Motion Compensated Unsupervised Deep Learning for 5D MRI

We propose an unsupervised deep learning algorithm for the motion-compensated reconstruction of 5D cardiac MRI data from 3D radial acquisitions. Ungated free-breathing 5D MRI simplifies the scan planning, improves patient comfort, and offers several clinical benefits over breath-held 2D exams, including isotropic spatial resolution and the ability to reslice the data to arbitrary views. However, the current reconstruction algorithms for 5D MRI take very long computational time, and their outcome is greatly dependent on the uniformity of the binning of the acquired data into different physiological phases. The proposed algorithm is a more data-efficient alternative to current motion-resolved reconstructions. This motion-compensated approach models the data in each cardiac/respiratory bin as Fourier samples of the deformed version of a 3D image template. The deformation maps are modeled by a convolutional neural network driven by the physiological phase information. The deformation maps and the template are then jointly estimated from the measured data. The cardiac and respiratory phases are estimated from 1D navigators using an auto-encoder. The proposed algorithm is validated on 5D bSSFP datasets acquired from two subjects.Comment: MICCAI 2023 conference pape

Fat-free noncontrast whole-heart CMR with fast and power-optimized off-resonant water excitation pulses

Background: Cardiovascular MRI (CMR) faces challenges due to the interference of bright fat signals in visualizing anatomical structures. Effective fat suppression is crucial when using whole-heart CMR. Conventional methods often fall short due to rapid fat signal recovery and water-selective off-resonant pulses come with tradeoffs between scan time and RF energy deposit. A lipid-insensitive binomial off-resonant (LIBOR) RF pulse is introduced, addressing concerns about RF energy and scan time for CMR at 3T. Methods: A short LIBOR pulse was developed and implemented in a free-breathing respiratory self-navigated whole-heart sequence at 3T. A BORR pulse with matched duration, as well as previously used LIBRE pulses, were implemented and optimized for fat suppression in numerical simulations and validated in healthy subjects (n=3). Whole-heart CMR was performed in healthy subjects (n=5) with all four pulses. The SNR of ventricular blood, skeletal muscle, myocardium, and subcutaneous fat, and the coronary vessel sharpness and length were compared. Results: Experiments validated numerical findings and near homogeneous fat suppression was achieved with all pulses. Comparing the short pulses (1ms), LIBOR reduced the RF power two-fold compared with LIBRE, and three-fold compared with BORR, and LIBOR significantly decreased overall fat SNR. The reduction in RF duration shortened the whole-heart acquisition from 8.5min to 7min. No significant differences in coronary arteries detection and sharpness were found when comparing all four pulses. Conclusion: LIBOR enabled whole-heart CMR under 7 minutes at 3T, with large volume fat signal suppression, while reducing RF power compared with LIBRE and BORR. LIBOR is an excellent candidate to address SAR problems encountered in CMR where fat suppression remains challenging and short RF pulses are required.Comment: 25 pages, 7 figures, 2 table

Self-navigation with compressed sensing for 2D translational motion correction in free-breathing coronary MRI:a feasibility study

PURPOSE: Respiratory motion correction remains a challenge in coronary magnetic resonance imaging (MRI) and current techniques, such as navigator gating, suffer from sub-optimal scan efficiency and ease-of-use. To overcome these limitations, an image-based self-navigation technique is proposed that uses "sub-images" and compressed sensing (CS) to obtain translational motion correction in 2D. The method was preliminarily implemented as a 2D technique and tested for feasibility for targeted coronary imaging. METHODS: During a 2D segmented radial k-space data acquisition, heavily undersampled sub-images were reconstructed from the readouts collected during each cardiac cycle. These sub-images may then be used for respiratory self-navigation. Alternatively, a CS reconstruction may be used to create these sub-images, so as to partially compensate for the heavy undersampling. Both approaches were quantitatively assessed using simulations and in vivo studies, and the resulting self-navigation strategies were then compared to conventional navigator gating. RESULTS: Sub-images reconstructed using CS showed a lower artifact level than sub-images reconstructed without CS. As a result, the final image quality was significantly better when using CS-assisted self-navigation as opposed to the non-CS approach. Moreover, while both self-navigation techniques led to a 69% scan time reduction (as compared to navigator gating), there was no significant difference in image quality between the CS-assisted self-navigation technique and conventional navigator gating, despite the significant decrease in scan time. CONCLUSIONS: CS-assisted self-navigation using 2D translational motion correction demonstrated feasibility of producing coronary MRA data with image quality comparable to that obtained with conventional navigator gating, and does so without the use of additional acquisitions or motion modeling, while still allowing for 100% scan efficiency and an improved ease-of-use. In conclusion, compressed sensing may become a critical adjunct for 2D translational motion correction in free-breathing cardiac imaging with high spatial resolution. An expansion to modern 3D approaches is now warranted

Comparison of phosphodiesterase 10A and dopamine transporter levels as markers of disease burden in early Parkinson's disease

BACKGROUND: Recent work has shown loss of phosphodiesterase 10A levels in middle-stage and advanced treated patients with PD, which was associated with motor symptom severity. OBJECTIVES: To assess phosphodiesterase 10A levels in early PD and compare with loss of dopamine transporter as markers of disease burden. METHODS: Seventy-eight subjects were included in this study (17 early de novo, 15 early l-dopa-treated, 24 moderate-advanced l-dopa-treated patients with PD, and 22 healthy controls). All participants underwent [11 C]IMA107 PET, [11 C]PE2I PET, and 3-Tesla MRI scan. RESULTS: Early de novo PD patients showed loss of [11 C]IMA107 and of [11 C]PE2I binding in caudate and putamen (P < 0.001); early l-dopa-treated PD patients showed additional loss of [11 C]IMA107 in the caudate (P < 0.001; annual decline 3.6%) and putamen (P < 0.001; annual decline 2.8%), but loss of [11 C]PE2I only in the putamen (P < 0.001; annual decline 6.8%). Lower [11 C]IMA107 correlated with lower [11 C]PE2I in the caudate (rho = 0.51; P < 0.01) and putamen (rho = 0.53; P < 0.01). Longer disease duration correlated with lower [11 C]IMA107 in the caudate (rho = -0.72; P < 0.001) and putamen (rho = -0.48; P < 0.01), and with lower [11 C]PE2I only in the putamen (rho = -0.65; P < 0.001). Higher burden of motor symptoms correlated with lower [11 C]IMA107 in the caudate (rho = -0.42; P < 0.05) and putamen (rho = -0.41; P < 0.05), and with lower [11 C]PE2I only in the putamen (rho = -0.69; P < 0.001). CONCLUSION: Our findings demonstrate loss of phosphodiesterase 10A levels very early in the course of PD and is associated with the gradual and progressive increase of motor symptoms. Phosphodiesterase 10A imaging shows similar potential with dopamine transporter imaging to follow disease progression. © 2019 International Parkinson and Movement Disorder Society

MR Volumetry of Lung Nodules: A Pilot Study

Introduction: Computed tomography (CT) is currently the reference modality for the detection and follow-up of pulmonary nodules. While 2D measurements are commonly used in clinical practice to assess growth, increasingly 3D volume measurements are being recommended. The goal of this pilot study was to evaluate preliminarily the capabilities of 3D MRI using ultra-short echo time for lung nodule volumetry, as it would provide a radiation-free modality for this task.Material and Methods: Artificial nodules were manufactured out of Agar and measured using an ultra-short echo time MRI sequence. CT data were also acquired as a reference. Image segmentation was carried out using an algorithm based on signal intensity thresholding (SIT). For comparison purposes, we also performed manual slice by slice segmentation. Volumes obtained with MRI and CT were compared. Finally, the volumetry of a lung nodule was evaluated in one human subject in comparison with CT.Results: Using the SIT technique, minimal bias was observed between CT and MRI across the entire range of volumes (2%) with limits of agreement below 14%. Comparison of manually segmented MRI and CT resulted in a larger bias (8%) and wider limits of agreement (−23% to 40%). In vivo, nodule volume differed of &lt;16% between modalities with the SIT technique.Conclusion: This pilot study showed very good concordance between CT and UTE-MRI to quantify lung nodule volumes, in both a phantom and human setting. Our results enhance the potential of MRI to quantify pulmonary nodule volume with similar performance to CT