Integration of cardiac and respiratory motion into MRI roadmaps fused with x-ray.
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ABSTRACT:
SUBMITTER: Faranesh AZ
PROVIDER: S-EPMC3579864 | biostudies-other | 2013 Mar
REPOSITORIES: biostudies-other
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Project description:PurposeIn this work, we integrated the pilot tone (PT) navigation system into a reconstruction framework for respiratory and cardiac motion-resolved 5D flow. We tested the hypotheses that PT would provide equivalent respiratory curves, cardiac triggers, and corresponding flow measurements to a previously established self-gating (SG) technique while being independent from changes to the acquisition parameters.MethodsFifteen volunteers and 9 patients were scanned with a free-running 5D flow sequence, with PT integrated. Respiratory curves and cardiac triggers from PT and SG were compared across all subjects. Flow measurements from 5D flow reconstructions using both PT and SG were compared to each other and to a reference electrocardiogram-gated and respiratory triggered 4D flow acquisition. Radial trajectories with variable readouts per interleave were also tested in 1 subject to compare cardiac trigger quality between PT and SG.ResultsThe correlation between PT and SG respiratory curves were 0.95 ± 0.06 for volunteers and 0.95 ± 0.04 for patients. Heartbeat duration measurements in volunteers and patients showed a bias to electrocardiogram measurements of, respectively, 0.16 ± 64.94 ms and 0.01 ± 39.29 ms for PT versus electrocardiogram and of 0.24 ± 63.68 ms and 0.09 ± 32.79 ms for SG versus electrocardiogram. No significant differences were reported for the flow measurements between 5D flow PT and from 5D flow SG. A decrease in the cardiac triggering quality of SG was observed for increasing readouts per interleave, whereas PT quality remained constant.ConclusionPT has been successfully integrated in 5D flow MRI and has shown equivalent results to the previously described 5D flow SG technique, while being completely acquisition-independent.
| S-EPMC8627452 | biostudies-literature
Project description:Diffusion-weighted images of the liver exhibit signal dropout from cardiac and respiratory motion, particularly in the left lobe. These artifacts cause bias and variance in derived parameters that quantify intravoxel incoherent motion. Many models of diffusion have been proposed, but few separate attenuation from diffusion or perfusion from that of bulk motion. The error model proposed here (Beta*LogNormal) is intended to accomplish that separation by modeling stochastic attenuation from bulk motion as multiplication by a Beta-distributed random variate. Maximum likelihood estimation with this error model can be used to derive intravoxel incoherent motion parameters separate from signal dropout, and does not require a priori specification of parameters to do so. Liver intravoxel incoherent motion parameters were derived for six healthy subjects under this error model and compared with least-squares estimates. Least-squares estimates exhibited bias due to cardiac and respiratory gating and due to location within the liver. Bias from these factors was significantly reduced under the Beta*LogNormal model, as was within-organ parameter variance. Similar effects were appreciable in diffusivity maps in two patients with focal liver lesions. These results suggest that, relative to least-squares estimation, the Beta*LogNormal model accomplishes the intended reduction of bias and variance from bulk motion in liver diffusion imaging.
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Project description:PurposeGastrointestinal motion patterns such as peristalsis and segmental contractions can alter the shape and position of the stomach and intestines with respect to other irradiated organs during radiation therapy. Unfortunately, these deformations are concealed by conventional four-dimensional (4D)-MRI techniques, which were developed to visualize respiratory motion by binning acquired data into respiratory motion states without considering the phases of GI motion. We present a method to reconstruct breathing-compensated images showing the phases of periodic gastric motion and study the effect of this motion on regional anatomical structures.MethodsSixty-seven DCE-MRI examinations were performed on patients undergoing MRI simulation for hepatocellular carcinoma using a golden-angle stack-of-stars sequence that collected 2000 radial spokes over 5 min. The collected data were reconstructed using a method with integrated respiratory motion correction into a time series of 3D image volumes without visible breathing motion. From this series, a gastric motion signal was extracted by temporal filtering of time-intensity curves in the stomach. Using this motion signal, breathing-corrected back-projection images were sorted according to the gastric phase and reconstructed into 21 gastric motion state images showing the phases of gastric motion.ResultsReconstructed image volumes showed gastric motion states clearly with no visible breathing motion or related artifacts. The mean frequency of the gastric motion signal was 3 cycles/min with a standard deviation of 0.27 cycles/min.ConclusionsPeriodic gastrointestinal motion can be visualized without confounding respiratory motion using the presented GI 4D MRI technique. GI 4D MRIs may help define internal target volumes for treatment planning, aid in planning organ at risk volume definition, or support motion model development for gastrointestinal motion tracking algorithms for real-time MR-guided radiation therapy.
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Project description:IntroductionRespiratory motion models establish a correspondence between respiratory-correlated (RC) 4-dimensional (4D) imaging and respiratory surrogates, to estimate time-resolved (TR) 3D breathing motion. To evaluate the performance of motion models on real patient data, a validation framework based on magnetic resonance imaging (MRI) is proposed, entailing the use of RC 4DMRI to build the model, and on both (i) TR 2D cine-MRI and (ii) additional 4DMRI data for testing intra-/inter-fraction breathing motion variability.MethodsRepeated MRI data were acquired in 7 patients with abdominal lesions. The considered model relied on deformable image registration (DIR) for building the model and compensating for inter-fraction baseline variations. Both 2D and 3D validation were performed, by comparing model estimations with the ground truth 2D cine-MRI and 4DMRI respiratory phases, respectively.ResultsThe median DIR error was comparable to the voxel size (1.33 × 1.33 × 5 mm3 ), with higher values in the presence of large inter-fraction motion (median value: 2.97 mm). In the 2D validation, the median estimation error on anatomical landmarks' position resulted below 4 mm in every scenario, whereas in the 3D validation it was 1.33 mm and 4.21 mm when testing intra- and inter-fraction motion, respectively. The range of motion described in the cine-MRI was comparable to the motion of the building 4DMRI, being always above the estimation error. Overall, the model performance was dependent on DIR error, presenting reduced accuracy when inter-fraction baseline variations occurred.ConclusionsResults suggest the potential of the proposed framework in evaluating global motion models for organ motion management in MRI-guided radiotherapy.
| S-EPMC8251859 | biostudies-literature
Project description:Cardiac PET is a versatile imaging technique providing important diagnostic information about ischemic heart diseases. Respiratory and cardiac motion of the heart can strongly impair image quality and therefore diagnostic accuracy of cardiac PET scans. The aim of this study was to investigate a new cardiac PET/MR approach providing respiratory and cardiac motion-compensated MR and PET images in less than 5 min. Methods: Free-breathing 3-dimensional MR data were acquired and retrospectively binned into multiple respiratory and cardiac motion states. Three-dimensional cardiac and respiratory motion fields were obtained with a nonrigid registration algorithm and used in motion-compensated MR and PET reconstructions to improve image quality. The improvement in image quality and diagnostic accuracy of the technique was assessed in simultaneous 18F-FDG PET/MR scans of a canine model of myocardial infarct and was demonstrated in a human subject. Results: MR motion fields were successfully used to compensate for in vivo cardiac motion, leading to improvements in full width at half maximum of the canine myocardium of 13% ± 5%, similar to cardiac gating but with a 90% ± 57% higher contrast-to-noise ratio between myocardium and blood. Motion correction led to an improvement in MR image quality in all subjects, with an increase in sharpness of the canine coronary arteries of 85% ± 72%. A functional assessment showed good agreement with standard MR cine scans with a difference in ejection fraction of -2% ± 3%. MR-based respiratory and cardiac motion information was used to improve the PET image quality of a human in vivo scan. Conclusion: The MR technique presented here provides both diagnostic and motion information that can be used to improve MR and PET image quality. Reliable respiratory and cardiac motion correction could make cardiac PET results more reproducible.
| S-EPMC5414502 | biostudies-literature
Project description:Head motion represents one of the greatest technical obstacles in magnetic resonance imaging (MRI) of the human brain. Accurate detection of artifacts induced by head motion requires precise estimation of movement. However, head motion estimates may be corrupted by artifacts due to magnetic main field fluctuations generated by body motion. In the current report, we examine head motion estimation in multiband resting state functional connectivity MRI (rs-fcMRI) data from the Adolescent Brain and Cognitive Development (ABCD) Study and comparison 'single-shot' datasets. We show that respirations contaminate movement estimates in functional MRI and that respiration generates apparent head motion not associated with functional MRI quality reductions. We have developed a novel approach using a band-stop filter that accurately removes these respiratory effects from motion estimates. Subsequently, we demonstrate that utilizing a band-stop filter improves post-processing fMRI data quality. Lastly, we demonstrate the real-time implementation of motion estimate filtering in our FIRMM (Framewise Integrated Real-Time MRI Monitoring) software package.
| S-EPMC7307712 | biostudies-literature
Project description:PurposeTo implement, validate, and apply a self-gated free-running whole-heart five-dimensional (5D) flow MRI framework to evaluate respiration-driven effects on three-dimensional (3D) hemodynamics in a clinical setting.Materials and methodsIn this prospective study, a free-running five-dimensional (5D) flow sequence was implemented with 3D radial sampling, self-gating, and a compressed-sensing reconstruction. The 5D flow was evaluated in a pulsatile phantom and adult participants with aortic and/or valvular disease who were enrolled between May and August 2019. Conventional twofold-accelerated four-dimensional (4D) flow of the thoracic aorta with navigator gating was performed as a reference comparison. Continuous parameters were evaluated for parameter normality and were compared between conventional 4D flow and 5D flow using a signed-rank or two-tailed paired t test. Differences between respiratory states were evaluated using a repeated-measure analysis of variance or a nonparametric Friedman test.ResultsA total of 20 adult participants (mean age, 49 years ± 17 [standard deviation]; 18 men and two women) were included. In vitro 5D flow results showed excellent agreement with conventional 4D flow-derived values (peak and net flow, <7% difference over all quantified planes). Whole-heart 5D flow data were collected in all participants in 7.65 minutes ± 0.35 (acceleration rate = 36.0-76.9) versus 9.88 minutes ± 3.17 for conventional aortic 4D flow. In vivo, 5D flow demonstrated moderate agreement with conventional 4D flow but demonstrated overestimation in net flow and peak velocity (up to 26% and 12%, respectively) in the ascending aorta and underestimation (<12%) in the arch and descending aorta. Respiratory-resolved analyses of caval veins showed significantly increased net and peak flow in the inferior vena cava in end inspiration compared with end expiration, and the opposite trend was shown in the superior vena cava.ConclusionA free-running 5D flow MRI framework consistently captured cardiac and respiratory motion-resolved 3D hemodynamics in less than 8 minutes. Supplemental material is available for this article. © RSNA, 2020.
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Project description:OBJECTIVES:To determine whether X-ray fused with MRI (XFM) is beneficial for select transcatheter congenital heart disease interventions. BACKGROUND:Complex transcatheter interventions often require three-dimensional (3D) soft tissue imaging guidance. Fusion imaging with live X-ray fluoroscopy can potentially improve and simplify procedures. METHODS:Patients referred for select congenital heart disease interventions were prospectively enrolled. Cardiac MRI data was overlaid on live fluoroscopy for procedural guidance. Likert scale operator assessments of value were recorded. Fluoroscopy time, radiation exposure, contrast dose, and procedure time were compared to matched cases from our institutional experience. RESULTS:Forty-six patients were enrolled. Pre-catheterization, same day cardiac MRI findings indicated intervention should be deferred in nine patients. XFM-guided cardiac catheterization was performed in 37 (median age 8.7 years [0.5-63?years]; median weight 28?kg [5.6-110?kg]) with the following prespecified indications: pulmonary artery (PA) stenosis (n =?13), aortic coarctation (n =?12), conduit stenosis/insufficiency (n = 9), and ventricular septal defect (n = 3). Diagnostic catheterization showed intervention was not indicated in 12 additional cases. XFM-guided intervention was performed in the remaining 25. Fluoroscopy time was shorter for XFM-guided intervention cases compared to matched controls. There was no significant difference in radiation dose area product, contrast volume, or procedure time. Operator Likert scores indicated XFM provided useful soft tissue guidance in all cases and was never misleading. CONCLUSIONS:XFM provides operators with meaningful three-dimensional soft tissue data and reduces fluoroscopy time in select congenital heart disease interventions.
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Project description:Accurate fusion of late gadolinium enhancement magnetic resonance imaging (MRI) and electro-anatomical voltage mapping (EAM) is required to evaluate the potential of MRI to identify the substrate of ventricular tachycardia. However, both datasets are not acquired at the same cardiac phase and EAM data is corrupted with respiratory motion limiting the accuracy of current rigid fusion techniques. Knowledge of cardiac and respiratory motion during EAM is thus required to enhance the fusion process. In this study, we propose a novel approach to characterize both cardiac and respiratory motion from EAM data using the temporal evolution of the 3D catheter location recorded from clinical EAM systems. Cardiac and respiratory motion components are extracted from the recorded catheter location using multi-band filters. Filters are calibrated for each EAM point using estimates of heart rate and respiratory rate. The method was first evaluated in numerical simulations using 3D models of cardiac and respiratory motions of the heart generated from real time MRI data acquired in 5 healthy subjects. An accuracy of 0.6-0.7 mm was found for both cardiac and respiratory motion estimates in numerical simulations. Cardiac and respiratory motions were then characterized in 27 patients who underwent LV mapping for treatment of ventricular tachycardia. Mean maximum amplitude of cardiac and respiratory motion was 10.2±2.7 mm (min?=?5.5, max?=?16.9) and 8.8±2.3 mm (min?=?4.3, max?=?14.8), respectively. 3D Cardiac and respiratory motions could be estimated from the recorded catheter location and the method does not rely on additional imaging modality such as X-ray fluoroscopy and can be used in conventional electrophysiology laboratory setting.
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Project description:Cine cardiac magnetic resonance imaging (MRI) is widely used for the diagnosis of cardiac diseases thanks to its ability to present cardiovascular features in excellent contrast. As compared to computed tomography (CT), MRI, however, requires a long scan time, which inevitably induces motion artifacts and causes patients' discomfort. Thus, there has been a strong clinical motivation to develop techniques to reduce both the scan time and motion artifacts. Given its successful applications in other medical imaging tasks such as MRI super-resolution and CT metal artifact reduction, deep learning is a promising approach for cardiac MRI motion artifact reduction. In this paper, we propose a novel recurrent generative adversarial network model for cardiac MRI motion artifact reduction. This model utilizes bi-directional convolutional long short-term memory (ConvLSTM) and multi-scale convolutions to improve the performance of the proposed network, in which bi-directional ConvLSTMs handle long-range temporal features while multi-scale convolutions gather both local and global features. We demonstrate a decent generalizability of the proposed method thanks to the novel architecture of our deep network that captures the essential relationship of cardiovascular dynamics. Indeed, our extensive experiments show that our method achieves better image quality for cine cardiac MRI images than existing state-of-the-art methods. In addition, our method can generate reliable missing intermediate frames based on their adjacent frames, improving the temporal resolution of cine cardiac MRI sequences.
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