Lung iodine mapping by subtraction with image registration allowing for tissue sliding

被引:4
|
作者
Mohr, Brian [1 ]
Brink, Monique [2 ]
Oostveen, Luuk J. [2 ]
Schuijf, Joanne D. [3 ]
Prokop, Mathias [2 ]
机构
[1] Toshiba Med Visualizat Syst Europe, Edinburgh, Midlothian, Scotland
[2] Radboud Univ Nijmegen, Med Ctr, Nijmegen, Netherlands
[3] Toshiba Med Syst Europe, Zoetermeer, Netherlands
来源
MEDICAL IMAGING 2016: IMAGE PROCESSING | 2016年 / 9784卷
关键词
Registration; Subtraction; Lung; CT;
D O I
10.1117/12.2216262
中图分类号
O43 [光学];
学科分类号
070207 ; 0803 ;
摘要
Pulmonary embolism is a fairly common and serious entity, so rapid diagnosis and treatment has a significant impact on morbidity and mortality rates. Iodine maps representing tissue perfusion enhancement are commonly generated by dual-energy CT acquisitions to provide information about the effect of the embolism on pulmonary perfusion. Alternatively, the iodine map can be generated by subtracting pre-from post-contrast CT scans as previously reported. Although accurate image registration is essential, subtraction has the advantage of a higher signal-to-noise ratio and suppression of bone. This paper presents an improvement over the previously reported registration algorithm. Significantly, allowance for sliding motion at tissue boundaries is included in the regularization. Pre- and post-contrast helical CT scans were acquired for thirty subjects using a Toshiba Aquilion ONE (R) scanner. Ten of these subjects were designated for algorithm development, while the remaining twenty were reserved for qualitative clinical evaluation. Quantitative evaluation was performed against the previously reported method and using publicly available data for comparison against other methods. Comparison of 100 landmarks in seven datasets shows no change in the mean Euclidean error of 0.48 mm, compared to the previous method. Evaluation in the publicly available DIR-Lab data with 300 annotations results in a mean Euclidean error of 1.17 mm in the ten 4DCT cases and 3.37 mm in the ten COPDGene cases. Clinical evaluation on a sliding scale from 1 (excellent) to 5 (non-diagnostic) indicates a slight, but non-significant, improvement in registration adequacy from 3.1 to 2.9.
引用
收藏
页数:8
相关论文
共 50 条
  • [1] Computed tomography lung iodine contrast mapping by image registration and subtraction
    Goatman, Keith
    Plakas, Costas
    Schuijf, Joanne
    Beveridge, Erin
    Prokop, Mathias
    MEDICAL IMAGING 2014: IMAGE PROCESSING, 2014, 9034
  • [2] Estimation of lung lobar sliding using image registration
    Amelon, Ryan
    Cao, Kunlin
    Reinhardt, Joseph M.
    Christensen, Gary E.
    Raghavan, Madhavan
    MEDICAL IMAGING 2012: BIOMEDICAL APPLICATIONS IN MOLECULAR, STRUCTURAL, AND FUNCTIONAL IMAGING, 2012, 8317
  • [3] Image registration for digital subtraction angiography
    Meijering, EHW
    Zuiderveld, KJ
    Viergever, MA
    INTERNATIONAL JOURNAL OF COMPUTER VISION, 1999, 31 (2-3) : 227 - 246
  • [4] Image Registration for Digital Subtraction Angiography
    Erik H.W. Meijering
    Karel J. Zuiderveld
    Max A. Viergever
    International Journal of Computer Vision, 1999, 31 : 227 - 246
  • [5] Image registration and subtraction for the visualization of change in diabetic retinopathy screening
    McRitchie, Ian N.
    Hart, Patricia M.
    Winder, R. John
    COMPUTERIZED MEDICAL IMAGING AND GRAPHICS, 2006, 30 (03) : 139 - 145
  • [6] Deformable image registration for temporal subtraction of chest radiographs
    Li, Min
    Castillo, Edward
    Luo, Hong-Yan
    Zheng, Xiao-Lin
    Castillo, Richard
    Meshkov, Dmitriy
    Guerrero, Thomas
    INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY, 2014, 9 (04) : 513 - 522
  • [7] DISCRIMINATIVE SLIDING PRESERVING REGULARIZATION IN MEDICAL IMAGE REGISTRATION
    Ruan, Dan
    Esedoglu, Selim
    Fessler, Jeffrey A.
    2009 IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING: FROM NANO TO MACRO, VOLS 1 AND 2, 2009, : 430 - 433
  • [8] Boundary-aware registration network for 4D-CT lung image with sliding motion
    Duan, Luwen
    Cao, Yuzhu
    Wang, Ziyu
    Liu, Desen
    Fu, Tianxiao
    Yuan, Gang
    Zheng, Jian
    BIOMEDICAL SIGNAL PROCESSING AND CONTROL, 2023, 86
  • [9] Multimodal Image Registration of Lung images
    Veduruparthi, Bijju Kranthi
    Mukherjee, Jayanta
    Das, Partha Pratim
    Chatterjee, Sanjoy
    Ray, Soumendranath
    Sen, Partha
    2015 FIFTH NATIONAL CONFERENCE ON COMPUTER VISION, PATTERN RECOGNITION, IMAGE PROCESSING AND GRAPHICS (NCVPRIPG), 2015,
  • [10] Multimodal macula mapping by deformable image registration
    Baptista, P.
    Ferreira, J.
    Bernardes, R.
    Dias, J.
    Cunha-Vaz, J.
    COMPUTATIONAL MODELLING OF OBJECTS REPRESENTED IN IMAGES: FUNDAMENTALS, METHODS AND APPLICATIONS, 2007, : 85 - +