• Choirul Anam Department of Physics, Faculty of Sciences and Mathematics, Diponegoro University, 50275, Central Java, Indonesia
  • Riska Amilia Department of Physics, Faculty of Sciences and Mathematics, Diponegoro University, 50275, Central Java, Indonesia
  • Ariij Naufal Department of Physics, Faculty of Sciences and Mathematics, Diponegoro University, 50275, Central Java, Indonesia
  • Geoff Dougherty Department of Applied Physics and Medical Imaging, California State University Channel Islands, Camarillo, CA 93012, USA



CT number linearity, Catphan phantom, CT scan, image quality


This study aims to develop an algorithm for automatically measuring CT number linearity in three different types of Catphan phantom. We used a sensitometry module image from three Catphan phantoms (types 500, 504, and 604). Each phantom and its air material were segmented. Based on the centroid of the air material, the coordinates for every object within the sensitometry modules were determined. The average CT numbers for every object were calculated and graphs of CT number linearity were automatically generated. Accurate segmentation of each object in the sensitometry modules produced accurate graphs of CT number linearity for each phantom. The linear regression of the Catphan 604 failed to pass the tolerance level, while the other two phantoms passed with R2 > 0.99. The automatic CT number linearity measurements were easy, fast, and more objective than manual measurements.  


Pola, A., Corbella, D., Righini, A., Torresin, A., Colombo, P. E., Vismara, L., Trombetta, L., Maddalo, M., Introini, M. V., Tinelli, D., Strohmenger, L., Garattini, G., Munari, A., & Triulzi, F. 2018. Computed Tomography Use in a Large Italian Region: Trend Analysis 2004-2014 of Emergency and Outpatient CT Examinations in Children and Adults. European Radiology. 28(6): 2308-2318.

Kim, N. T., Kwon, S. S., Park, M. S., Lee, K. M., & Sung, K. H. 2022. National Trends in Pediatric CT Scans in South Korea: A Nationwide Cohort Study. Taehan Yongsang Uihakhoe chi. 83(1): 138-148.

Huang, C. C., Effendi, F. F., Kosik, R. O., Lee, W. J., Wang, L. J., Juan, C. J., & Chan, W. P. 2023. Utilization of CT and MRI Scanning in Taiwan, 2000-2017. Insights into Imaging. 14(1): 23.

Smith-Bindman, R., Kwan, M. L., Marlow, E. C., Theis, M. K., Bolch, W., Cheng, S. Y., Bowles, E. J. A., Duncan, J. R., Greenlee, R. T., Kushi, L. H., Pole, J. D., Rahm, A. K., Stout, N. K., Weinmann, S., & Miglioretti, D. L. 2019. Trends in Use of Medical Imaging in US Health Care Systems and in Ontario, Canada, 2000-2016. JAMA. 322(9): 843-856.

Kwan, M. L., Miglioretti, D. L., Marlow, E. C., Aiello Bowles, E. J., Weinmann, S., Cheng, S. Y., Deosaransingh, K. A., Chavan, P., Moy, L. M., Bolch, W. E., Duncan, J. R., Greenlee, R. T., Kushi, L. H., Pole, J. D., Rahm, A. K., Stout, N. K., Smith-Bindman, R., & Radiation-Induced Cancers Study Team. 2019. Trends in Medical Imaging During Pregnancy in the United States and Ontario, Canada, 1996 to 2016. JAMA Network Open. 2(7): e197249.

Seeram E. 2018. Computed Tomography: A Technical Review. Radiologic Technology. 89(3): 279CT-302CT.

Seeram E. 2014. Computed Tomography Dose Optimization. Radiologic Technology. 85(6): 655CT-675CT.

Alresheedi, N., Walton, L., Hogg, P., Webb, J., & Tootell, A. 2021. Evaluation of X-ray Table Mattresses for Radiation Attenuation and Impact on Image Quality. Radiography (London, England: 1995). 27(1): 215-220.

Ma, X., Figl, M., Unger, E., Buschmann, M., & Homolka, P. 2022. X-ray Attenuation of Bone, Soft and Adipose Tissue in CT from 70 to 140 kV and Comparison with 3D Printable Additive Manufacturing Materials. Scientific Reports. 12(1): 14580.

Kunert, P., Trinkl, S., Giussani, A., Reichert, D., & Brix, G. 2022. Tissue Equivalence of 3D Printing Materials with Respect to Attenuation and Absorption of X-rays used for Diagnostic and Interventional Imaging. Medical Physics. 49(12): 7766-7778.

DenOtter TD, Schubert J. Hounsfield Unit. [Updated 2023 Mar 6]. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing.

Ai, H. A., Meier, J. G., & Wendt, R. E. 2018. HU Deviation in Lung and Bone Tissues: Characterization and a Corrective Strategy. Medical Physics. 45(5): 2108-2118.

Levine, Z. H., Peskin, A. P., Holmgren, A. D., & Garboczi, E. J. 2018. Preliminary X-ray CT Investigation to Link Hounsfield Unit Measurements with the International System of Units (SI). PloS One. 13(12): e0208820.

Egashira, R., & Raghu, G. 2022. Quantitative Computed Tomography of the Chest for Fibrotic Lung Diseases: Prime Time for Its Use in Routine Clinical Practice? Respirology (Carlton, Vic.). 27(12): 1008-1011.

Ito, K., Kondo, T., Andreu-Arasa, V. C., Li, B., Hirahara, N., Muraoka, H., Sakai, O., & Kaneda, T. 2022. Quantitative Assessment of the Maxillary Sinusitis using Computed Tomography Texture Analysis: Odontogenic vs Non-odontogenic Etiology. Oral Radiology. 38(3): 315-324.

Zhou, G., Li, F., Wang, K., Lin, X., & Yu, X. 2020. Research on a Quantitative Method for Three-dimensional Computed Tomography of Chemiluminescence. Applied Optics. 59(17): 5310-5318.

Title, R. S., Harper, K., Nelson, E., Evans, T., Tello R. 2005. Observer Performance in Assessing Anemia on Thoracic CT. AJR Am J Roentgenol. 185(5): 1240-1244.

Jung, C., Groth, M., Bley, T. A., Henes, F. O., Treszl, A., Adam, G., & Bannas, P. 2012. Assessment of Anemia during CT Pulmonary Angiography. European Journal of Radiology. 81(12): 4196-4202.

Zhou, Q. Q., Yu, Y. S., Chen, Y. C., Ding, B. B., Fang, S. Y., Yang, X., Zhang, B., & Zhang, H. 2018. Optimal Threshold for the Diagnosis of Anemia Severity on Unenhanced Thoracic CT: A Preliminary Study. European Journal of Radiology. 108: 236-241.

Kwan, A. C., Gransar, H., Tzolos, E, et al. Kwan, A. C., Gransar, H., Tzolos, E., Chen, B., Otaki, Y., Klein, E., Pope, A. J., Han, D., Howarth, A., Jain, N., Dey, D., Miller, R. J., Cheng, V., Azarbal, B., & Berman, D. S. 2021. The Accuracy of Coronary CT Angiography in Patients with Coronary Calcium Score above 1000 Agatston Units: Comparison with Quantitative Coronary Angiography. Journal of Cardiovascular Computed Tomography. 15(5): 412-418.

Gatti, M., Gallone, G., Poggi, V., Bruno, F., Serafini, A., Depaoli, A., De Filippo, O., Conrotto, F., Darvizeh, F., Faletti, R., De Ferrari, G. M., Fonio, P., & D'Ascenzo, F. 2022. Diagnostic Accuracy of Coronary Computed Tomography Angiography for the Evaluation of Obstructive Coronary Artery Disease in Patients Referred for Transcatheter Aortic Valve Implantation: A Systematic Review and Meta-Analysis. European Radiology. 32(8).

Guan, X., Yao, L., Tan, Y., Shen, Z., Zheng, H., Zhou, H., Gao, Y., Li, Y., Ji, W., Zhang, H., Wang, J., Zhang, M., & Xu, X. 2021. Quantitative and Semi-quantitative CT Assessments of Lung Lesion Burden in COVID-19 Pneumonia. Scientific Reports. 11(1): 5148.

Lu, W., Wei, J., Xu, T., Ding, M., Li, X., He, M., Chen, K., Yang, X., She, H., & Huang, B. 2021. Quantitative CT for Detecting COVID 19 Pneumonia in Suspected Cases. BMC Infectious Diseases. 21(1): 836.

Zhang, K., Liu, X., Shen, J., Li, Z., Sang, Y., Wu, X., Zha, Y., Liang, W., Wang, C., Wang, K., Ye, L., Gao, M., Zhou, Z., Li, L., Wang, J., Yang, Z., Cai, H., Xu, J., Yang, L., Cai, W., … Wang, G. (2020). Clinically Applicable AI System for Accurate Diagnosis, Quantitative Measurements, and Prognosis of COVID-19 Pneumonia Using Computed Tomography. Cell. 182(5): 1360.

Thakur, S. K., Singh, D. P., & Choudhary, J. 2020. Lung Cancer Identification: A Review on Detection and Classification. Cancer Metastasis Reviews. 39(3): 989-998.

Mehnati, P., Jafari, T. M., & Ghavami, M. 2020. CT Role in the Assessment of Existence of Breast Cancerous Cells. Journal of Biomedical Physics & Engineering. 10(3): 349-356.

Suyudi, I., Anam, C., Sutanto, H., Triadyaksa, P., & Fujibuchi, T. 2020. Comparisons of Hounsfield Unit Linearity between Images Reconstructed using an Adaptive Iterative Dose Reduction (AIDR) and a Filter Back-Projection (FBP) Techniques. Journal of Biomedical Physics & Engineering. 10(2): 215-224.

Roa, A. M., Andersen, H. K., & Martinsen, A. C. 2015. CT Image Quality Over Time: Comparison of Image Quality for Six Different CT Scanners Over a Six-year Period. Journal of Applied Clinical Medical Physics. 16(2): 4972.

Nute, J. L., Rong, J., Stevens, D. M., Darensbourg, B. J., Cheng, J., Wei, W., Hobbs, B. P., & Cody, D. D. 2013. Evaluation of over 100 Scanner-years of Computed Tomography Daily Quality Control Data. Medical Physics. 40(5): 051908.

Alikhani, B., Jamali, L., Raatschen, H. J., Wacker, F., & Werncke, T. 2017. Impact of CT Parameters on the Physical Quantities Related to Image Quality for Two MDCT Scanners using the ACR Accreditation Phantom: A Phantom Study. Radiography (London, England: 1995). 23(3): 202-210.

Mansour, Z., Mokhtar, A., Sarhan, A., Ahmed, M. T., El-Diasty, T. 2016. Quality Control of Image using American College of Radiology (ACR) Phantom. Egypt J Radiol Nucl Med. 47(4): 1665-1671.

Nowik, P., Bujila, R., Poludniowski, G., & Fransson, A. 2015. Quality Control of CT Systems by Automated Monitoring of Key Performance Indicators: A Two-Year Study. Journal of Applied Clinical Medical Physics. 16(4): 254-265.

An, H. J., Son, J., Jin, H., Sung, J., Chun, M. 2019. Acceptance Test and clinical commissioning of CT simulator. Prog Med Phys. 30(4): 160

Njiki, C. , Ndjaka Manyol, J. , Ebele Yigbedeck, Y. , Abou’ou, D., Yimele, B. and Sabouang, J. 2018. Assessment of Image Quality Parameters for Computed Tomography in the City of Yaoundé. Open Journal of Radiology. 8: 37-44.

Garayoa, J., & Castro, P. 2013. A Study on Image Quality Provided by a Kilovoltage Cone-beam Computed Tomography. Journal of Applied Clinical Medical Physics. 14(1): 3888.

Won, H. S., Chung, J. B., Choi, B. D., Park, J. H., & Hwang, D. G. 2016. Accuracy of Automatic Matching of Catphan 504 Phantom in Cone-beam Computed Tomography for Tube Current-exposure Time Product. Journal of Applied Clinical Medical Physics. 17(6): 421-428.

The Phantom Laboratory. Smari Image Analysis.

QA Benchmark. AutoQA Plus CT.

Karius, A., & Bert, C. 2022. QAMaster: A New Software Framework for Phantom-based Computed Tomography Quality Assurance. Journal Of Applied Clinical Medical Physics. 23(4): e13588.

Standard Imaging. QA Pilot.

Medical Physics Perth. CT Quality Inspector.

Anam, C., Naufal, A., Fujibuchi, T., Matsubara, K., & Dougherty, G. 2022. Automated Development of the Contrast-detail Curve based on Statistical Low-contrast Detectability in CT Images. Journal of Applied Clinical Medical Physics. 23(9): e13719.

Anam, C., Amilia, R., Naufal, A., Budi, W. S., Maya, A. T., & Dougherty, G. 2022. The Automated Measurement of CT Number Linearity using an ACR Accreditation Phantom. Biomedical Physics & Engineering Express. 9(1): 10.1088/2057-1976/aca9d5.

Regulation of the nuclear energy supervisory agency of the Republic of Indonesia number 2 concerning the conformity test of diagnostic and interventional radiology x-ray machine Nuclear Energy Regulatory Agency of Indonesia 2018.

Hatton, J., McCurdy, B., & Greer, P. B. 2009). Cone Beam Computerized Tomography: The Effect of Calibration of the Hounsfield Unit Number to Electron Density on Dose Calculation Accuracy for Adaptive Radiation Therapy. Physics in Medicine and Biology. 54(15): N329-N346.

Guan, H., & Dong, H. 2009. Dose Calculation Accuracy using Cone-beam CT (CBCT) for Pelvic Adaptive Radiotherapy. Physics in Medicine and Biology. 54(20): 6239-6250.

Institute of Physics and Engineering in Medicine. 2003. Measurements of the Performance Characteristics of Diagnostic x-ray Systems Used in Medicine. Part III. Computed Tomography x-ray Scanners. IPEM Report 32. 2nd ed. York: IPEM. 2003: 1-98.

Afifi, M. B., Abdelrazek, A., Deiab, N. A., Abd El-Hafez, A. I., El-Farrash, A. H. 2020. The Effects of CT x-ray Tube Voltage and Current Variations on the Relative Electron Density (RED) and CT Number Conversion Curves. Journal of Radiation Research and Applied Sciences. 13(1): 1-11.

Ohno, Y., Fujisawa, Y., Fujii, K., Sugihara, N., Kishida, Y., Seki, S., & Yoshikawa, T. 2019. Effects of Acquisition Method and Reconstruction Algorithm for CT Number Measurement on Standard-dose CT and Reduced-dose CT: A QIBA Phantom Study. Japanese Journal of Radiology. 37(5): 399-411.

Gierada, D. S., Bierhals, A. J., Choong, C. K., Bartel, S. T., Ritter, J. H., Das, N. A., Hong, C., Pilgram, T. K., Bae, K. T., Whiting, B. R., Woods, J. C., Hogg, J. C., Lutey, B. A., Battafarano, R. J., Cooper, J. D., Meyers, B. F., & Patterson, G. A. 2010. Effects of CT Section Thickness and Reconstruction Kernel on Emphysema Quantification Relationship to the Magnitude of the CT Emphysema Index. Academic Radiology. 17(2): 146-156.






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