Technical Note: Density correction to improve CT number mapping in thoracic deformable image registration

被引:4
作者
Yang, Jinzhong [1 ]
Zhang, Yongbin [2 ]
Zhang, Zijian [1 ,3 ]
Zhang, Lifei [1 ]
Batter, Peter [1 ]
Court, Laurence [1 ]
机构
[1] Univ Texas MD Anderson Canc Ctr, Dept Radiat Phys, Houston, TX 77030 USA
[2] Univ Cincinnati, Proton Therapy Ctr, Med Ctr, Liberty Township, OH USA
[3] Cent S Univ, Xiangya Hosp, Changsha, Hunan, Peoples R China
基金
美国国家卫生研究院;
关键词
deformable image registration; density correction; Jacobian; lung cancer; NORMAL ORGAN WEIGHTS; II-THE-BRAIN; CANCER-PATIENTS; RADIOTHERAPY; CHAIR; VALIDATION; POSITION; SPLEEN; LUNGS; LIVER;
D O I
10.1002/mp.13502
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
Purpose To improve the accuracy of computed tomography (CT) number mapping inside the lung in deformable image registration with large differences in lung volume for applications in vertical CT imaging and adaptive radiotherapy. Methods The deep inspiration breath hold (DIBH) CT image and the end of exhalation (EE) phase image in four-dimensional CT of 14 thoracic cancer patients were used in this study. Lung volumes were manually delineated. A Demons-based deformable registration was first applied to register the EE CT to the DIBH CT for each patient, and the resulting deformation vector field deformed the EE CT image to the DIBH CT space. Given that the mass of the lung remains the same during respiration, we created a mass-preserving model to correlate lung density variations with volumetric changes, which were characterized by the Jacobian derived from the deformation field. The Jacobian determinant was used to correct the lung CT numbers transferred from the EE CT image. The absolute intensity differences created by subtracting the deformed EE CT from the DIBH CT with and without density correction were compared. Results The ratio of DIBH CT to EE CT lung volumes was 1.6 on average. The deformable registration registered the lung shape well, but the appearance of voxel intensities inside the lung was different, demonstrating the need for density correction. Without density correction, the mean and standard deviation of the absolute intensity difference between the deformed EE CT and the DIBH CT inside the lung were 54.5 +/- 45.5 for all cases. After density correction, these numbers decreased to 18.1 +/- 34.9, demonstrating greater accuracy. The cumulative histogram of the intensity difference also showed that density correction improved CT number mapping greatly. Conclusion Density correction improves CT number mapping inside the lung in deformable image registration for difficult cases with large lung volume differences.
引用
收藏
页码:2330 / 2336
页数:7
相关论文
共 50 条
  • [1] Density Correction for Deformable Image Registration to Improve CT Number Mapping
    Yang, J.
    Zhang, Y.
    Zhang, L.
    Balter, P.
    Court, L.
    MEDICAL PHYSICS, 2014, 41 (06) : 400 - +
  • [2] Technical Note: The impact of deformable image registration methods on dose warping
    Qin, An
    Liang, Jian
    Han, Xiao
    O'Connell, Nicolette
    Yan, Di
    MEDICAL PHYSICS, 2018, 45 (03) : 1287 - 1294
  • [3] Technical Note: Deformable image registration on partially matched images for radiotherapy applications
    Yang, Deshan
    Goddu, S. Murty
    Lu, Wei
    Pechenaya, Olga L.
    Wu, Yu
    Deasy, Joseph O.
    El Naqa, Issam
    Low, Daniel A.
    MEDICAL PHYSICS, 2010, 37 (01) : 141 - 145
  • [4] Evaluation of various deformable image registration algorithms for thoracic images
    Kadoya, Noriyuki
    Fujita, Yukio
    Katsuta, Yoshiyuki
    Dobashi, Suguru
    Takeda, Ken
    Kishi, Kazuma
    Kubozono, Masaki
    Umezawa, Rei
    Sugawara, Toshiyuki
    Matsushita, Haruo
    Jingu, Keiichi
    JOURNAL OF RADIATION RESEARCH, 2014, 55 (01) : 175 - 182
  • [5] Evaluation of deformable image registration accuracy for CT images of the thorax region
    Sarudis, Sebastian
    Karlsson, Anna
    Bibac, Dan
    Nyman, Jan
    Back, Anna
    PHYSICA MEDICA-EUROPEAN JOURNAL OF MEDICAL PHYSICS, 2019, 57 : 191 - 199
  • [6] Registration error of the liver CT using deformable image registration of MIM Maestro and Velocity AI
    Fukumitsu, Nobuyoshi
    Nitta, Kazunori
    Terunuma, Toshiyuki
    Okumura, Toshiyuki
    Numajiri, Haruko
    Oshiro, Yoshiko
    Ohnishi, Kayoko
    Mizumoto, Masashi
    Aihara, Teruhito
    Ishikawa, Hitoshi
    Tsuboi, Koji
    Sakurai, Hideyuki
    BMC MEDICAL IMAGING, 2017, 17
  • [7] Evaluation of the effect of user-guided deformable image registration of thoracic images on registration accuracy among users
    Nakajima, Yujiro
    Kadoya, Noriyuki
    Kanai, Takayuki
    Saito, Masahide
    Kito, Satoshi
    Hashimoto, Shimpei
    Karasawa, Katsuyuki
    Jingu, Keiichi
    MEDICAL DOSIMETRY, 2020, 45 (03) : 206 - 212
  • [8] A method to estimate the effect of deformable image registration uncertainties on daily dose mapping
    Murphy, Martin J.
    Salguero, Francisco J.
    Siebers, Jeffrey V.
    Staub, David
    Vaman, Constantin
    MEDICAL PHYSICS, 2012, 39 (02) : 573 - 580
  • [9] Impact of deformable image registration accuracy on thoracic images with different regularization weight parameter settings
    Miura, Hideharu
    Ozawa, Shuichi
    Nakao, Minoru
    Furukawa, Kengo
    Doi, Yoshiko
    Kawabata, Hideo
    Kenjou, Masahiro
    Nagata, Yasushi
    PHYSICA MEDICA-EUROPEAN JOURNAL OF MEDICAL PHYSICS, 2017, 42 : 108 - 111
  • [10] Deformable registration of CT and cone-beam CT by local CBCT intensity correction
    Park, Seyoun
    Plishker, William
    Shekhar, Raj
    Quon, Harry
    Wong, John
    Lee, Junghoon
    MEDICAL IMAGING 2015: IMAGE PROCESSING, 2015, 9413