Quantitative Assessment of Motion Consistency in Projection Data of Chest Tomosynthesis

PDF (1593KB), PP.314-324

Views: 0 Downloads: 0

Author(s)

Oleksandra Miroshnychenko 1,* Yurii Khobta 2 Sergii Miroshnychenko 3 Andrii Nevgasymyi 4

1. Dept of ERMIT, National University "Kyiv Aviation Institute", Kyiv, Ukraine

2. Deprt of Design and Development, Teleoptic PRC, ltd, Kyiv, Ukraine

3. Dept of Design and Development, Teleoptika PRA, llc, Kyiv region, Ukraine

4. Dept of Design and Development, Teleoptic PRC, ltd, Kyiv, Ukraine

* Corresponding author.

DOI: https://doi.org/10.5815/ijem.2026.03.19

Received: 24 Jan. 2026 / Revised: 15 Mar. 2026 / Accepted: 9 May 2026 / Published: 8 Jun. 2026

Index Terms

Chest tomosynthesis, projection data, temporal consistency, image reconstruction, region of interest, intensity analysis, motion effects

Abstract

Chest digital tomosynthesis (DTS) provides a compromise between conventional radiography and computed tomography in terms of radiation dose and diagnostic capability. Most reconstruction algorithms used in DTS assume a stationary object during acquisition. However, projection data are acquired over a finite time interval, during which internal anatomical structures may exhibit temporal variability. In this study, projection-domain intensity variations were analyzed to assess temporal consistency of DTS data. Mean intensity values were measured across multiple regions of interest (ROIs), forming temporal intensity profiles for anatomically distinct regions. Additionally, intensity profiles across anatomical transitions were evaluated in both projection data and reconstructed slices. The results show that while global intensity variations are primarily driven by acquisition geometry, certain regions exhibit local fluctuations, indicating reduced temporal consistency. Comparative analysis revealed that regions with increased variability correspond to degraded contrast and broadened transition boundaries in reconstructed slices. In particular, the heart–lung interface showed a significant contrast reduction compared to the stomach–lung interface, despite similar contrast levels in projection images. These findings indicate that even small temporal inconsistencies in projection data can lead to cumulative reconstruction errors. The proposed ROI-based analysis provides a simple approach for identifying such regions directly from projection data and suggests directions for improving reconstruction quality, including more consistent 3-D reconstruction of cardiac slices across different phases of the cardiac cycle.

Cite This Paper

Oleksandra Miroshnychenko, Yurii Khobta, Sergii Miroshnychenko, Andrii Nevgasymyi, "Quantitative Assessment of Motion Consistency in Projection Data of Chest Tomosynthesis", International Journal of Engineering and Manufacturing (IJEM), Vol.16, No.3, pp.314-324, 2026. DOI:10.5815/ijem.2026.03.19

Reference

[1]J. T. Dobbins III, “Tomosynthesis Imaging: Recent Advances and Clinical Applications,” Med. Phys., vol. 45, no. 6, pp. e639–e654, 2018. doi: 10.1002/mp.12957.
[2]O. Miroshnychenko, S. Miroshnychenko, A. Nevgasymyi, R. Panteyev, D. Radko, “The Concept of a Mobile X-Ray System with Tomosynthesis,” Proc. IEEE Int. Conf. Electronics and Nanotechnology (ELNANO), 2024, pp. 452–455.
[3]A. Kumar, S. Singh, “Contrast Enhancement Techniques for Medical Images: A Review,” Int. J. Image, Graphics and Signal Processing (IJIGSP), MECS Press, vol. 10, no. 3, pp. 1–10, 2018. doi: 10.5815/ijigsp.2018.03.01.
[4]H. Zhao, G. Wang, “Motion Compensation in Tomosynthesis Reconstruction: A Review,” Med. Phys., vol. 45, no. 3, pp. e168–e182, 2018. doi: 10.1002/mp.12712.
[5]O. Miroshnychenko, S. Miroshnychenko, Y. Khobta, A. Nevgasymyi, “Assessment of Compliance of Digital X-ray Tomosynthesis Images with Image Quality Requirements,” Proc. IEEE ELNANO, 2022, pp. 401–404.
[6]O. Miroshnychenko, S. Miroshnychenko, Y. Khobta, A. Nevgasymyi, “Contrast Comparison of Chest Pathologies in Radiography and Tomosynthesis,” Proc. ISETC, 2020.
[7]M. A. Sattar, S. Lee, “A Review of Image Reconstruction Techniques in Medical Imaging,” Int. J. Information Technology and Computer Science (IJITCS), MECS Press, vol. 9, no. 1, pp. 1–10, 2017. doi: 10.5815/ijitcs.2017.01.01.
[8]I. Sechopoulos, S. Vedantham, “Digital Breast Tomosynthesis: Current State and Future Perspectives,” Med. Phys., vol. 46, no. 12, pp. e913–e928, 2019. doi: 10.1002/mp.13701.
[9]Y. Zhang, H. Yu, G. Wang, “A Review of Computational Algorithms for Tomographic Imaging,” IEEE Trans. Med. Imaging, vol. 37, no. 2, pp. 517–530, 2018. doi: 10.1109/TMI.2017.2766876.
[10]R. Sharma, P. Gupta, “Region of Interest-Based Medical Image Analysis: Recent Advances,” Int. J. Image, Graphics and Signal Processing (IJIGSP), MECS Press, vol. 9, no. 5, pp. 12–20, 2017. doi: 10.5815/ijigsp.2017.05.02.
[11]K. Verma, A. Jain, “Noise Analysis and Reduction Techniques in Medical Imaging,” Int. J. Intelligent Systems and Applications (IJISA), MECS Press, vol. 9, no. 9, pp. 45–52, 2017. doi: 10.5815/ijisa.2017.09.05.
[12]J. Granstedt et al., “Clinical Evaluation of Chest Tomosynthesis for Pulmonary Nodule Detection,” Eur. Radiol., vol. 27, no. 12, pp. 4979–4987, 2017. doi: 10.1007/s00330-017-4851-1.
[13]M. Båth et al., “Improved Detection of Pulmonary Nodules with Digital Chest Tomosynthesis,” Eur. Radiol., vol. 26, no. 5, pp. 1342–1349, 2016. doi: 10.1007/s00330-015-3922-7.
[14]J. T. Dobbins III, “Advanced Tomosynthesis Imaging Techniques and Clinical Applications,” Med. Phys., vol. 45, no. 6, pp. e639–e654, 2018.
[15]S. Patel, R. Mehta, “Medical Image Processing Techniques for Diagnosis: A Survey,” Int. J. Intelligent Systems and Applications (IJISA), MECS Press, vol. 10, no. 2, pp. 1–9, 2018. doi: 10.5815/ijisa.2018.02.01.
[16]I. Sechopoulos, “Advances in Digital Breast Tomosynthesis Imaging,” Med. Phys., vol. 47, no. 3, pp. e102–e115, 2020. doi: 10.1002/mp.13970.Shor, P. W. (1999). Polynomial-time algorithms for prime factorization and discrete logarithms on a quantum computer. SIAM Journal on Computing, 26(5), 1484-1509. DOI: 10.1137/S0097539791191494