搜索

x

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

基于可分离编码的高分辨X射线荧光成像技术研究

孙世峰

引用本文:
Citation:

基于可分离编码的高分辨X射线荧光成像技术研究

孙世峰

High-resolution coded aperture X-ray fluorescence imaging with separable masks

Sun Shi-Feng
PDF
HTML
导出引用
  • 相比于传统基于毛细管或针孔的 X 射线成像系统, 编码孔径成像系统具有结构简单、灵敏度高、扩展性强等优势,使其在 X 射线荧光成像中极具潜力. 本工作应用新型编码孔径成像计算模型, 设计了一种基于可分离编码的X射线成像系统. 利用Geant4蒙特卡罗仿真对系统的性能进行了研究, 并根据快速迭代收缩阈值算法进行了图像重建. 模拟及分析结果显示, 近场成像时, 与传统基于卷积模型的成像系统不同, 该系统的性能不受准直效应的影响. 成像系统的空间分辨率约为65 μm, 并能够准确地重建出不同能量的线源和形状复杂物体的图像. 重建图像的质量受校准时所用X射线能量和物体发射X射线能量的影响, 两者差异越小, 重建图像的质量越高. 三维重建结果显示, 系统能够从单次获取的二维投影图像, 正确地重建出物体与系统的距离, 轴向空间分辨率约为1.1 mm.
    Compared with traditional X-ray imaging systems based on polycapillary X-ray optics or a pinhole, coded aperture imaging system has the advantages in simple structure, high sensitivity, and strong expandability, which make it possess the potential applications in X-ray fluorescence imaging. In this work, a new coded aperture X-ray imaging system based on a novel imaging model which decomposes the mask projections into a superposition of two separable functions is designed and proposed for high-resolution X-ray imaging. The performance of the system is demonstrated by using the Geant4 package. To reduce the computational complexity of calibration and image reconstruction, a separable mask with 90 × 90 pixels is used. The mask is designed by selecting the central part of the original rank 463 modified uniformly redundant arrays. The mask is made of platinum foil with a pixel pitch of 25 microns. To study the effect of mask thickness on system performance, the mask thickness is varied from 25 to 200 microns. The active area of the Si detector employed in the system is 2 mm × 2 mm, divided into 80 × 80 pixels, each with a size of 25 μm × 25 μm. The field of view of the system is equal to the area of the detector, which is 2 mm × 2 mm. The detector is parallel to and center-aligned with the mask with a fixed distance of 2.0 mm. The images are reconstructed by using the fast iterative shrinkage-thresholding algorithm. The high-quality reconstructed images of different energy line sources and complex shaped objects are obtained. The simulation and analysis results indicate that for the near-field imaging, unlike imaging systems based on the conventional convolution model, the system has the performance that is not affected by the aperture collimation effect. The spatial resolution of the imaging system is about 65 microns. The calibrated matrices used have an important influence on the image quality. The quality of the reconstructed image is affected by the energy of X-rays used during calibration and the energy of X-rays emitted from the object; the smaller the difference between these two energy values, the higher the quality of the reconstructed images will be. The three-dimensional reconstruction results show that the system can correctly estimate the distance between the object and the system from a single two-dimensional projection. The axial spatial resolution of the system is about 1.1 mm.
      通信作者: 孙世峰, sunshf@ncepu.edu.cn
    • 基金项目: 国家自然科学基金(批准号: 11805066)和核动力技术创新中心(批准号: HDLCXZX-2020-HD-018)资助的课题
      Corresponding author: Sun Shi-Feng, sunshf@ncepu.edu.cn
    • Funds: Project supported by the National Natural Science Foundation of China (Grant No. 11805066) and the Nuclear Power Technology Innovation Centre (Grant No. HDLCXZX-2020-HD-018)
    [1]

    Terzano R, Denecke M A, Falkenberg G, Miller B, Paterson D, Janssens K 2019 Pure Appl. Chem. 91 1029Google Scholar

    [2]

    Romano F P, Caliri C, Cosentino L, Gammino S, Giuntini L, Mascali D, Neri L, Pappalardo L, Rizzo F, Taccetti F 2014 Anal. Chem. 86 10892Google Scholar

    [3]

    Tsuji K, Matsuno T, Takimoto Y, Yamanashi M, Kometani N, Sasaki Y C, Hasegawa T, Kato S, Yamada T, Shoji T, Kawahara N 2015 Spectrom. Acta B 113 43Google Scholar

    [4]

    Buzanich G, Radtke M, Reinholz U, Riesemeier H, Thünemann A F, Streli C 2012 J. Anal. At. Spectrom. 27 1875Google Scholar

    [5]

    Rauwolf M, Turyanskaya A, Roschger A, Prost J, Simon R, Scharf O, Radtke M, Schoonjans T, Buzanich A G, Klaushofer K, Wobrauschek P, Hofstaetter J G, Roschger P, Streli C 2017 J. Synchrotron Radiat. 24 307Google Scholar

    [6]

    Zhang H, Jiang S W, Liao J, Deng J J, Liu J, Zhang Y B, Zheng G A 2019 Opt. Express 27 7498Google Scholar

    [7]

    强鹏飞, 盛立志, 李林森, 闫永清, 刘哲, 周晓红 2019 68 160702Google Scholar

    Qiang P F, Sheng L Z, Li L S, Yan Y Q, Liu Z, Zhou X H 2019 Acta Phys. Sin. 68 160702Google Scholar

    [8]

    Dicke R H 1968 Astrophys. J. 153 L101Google Scholar

    [9]

    Ables J G 1968 Astron. Soc. Aust. 1 172Google Scholar

    [10]

    Cieślak M J, Gamage K A A, Glover R 2016 Radiat. Meas. 92 59Google Scholar

    [11]

    Liu Y T, Xiao X, Zhang Z M, Wei L 2020 Nucl. Instrum. Methods Phys. Res. A 957 163385Google Scholar

    [12]

    Sun S F, Liu Y, Ouyang X P 2020 Radiat. Phys. Chem. 174 108891Google Scholar

    [13]

    Haboub A, MacDowell A A, Marchesini S, Parkinson D Y 2014 Rev. Sci. Instrum. 85 063704Google Scholar

    [14]

    Kulow A, Buzanich A G, Reinholz U, Streli C, Radtke M 2020 J. Anal. At. Spectrom. 35 347Google Scholar

    [15]

    DeWeert M J, Farm B P 2015 Opt. Eng. 54 023102Google Scholar

    [16]

    Asif M S, Ayremlou A, Sankaranarayanan A, Veeraraghavan A, Baraniuk R G 2017 IEEE Trans. Comput. Imaging 3 384Google Scholar

    [17]

    Adams J K, Boominathan V, Avants B W, Vercosa D G, Ye F, Baraniuk R G, Robinson J T, Veeraraghavan A 2017 Sci. Adv. 3 e1701548Google Scholar

    [18]

    Sun S F, Liu Y, Ouyang X P 2020 Nucl. Instrum. Methods Phys. Res. A 951 163001Google Scholar

    [19]

    Beck A, Teboulle M 2009 SIAM J. Imaging Sci. 2 183Google Scholar

    [20]

    Allison J, Amako K, Apostolakis J, Arce P, Asai M, Aso T, Bagli E, Bagulya A, Banerjee S, Barrand G 2016 Nucl. Instrum. Methods Phys. Res. A 835 186Google Scholar

    [21]

    Wang Z, Bovik A C 2002 IEEE Signal Process. Lett. 9 81Google Scholar

  • 图 1  编码孔径成像示意图

    Fig. 1.  Schematics diagram of coded aperture imaging

    图 2  模拟选用的可编码准直器图样

    Fig. 2.  The mask pattern used in the simulation.

    图 3  线源二维重建图像及其沿横纵向分布 (a)准直器厚度25微米时的线源重建图像; (b)不同准直器厚度时的横向分布; (c)不同准直器厚度时的纵向分布

    Fig. 3.  2 d reconstructed image of the line source and its horizontal and vertical distribution: (a) Reconstructed image of the line source when the mask thickness was 25 micron; (b) horizontal distribution for different mask thickness; (c) vertical distribution for different mask thickness.

    图 4  物体的原始图像及重建图像 (a)原始图像; (b)准直器厚度25微米时的重建图像

    Fig. 4.  Original image and reconstructed image of the object: (a) Original image; (b) reconstructed image when the mask thickness was 25 micron.

    图 5  校准能量10 keV情况下物体不同能量时的重建图像 (a) 6 keV; (b) 8 keV; (c) 16 keV

    Fig. 5.  Reconstructed images of the object with different energies at a calibration energy of 10 keV: (a) 6 keV; (b) 8 keV; (c) 16 keV.

    图 6  校准能量16 keV情况下物体不同能量时的重建图像 (a) 6 keV; (b) 8 keV; (c) 16 keV

    Fig. 6.  Reconstructed images of the object with different energies at a calibration energy of 16 keV: (a) 6 keV; (b) 8 keV; (c) 16 keV.

    图 7  重建图像质量的定量评价结果随物体能量和校准能量的变化情况 (a) RMSE; (b) UQI

    Fig. 7.  Quantitative evaluation results of reconstructed images change with the object energy and calibration energy: (a) RMSE; (b) UQI

    图 8  线源的三维重建图像

    Fig. 8.  3D reconstructed image of the line source.

    图 9  复杂物体的三维重建图像

    Fig. 9.  3D reconstructed image of the complex object.

    Baidu
  • [1]

    Terzano R, Denecke M A, Falkenberg G, Miller B, Paterson D, Janssens K 2019 Pure Appl. Chem. 91 1029Google Scholar

    [2]

    Romano F P, Caliri C, Cosentino L, Gammino S, Giuntini L, Mascali D, Neri L, Pappalardo L, Rizzo F, Taccetti F 2014 Anal. Chem. 86 10892Google Scholar

    [3]

    Tsuji K, Matsuno T, Takimoto Y, Yamanashi M, Kometani N, Sasaki Y C, Hasegawa T, Kato S, Yamada T, Shoji T, Kawahara N 2015 Spectrom. Acta B 113 43Google Scholar

    [4]

    Buzanich G, Radtke M, Reinholz U, Riesemeier H, Thünemann A F, Streli C 2012 J. Anal. At. Spectrom. 27 1875Google Scholar

    [5]

    Rauwolf M, Turyanskaya A, Roschger A, Prost J, Simon R, Scharf O, Radtke M, Schoonjans T, Buzanich A G, Klaushofer K, Wobrauschek P, Hofstaetter J G, Roschger P, Streli C 2017 J. Synchrotron Radiat. 24 307Google Scholar

    [6]

    Zhang H, Jiang S W, Liao J, Deng J J, Liu J, Zhang Y B, Zheng G A 2019 Opt. Express 27 7498Google Scholar

    [7]

    强鹏飞, 盛立志, 李林森, 闫永清, 刘哲, 周晓红 2019 68 160702Google Scholar

    Qiang P F, Sheng L Z, Li L S, Yan Y Q, Liu Z, Zhou X H 2019 Acta Phys. Sin. 68 160702Google Scholar

    [8]

    Dicke R H 1968 Astrophys. J. 153 L101Google Scholar

    [9]

    Ables J G 1968 Astron. Soc. Aust. 1 172Google Scholar

    [10]

    Cieślak M J, Gamage K A A, Glover R 2016 Radiat. Meas. 92 59Google Scholar

    [11]

    Liu Y T, Xiao X, Zhang Z M, Wei L 2020 Nucl. Instrum. Methods Phys. Res. A 957 163385Google Scholar

    [12]

    Sun S F, Liu Y, Ouyang X P 2020 Radiat. Phys. Chem. 174 108891Google Scholar

    [13]

    Haboub A, MacDowell A A, Marchesini S, Parkinson D Y 2014 Rev. Sci. Instrum. 85 063704Google Scholar

    [14]

    Kulow A, Buzanich A G, Reinholz U, Streli C, Radtke M 2020 J. Anal. At. Spectrom. 35 347Google Scholar

    [15]

    DeWeert M J, Farm B P 2015 Opt. Eng. 54 023102Google Scholar

    [16]

    Asif M S, Ayremlou A, Sankaranarayanan A, Veeraraghavan A, Baraniuk R G 2017 IEEE Trans. Comput. Imaging 3 384Google Scholar

    [17]

    Adams J K, Boominathan V, Avants B W, Vercosa D G, Ye F, Baraniuk R G, Robinson J T, Veeraraghavan A 2017 Sci. Adv. 3 e1701548Google Scholar

    [18]

    Sun S F, Liu Y, Ouyang X P 2020 Nucl. Instrum. Methods Phys. Res. A 951 163001Google Scholar

    [19]

    Beck A, Teboulle M 2009 SIAM J. Imaging Sci. 2 183Google Scholar

    [20]

    Allison J, Amako K, Apostolakis J, Arce P, Asai M, Aso T, Bagli E, Bagulya A, Banerjee S, Barrand G 2016 Nucl. Instrum. Methods Phys. Res. A 835 186Google Scholar

    [21]

    Wang Z, Bovik A C 2002 IEEE Signal Process. Lett. 9 81Google Scholar

  • [1] 舒盼盼, 赵朋程. 高功率微波介质窗气体侧击穿特性的粒子-蒙特卡洛碰撞模拟研究.  , 2024, 73(23): 1-10. doi: 10.7498/aps.73.20241177
    [2] 陈松懋, 苏秀琴, 郝伟, 张振扬, 汪书潮, 朱文华, 王杰. 基于光子计数激光雷达的自适应门控抑噪及三维重建算法.  , 2022, 71(10): 104202. doi: 10.7498/aps.71.20211697
    [3] 陈洁, 周昕, 白星, 李聪, 徐昭, 倪洋. 强散射过程与双随机相位加密过程的等价性分析.  , 2021, 70(13): 134201. doi: 10.7498/aps.70.20201903
    [4] 乔志伟. 总变差约束的数据分离最小图像重建模型及其Chambolle-Pock求解算法.  , 2018, 67(19): 198701. doi: 10.7498/aps.67.20180839
    [5] 王心怡, 范全平, 魏来, 杨祖华, 张强强, 陈勇, 彭倩, 晏卓阳, 肖沙里, 曹磊峰. Fresnel波带片编码成像的高分辨重建.  , 2017, 66(5): 054203. doi: 10.7498/aps.66.054203
    [6] 张雷雷, 唐立金, 张慕阳, 梁艳梅. 对称照明在傅里叶叠层成像中的应用.  , 2017, 66(22): 224201. doi: 10.7498/aps.66.224201
    [7] 阮聪, 孙晓民, 宋亦旭. 元胞方法与蒙特卡洛方法相结合的薄膜生长过程模拟.  , 2015, 64(3): 038201. doi: 10.7498/aps.64.038201
    [8] 韩玉, 李磊, 闫镔, 席晓琦, 胡国恩. 一种基于Radon逆变换的半覆盖螺旋锥束CT重建算法.  , 2015, 64(5): 058704. doi: 10.7498/aps.64.058704
    [9] 何林阳, 刘晶红, 李刚. 基于多相组重建的航空图像超分辨率算法.  , 2015, 64(11): 114208. doi: 10.7498/aps.64.114208
    [10] 杜劲松, 高扬, 毕欣, 齐伟智, 黄林, 荣健. S波段微波热致超声成像系统研究.  , 2015, 64(3): 034301. doi: 10.7498/aps.64.034301
    [11] 刘扬阳, 吕群波, 吴戈, 裴琳琳, 王建威. 编码孔径光谱成像仪光学简化彗差对图谱反演误差分析.  , 2015, 64(5): 054205. doi: 10.7498/aps.64.054205
    [12] 周树波, 袁艳, 苏丽娟. 基于双阈值Huber范数估计的图像正则化超分辨率算法.  , 2013, 62(20): 200701. doi: 10.7498/aps.62.200701
    [13] 汪先超, 闫镔, 刘宏奎, 李磊, 魏星, 胡国恩. 一种圆轨迹锥束CT中截断投影数据的高效重建算法.  , 2013, 62(9): 098702. doi: 10.7498/aps.62.098702
    [14] 宁方立, 何碧静, 韦娟. 基于lp范数的压缩感知图像重建算法研究.  , 2013, 62(17): 174212. doi: 10.7498/aps.62.174212
    [15] 崔振国, 勾成俊, 侯氢, 毛莉, 周晓松. 低能中子在锆中产生的辐照损伤的计算机模拟研究.  , 2013, 62(15): 156105. doi: 10.7498/aps.62.156105
    [16] 刘扬阳, 吕群波, 曾晓茹, 黄旻, 相里斌. 静态计算光谱成像仪图谱反演的关键数据处理技术.  , 2013, 62(6): 060203. doi: 10.7498/aps.62.060203
    [17] 王胜, 邹宇斌, 温伟伟, 李航, 刘树全, 王浒, 陆元荣, 唐国有, 郭之虞. 基于小型加速器的编码中子源成像研究.  , 2013, 62(12): 122801. doi: 10.7498/aps.62.122801
    [18] 杨昆, 刘新新, 李晓苇. 数据插值对正电子发射断层成像设备的图像重建影响的研究.  , 2013, 62(14): 147802. doi: 10.7498/aps.62.147802
    [19] 周光照, 王玉丹, 任玉琦, 陈灿, 叶琳琳, 肖体乔. 相干X射线衍射成像三维重建的数字模拟研究.  , 2012, 61(1): 018701. doi: 10.7498/aps.61.018701
    [20] 万 雄, 于盛林, 王长坤, 乐淑萍, 李冰颖, 何兴道. 多目标优化发射层析算法在等离子体场光谱诊断中的应用.  , 2004, 53(9): 3104-3113. doi: 10.7498/aps.53.3104
计量
  • 文章访问数:  6675
  • PDF下载量:  124
  • 被引次数: 0
出版历程
  • 收稿日期:  2020-05-07
  • 修回日期:  2020-06-04
  • 上网日期:  2020-06-13
  • 刊出日期:  2020-10-05

/

返回文章
返回
Baidu
map