| Computer # |
| 1901.
 | 33 | Inline SNR-driven automatic quality control of UTE pulmonary imaging for patient-specific scan time adaptation Pierre Daudé1, Ahsan Javed1, Rajiv Ramasawmy1, Kelvin Chow2, and Adrienne Campbell-Washburn1
1Laboratory of Imaging Technology, National Heart, Lung & Blood Institute, NIH, Bethesda, MD, United States, 2Siemens Healthcare Ltd., Calgary, AB, Canada Keywords: Image Reconstruction, Low-Field MRI, MR value Motivation: Image quality with fixed scan-duration is patient-dependent, leading to potentially insufficient quality for some patients and unnecessarily long scan time for others. Goal(s): We propose inline automatic quality control based on signal-to-noise ratio (SNR) to efficiently achieve consistent diagnostic image quality for 3D pulmonary imaging. Approach: We designed a closed-loop feedback framework between image reconstruction and data acquisition to automatically stop the acquisition when a target SNR is achieved. 6 healthy volunteers (HV) were imaged at 0.55T. Results: Target SNR was achieved at 3mins 57s±1min 9s across the population. Impact: The
inline automatic quality control enables a subject-specific optimized scan time
while ensuring sufficient data for highly resolved complex reconstruction. The
distribution of early stopping times (1min 9s) across the population revealed
the value of subject-specific scan time. |
| 1902.
 | 34 | Spatiotemporally Encoded Single-Sided MRI with a Numerical Point-Spread Function Reconstruction Meredith Sadinski1, Muller De Matos Gomes1, Aleksander Nacev1, and William Grissom2
1Promaxo, Oakland, CA, United States, 2Case Western Reserve University, Cleveland, OH, United States Keywords: Image Reconstruction, Prostate Motivation: Motivation: Single-sided MRI scanners offer excellent interventional access but require novel approaches to image acquisition. Goal(s): To generate distortion-free spatiotemporally encoded images with a single-sided low-field MRI scanner and validate the acquisitions in vivo. Approach: Spatiotemporally encoded images were collected in phantom and volunteer and reconstructed using a numerical PSF-based reconstruction. Results: Images of male and female subjects showed anatomic structures. Reconstruction with a numerical modeled PSF increased visibility of these structures and improved signal across the field of view in both phantom and human images relative to using a sinc-based PSF. Impact: This methodology can be used for translation of an xSPEN encoded sequence into clinical practice and aid other researchers facing similar reconstruction challenges. |
| 1903.
 | 35 | Iterative Low-Rank Infilling Approach for Zero Echo-Time (ZTE) Imaging Zimu Huo1, José de Arcos2, Florian Wiesinger3, Joshua Kaggie1, and Martin Graves1
1Department of Radiology, Univeristy of Cambridge, Cambridge, United Kingdom, 2GE Healthcare, Little Chalfont, Amersham, United Kingdom, 3GE Healthcare, Munich, Germany Keywords: Image Reconstruction, Parallel Imaging Motivation: The short, but non-zero, time taken to switch between transmit and receive results in a dead-time gap in Zero Echo-Time (ZTE) imaging, which leads to substantial reconstruction artifacts. Goal(s): Determine the missing data in the dead-time gap without any additional acquisition. Approach: We reformulate the reconstruction problem as a nuclear norm minimization problem to implicitly fill the missing data through iterative reconstruction. Results: The proposed method demonstrates that the missing data can be filled using low-rank without sacrificing image quality. Impact: We present a method for filling the dead-time gap in ZTE imaging using low-rank that does not require the collection of additional data. |
| 1904.
 | 36 | Enhancing Self-Navigated Interleaved Spiral with ESPIRiT (eSNAILS) Xingwang Yong1,2,3, Shohei Fujita2,3,4,5, Yohan Jun2,3, Jaejin Cho2,3, Qiang Liu6, Yi Zhang1, and Berkin Bilgic2,3,7
1Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China, 2Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States, 3Department of Radiology, Harvard Medical School, Boston, MA, United States, 4Department of Radiology, Juntendo University, Tokyo, Japan, 5Department of Radiology, The University of Tokyo, Tokyo, Japan, 6Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States, 7Harvard/MIT Health Sciences and Technology, Cambridge, MA, United States Keywords: Image Reconstruction, Data Acquisition, multi-shot; self-navigation Motivation: Current methods for estimation of shot-to-shot phase variations in multi-shot DWI may not fully exploit the correlations in data. Goal(s): To propose a method which efficiently uses correlations between shots and coils to calculate composite sensitivities and then improve multi-shot DWI reconstruction. Approach: A multi-shot, dual density spiral sequence was designed, with each shot having fully sampled k-space center and undersampled periphery. The center of all shots and all coils are concatenated and fed into ESPIRiT to estimate sensitivities. Results: The proposed method successfully estimated shot-to-shot phase variations and yielded comparable results to the reference locally low-rank regularized reconstruction which requires parameter tuning. Impact: The proposed eSNAILS
demonstrated the ability of estimating composite sensitivities that incorporate
shot-to-shot phase variations. Compared to low-rank modeling methods that assume
phase smoothness, eSNAILS can handle cases where there are abrupt phase changes
and does not require parameter tuning. |
| 1905.
 | 37 | Improved Region-Optimized Virtual Coils for Cartesian Acquisition Geometries Chin-Cheng Chan1, Christopher Nguyen2, and Justin P. Haldar1
1Signal and Image Processing Institute, Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, United States, 2Cardiovascular Innovation Research Center, Heart, Vascular, and Thoracic Institute, Cleveland Clinic, Cleveland, OH, United States Keywords: Image Reconstruction, Image Reconstruction, accelerated acquisition, beamforming, reduced field-of-view imaging, signal suppression, region-optimized virtual coils, coil compression Motivation: ROVir (Region-Optimized Virtual coils) is a technique that constructs MRI virtual coils in a way that seeks to simultaneously maximize the amount of information captured by the smallest number of virtual coils (coil compression/dimensionality reduction) while also suppressing signal from undesired spatial regions (avoiding aliasing/leakage artifacts). Although ROVir generally performs well, its performance can sometimes be limited by coil geometry. Goal(s): To improve the performance of ROVir. Approach: We exploit the structure of Cartesian imaging, calculating distinct ROVir weights for each position along the fully-sampled readout. Results: The proposed approach enables substantially better dimensionality reduction and signal suppression performance. Impact: The proposed approach provides substantially better signal suppression and coil compression for Cartesian acquisitions, alleviating burdens on data acquisition (reducing the need for sequence-based signal suppression and enabling reduced-FOV imaging) and reducing the computational complexity of image reconstruction. |
| 1906.
 | 38 | Improvement in Fat-Water Separation Using Modeled Gradient Impulse Response with Two-Point Dixon Radial Imaging James Hao Wang1,2, Ali Pirasteh1,2, and Alan McMillan1,2
1Medical Physics, UW Madison, Madison, WI, United States, 2Radiology, UW Madison, Madison, WI, United States Keywords: Image Reconstruction, Image Reconstruction, GIRF, Radial, PDFF, 2-Point Dixon Motivation: Radial imaging is desirable for whole body imaging due to its motion robustness. However, two-point Dixon with bipolar readouts can have reduced quality due to gradient errors. Goal(s): To develop improved imaging capabilities by leveraging gradient impulse response function (GIRF) measurements to correct for gradient errors and yield better image quality. Approach: A radial two-point Dixon acquisition was compared using GIRF-corrected and uncorrected reconstructions in phantoms and a human volunteer. Results: The use of GIRF in the reconstruction of two-point Dixon radial imaging provides improved image quality and better fat-water separation and provided similar quality to a Cartesian acquisition. Impact: This work demonstrates improved image quality and improved fat-water separation by incorporating gradient impulse response function compensation in two-point Dixon radial imaging for an intended application of motion-robust whole body MR imaging. |
| 1907.
 | 39 | Partial Separable Model Combined with Spatial Constraint for Interventional MRI Reconstruction Rui Li1, Haozhong Sun1, Zhongsen Li1, Ziming Xu1, and Huijun Chen1
1Tsinghua University, Beijing, China Keywords: Image Reconstruction, Simulations Motivation: Real time interventional MRI (i-MRI) is essential for MR image guided therapy, but the requirement of high temporal resolution presents a great challenge for reconstruction of real-time i-MRI. Goal(s): Our goal is modifying an improved Partial Separable (PS) model called PS-R model to explore the potential of the modified model to achieve real-time i-MRI. Approach: We modified PS-R model with spatial constraint (PS-RSC) and performed retrospective experiments on simulation intervention MRI images to verify the effectiveness of the PS-RSC model. Results: Satisfying results were obtained with only 7 k-space lines for reconstructing one frame, giving an acceleration up to 40 folds. Impact: The reconstruction results of the simulation experiment showed that our method could obviously accelerate the acquisition time with good needle positioning, thus suggesting it has the potential for many MR guided interventional MRI application scenarios, such as MR image-guided therapy. |
| 1908.
 | 40 | The software system of an MR compatible dedicated brain PET Jiamin Liu1, Ning Ren1, Tianyi Zeng1, Zhonghua Kuang1, Xiaohui Wang1, Zheng Liu1, Hairong Zheng1,2, Dong Liang1,2, Yongfeng Yang1,2, and Zhanli Hu1,2
1Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China, Shenzhen, China, 2Key Laboratory of Biomedical Imaging Science and System, Chinese Academy of Sciences., Shenzhen, China Keywords: Image Reconstruction, Image Reconstruction, Simultaneous PET/MRI Imaging, dedicated scanner Motivation: Dedicated brain PET devices can acquire high-quality images while also allowing for simultaneous imaging with MRI equipment. Goal(s): The implemention of software system of a MR compatible brain PET including data acquisition, sinogram generation, imaging reconstructionis presented. Approach: We designed a virtual crystal-based sinogram generation method and implemented OSEM image reconstruction software with various acceleration strategies. Results: The functionality of the software system and the imaging capability of the PET scanner were demonstrated by simultaneous PET and MRI imaging of the human brain. Impact: The sinogram generation method and image reconstruction
acceleration strategies developed in this work can also be used for other PET
scanners using high DOI resolution depth encoding detectors. |
| 1909.
 | 41 | Improved receive coil signal combination for large volume single voxel spectroscopy at ultra-high field Mark A Elliott1, Neil Wilson1, Sophia Swago1, Ravi Prakash Nanga1, Ravinder Reddy1, and Walter Witschey1
1CAMIPM, Dept. of Radiology, University of Pennsylvania, Philadelphia, PA, United States Keywords: Image Reconstruction, Spectroscopy, ultrahigh field Motivation: Spectroscopic detection of low concentration metabolites (< 1mM) requires either lengthy signal averaging or large volume voxels. The latter can lead to significant variations in B0 across the volume, especially at ultrahigh field. Goal(s): Correct for B0 heterogeneity in large volume SVS when detecting with multi-channel receive coils. Approach: Using a metric for receive signal similarity, apply frequency and phase alignment of individual channel signals, prior to coil combination. Results: The method was applied to 18 in vivo scans of 1H brain spectra at 7 T. Narrower linewidths and increased SNR were observed for water and down-field NAD+ resonances. Impact: This approach provides an
automated and robust method to improve spectral resolution and signal-to-noise
ratio with large volume single voxel spectroscopy of low concentration
metabolites when using multichannel receive arrays. |
| 1910.
 | 42 | Explicit analytical solution for bSSFP with 3 general phase-cycled acquisitions Yiyun Dong1, Qing-San Xiang2, and Michael Hoff3
1Physics, University of Washington, Seattle, WA, United States, 2Radiology, University of British Columbia, Vancouver, BC, Canada, 3Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States Keywords: Image Reconstruction, Image Reconstruction, Geometric demodulation, bSSFP, Phase-cycles, ellipse, elliptical signal model Motivation: Unlocking the phase-cycled bSSFP system with high efficiency, accuracy, and flexibility. Goal(s): To find and validate an explicit analytical solution to the bSSFP MRI framework using three general phase-cycled acquisitions. Approach: Using the elliptical signal model and trigonometrical relationships, this study obtains explicit, general, and closed form expressions of an analytical solution to the bSSFP system with only 3 acquisitions. To reduce noise sensitivity, the solution was also linearized using regional optimal weighted averaging. Results: Analytical results showed efficacy via validation by simulated data with noise covering all possible scan parameters. Impact: Phase-cycled bSSFP imaging is becoming prominent, and has recently been analytically unlocked with only 3 acquisitions. This development will allow even more widespread use of the technique due to its associated multi-parametric capability. |
| 1911.
 | 43 | MR-blob: Coordinate-Transformed Blobs for Parallel MRI Reconstruction Imraj RD Singh1, Željko Kereta1, Alexander Denker2, Riccardo Barbano3, Bangti Jin4, Kris Thielemans5,6, and Simon Arridge1
1Department of Computer Science, University College London, London, United Kingdom, 2Center of Industrial Mathematics (ZeTeM), University of Bremen, Bremen, Germany, 3School of Computation, Information and Technology, Technical University of Munich, Munich, Germany, 4Department of Mathematics, The Chinese University of Hong Kong, Hong Kong, Hong Kong, 5Institute of Nuclear Medicine, University College London, London, United Kingdom, 6Centre for Medical Image Computing, University College London, London, United Kingdom Keywords: Image Reconstruction, Parallel Imaging, blobs Motivation: Reducing the number of parameters needed to represent and reconstruct parallel MRI measurements. Goal(s): Reconstruct parallel MRI measurements with coordinate-transformed Gaussian functions (blobs) where the forward model is formulated directly. We term this MR-blob. Approach: MR-blob directly represents parallel MRI measurements; where coil sensitivities are modelled as isotropic Gaussians and the image is represented by coordinate-transformed blobs. Results: Noisy, undersampled parallel MRI simulations of Shepp-Logan phantom are reconstructed with a pixelised image, a coordinate-transformed blob-based image, and MR-blob; all with total variation regularisation. Quality measures are shown to be consistent across methods and regularisation strengths. Impact: Parameter-efficient image representations have the potential to reduce computational burden. This work defines parallel MRI forward model for coordinate-transformed blobs. This includes auto-calibrating coil sensitivities that re-scale and translate to fit the parallel MRI measurements. |
| 1912.
 | 44 | Quantitively Accurate Bipolar Quantitative Chemical Shift Encoded Imaging Using the Gradient Impulse Response Function James Hao Wang1,2, Diego Hernando1,2, Ali Pirasteh1,2, and Alan McMillan1,2
1Medical Physics, UW Madison, Madison, WI, United States, 2Radiology, UW Madison, Madison, WI, United States Keywords: Image Reconstruction, Quantitative Imaging, GIRF, PDFF, CSE Motivation: Quantitative chemical shift encoded (CSE) imaging enables accurate measurement of proton density fat fraction (PDFF) but requires breath holding that can be affected by motion artifacts. Goal(s): We aim to develop a more rapid, bipolar readout CSE acquisition. Approach: Gradient impulse response function (GIRF) was used to account for gradient-related phase errors that complicate PDFF measurements in bipolar acquisitions. Results: Bipolar CSE with GIRF correction enabled accurate PDFF measurement with similar results to conventional unipolar readout CSE with a 40% reduction in scan time. Impact: This study explores the application of GIRF and how it could be used in a bipolar CSE for PDFF measurement. Results are comparable to conventional unipolar CSE but yield significant scan time reduction. |
| 1913.
 | 45 | 31P MRSI multi-channel signal combination using 23Na coil sensitivity profiles at 7T: further evaluations in silico, on phantom, and in vivo Jiying Dai1,2, Mark Gosselink1, Alexander J. E. Raaijmakers1,3, and Dennis W. J. Klomp1
1UMC Utrecht, Utrecht, Netherlands, 2Tesla Dynamic Coils B.V., Zaltbommel, Netherlands, 3Eindhoven University of Technology, Eindhoven, Netherlands Keywords: Image Reconstruction, Spectroscopy, multi-channel signal combination Motivation: Multi-channel 31P MRSI signal combination is challenging due to low intrinsic SNR. Goal(s): Improve multi-channel 31P signal combination for a multi-tuned 31P/23Na head coil receiver array. Approach: Combining 15-channel 31P data using the 23Na sensitivity maps received with the same dual-tuned array. The results are evaluated quantitatively and qualitatively in silico, on phantom scans, and in vivo. Results: Simulation shows <5% SNR-decrease by combining 31P signals using 23Na sensitivities. Phantom data shows 25% SNR-decrease but also potential for mitigating point-spreading effect. In vivo data analysis shows 5% SNR increase when the intrinsic SNR is low but 15% decrease after the dataset is denoised. Impact: This study explores an alternative approach for multi-channel 31P MRSI signal combination. The performance is positive when intrinsic SNR is low. This approach can be extended to other X-nuclei with not-too-far Larmor frequencies if multi-tuned receiver antennas are used. |
| 1914.
 | 46 | Optimizing the T1-mapping GOAL-SNAP MRA with Histogram-Matching-Based Multi-Frame Combination Jiaqi Dou1, Xiaoming Liu2,3, Ziming Xu1, Song Tian4, Jing Wang2, and Huijun Chen1
1Center for Biomedical Imaging Research, Tsinghua University, Beijing, China, 2Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Beijing, China, 3Hubei Province Key Laboratory of Molecular Imaging , Wuhan, China, 4Philips Healthcare, Beijing, China Keywords: Image Reconstruction, Vessels Motivation: GOAL-SNAP, a sequence designed for T1 value measurement of vessel walls, shows promise in generating MRA images for intracranial vessel visualization, albeit with opportunities for further improvement. Goal(s): To introduce a novel approach to optimize GOAL-SNAP MRA. Approach: A histogram-matching-based approach was developed to optimize GOAL-SNAP MRA. This technique combined multiple GOAL-SNAP frames and utilizes histogram matching to suppress background signals and improve the contrast between vessels and background. Results: Contrast-to-noise (CNR) and visualization evaluation demonstrated the effectiveness of the optimized GOAL-SNAP MRA, exhibiting comparable performance to TOF and superior visualization of distal vessels. Impact: A novel histogram-matching-based multi-frame combination approach improved GOAL-SNAP MRA for intracranial vessel visualization and vascular stenosis assessment, with comparable performance to TOF and superior visualization of distal vessels. |
| 1915.
 | 47 | Coil Sensitivity Estimation and Complex Image Combination for 96-Channel Receive Array at 7T Hannah Kempfert1, Jingjia Chen2,3, and Chunlei Liu1,4
1Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA, United States, 2Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY, United States, 3Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York, NY, United States, 4Helen Wills Neuroscience Institute, University of California, Berkeley, CA, United States Keywords: Image Reconstruction, Data Processing Motivation: High density receive arrays can improve SNR and parallel imaging capability; however, they also introduce significant image reconstruction challenges. Goal(s): We aim to find a reconstruction method that will produce consistent and high-quality complex images for high-channel-count receive arrays at 7T. Approach: Several existing sensitivity map estimation methods and coil combination methods were tested for 8-channel and 32-channel datasets, and an ultrahigh resolution 96-channel dataset acquired at 7T. Results: Existing reconstruction methods did not produce consistent results for the 96-channel dataset. Compression of high-quality sensitivity maps reduced data size by a factor of 100 while maintaining image quality. Impact: This work explores
the unique reconstruction challenges in high-channel-count receive arrays by
assessing performance of existing reconstruction techniques on an ultrahigh
resolution dataset acquired with a 96-channel receive array, establishing a need
for more research in effective reconstruction methods.
|
| 1916.
 | 48 | Reconstruction of Highly Underdamped 3D Spiral and Golden Angle Radial MRI Data Using Spherical Fourier-Legendre Transform Mojtaba Shafiekhani1, Vahid Ghodrati2, and Abbas Nasiraei Moghaddam3
1Department of Radiology, Division of Medical Physics, University Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Freiburg im Breisgau, Germany, 2Department of Radiological Sciences, University of California Los Angeles, Los Angeles, CA, United States, 3Department of Biomedical Engineering, Amirkabir University of Technology, Tehran, Iran (Islamic Republic of) Keywords: Image Reconstruction, Image Reconstruction Motivation: Fast imaging is crucial in MRI for various applications. This study proposes a novel method to reconstruct highly undersampled MRI data for faster and higher-quality imaging. Goal(s): Reconstruct the highly undersampled 3D golden-angle and spiral radial MRI data. Approach: We proposed a novel image reconstruction method for 3D radial MRI data at high acceleration rates, based on the spherical Fourier-Legendre transform. This method reconstructs images directly in the spherical coordinates without using interpolation operator in k-space domain. Results: The feasibility of the proposed method is proven by reconstructing highly undersampled 3D golden-angle and spiral radial data of digital Shepp-Logan and in-vitro ACR phantoms. Impact: A nivel image reconstruction method for fast and high-quality radial MRI imaging. It focuses
on highly undersampled 3D golden-angle and spiral radial MRI data,
reconstructing images directly in spherical coordinates without any frequency interpolation. |