INVESTIGATING THE EFFICACY OF INTEGRATED DETECTOR AND PHOTON COUNTING APPROACHES FOR BREAST LESION DECOMPOSITION IN BREAST CANCER
DOI:
https://doi.org/10.33003/fjs-2025-0905-3688Keywords:
Breast cancer, Photon counting, Integrated detector, Lesion decomposition, Diagnostic imagingAbstract
The World Health Organization (WHO) reports that breast cancer is the primary cancer diagnosis for women worldwide, accounting for 11.7% of new cancer cases. In Nigeria, 49% of women 36 and above are diagnosed with breast cancer, ranking Nigeria second in Africa. The National Cancer Control Programme (NCCP) seeks to improve patient survival through early diagnosis using various screening modalities, including Computed Tomography (CT). This study explores spectral decomposition techniques for lesion contrast enhancement in breast CT, building on previous research that highlights the benefits of CT over 2-D mammography. The work featured in this paper presents an idealized scenario of noiseless images devoid of scatter or photon noise to investigate the intrinsic characteristics of contrast in CT imaging. A 2-D breast phantom with a diameter of 100mm was built and employed in photon counting methodology to simulate breast lesions. Three distinct experiments were conducted across photon energies in bins of 20keV, 10keV, 5kev and examined spectrally. The decomposition lesion showed substantially greater contrast of about 70% within the energy spectrum of 1-60 keV in comparison to conventional integrating CT methodologies. Contrast values have been attained at lower energy bins, which agrees with the promising features of photon counting technology for early detection of lesions and correlate with the attenuation of glandular tissues. The photon counting technique has exhibited potential for the visualization of synthetic images in bins predicated on contrast analyses.
References
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Karrar, H., Nouh, M. A., Alanazi, B., Hakami, A. A., Alzahrani, W. J. S., Aldoweirah, W. K., Alanazi, W. S., Alshammari, N. E., Alanazi, B. M., Alharbi, R. abed, Alshammari, N. H.,Lawrence, R. A., and Liu, L. (2022). Anatomy of the Breast. https://doi.org/10.1016/B978-0-323-68013-4.00002-X
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Lloyd, M. C., Gatenby, R. A., and Brown, J. S. (2017). Ecology of the Metastatic Process. https://doi.org/10.1016/B978-0-12-804310-3.00011-9
Mani, D. (2022). Alveolar cells in the mammary gland: lineage commitment and cell death. Biochemical Journal. https://doi.org/10.1042/bcj20210734
McCullagh, P. (2022). Evaluation and comparison of a CdTe based photon counting detector with an energy integrating detector for X-ray phase sensitive imaging of breast cancer. Journal of X-Ray Science and Technology, 30(2), 207219. https://doi.org/10.3233/xst-211028
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Sartoretti, T., Wildberger, J. E., Flohr, T., & Alkadhi, H. (2023). Photon-counting detector CT: early clinical experience review. British Journal of Radiology. https://doi.org/10.1259/bjr.20220544
Schaeffer, C., Glick, S., and Ghammraoui, B. (2024). Spectral correction of photon-counting detectors in contrast-enhanced mammography using a convolutional neural network. https://doi.org/10.1117/12.3000734
Schaeffer, C. J., Ghammraoui, B., Taguchi, K., and Glick, S. J. (2022). Theoretical comparison and optimization of CdTe and GaAs photon-counting detectors for contrast-enhanced spectral mammography. Medical Imaging 2022: Physics of Medical Imaging, 12031,120310B. https://doi.org/10.1117/12.2611134
Shinohara, N., Nishihara, T., and Oguchi, S. (2024). Study of photon-counting images compared with energy-integrated images for mammography. Acta IMEKO, 13(4), 17. https://doi.org/10.21014/actaimeko.v13i4.1740
Siddig, A., Al-Astani Tengku Din, T. A. D., Mohd Nafi, S. N., Yahya, M. M., Sulong, S., and Wan Abdul Rahman, W. F. (2021). The Unique Biology behind the Early Onset of Breast Cancer.Genes. https://doi.org/10.3390/GENES12030372
Ye, D. H., Buzzard, G. T., Ruby, M., and Bouman, C. A. (2018, November 1). Deep back projection for sparse-view ct reconstruction.IEEE Global Conference on Signal and Information Processing. https://doi.org/10.1109/GLOBALSIP.2018.8646669
Alekseev, N.P. (2021). The Structure of the Lactating Mammary Gland of a Woman. https://doi.org/10.1007/978-3-030-66364-3_3
Alkadhi, H., and Runge, V. M. (2023). The Future Arrived: Photon-Counting Detector CT. Investigative Radiology. https://doi.org/10.1097/RLI.0000000000000985
Alzahrani, H. S., Alghamdi, A. S. A., and Alanazi, F. W. (2022). Clinical Aspects of Breast Cancer.World Family Medicine Journal /Middle East Journal of Family Medicine. https://doi.org/10.5742/mewfm.2022.9525148
Anggraeni, F. D., Sukartini, T., and Nihayati, H. E. (2023). Quality of life of breast cancer patients.Pediomaternal Nursing Journal. https://doi.org/10.20473/pmnj.v9i1.42249
Badal, A., Clark, M., and Ghammraoui, B. (2018). Reproducing two-dimensional mammograms with three-dimensional printed phantoms. Journal of Medical Imaging. https://doi.org/10.1117/1.JMI.5.3.033501
Barber, W. C., Wessel, J. C., Malakhov, N., Wawrzyniak, G., Hartsough, N. E., Noss-Ulseth, E., and Iwanczyk, J. S. (2015). Photon counting systems for breast imaging.Nuclear Science Symposium and Medical Imaging Conference. https://doi.org/10.1109/NSSMIC.2015.7582134
Biswas, S. K., Banerjee, S., Baker, G. W., Kuo, C.-Y., and Chowdhury, I. (2022). The Mammary Gland: Basic Structure and Molecular Signaling during Development. International Journal of Molecular Sciences. https://doi.org/10.3390/ijms23073883
Braitmaier, M., Kollhorst, B., Heinig, M., Langner, I., Czwikla, J., Heinze, F., Buschmann, L., Minner up, H., Garca-Albniz, X., Hense, H.-W., Karch, A., Zeeb, H., Haug, U., and Didelez, V. (2022). Effectiveness of Mammography Screening on Breast Cancer Mortality A Study Protocol for Emulation of Target Trials Using German Health Claims Data. Clinical Epidemiology. https://doi.org/10.2147/CLEP.S376107
Bulbul, A. A. M., Bulbul, A. A. M., Rahaman, H., Biswas, S., Hossain, B., and Nahid, A. A. (2020). Design and numerical analysis of a PCF-based bio-sensor for breast cancer cell detection in the THz regime.Sensing and Bio-Sensing Research. https://doi.org/10.1016/J.SBSR.2020.100388
Chernaya, A., Ulyanova, R., Bagnenko, S. S., Krivorotko, P., Artemyeva, A. S., Zhiltsova, E. K., Zhiltsova, Z., Novikov, S., Danilov, V., Krzhivitsky, P. I., Meshkova, I., and Zaitsev, A. N. (2023). ontrast enhanced spectral mammography as a tool for accurate diagnosis of cancer on the background of the dense breast tissue. Medicinska Vizualizaci. https://doi.org/10.24835/1607-0763-1152
Cierniak, R. (2010). A neural network optimization-based method of image reconstruction from projections. https://doi.org/10.1007/978-3-642-12145-6_25
Conti, B., Bochaton, A., Charreire, H., Kitzis-Bonsang, H., Desprs, C., Baffert, S., and Ngo, C. (2022). Influence of geographic access and socioeconomic characteristics on breast cancer outcomes: A systematic review. PLOS ONE. https://doi.org/10.1371/journal.pone.0271319
Edwin, L., Benjamin, P. F., Cristina, V. I., Christian, S.,Gavin, E. M., Elizabeth, R .W., Daniel, C., Grant, J. J., and Jianwei, M. (2008). Radiation dose reduction and image enhancement in biological imaging through equally-sloped tomography. Journal of structural biology, 164(2), 221227. https://doi.org/10.1016/j.jsb.2008.05.014
Elangovan, P., Mackenzie, A., Dance, D.R., Young, K.C., Cooke, V., Wilkinson, L., Given-Wilson, R, M., Wallis, M.G., and Wells, K.(2017). Design and Validation of realistic breast models for use in multiple alternative forced choice virtual clinical trials. Physics in medicine and biology, 62(7), 2778-2794. https://doi.org/10.1088/1361-6560/aa622c
Gao, Y., Chang, S.-H., Pomeroy, M. J., Li, L., & Liang, Z. J. (2023, April 7). Using virtual monoenergetic images in KarhunenLove domain to differentiate lesion pathology. https://doi.org/10.1117/12.2654390
Gomes, M. J. (2022). Photon-Counting Detectors in Computed Tomography: A Review.Journal of Health and Allied Sciences NU. https://doi.org/10.1055/s-0042-1749180
Gong, X.-T., Li, Q., Gu, L., Chen, C., Liu, X., Zhang, X., Wang, B., Sun, C., Yang, D., Li, L., and Wang, Y. (2023). Conventional ultrasound and contrast-enhanced ultrasound radiomics in breast cancer and molecular subtype diagnosis.Frontiers in Oncology. https://doi.org/10.3389/fonc.2023.1158736
Grigorievskaya, Z. V., Denchik, D. A., Khaylenko, D., Rumyantsev, A. A., and Petrovsky, A. (2023). The main causative agents of infectious complications during reconstructive surgery using allomaterials in patients with breast cancer. . https://doi.org/10.17650/2686-9594-2023-13-2-38-45
Hashem, L. M. B., Abd El Hamid, N. O., Kamal, R., Mansour, S., Lasheen, S., and Tohamey, Y. M. (2021). Does contrast-enhanced mammography have an impact on the detection of cancer in patients with risk of developing breast cancer?Egyptian Journal of Radiology and Nuclear Medicine. https://doi.org/10.1186/S43055-021-00447-8
Ikubor, J. E., and Tobi, A. C. (2022). Estimation of lifetime attributable risk incidence and mortality for breast cancer attributed to computed tomography of the head in the Niger Delta Region of Nigeria.The Egyptian Journal of Radiology and Nuclear Medicine. https://doi.org/10.1186/s43055-022-00786-0
Karrar, H., Nouh, M. A., Alanazi, B., Hakami, A. A., Alzahrani, W. J. S., Aldoweirah, W. K., Alanazi, W. S., Alshammari, N. E., Alanazi, B. M., Alharbi, R. abed, Alshammari, N. H.,Lawrence, R. A., and Liu, L. (2022). Anatomy of the Breast. https://doi.org/10.1016/B978-0-323-68013-4.00002-X
Lee, S. C., Tchelepi, H., Khadem, N., Desai, B., Yamashita, M., and Hovanessian-Larsen, L. (2022). Imaging of Benign and Malignant Breast Lesions Using Contrast-Enhanced Ultrasound.Ultrasound Quarterly. https://doi.org/10.1097/RUQ.0000000000000574
Lloyd, M. C., Gatenby, R. A., and Brown, J. S. (2017). Ecology of the Metastatic Process. https://doi.org/10.1016/B978-0-12-804310-3.00011-9
Mani, D. (2022). Alveolar cells in the mammary gland: lineage commitment and cell death. Biochemical Journal. https://doi.org/10.1042/bcj20210734
McCullagh, P. (2022). Evaluation and comparison of a CdTe based photon counting detector with an energy integrating detector for X-ray phase sensitive imaging of breast cancer. Journal of X-Ray Science and Technology, 30(2), 207219. https://doi.org/10.3233/xst-211028
Nakamura, Y. A., Higaki, T., Kondo, S., Kawashita, I., Takahashi, I., and Awai, K. (2022). An introduction to photon-counting detector CT (PCD CT) for radiologists.Japanese Journal of Radiology. https://doi.org/10.1007/s11604-022-01350-6
Olasehinde, O., Alatise, O. I., Omisore, A. D., Wuraola, F., Odujoko, O. O., Romanoff, A., Akinkuolie, A. A., Arowolo, O., Adisa, A. O., Knapp, G. C., Famurewa, O. C., Omisile, I., Onabanjo, E., Constable, J., Go, O.-E., Adesunkanmi, A.-R. K., Lawal, O. O., and Kingham, T. P. (2021). Contemporary management of breast cancer in Nigeria: Insights from an institutional database.International Journal of Cancer. https://doi.org/10.1002/IJC.33484
Prevalence of women breast cancer. (2023). Cellular, Molecular and Biomedical Reports. https://doi.org/10.55705/cmbr.2023.384467.1095
Sartoretti, T., Wildberger, J. E., Flohr, T., & Alkadhi, H. (2023). Photon-counting detector CT: early clinical experience review. British Journal of Radiology. https://doi.org/10.1259/bjr.20220544
Schaeffer, C., Glick, S., and Ghammraoui, B. (2024). Spectral correction of photon-counting detectors in contrast-enhanced mammography using a convolutional neural network. https://doi.org/10.1117/12.3000734
Schaeffer, C. J., Ghammraoui, B., Taguchi, K., and Glick, S. J. (2022). Theoretical comparison and optimization of CdTe and GaAs photon-counting detectors for contrast-enhanced spectral mammography. Medical Imaging 2022: Physics of Medical Imaging, 12031,120310B. https://doi.org/10.1117/12.2611134
Shinohara, N., Nishihara, T., and Oguchi, S. (2024). Study of photon-counting images compared with energy-integrated images for mammography. Acta IMEKO, 13(4), 17. https://doi.org/10.21014/actaimeko.v13i4.1740
Siddig, A., Al-Astani Tengku Din, T. A. D., Mohd Nafi, S. N., Yahya, M. M., Sulong, S., and Wan Abdul Rahman, W. F. (2021). The Unique Biology behind the Early Onset of Breast Cancer.Genes. https://doi.org/10.3390/GENES12030372
Ye, D. H., Buzzard, G. T., Ruby, M., and Bouman, C. A. (2018, November 1). Deep back projection for sparse-view ct reconstruction.IEEE Global Conference on Signal and Information Processing. https://doi.org/10.1109/GLOBALSIP.2018.8646669
Published
2025-05-31
How to Cite
Alumuku, L., & Iortile, J. T. (2025). INVESTIGATING THE EFFICACY OF INTEGRATED DETECTOR AND PHOTON COUNTING APPROACHES FOR BREAST LESION DECOMPOSITION IN BREAST CANCER. FUDMA JOURNAL OF SCIENCES, 9(5), 351 - 361. https://doi.org/10.33003/fjs-2025-0905-3688
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Research Articles
FUDMA Journal of Sciences
