3D-2D image registration in the presence of soft-tissue deformation in image-guided transbronchial interventions

被引:1
|
作者
Vijayan, R. [1 ]
Sheth, N. [1 ]
Mekki, L. [1 ]
Lu, A. [1 ]
Uneri, A. [1 ]
Sisniega, A. [1 ]
Magaraggia, J. [2 ]
Kleinszig, G. [2 ]
Vogt, S. [2 ]
Thiboutot, J. [3 ]
Lee, H. [3 ]
Yarmus, L. [3 ]
Siewerdsen, J. H. [1 ,4 ]
机构
[1] Johns Hopkins Univ, Dept Biomed Engn, Baltimore, MD 21218 USA
[2] Siemens Healthineers, Erlangen, Germany
[3] Johns Hopkins Med Inst, Div Pulm & Crit Care Med, Baltimore, MD USA
[4] Univ Texas MD Anderson Canc Ctr, Dept Imaging Phys, Houston, TX 77070 USA
来源
PHYSICS IN MEDICINE AND BIOLOGY | 2023年 / 68卷 / 01期
关键词
deformable image registration; 3D-2D image registration; pulmonary interventions; fluoroscopy; cone-beam CT; lung nodules; transbronchial biopsy; ENDOBRONCHIAL ULTRASOUND; RESPIRATORY MOTION; LUNG; CT; FLUOROSCOPY; BRONCHOSCOPY; FEASIBILITY; ALGORITHM; ACCURACY; BIOPSY;
D O I
10.1088/1361-6560/ac9e3c
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
Purpose. Target localization in pulmonary interventions (e.g. transbronchial biopsy of a lung nodule) is challenged by deformable motion and may benefit from fluoroscopic overlay of the target to provide accurate guidance. We present and evaluate a 3D-2D image registration method for fluoroscopic overlay in the presence of tissue deformation using a multi-resolution/multi-scale (MRMS) framework with an objective function that drives registration primarily by soft-tissue image gradients. Methods. The MRMS method registers 3D cone-beam CT to 2D fluoroscopy without gating of respiratory phase by coarse-to-fine resampling and global-to-local rescaling about target regions-of-interest. A variation of the gradient orientation ( GO iso). Phantom studies determined nominal algorithm parameters and capture range. Preclinical studies used a freshly deceased, ventilated porcine specimen to evaluate performance in the presence of real tissue deformation and a broad range of 3D-2D image mismatch. Results. Nominal algorithm parameters were identified that provided robust performance over a broad range of motion (0-20 mm), including an adaptive parameter selection technique to accommodate unknown mismatch in respiratory phase. The GO & PRIME; iso = 1.2 mm, compared to 6.2 mm for conventional GO. GO & PRIME;
引用
收藏
页数:19
相关论文
共 50 条
  • [21] Soft-Tissue Modeling and Image-Guided Control of Steerable Needles
    Sadati, Nasser
    Torabi, Meysam
    Vaziri, Reza
    Dehestani-Ardekani, Reza
    2009 ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY, VOLS 1-20, 2009, : 5122 - +
  • [22] Dynamic Tracking of a Deformable Tissue Based on 3D-2D MR-US Image Registration
    Marami, Bahram
    Sirouspour, Shahin
    Fenster, Aaron
    Capson, David W.
    MEDICAL IMAGING 2014: IMAGE-GUIDED PROCEDURES, ROBOTIC INTERVENTIONS, AND MODELING, 2014, 9036
  • [23] Registration methods to enable augmented reality-assisted 3D image-guided interventions
    Park, Brian
    Hunt, Stephen
    Nadolski, Gregory
    Gade, Terence
    15TH INTERNATIONAL MEETING ON FULLY THREE-DIMENSIONAL IMAGE RECONSTRUCTION IN RADIOLOGY AND NUCLEAR MEDICINE, 2019, 11072
  • [24] Measuring and modeling soft tissue deformation for image guided interventions
    Hawkes, DJ
    Edwards, PJ
    Barratt, D
    Blackall, JM
    Penney, GP
    Tanner, C
    SURGERY SIMULATION AND SOFT TISSUE MODELING, PROCEEDINGS, 2003, 2673 : 1 - 14
  • [25] Image-based marker tracking and registration for intraoperative 3D image-guided interventions using augmented reality
    Cao, Andong
    Dhanaliwala, Ali
    Shi, Jianbo
    Gade, Terence P.
    Park, Brian J.
    MEDICAL IMAGING 2020: IMAGING INFORMATICS FOR HEALTHCARE, RESEARCH, AND APPLICATIONS, 2020, 11318
  • [26] Sliding-based image-guided 3D needle steering in soft tissue
    Fallahi, Bita
    Rossa, Carlos
    Sloboda, Ron S.
    Usmani, Nawaid
    Tavakoli, Mahdi
    CONTROL ENGINEERING PRACTICE, 2017, 63 : 34 - 43
  • [27] Image-guided soft-tissue foreign body extraction - Success and pitfalls
    Bradley, Mike
    CLINICAL RADIOLOGY, 2012, 67 (06) : 531 - 534
  • [28] Predictors of diagnostic success in image-guided pediatric soft-tissue biopsies
    Michael Acord
    Raja Shaikh
    Pediatric Radiology, 2015, 45 : 1529 - 1534
  • [29] Predictors of diagnostic success in image-guided pediatric soft-tissue biopsies
    Acord, Michael
    Shaikh, Raja
    PEDIATRIC RADIOLOGY, 2015, 45 (10) : 1529 - 1534
  • [30] Tissue deformation and shape models in image-guided interventions: a discussion paper
    Hawkes, DJ
    Barratt, D
    Blackall, JM
    Chan, C
    Edwards, PJ
    Rhode, K
    Penney, GP
    McClelland, J
    Hill, DLG
    MEDICAL IMAGE ANALYSIS, 2005, 9 (02) : 163 - 175