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Register a new userIn-Vitro MPI-Guided IVOCT Catheter Tracking in Real Time for Motion Artifact Compensation
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Florian Griese
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Purpose: Using 4D magnetic particle imaging (MPI), intravascular optical coherence tomography (IVOCT) catheters are tracked in real time in order to compensate for image artifacts related to relative motion. Our approach demonstrates the feasibility for bimodal IVOCT and MPI in-vitro experiments. Material and Methods: During IVOCT imaging of a stenosis phantom the catheter is tracked using MPI. A 4D trajectory of the catheter tip is determined from the MPI data using center of mass sub-voxel strategies. A custom built IVOCT imaging adapter is used to perform different catheter motion profiles: no motion artifacts, motion artifacts due to catheter bending, and heart beat motion artifacts. Two IVOCT volume reconstruction methods are compared qualitatively and quantitatively using the DICE metric and the known stenosis length. Results: The MPI-tracked trajectory of the IVOCT catheter is validated in multiple repeated measurements calculating the absolute mean error and standard deviation. Both volume reconstruction methods are compared and analyzed whether they are capable of compensating the motion artifacts. The novel approach of MPI-guided catheter tracking corrects motion artifacts leading to a DICE coefficient with a minimum of 86% in comparison to 58% for a standard reconstruction approach. Conclusions: IVOCT catheter tracking with MPI in real time is an auspicious method for radiation free MPI-guided IVOCT interventions. The combination of MPI and IVOCT can help to reduce motion artifacts due to catheter bending and heart beat for optimized IVOCT volume reconstructions.
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The MR-Linac is a combination of an MR-scanner and radiotherapy linear accelerator (Linac) which holds the promise to increase the precision of radiotherapy treatments with MR-guided radiotherapy by monitoring motion during radiotherapy with MRI, and adjusting the radiotherapy plan accordingly. Optimal MR-guidance for respiratory motion during radiotherapy requires MR-based 3D motion estimation with a latency of 200-500 ms. Currently this is still challenging since typical methods rely on MR-images, and are therefore limited by the 3D MR-imaging latency. In this work, we present a method to perform non-rigid 3D respiratory motion estimation with 170 ms latency, including both acquisition and reconstruction. The proposed method called real-time low-rank MR-MOTUS reconstructs motion-fields directly from k-space data, and leverages an explicit low-rank decomposition of motion-fields to split the large scale 3D+t motion-field reconstruction problem posed in our previous work into two parts: (I) a medium-scale offline preparation phase and (II) a small-scale online inference phase which exploits the results of the offline phase for real-time computations. The method was validated on free-breathing data of five volunteers, acquired with a 1.5T Elekta Unity MR-Linac. Results show that the reconstructed 3D motion-field are anatomically plausible, highly correlated with a self-navigation motion surrogate (R = 0.975 +/- 0.0110), and can be reconstructed with a total latency of 170 ms that is sufficient for real-time MR-guided abdominal radiotherapy.
Fluorine-19 (19F) MRI of injected perfluorocarbon emulsions (PFCs) allows for the non-invasive quantification of inflammation and cell tracking, but suffers from a low signal-to-noise ratio and extended scan time. To address this limitation, we tested the hypothesis that a 19F MRI pulse sequence that combines a specific undersampling regime with signal averaging has increased sensitivity and robustness against motion artifacts compared to a non-averaged fully-sampled dataset, when both are reconstructed with compressed sensing. To this end, numerical simulations and phantom experiments were performed to characterize the point spread function (PSF) of undersampling patterns and the vulnerability to noise of acquisition-reconstruction strategies with paired numbers of x signal averages and acceleration factor x (NAx-AFx). At all investigated noise levels, the DSC of the acquisition-reconstruction strategies strongly depended on the regularization parameters and acceleration factor. In phantoms, motion robustness of an NA8-AF8 undersampling pattern versus NA1-AF1 was evaluated with simulated and real motions. Differences were assessed with Dice similarity coefficients (DSC), and were consistently higher for NA8-AF8 compared to NA1-AF1 strategy, for both simulated and real cyclic motions (P<0.001). Both acquisition-reconstruction strategies were validated in vivo in mice (n=2) injected with perfluoropolyether. These images displayed a sharper delineation of the liver with the NA8-AF8 strategy than with the NA1-AF1 strategy. In conclusion, we validated the hypothesis that in 19F MRI, the combination of undersampling and averaging improves both the sensitivity and the robustness against motion artifacts compared to a non-averaged fully-sampled dataset, when both are reconstructed with compressed sensing.
In recent years, Independent Component Analysis (ICA) has successfully been applied to remove noise and artifacts in images obtained from Three-dimensional Polarized Light Imaging (3D-PLI) at the mesoscale (i.e., 64 $mu$m). Here, we present an automatic denoising procedure for gray matter regions that allows to apply the ICA also to microscopic images, with reasonable computational effort. Apart from an automatic segmentation of gray matter regions, we applied the denoising procedure to several 3D-PLI images from a rat and a vervet monkey brain section.
Purpose: To study the accuracy of motion information extracted from beat-to-beat 3D image-based navigators (3D iNAVs) collected using a variable-density cones trajectory with different combinations of spatial resolutions and scan acceleration factors. Methods: Fully sampled, breath-held 4.4 mm 3D iNAV datasets for six respiratory phases are acquired in a volunteer. Ground truth translational and nonrigid motion information is derived from these datasets. Subsequently, the motion estimates from synthesized undersampled 3D iNAVs with isotropic spatial resolutions of 4.4 mm (acceleration factor = 10.9), 5.4 mm (acceleration factor = 7.2), 6.4 mm (acceleration factor = 4.2), and 7.8 mm (acceleration factor = 2.9) are assessed against the ground truth information. The undersampled 3D iNAV configuration with the highest accuracy motion estimates in simulation is then compared with the originally proposed 4.4 mm undersampled 3D iNAV in six volunteer studies. Results: The simulations indicate that for navigators beyond certain scan acceleration factors, the accuracy of motion estimates is compromised due to errors from residual aliasing and blurring/smoothening effects following compressed sensing reconstruction. The 6.4 mm 3D iNAV achieves an acceptable spatial resolution with a small acceleration factor, resulting in the highest accuracy motion information among all assessed undersampled 3D iNAVs. Reader scores for six volunteer studies demonstrate superior coronary vessel sharpness when applying an autofocusing nonrigid correction technique using the 6.4 mm 3D iNAVs in place of 4.4 mm 3D iNAVs. Conclusion: Undersampled 6.4 mm 3D iNAVs enable motion tracking with improved accuracy relative to previously proposed undersampled 4.4 mm 3D iNAVs.
An approach to reduce motion artifacts in Quantitative Susceptibility Mapping using deep learning is proposed. We use an affine motion model with randomly created motion profiles to simulate motion-corrupted QSM images. The simulated QSM image is paired with its motion-free reference to train a neural network using supervised learning. The trained network is tested on unseen simulated motion-corrupted QSM images, in healthy volunteers and in Parkinsons disease patients. The results show that motion artifacts, such as ringing and ghosting, were successfully suppressed.
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