Quantum 3D FFT in Tomography

被引:0
作者
Koukiou, Georgia [1 ]
Anastassopoulos, Vassilis [1 ]
机构
[1] Univ Patras, Phys Dept, Elect Lab, Patras 26504, Greece
来源
APPLIED SCIENCES-BASEL | 2023年 / 13卷 / 06期
关键词
Quantum Fourier Transform; quantum circuits; tomography; radon transform; quantum 3D back projection; ALGORITHMS;
D O I
10.3390/app13064009
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
The Radon transform constitutes the conventional tool for tomosynthesis, i.e., the composition of cross-sections of an object from its projections. It is actually a version of the Fourier Transform, which is accompanied by the appropriate digital high pass filters for correct distribution of energy among the reconstructed frequency components. The Radon transform and its inverse are employed in their 2D and 3D versions, respectively, and the whole procedure is verified by the a priori known cross-sections to be reconstructed (known fandom). Usually, 3D medical image cubes, which are to be reconstructed, require powerful computational tools since the 2D projections are of high-resolution containing millions of pixels. Although the 3D FFT is very fast, the large number of projections will result in a 3D spectrum of very large dimensions. Inverting this spectrum with the inverse 3D FFT is extremely time consuming. In this work, the implementation of the 2D Radon transform using the 2D Quantum Fourier Transform is analytically presented. Simultaneously, its inverse version is realized by means of the Quantum inverse 3D FFT. For this purpose, a review of the necessary quantum computational units is presented for the implementation of the quantum 3D FFT and simultaneously simple examples of tomosynthesis are given by means of the quantum version of the 2D Radon transform and its inverse 3D counterpart. The whole procedure of the quantum tomosynthesis is analytically described.
引用
收藏
页数:15
相关论文
共 37 条
[1]   Atomic-scale imaging of a 27-nuclear-spin cluster using a quantum sensor [J].
Abobeih, M. H. ;
Randall, J. ;
Bradley, C. E. ;
Bartling, H. P. ;
Bakker, M. A. ;
Degen, M. J. ;
Markham, M. ;
Twitchen, D. J. ;
Taminiau, T. H. .
NATURE, 2019, 576 (7787) :411-+
[2]  
[Anonymous], 2020, quantum fourier transform, grovers algorithm, and the
[3]   Quantum circuit for the fast Fourier transform [J].
Asaka, Ryo ;
Sakai, Kazumitsu ;
Yahagi, Ryoko .
QUANTUM INFORMATION PROCESSING, 2020, 19 (08)
[4]   Approximate quantum Fourier transform and decoherence [J].
Barenco, A ;
Ekert, A ;
Suominen, KA ;
Torma, P .
PHYSICAL REVIEW A, 1996, 54 (01) :139-146
[5]   Quantum Image Filtering in the Frequency Domain [J].
Caraiman, Simona ;
Manta, Vasile I. .
ADVANCES IN ELECTRICAL AND COMPUTER ENGINEERING, 2013, 13 (03) :77-84
[6]  
Coppersmith D., 2002, arXiv
[7]  
Divincenzo D.P., 1998, ARXIV, DOI [10.1098/rspa.1998.0159, DOI 10.1098/RSPA.1998.0159]
[8]   Twisted photons: new quantum perspectives in high dimensions [J].
Erhard, Manuel ;
Fickler, Robert ;
Krenn, Mario ;
Zeilinger, Anton .
LIGHT-SCIENCE & APPLICATIONS, 2018, 7 :17146-17146
[9]   Scalability of Shor's algorithm with a limited set of rotation gates [J].
Fowler, AG ;
Hollenberg, LCL .
PHYSICAL REVIEW A, 2004, 70 (03) :032329-1
[10]   Computational coherent imaging by rotating a cylindrical lens [J].
Geng, Yong ;
Tan, Jiubin ;
Guo, Cheng ;
Shen, Cheng ;
Ding, Weiqiang ;
Liu, Shutian ;
Liu, Zhengjun .
OPTICS EXPRESS, 2018, 26 (17) :22110-22122