Fast spectroscopic imaging using online optimal sparse k-space acquisition and projections onto convex sets reconstruction

被引:8
|
作者
Gao, Yun
Strakowski, Stephen M.
Reeves, Stanley J.
Hetherington, Hoby P.
Chu, Wen-Jang
Lee, Jing-Huei
机构
[1] Univ Cincinnati, Ctr Imaging Res, Coll Med, Cincinnati, OH 45267 USA
[2] Univ Cincinnati, Coll Med, Dept Psychiat, Cincinnati, OH USA
[3] Univ Cincinnati, Coll Med, Dept Biochem Engn, Cincinnati, OH USA
[4] Auburn Univ, Dept Elect Engn, Auburn, AL 36849 USA
[5] Albert Einstein Coll Med, Dept Radiol, Bronx, NY 10467 USA
[6] Albert Einstein Coll Med, Dept Physiol, Bronx, NY 10467 USA
[7] Albert Einstein Coll Med, Dept Biophys, Bronx, NY 10467 USA
关键词
fast spectroscopic imaging; sequential forward array selection; projections onto convex sets reconstruction; partial k-space sampling; region of support;
D O I
10.1002/mrm.20905
中图分类号
R8 [特种医学]; R445 [影像诊断学];
学科分类号
1002 ; 100207 ; 1009 ;
摘要
Long acquisition times, low resolution, and voxel contamination are major difficulties in the application of magnetic resonance spectroscopic imaging (MRSI). To overcome these difficulties, an online-optimized acquisition of k-space, termed sequential forward array selection (SFAS), was developed to reduce acquisition time without sacrificing spatial resolution. A 2D proton MRSI region of interest (ROI) was defined from a scout image and used to create a region of support (ROS) image. The ROS was then used to optimize and obtain a subset of k-space (i.e., a subset of nonuniform phase encodings) and hence reduce the acquisition time for MRSI. Reconstruction and processing software was developed in-house to process and reconstruct MRSI using the projections onto convex sets method. Phantom and in vivo studies showed that good-quality MRS images are obtainable with an approximately 80% reduction of data acquisition time. The reduction of the acquisition time depends on the area ratio of ROS to FOV (i.e., the smaller the ratio, the greater the time reduction). It is also possible to obtain higher-resolution MRS images within a reasonable time using this approach. MRSI with a resolution of 64 x 64 is possible with the acquisition time of the same as 24 x 24 using the traditional full k-space method.
引用
收藏
页码:1265 / 1271
页数:7
相关论文
共 50 条
  • [41] Simultaneous Multislice Accelerated Interleaved EPI DWI Using Generalized Blipped-CAIPI Acquisition and 3D K-Space Reconstruction
    Dai, Erpeng
    Ma, Xiaodong
    Zhang, Zhe
    Yuan, Chun
    Guo, Hua
    MAGNETIC RESONANCE IN MEDICINE, 2017, 77 (04) : 1593 - 1605
  • [42] Non-water-suppressed short-echo-time magnetic resonance spectroscopic imaging using a concentric ring k-space trajectory
    Emir, Uzay E.
    Burns, Brian
    Chiew, Mark
    Jezzard, Peter
    Thomas, M. Albert
    NMR IN BIOMEDICINE, 2017, 30 (07)
  • [43] Scan-specific robust artificial-neural-networks for k-space interpolation (RAKI) reconstruction: Database-free deep learning for fast imaging
    Akcakaya, Mehmet
    Moeller, Steen
    Weingaertner, Sebastian
    Ugurbil, Kamil
    MAGNETIC RESONANCE IN MEDICINE, 2019, 81 (01) : 439 - 453
  • [44] Technical innovation in dynamic contrast-enhanced magnetic resonance imaging of musculoskeletal tumors: an MR angiographic sequence using a sparse k-space sampling strategy
    Laura M. Fayad
    Charles Mugera
    Theodoros Soldatos
    Aaron Flammang
    Filippo del Grande
    Skeletal Radiology, 2013, 42 : 993 - 1000
  • [45] Technical innovation in dynamic contrast-enhanced magnetic resonance imaging of musculoskeletal tumors: an MR angiographic sequence using a sparse k-space sampling strategy
    Fayad, Laura M.
    Mugera, Charles
    Soldatos, Theodoros
    Flammang, Aaron
    del Grande, Filippo
    SKELETAL RADIOLOGY, 2013, 42 (07) : 993 - 1000
  • [46] Adaptive-size dictionary learning using information theoretic criteria for image reconstruction from undersampled k-space data in low field magnetic resonance imaging
    Emmanuel Ahishakiye
    Martin Bastiaan Van Gijzen
    Julius Tumwiine
    Johnes Obungoloch
    BMC Medical Imaging, 20
  • [47] Adaptive-size dictionary learning using information theoretic criteria for image reconstruction from undersampled k-space data in low field magnetic resonance imaging
    Ahishakiye, Emmanuel
    Van Gijzen, Martin Bastiaan
    Tumwiine, Julius
    Obungoloch, Johnes
    BMC MEDICAL IMAGING, 2020, 20 (01)
  • [48] IMAGE-RECONSTRUCTION USING PROJECTION ONTO CONVEX-SETS, MODEL CONSTRAINTS, AND LINEAR PREDICTION-THEORY FOR THE REMOVAL OF PHASE, MOTION, AND GIBBS ARTIFACTS IN MAGNETIC-RESONANCE AND ULTRASOUND IMAGING
    HAACKE, EM
    LIANG, ZP
    BOADA, F
    OPTICAL ENGINEERING, 1990, 29 (05) : 555 - 566
  • [49] Ferumoxytol-Enhanced 5D Multiphase Steady-State Imaging Using Rotating Cartesian K-Space With Low-Rank Reconstruction for Pediatric Congenital Heart Disease
    Zhao, Zixuan
    Lee, Hsu-Lei
    Ruan, Dan
    Ming, Zhengyang
    Han, Fei
    Bedayat, Arash
    Christodoulou, Anthony G.
    Finn, J. Paul
    Nguyen, Kim-Lien
    JOURNAL OF MAGNETIC RESONANCE IMAGING, 2025, 61 (03) : 1311 - 1322
  • [50] Diffusion Weighted Imaging A Comprehensive Evaluation of a Fast Spin Echo DWI Sequence With BLADE (PROPELLER) k-Space Sampling at 3 T, Using a 32-Channel Head Coil in Acute Brain Ischemia
    Attenberger, Ulrike I.
    Runge, Val M.
    Stemmer, Alto
    Williams, Kenneth D.
    Naul, L. Gill
    Michaely, Henrik J.
    Schoenberg, Stefan O.
    Reiser, Maximilian F.
    Wintersperger, Bernd J.
    INVESTIGATIVE RADIOLOGY, 2009, 44 (10) : 656 - 661