• Journal of Radiation Research and Radiation Processing
  • Vol. 41, Issue 3, 030301 (2023)
Nannan CAO1,2,3,4 and Xinye NI1,2,3,4,*
Author Affiliations
  • 1Department of Radiotherapy, Changzhou Second Peope’s Hospital, Nanjing Medical University, Changzhou 213003, China
  • 2Jiangsu Province Engineering Research Center of Medical Physics, Changzhou 213003, China
  • 3Edical Physics Research Center, Nanjing Medical University, Changzhou 213003, China
  • 4Changzhou Key Laboratory of Medical Physics, Changzhou 213003, China
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    DOI: 10.11889/j.1000-3436.2022-0118 Cite this Article
    Nannan CAO, Xinye NI. Research on motion management in stereotactic body radiotherapy for lung cancer[J]. Journal of Radiation Research and Radiation Processing, 2023, 41(3): 030301 Copy Citation Text show less
    Respiratory motion model construction process[11]
    Fig. 1. Respiratory motion model construction process[11]
    Reconstruction based on FDK; MC-FDK; RPCA[19]
    Fig. 2. Reconstruction based on FDK; MC-FDK; RPCA19

    文献

    Literature

    方法

    Method

    结果

    results

    [7]

    智能4D-CT;模体

    Intelligent 4D-CT; phantom

    呼吸伪影减小

    Motion artifacts were reduced

    [8]

    呼吸自适应断层扫描;模体

    Respiratory adaptive computed tomography; phantom

    靶区和肺≥4 mm伪影发生频率降低70%和76%;呼吸信号不规律减少27.3%

    The frequency of respiratory-induced image artifacts ≥ 4 mm decreased by 70% and 76% for the tumor and lung; Irregular respiratory signals decreased by 27.3%

    [9]

    呼吸运动引导4D-CT;模体

    Respiratory motion guide 4D-CT; phantom

    成像剂量减少17.8%;呼吸信号不规律减少12.6%

    Imaging dose decreased by 17.8%; Irregular respiratory signals decreased by 12.6%

    [10]

    基于B样条配准;主成分分析

    B-splines; principal component analysis

    模型误差为(2.35±0.26) mm\(2.48±0.18) mm

    Model error was (2.35±0.26) mm\(2.48±0.18) mm

    [11]

    基于B样条配准;主成分分析;贝叶斯原理

    B-splines; principal component analysis; bayesian

    单周期\双周期构建模型误差为(0.57±0.06) mm\(1.52±0.41) mm

    Error of model based on Single-cycle\Double cycles was(0.57±0.06) mm\(1.52±0.41) mm

    [12]

    基于SUM、MIP和EXS勾画靶区

    Target contouring based SUM; MIP; EXS

    MIP、EXS勾画靶区与SUM勾画靶区的体积比分别为0.888±0.061、0.883±0.064

    The ratios of for MIP and EXS to SUM were 0.888±0.061, 0.883±0.064

    [13]

    基于MIP和AVG勾画靶区

    Target contouring based MIP and AVG

    MIP勾画不规则运动范围较大的靶区 ((20.8±2.6) mm)剂量一致性差

    MIP delineation of moving irregularly target with a larger range ((20.8±2.6) mm) has poor dose consistency

    [14]

    基于模型4D-CT

    Model-based 4D-CT

    ITV区域内模型平均误差为(1.71±0.81) mm;呼吸伪影减少

    Mean model error within the ITV regions was (1.71±0.81) mm; motion artifacts were reduced

    Table 1. Research on the optimization of 4D-CT from different aspects

    文献

    literature

    算法

    algorithm

    结果

    result

    [19]

    鲁棒主成分分析;

    霍恩&舒克的光流法

    Robust principal component analysis;

    Horn and Schunck optical flow method

    PSNR与SSIM分别提高25.4%与7.6%(MC-FDK);PSNR与SSIM分别提高37.9%与17.6%(FDK)

    The improvements of PSNR by 25.4% and SSIM by 7.6% (MC-FDK) and of PSNR by 37.9% and SSIM by 17.6% (FDK)

    [21]4D-AirNet

    60个角度下PSNR与SSIM分别为49.02与0.9855

    PSNR and SSIM (AP-AirNet) were 49.02 and 0.9855 respectively under 60-view

    [22]

    自适应4D-CBCT;MC-FDK

    Adaptive 4D-CBCT; MC-FDK

    成像剂量减少85%;扫描时间减少70%

    Imaging dose was reduced by 85%; scan times were reduced by 70%

    [23]FeaCo-DCN

    18个呼吸周期下PSNR与SSIM最大为32.73和0.935;扫描时间减少约90%

    PSNR and SSIM were up to 32.73 and 0.935 under 18 breathing cycles;

    Scanning time was reduced approximately 90%

    [24]SMEIR; U-netUQI为0.96\0.97; UQI were 0.96\0.97
    [25]CycleGAN

    PSNR和SSIM分别提高了约18%和51%

    The improvements of PSNR by 25.4% and SSIM by 7.6% approximately.

    [26]

    光流约束

    Optical low (OF) constraint

    SSIM提高了2.8% (ART-TV)和23.4% (FDK)

    SSIM increases 2.8% (ART-TV) and 23.4% (FDK)

    [27]

    U-net;迁移学习

    U-net; transfer learning

    PSNR和SSIM分别为38.42和0.958

    PSNR and SSIM were 38.42 and 0.958, respectively

    Table 2. Optimization of 4D-CBCT algorithm in the past 3 years

    文献

    Literature

    方法Method

    靶区

    Target

    结果

    Results

    [44]ACT

    下叶肺肿瘤

    Lower lobe lung tumor

    运动范围最大减少6.4 mm

    The range of motion is reduced by up to 6.4 mm

    [45]ACT

    上/中叶肺肿瘤;下叶肺肿瘤

    Upper/mid-lobe lung tumor;

    lower lobe lung tumor

    运动范围分别减少0.8 mm;3.5 mm

    The range of motion is reduced by 0.8 mm and 3.5 mm respectively

    [46]ACT

    肺肿瘤

    Lung tumor

    运动范围减少7.5 mm

    The range of motion is reduced by 7.5 mm

    [47]DIBH

    肺肿瘤

    Lung tumor

    PTV从148 mL减少至110 mL

    Planning target volumes were reduced from 148 mL to 110 mL

    [48]DIBH

    肺肿瘤

    Lung tumor

    PTV减少6%

    PTV reduced by 6%

    [49]DIBH

    肺肿瘤

    Lung tumor

    平均肺剂量减少29%

    There was a 29% reduction in the mean lung dose

    [50]ABC

    肺肿瘤

    Lung tumor

    PTV平均肺剂量减少25% There was a 25% reduction in the mean lung dose of planning target volume
    [51]RG

    肺肿瘤

    Lung tumor

    放射性肺炎风险降低11%

    The risk of Radiation pneumonia is reduced by 11%

    Table 3. Respiratory motion management techniques reported in literatures
    Nannan CAO, Xinye NI. Research on motion management in stereotactic body radiotherapy for lung cancer[J]. Journal of Radiation Research and Radiation Processing, 2023, 41(3): 030301
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