Real-time motion analytics during brain MRI improve data quality and reduce costs.
Ontology highlight
ABSTRACT:
SUBMITTER: Dosenbach NUF
PROVIDER: S-EPMC5731481 | biostudies-literature | 2017 Nov
REPOSITORIES: biostudies-literature
Publications
Real-time motion analytics during brain MRI improve data quality and reduce costs.
Dosenbach Nico U F NUF Koller Jonathan M JM Earl Eric A EA Miranda-Dominguez Oscar O Klein Rachel L RL Van Andrew N AN Snyder Abraham Z AZ Nagel Bonnie J BJ Nigg Joel T JT Nguyen Annie L AL Wesevich Victoria V Greene Deanna J DJ Fair Damien A DA
NeuroImage 20170810
Head motion systematically distorts clinical and research MRI data. Motion artifacts have biased findings from many structural and functional brain MRI studies. An effective way to remove motion artifacts is to exclude MRI data frames affected by head motion. However, such post-hoc frame censoring can lead to data loss rates of 50% or more in our pediatric patient cohorts. Hence, many scanner operators collect additional 'buffer data', an expensive practice that, by itself, does not guarantee su ...[more]
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Project description:Imaging the infant brain with MRI has improved our understanding of early neurodevelopment. However, head motion during MRI acquisition is detrimental to both functional and structural MRI scan quality. Though infants are typically scanned while asleep, they commonly exhibit motion during scanning causing data loss. Our group has shown that providing MRI technicians with real-time motion estimates via Framewise Integrated Real-Time MRI Monitoring (FIRMM) software helps obtain high-quality, low motion fMRI data. By estimating head motion in real time and displaying motion metrics to the MR technician during an fMRI scan, FIRMM can improve scanning efficiency. Here, we compared average framewise displacement (FD), a proxy for head motion, and the amount of usable fMRI data (FD ≤ 0.2 mm) in infants scanned with (n = 407) and without FIRMM (n = 295). Using a mixed-effects model, we found that the addition of FIRMM to current state-of-the-art infant scanning protocols significantly increased the amount of usable fMRI data acquired per infant, demonstrating its value for research and clinical infant neuroimaging.
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Project description:ObjectivesThe breathing phase for the determination of thoracic indices in patients with pectus excavatum is not standardized. The aim of this study was to identify the best period for reliable assessments of morphologic indices by dynamic observations of the chest wall using real-time MRI.MethodsIn this prospective study, patients with pectus excavatum underwent morphologic evaluation by real-time MRI at 3 T between January 2020 and June 2021. The Haller index (HI), correction index (CI), modified asymmetry index (AI), and modified eccentricity index (EI) were determined during free, quiet, and forced breathing respectively. Breathing-related differences in the thoracic indices were analyzed with the Wilcoxon signed-rank test. Motion of the anterior chest wall was analyzed as well.ResultsA total of 56 patients (11 females and 45 males, median age 15.4 years, interquartile range 14.3-16.9) were included. In quiet expiration, the median HI in the cohort equaled 5.7 (4.5-7.2). The median absolute differences (Δ) in the thoracic indices between peak inspiration and peak expiration were ΔHI = 1.1 (0.7-1.6, p < .001), ΔCI = 4.8% (1.3-7.5%, p < .001), ΔAI = 3.0% (1.0-5.0%, p < .001), and ΔEI = 8.0% (3.0-14.0%, p < .05). The indices varied significantly during different inspiratory phases, but not during expiration (p > .05 each). Furthermore, the dynamic evaluation revealed three distinctive movement patterns of the funnel chest.ConclusionsReal-time MRI reveals patterns of chest wall motion and indicate that thoracic indices of pectus excavatum should be assessed in the end-expiratory phase of quiet expiration.Key points• The thoracic indices in patients with pectus excavatum depend on the breathing phase. • Quiet expiration represents the best breathing phase for determining thoracic indices. • Real-time MRI can identify different chest wall motion patterns in pectus excavatum.
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Project description:PURPOSE:To demonstrate a tagging method compatible with RT-MRI for the study of speech production. METHODS:Tagging is applied as a brief interruption to a continuous real-time spiral acquisition. Tagging can be initiated manually by the operator, cued to the speech stimulus, or be automatically applied with a fixed frequency. We use a standard 2D 1-3-3-1 binomial SPAtial Modulation of Magnetization (SPAMM) sequence with 1 cm spacing in both in-plane directions. Tag persistence in tongue muscle is simulated and validated in vivo. The ability to capture internal tongue deformations is tested during speech production of American English diphthongs in native speakers. RESULTS:We achieved an imaging window of 650-800 ms at 1.5T, with imaging signal to noise ratio ? 17 and tag contrast to noise ratio ? 5 in human tongue, providing 36 frames/s temporal resolution and 2 mm in-plane spatial resolution with real-time interactive acquisition and view-sharing reconstruction. The proposed method was able to capture tongue motion patterns and their relative timing with adequate spatiotemporal resolution during the production of American English diphthongs and consonants. CONCLUSION:Intermittent tagging during real-time MRI of speech production is able to reveal the internal deformations of the tongue. This capability will allow new investigations of valuable spatiotemporal information on the biomechanics of the lingual subsystems during speech without reliance on binning speech utterance repetition.
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Project description:BackgroundNon-invasive imaging techniques such as magnetic resonance imaging (MRI) provide the ability to evaluate the complex anatomy of bone and soft tissues of the wrist without the use of ionizing radiation. Dynamic instability of wrist--occurring during joint motion--is a complex condition that has assumed increased importance in musculoskeletal medicine. The objective of this study was to develop an MRI protocol for evaluating the wrist during continuous active motion, to show that dynamic imaging of the wrist is realizable, and to demonstrate that the resulting anatomical images enable the measurement of metrics commonly evaluated for dynamic wrist instability.MethodsA 3-Tesla "active-MRI" protocol was developed using a bSSFP sequence with 475 ms temporal resolution for continuous imaging of the moving wrist. Fifteen wrists of 10 asymptomatic volunteers were scanned during active supination/pronation, radial/ulnar deviation, "clenched-fist", and volarflexion/dorsiflexion maneuvers. Two physicians evaluated distal radioulnar joint (DRUJ) congruity, extensor carpi ulnaris (ECU) tendon translation, the scapholunate (SL) interval, and the SL, radiolunate (RL) and capitolunate (CL) angles from the resulting images.ResultsThe mean DRUJ subluxation ratio was 0.04 in supination, 0.10 in neutral, and 0.14 in pronation. The ECU tendon was subluxated or translated out of its groove in 3 wrists in pronation, 9 wrists in neutral, and 11 wrists in supination. The mean SL interval was 1.43 mm for neutral, ulnar deviation, radial deviation positions, and increased to 1.64 mm during the clenched-fist maneuver. Measurement of SL, RL and CL angles in neutral and dorsiflexion was also accomplished.ConclusionThis study demonstrates the initial performance of active-MRI, which may be useful in the investigation of dynamic wrist instability in vivo.
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Project description:PurposeTo improve motion robustness of functional fetal MRI scans by developing an intrinsic real-time motion correction method. MRI provides an ideal tool to characterize fetal brain development and growth. It is, however, a relatively slow imaging technique and therefore extremely susceptible to subject motion, particularly in functional MRI experiments acquiring multiple Echo-Planar-Imaging-based repetitions, for example, diffusion MRI or blood-oxygen-level-dependency MRI.MethodsA 3D UNet was trained on 125 fetal datasets to track the fetal brain position in each repetition of the scan in real time. This tracking, inserted into a Gadgetron pipeline on a clinical scanner, allows updating the position of the field of view in a modified echo-planar imaging sequence. The method was evaluated in real-time in controlled-motion phantom experiments and ten fetal MR studies (17 + 4-34 + 3 gestational weeks) at 3T. The localization network was additionally tested retrospectively on 29 low-field (0.55T) datasets.ResultsOur method achieved real-time fetal head tracking and prospective correction of the acquisition geometry. Localization performance achieved Dice scores of 84.4% and 82.3%, respectively for both the unseen 1.5T/3T and 0.55T fetal data, with values higher for cephalic fetuses and increasing with gestational age.ConclusionsOur technique was able to follow the fetal brain even for fetuses under 18 weeks GA in real-time at 3T and was successfully applied "offline" to new cohorts on 0.55T. Next, it will be deployed to other modalities such as fetal diffusion MRI and to cohorts of pregnant participants diagnosed with pregnancy complications, for example, pre-eclampsia and congenital heart disease.
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Project description:PurposeTo develop an MRI acquisition and reconstruction framework for volumetric cine visualization of the fetal heart and great vessels in the presence of maternal and fetal motion.MethodsFour-dimensional (4D) depiction was achieved using a highly-accelerated multi-planar real-time balanced steady-state free precession acquisition combined with retrospective image-domain techniques for motion correction, cardiac synchronization and outlier rejection. The framework was validated using a numerical phantom and evaluated in a study of 20 mid- to late-gestational age human fetal subjects (23-33 weeks gestational age). Reconstructed MR data were compared with matched ultrasound. A preliminary assessment of flow-sensitive reconstruction using the velocity information encoded in the phase of real-time images is included.ResultsReconstructed 4D data could be visualized in any two-dimensional plane without the need for highly specific scan plane prescription prior to acquisition or for maternal breath hold to minimize motion. Reconstruction was fully automated aside from user-specified masks of the fetal heart and chest. The framework proved robust when applied to fetal data and simulations confirmed that spatial and temporal features could be reliably recovered. Evaluation suggested the reconstructed framework has the potential to be used for comprehensive assessment of the fetal heart, either as an adjunct to ultrasound or in combination with other MRI techniques.ConclusionsThe proposed methods show promise as a framework for motion-compensated 4D assessment of the fetal heart and great vessels.
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Project description:Purpose. Real-time three-dimensional (3D) magnetic resonance (MR) imaging is challenging because of slow MR signal acquisition, leading to highly under-sampled k-space data. Here, we proposed a deep learning-based, k-space-driven deformable registration network (KS-RegNet) for real-time 3D MR imaging. By incorporating prior information, KS-RegNet performs a deformable image registration between a fully-sampled prior image and on-board images acquired from highly-under-sampled k-space data, to generate high-quality on-board images for real-time motion tracking.Methods. KS-RegNet is an end-to-end, unsupervised network consisting of an input data generation block, a subsequent U-Net core block, and following operations to compute data fidelity and regularization losses. The input data involved a fully-sampled, complex-valued prior image, and the k-space data of an on-board, real-time MR image (MRI). From the k-space data, under-sampled real-time MRI was reconstructed by the data generation block to input into the U-Net core. In addition, to train the U-Net core to learn the under-sampling artifacts, the k-space data of the prior image was intentionally under-sampled using the same readout trajectory as the real-time MRI, and reconstructed to serve an additional input. The U-Net core predicted a deformation vector field that deforms the prior MRI to on-board real-time MRI. To avoid adverse effects of quantifying image similarity on the artifacts-ridden images, the data fidelity loss of deformation was evaluated directly in k-space.Results. Compared with Elastix and other deep learning network architectures, KS-RegNet demonstrated better and more stable performance. The average (±s.d.) DICE coefficients of KS-RegNet on a cardiac dataset for the 5- , 9- , and 13-spoke k-space acquisitions were 0.884 ± 0.025, 0.889 ± 0.024, and 0.894 ± 0.022, respectively; and the corresponding average (±s.d.) center-of-mass errors (COMEs) were 1.21 ± 1.09, 1.29 ± 1.22, and 1.01 ± 0.86 mm, respectively. KS-RegNet also provided the best performance on an abdominal dataset.Conclusion. KS-RegNet allows real-time MRI generation with sub-second latency. It enables potential real-time MR-guided soft tissue tracking, tumor localization, and radiotherapy plan adaptation.
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