High-resolution, respiratory-resolved coronary MRA using a Phyllotaxis-reordered variable-density 3D cones trajectory.

PURPOSE: To develop a respiratory-resolved motion-compensation method for free-breathing, high-resolution coronary magnetic resonance angiography (CMRA) using a 3D cones trajectory. METHODS: To achieve respiratory-resolved 0.98 mm resolution images in a clinically relevant scan time, we undersample the imaging data with a variable-density 3D cones trajectory. For retrospective motion compensation, translational estimates from 3D image-based navigators (3D iNAVs) are used to bin the imaging data into four phases from end-expiration to end-inspiration. To ensure pseudo-random undersampling within each respiratory phase, we devise a phyllotaxis readout ordering scheme mindful of eddy current artifacts in steady state free precession imaging. Following binning, residual 3D translational motion within each phase is computed using the 3D iNAVs and corrected for in the imaging data. The noise-like aliasing characteristic of the combined phyllotaxis and cones sampling pattern is leveraged in a compressed sensing reconstruction with spatial and temporal regularization to reduce aliasing in each of the respiratory phases. RESULTS: In initial studies of six subjects, respiratory motion compensation using the proposed method yields improved image quality compared to non-respiratory-resolved approaches with no motion correction and with 3D translational correction. Qualitative assessment by two cardiologists and quantitative evaluation with the image edge profile acutance metric indicate the superior sharpness of coronary segments reconstructed with the proposed method (P < 0.01). CONCLUSION: We have demonstrated a new method for free-breathing, high-resolution CMRA based on a variable-density 3D cones trajectory with modified phyllotaxis ordering and respiratory-resolved motion compensation with 3D iNAVs.

Reconstruction of undersampled 3D non-Cartesian image-based navigators for coronary MRA using an unrolled deep learning model.

PURPOSE: To rapidly reconstruct undersampled 3D non-Cartesian image-based navigators (iNAVs) using an unrolled deep learning (DL) model, enabling nonrigid motion correction in coronary magnetic resonance angiography (CMRA). METHODS: An end-to-end unrolled network is trained to reconstruct beat-to-beat 3D iNAVs acquired during a CMRA sequence. The unrolled model incorporates a nonuniform FFT operator in TensorFlow to perform the data-consistency operation, and the regularization term is learned by a convolutional neural network (CNN) based on the proximal gradient descent algorithm. The training set includes 6,000 3D iNAVs acquired from 7 different subjects and 11 scans using a variable-density (VD) cones trajectory. For testing, 3D iNAVs from 4 additional subjects are reconstructed using the unrolled model. To validate reconstruction accuracy, global and localized motion estimates from DL model-based 3D iNAVs are compared with those extracted from 3D iNAVs reconstructed with l1 -ESPIRiT. Then, the high-resolution coronary MRA images motion corrected with autofocusing using the l1 -ESPIRiT and DL model-based 3D iNAVs are assessed for differences. RESULTS: 3D iNAVs reconstructed using the DL model-based approach and conventional l1 -ESPIRiT generate similar global and localized motion estimates and provide equivalent coronary image quality. Reconstruction with the unrolled network completes in a fraction of the time compared to CPU and GPU implementations of l1 -ESPIRiT (20× and 3× speed increases, respectively). CONCLUSIONS: We have developed a deep neural network architecture to reconstruct undersampled 3D non-Cartesian VD cones iNAVs. Our approach decreases reconstruction time for 3D iNAVs, while preserving the accuracy of nonrigid motion information offered by them for correction.

Combined T2 -preparation and multidimensional outer volume suppression for coronary artery imaging with 3D cones trajectories.

PURPOSE: To develop a modular magnetization preparation sequence for combined T2 -preparation and multidimensional outer volume suppression (OVS) for coronary artery imaging. METHODS: A combined T2 -prepared 1D OVS sequence with fat saturation was defined to contain a 90°-60 180°60 composite nonselective tip-down pulse, two 180°Y hard pulses for refocusing, and a -90° spectral-spatial sinc tip-up pulse. For 2D OVS, 2 modules were concatenated, selective in X and then Y. Bloch simulations predicted robustness of the sequence to B0 and B1 inhomogeneities. The proposed sequence was compared with a T2 -prepared 2D OVS sequence proposed by Luo et al, which uses a spatially selective 2D spiral tip-up. The 2 sequences were compared in phantom studies and in vivo coronary artery imaging studies with a 3D cones trajectory. RESULTS: Phantom results demonstrated superior OVS for the proposed sequence compared with the Luo sequence. In studies on 15 healthy volunteers, the proposed sequence had superior image edge profile acutance values compared with the Luo sequence for the right (P < .05) and left (P < .05) coronary arteries, suggesting superior vessel sharpness. The proposed sequence also had superior signal-to-noise ratio (P < .05) and passband-to-stopband ratio (P < .05). Reader scores and reader preference indicated superior coronary image quality of the proposed sequence for both the right (P < .05) and left (P < .05) coronary arteries. CONCLUSION: The proposed sequence with concatenated 1D spatially selective tip-ups and integrated fat saturation has superior image quality and suppression compared with the Luo sequence with 2D spatially selective tip-up.

Whole-heart coronary MR angiography using a 3D cones phyllotaxis trajectory.

PURPOSE: To develop a 3D cones steady-state free precession sequence with improved robustness to respiratory motion while mitigating eddy current artifacts for free-breathing whole-heart coronary magnetic resonance angiography. METHOD: The proposed sequence collects cone interleaves using a phyllotaxis pattern, which allows for more distributed k-space sampling for each heartbeat compared to a typical sequential collection pattern. A Fibonacci number of segments is chosen to minimize eddy current effects with the trade-off of an increased number of acquisition heartbeats. For verification, phyllotaxis-cones is compared to sequential-cones through simulations, phantom studies, and in vivo coronary scans with 8 subjects using 2D image-based navigators for retrospective motion correction. RESULTS: Simulated point spread functions and moving phantom results show less coherent motion artifacts for phyllotaxis-cones compared to sequential-cones. Assessment of the right and left coronary arteries using reader scores and the image edge profile acutance vessel sharpness metric indicate superior image quality and sharpness for phyllotaxis-cones. CONCLUSION: Phyllotaxis 3D cones results in improved qualitative image scores and coronary vessel sharpness for free-breathing whole-heart coronary magnetic resonance angiography compared to standard sequential ordering when using a steady-state free precession sequence.

Automatic tip selection for microtubule dynamics quantification.

Microtubule (MT) dynamics quantification includes modeling of elongation, rapid shortening, and pauses. It indicates the effect of the cancer treatment drug paclitaxel because the drug causes MTs to bundle, which will in turn inhibit successful mitosis of cancerous cells. Thus, automatic MT dynamics analysis has been researched intensely because it allows for faster evaluation of potential cancer treatments and better understanding of drug effects on a cell. However, most current literatures still use manual initialization. In this work, we propose an automatic initialization algorithm that selects isolated and active tips for tracking. We use a Gaussian match filter to enhance the MT structures, and a novel technique called Pixel Nucleus Analysis (PNA) for isolated MT tip detection. To find dynamic tips, we applied a masked FFT in the temporal domain followed by K-means clustering. To evaluate the selected tips, we used a low level tip linking algorithm, and show the results of applying the algorithm to a model image and five MCF-7 breast cancer cell line images captured using fluorescent confocal microscopy. Finally, we compare tip selection criteria with existing automatic selection algorithms. We conclude that the proposed analysis is an effective technique based on three criteria which include outer region selection, separation, and MT dynamics.