Iterative Reconstruction Algorithms of Computed Tomography for the Assessment of Small Pancreatic Lesions: Phantom Study

被引:10
|
作者
Choi, Jin Woo [1 ,2 ]
Lee, Jeong Min [1 ,2 ]
Yoon, Jeong-Hee [1 ,2 ]
Baek, Jee Hyun [1 ,2 ]
Han, Joon Koo [1 ,2 ]
Choi, Byung Ihn [1 ,2 ]
机构
[1] Seoul Natl Univ, Coll Med, Dept Radiol, Seoul 110744, South Korea
[2] Seoul Natl Univ Hosp, Dept Radiol, Seoul 110744, South Korea
关键词
computed tomography; iterative reconstruction; pancreas; pancreatic cancer; FILTERED BACK-PROJECTION; RADIATION-DOSE REDUCTION; INITIAL CLINICAL-EXPERIENCE; TUBE CURRENT MODULATION; MODERN DIAGNOSTIC MDCT; NOISE POWER SPECTRUM; IMAGE QUALITY; HELICAL CT; ABDOMINAL CT; LIVER CT;
D O I
10.1097/RCT.0b013e3182a2181e
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
Objective: To evaluate the image quality and radiation dose reduction of iterative reconstruction (IR) used for computed tomographic (CT) scanning of small pancreatic lesions. Methods: An anthropomorphic pancreas phantom with 16 small lesions was scanned using 4 kinds of CT scanners with different tube current-time products (75-250 mAs). The CT images were reconstructed using filtered back projection (FBP) and the relevant IR of each vendor (GE Healthcare, Philips Healthcare, Siemens Healthcare, Toshiba Medical Systems). The image qualities, dose reduction rate (in percent), and figure of merit (FOM) were evaluated in comparison with the reference images (250 mAs, FBP). Results: Image noise was markedly improved with the IR; therefore, a 36 to 60% dose reduction was possible. As a result, the final CT dose index volume can be diminished to 7.05 to 11.40 mGy with the IR algorithms. The IR demonstrated 1.52 to 7.84 times higher FOM than that of FBP. Particularly, an advanced fully IR showed outstanding results of FOM (6.06-7.84 times). Conclusions: Because IR can reduce image noise while maintaining image quality for the delineation of small pancreatic lesions, it can be used for pancreatic imaging with substantial radiation dose reduction.
引用
收藏
页码:911 / 923
页数:13
相关论文
共 50 条
  • [1] Evaluation of Image Quality for 7 Iterative Reconstruction Algorithms in Chest Computed Tomography Imaging: A Phantom Study
    Jensen, Kristin
    Hagemo, Guro
    Tingberg, Anders
    Steinfeldt-Reisse, Claudius
    Mynarek, Georg Karl
    Rivero, Rodriguez Jezabel
    Fosse, Erik
    Martinsen, Anne Catrine
    JOURNAL OF COMPUTER ASSISTED TOMOGRAPHY, 2020, 44 (05) : 673 - 680
  • [2] Objective performance assessment of five computed tomography iterative reconstruction algorithms
    Omotayo, Azeez
    Elbakri, Idris
    JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY, 2016, 24 (06) : 913 - 930
  • [3] Impact of Iterative Reconstruction Algorithms on Image Quality and Radiation Dose in Computed Tomography Scan of Patients with Malignant Pancreatic Lesions
    Asemanrafat, Mohamadhosein
    Chaparian, Ali
    Lotfi, Mehrzad
    Rasekhi, Alireza
    JOURNAL OF MEDICAL SIGNALS & SENSORS, 2022, 12 (01): : 69 - 75
  • [4] Initial Phantom Study Comparing Image Quality in Computed Tomography Using Adaptive Statistical Iterative Reconstruction and New Adaptive Statistical Iterative Reconstruction V
    Lim, Kyungjae
    Kwon, Heejin
    Cho, Jinhan
    Oh, Jongyoung
    Yoon, Seongkuk
    Kang, Myungjin
    Ha, Dongho
    Lee, Jinhwa
    Kang, Eunju
    JOURNAL OF COMPUTER ASSISTED TOMOGRAPHY, 2015, 39 (03) : 443 - 448
  • [5] Comparing five different iterative reconstruction algorithms for computed tomography in an ROC study
    Jensen, Kristin
    Martinsen, Anne Catrine T.
    Tingberg, Anders
    Aalokken, Trond Mogens
    Fosse, Erik
    EUROPEAN RADIOLOGY, 2014, 24 (12) : 2989 - 3002
  • [6] Effect of Unmatched System Models on Iterative Reconstruction in Computed Tomography: A Phantom Study
    Youngjin Lee
    Seungwan Lee
    Journal of the Korean Physical Society, 2020, 76 : 866 - 873
  • [7] Effect of Unmatched System Models on Iterative Reconstruction in Computed Tomography: A Phantom Study
    Lee, Youngjin
    Lee, Seungwan
    JOURNAL OF THE KOREAN PHYSICAL SOCIETY, 2020, 76 (09) : 866 - 873
  • [8] Improving Low -contrast Detectability and Noise Texture Pattern for Computed Tomography Using Iterative Reconstruction Accelerated with Machine Learning Method: A Phantom Study
    Funama, Yoshinori
    Takahashi, Hisashi
    Goto, Taiga
    Aoki, Yuko
    Yoshida, Ryo
    Kumagai, Yukio
    Awai, Kazuo
    ACADEMIC RADIOLOGY, 2020, 27 (07) : 929 - 936
  • [9] Evaluation of low-contrast detectability for iterative reconstruction in pediatric abdominal computed tomography: a phantom study
    Rubert, Nicholas
    Southard, Richard
    Hamman, Susan M.
    Robison, Ryan
    PEDIATRIC RADIOLOGY, 2020, 50 (03) : 345 - 356
  • [10] Hybrid Iterative Reconstruction for Low Radiation Dose Computed Tomography
    Sheng, Jinhua
    Chen, Bin
    Wang, Bocheng
    Liu, Qingqiang
    Ma, Yangjie
    Liu, Weixiang
    NEURAL INFORMATION PROCESSING (ICONIP 2018), PT VI, 2018, 11306 : 243 - 256