to the structural scan.…”

Section: Plos Onementioning

confidence: 99%

“…Despite the availability of effective tools, the supplementary information necessary for these techniques is not always available, which could be potentially due to scanner limitations, scan time constraints, acquisition difficulties / artifacts, or legacy considerations. Recently, we presented a deep learning synthesis approach, Synb0-DisCo, to estimate non-distorted (infinite bandwidth) minimally weighted images from T1 weighed (T1w) images [14]. Synb0-DisCo uses a 2.5D (multi-slice, multi-view) generative adversarial network (GAN) to perform the image synthesis process.…”

Section: Introductionmentioning

confidence: 99%

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Distortion correction of diffusion weighted MRI without reverse phase-encoding scans or field-maps

et al. 2020

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Diffusion magnetic resonance images may suffer from geometric distortions due to susceptibility induced off resonance fields, which cause geometric mismatch with anatomical images and ultimately affect subsequent quantification of microstructural or connectivity indices. State-of-the art diffusion distortion correction methods typically require data acquired with reverse phase encoding directions, resulting in varying magnitudes and orientations of distortion, which allow estimation of an undistorted volume. Alternatively, additional field maps acquisitions can be used along with sequence information to determine warping fields. However, not all imaging protocols include these additional scans and cannot take advantage of state-of-the art distortion correction. To avoid additional acquisitions, structural MRI (undistorted scans) can be used as registration targets for intensity driven correction. In this study, we aim to (1) enable susceptibility distortion correction with historical and/or limited diffusion datasets that do not include specific sequences for distortion correction and (2) avoid the computationally intensive registration procedure typically required for distortion correction using structural scans. To achieve these aims, we use deep learning (3D U-nets) to synthesize an undistorted b0 image that matches geometry of structural T1w images and intensity contrasts from diffusion images. Importantly, the training dataset is heterogenous, consisting of varying acquisitions of both structural and diffusion. We apply our approach to a withheld test set and show that distortions are successfully corrected after processing. We quantitatively evaluate the proposed distortion correction and intensitybased registration against state-of-the-art distortion correction (FSL topup). The results illustrate that the proposed pipeline results in b0 images that are geometrically similar to non-PLOS ONE

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Design and validation of the ADNI MR protocol

Arani,

Borowski,

Felmlee

et al. 2024

Alzheimer's & Dementia

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Phase four of the Alzheimer's Disease Neuroimaging Initiative (ADNI4) magnetic resonance imaging (MRI) protocols aim to maintain longitudinal consistency across two decades of data acquisition, while adopting new technologies. Here we describe and justify the study's design and targeted biomarkers. The ADNI4 MRI protocol includes nine MRI sequences. Some sequences require the latest hardware and software system upgrades and are continuously rolled out as they become available at each site. The main sequence additions/changes in ADNI4 are: (1) compressed sensing (CS) T1‐weighting, (2) pseudo‐continuous arterial spin labeling (ASL) on all three vendors (GE, Siemens, Philips), (3) multiple‐post‐labeling‐delay ASL, (4) 1 mm3 isotropic 3D fluid‐attenuated inversion recovery, and (5) CS 3D T2‐weighted. ADNI4 aims to help the neuroimaging community extract valuable imaging biomarkers and provide a database to test the impact of advanced imaging strategies on diagnostic accuracy and disease sensitivity among individuals lying on the cognitively normal to impaired spectrum.Highlights A summary of MRI protocols for phase four of the Alzheimer's Disease Neuroimaging Initiative (ADNI 4). The design and justification for the ADNI 4 MRI protocols. Compressed sensing and multi‐band advances have been applied to improve scan time. ADNI4 protocols aim to streamline safety screening and therapy monitoring. The ADNI4 database will be a valuable test bed for academic research.

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Synthesized b0 for diffusion distortion correction (Synb0-DisCo)

Cited by 117 publications

References 46 publications

A review on medical imaging synthesis using deep learning and its clinical applications

A review on medical imaging synthesis using deep learning and its clinical applications

Distortion correction of diffusion weighted MRI without reverse phase-encoding scans or field-maps

Design and validation of the ADNI MR protocol

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