Comprehensive assessment of nonuniform image quality: Application to imaging near metal

被引:0
|
作者
Toews, Alexander R. [1 ,2 ,5 ]
Lee, Philip K. [1 ]
Nayak, Krishna S. [3 ]
Hargreaves, Brian A. [1 ,2 ,4 ]
机构
[1] Stanford Univ, Radiol, Stanford, CA 94305 USA
[2] Stanford Univ, Elect Engn, Stanford, CA USA
[3] Univ Southern Calif, Ming Hsieh Dept Elect & Comp Engn, Los Angeles, CA USA
[4] Stanford Univ, Bioengn, Stanford, CA USA
[5] Richard M Lucas Ctr Imaging, Stanford, CA 94305 USA
基金
美国国家卫生研究院; 加拿大自然科学与工程研究理事会;
关键词
image quality; metal; phantom; point spread function; spatial resolution; susceptibility artifact; TO-NOISE RATIO; ARTIFACT CORRECTION; MR-IMAGES; QUANTITATIVE-EVALUATION; MAVRIC SL; IMPLANTS; REDUCTION; DISTORTION;
D O I
10.1002/mrm.30222
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
PurposeComprehensive assessment of image quality requires accounting for spatial variations in (i) intensity artifact, (ii) geometric distortion, (iii) signal-to-noise ratio (SNR), and (iv) spatial resolution, among other factors. This work presents an ensemble of methods to meet this need, from phantom design to image analysis, and applies it to the scenario of imaging near metal.MethodsA modular phantom design employing a gyroid lattice is developed to enable the co-registered volumetric quantitation of image quality near a metallic hip implant. A method for measuring spatial resolution by means of local point spread function (PSF) estimation is presented and the relative fitness of gyroid and cubic lattices is examined. Intensity artifact, geometric distortion, and SNR maps are also computed. Results are demonstrated with 2D-FSE and MAVRIC-SL scan protocols on a 3T MRI scanner.ResultsThe spatial resolution method demonstrates a worst-case error of 0.17 pixels for measuring in-plane blurring up to 3 pixels (full width at half maximum). The gyroid outperforms a cubic lattice design for the local PSF estimation task. The phantom supports four configurations toggling the presence/absence of both metal and structure with good spatial correspondence for co-registered analysis of the four quality factors. The marginal scan time to evaluate one scan protocol amounts to five repetitions. The phantom design can be fabricated in 2 days at negligible material cost.ConclusionThe phantom and associated analysis methods can elucidate complex image quality trade-offs involving intensity artifact, geometric distortion, SNR, and spatial resolution. The ensemble of methods is suitable for benchmarking imaging performance near metal.
引用
收藏
页码:2358 / 2372
页数:15
相关论文
共 50 条
  • [21] Application of Comprehensive Water Quality Identification Index in Water Quality Assessment of River
    Miao Qun
    Gao Ying
    Liu Zhiqiang
    Tan Xiaohui
    PROCEEDINGS OF THE 2009 WRI GLOBAL CONGRESS ON INTELLIGENT SYSTEMS, VOL I, 2009, : 333 - 337
  • [22] Application of rate of pollution loss on comprehensive assessment of sewage quality
    Sun, Jun
    Gao, Yi
    Ke, Chong-yi
    Zhang, Yu-xiang
    He, Zhong-rong
    Zhao, Shen-huang
    Qingdao Daxue Xuebao(Gongcheng Jishuban)/Journal of Qingdao University (Engineering and Technology Edition), 14 (03): : 58 - 59
  • [23] Comprehensive image quality assessment via predicting the distribution of opinion score
    Liu, Anan
    Wang, Jingting
    Liu, Jing
    Su, Yuting
    MULTIMEDIA TOOLS AND APPLICATIONS, 2019, 78 (17) : 24205 - 24222
  • [24] Comprehensive assessment of image quality in synthetic and digital mammography: a quantitative comparison
    Barca, Patrizio
    Lamastra, Rocco
    Aringhieri, Giacomo
    Tucciariello, Raffaele Maria
    Traino, Antonio
    Fantacci, Maria Evelina
    AUSTRALASIAN PHYSICAL & ENGINEERING SCIENCES IN MEDICINE, 2019, 42 (04) : 1141 - 1152
  • [25] A Comprehensive Study of Multimodal Large Language Models for Image Quality Assessment
    Wu, Tianhe
    Ma, Kede
    Liang, Jie
    Yang, Yujiu
    Zhang, Lei
    COMPUTER VISION - ECCV 2024, PT LXXIV, 2025, 15132 : 143 - 160
  • [26] Comprehensive assessment of image quality in synthetic and digital mammography: a quantitative comparison
    Patrizio Barca
    Rocco Lamastra
    Giacomo Aringhieri
    Raffaele Maria Tucciariello
    Antonio Traino
    Maria Evelina Fantacci
    Australasian Physical & Engineering Sciences in Medicine, 2019, 42 : 1141 - 1152
  • [27] Comprehensive image quality assessment via predicting the distribution of opinion score
    Anan Liu
    Jingting Wang
    Jing Liu
    Yuting Su
    Multimedia Tools and Applications, 2019, 78 : 24205 - 24222
  • [28] Recent application of imaging techniques for fruit quality assessment
    Pathmanaban, P.
    Gnanavel, B. K.
    Anandan, Shanmuga Sundaram
    TRENDS IN FOOD SCIENCE & TECHNOLOGY, 2019, 94 : 32 - 42
  • [29] Medical Imaging Image Quality Assessment with Monte Carlo Methods
    Michail, C. M.
    Karpetas, G. E.
    Fountos, G. P.
    Kalyvas, N. I.
    Martini, Niki
    Koukou, Vaia
    Valais, I. G.
    Kandarakis, I. S.
    4TH INTERNATIONAL CONFERENCE ON MATHEMATICAL MODELING IN PHYSICAL SCIENCES (IC-MSQUARE2015), 2015, 633
  • [30] Characteristic functionals in imaging and image-quality assessment: tutorial
    Clarkson, Eric
    Barrett, Harrison H.
    JOURNAL OF THE OPTICAL SOCIETY OF AMERICA A-OPTICS IMAGE SCIENCE AND VISION, 2016, 33 (08) : 1464 - 1475