A generalized 4D image registration scheme for targeted radionuclide therapy dosimetry

被引:8
|
作者
Papavasileiou, Periklis
Divoli, Antigoni
Hatziioannou, Kostas
Flux, Glenn D.
机构
[1] Papageorgiou Gen Hosp, Dept Phys Med, Thessaloniki 54603, Greece
[2] Inst Canc Res, Joint Dept Phys, Sutton, Surrey, England
[3] Royal Marsden Hosp NHS Fdn Trust, Sutton, Surrey, England
关键词
image registration; radionuclide therapy dosimetry; polynomial fitting;
D O I
10.1089/cbr.2007.310
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
An iterative, generalized four-dimensional (4D) method is presented in this paper that allows simultaneous registration of a series of single-photon emission computed tomography (SPECT) scans acquired in the course of a radionuclide therapy or pretherapy tracer study. The method combines temporal information with voxel-based similarity criteria to carry out simultaneous registration of the SPECT scans. A polynomial function was fitted to the maximum counts of each tumor site over the 4D study. Each tumor site was normalized to its maximum on the reference scan, and a template 4D dataset was generated, employing the polynomial fitting and the normalization map. Then, each 3D scan was registered to the corresponding simulated scan, using a 3D similarity criterion. The correlation coefficient (CC), the mutual information (MI), and the sum-of-absolute differences (SAD) similarity criteria were employed. Simulated data, based on a head-neck I-131-MIBG study, were used to compare the proposed method for 4D registration with sequential 3D registration. Sequential 3D registration resulted in residual registration errors of 3.5 +/- 2.5, 3.2 +/- 2.0, and 7.0 +/- 3.5 mm for the CC, MI, and SAD criteria respectively, whereas the corresponding 4D method gave errors of 2.4 +/- 1.6, 1.9 +/- 1.1, and 5.3 +/- 2.9 mm for the CC, MI, and SAD criteria, respectively. The 4D method was applied to Re-186 HEDP SPECT patient studies and registration was verified by a dual-cursor display tool.
引用
收藏
页码:160 / 165
页数:6
相关论文
共 45 条
  • [41] Motion Extraction of the Right Ventricle from 4D Cardiac Cine MRI Using A Deep Learning-Based Deformable Registration Framework
    Upendra, Roshan Reddy
    Hasan, S. M. Kamrul
    Simon, Richard
    Wentz, Brian Jamison
    Shontz, Suzanne M.
    Sacks, Michael S.
    Linte, Cristian A.
    2021 43RD ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE & BIOLOGY SOCIETY (EMBC), 2021, : 3795 - 3799
  • [42] Accelerated Digitally Reconstructed Radiograph Generation Scheme for 2D to 3D Image Registration of Vertebrae Based on Sparse Sampling and Multi-Resolution
    Bhat, Vidya
    Bhat, Shyamasunder N.
    Anitha, H.
    2017 NINTH INTERNATIONAL CONFERENCE ON ADVANCED COMPUTING (ICOAC), 2017, : 51 - 57
  • [43] Estimation of lung motion fields in 4D CT data by variational non-linear intensity-based registration: A comparison and evaluation study
    Werner, Rene
    Schmidt-Richberg, Alexander
    Handels, Heinz
    Ehrhardt, Jan
    PHYSICS IN MEDICINE AND BIOLOGY, 2014, 59 (15) : 4247 - 4260
  • [44] Self-Supervised Motion-Corrected Image Reconstruction Network for 4D Magnetic Resonance Imaging of the Body Trunk
    Kuestner, Thomas
    Pan, Jiazhen
    Gilliam, Christopher
    Qi, Haikun
    Cruz, Gastao
    Hammernik, Kerstin
    Blu, Thierry
    Rueckert, Daniel
    Botnar, Rene
    Prieto, Claudia
    Gatidis, Sergios
    APSIPA TRANSACTIONS ON SIGNAL AND INFORMATION PROCESSING, 2022, 11 (01)
  • [45] Stochastic Rank Correlation - A novel merit function for dual energy 2D/3D registration in image-modulated radiation therapy
    Birkfellner, W.
    11TH MEDITERRANEAN CONFERENCE ON MEDICAL AND BIOLOGICAL ENGINEERING AND COMPUTING 2007, VOLS 1 AND 2, 2007, 16 (1-2): : 834 - 834