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Comparison of noise-power spectrum and modulation-transfer function for CT images reconstructed with iterative and deep learning image reconstructions: An initial experience study

Setiawan Adiwasono M. B., Anam Choirul, Hidayanto Eko, Sutanto Heri, Naufal Ariij, Dougherty Geoff

Polish Journal of Medical Physics and Engineering

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2023

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Vol. 29, Iss. 2

104--112

EN

Deep learning image reconstruction (DLIR) is a very recent image reconstruction method that is already available for commercial use. We evaluated the quality of DLIR images and compared it to the quality of images from the latest adaptive statistical iterative reconstruction (ASIR-V) algorithm in terms of noise-power spectrum (NPS) and modulation-transfer function (MTF). We scanned a Revolution QA phantom (GE Healthcare, USA) and a 20 cm water phantom (GE Healthcare, USA) with our 512 multi-slice computed tomography (CT) scanner. Images of the tungsten wire within the Revolution QA phantom were reconstructed with a 50 mm field of view (FOV). The images were reconstructed with various ASIR-V strengths (i.e. 0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100%) and DLIRs (i.e. low, medium, and high) to assess the MTF. The images from the 20 cm water phantom were reconstructed with the same configuration to assess the NPS. The MTF was similar for both reconstruction algorithms of DLIR and ASiR-V. The peak frequency (fp) of the DLIR low was comparable to that from ASIR-V at 50, 60, 70%; the DLIR medium was comparable to ASIR-V at 80%; and the DLIR high was comparable to ASIR-V at 100%. The average frequency (fA) of the DLIR low was comparable to that from ASIR-V at 40%; the DLIR medium was comparable to ASIR-V at 50%; and the DLIR high was comparable to ASIR-V at 70%. Both the DLIR and ASIR-V were able to reduce noise, but they had a different texture. The noise in the DLIR images was more homogenous at high and low frequencies, while in the ASIR-V images, the noise was more concentrated at high frequencies. The MTF was similar for both reconstruction algorithms. The DLIR method showed a better noise reduction than the ASIR-V reconstruction.

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Automation of slice thickness measurements in computed tomography images of AAPM CT performance phantom using a non-rotational method

Ximenes Angelita D., Anam Choirul, Hidayanto Eko, Naufal Ariij, Rukmana Dito A., Dougherty Geoff

Polish Journal of Medical Physics and Engineering

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2022

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Vol. 28, Iss. 3

133--138

EN

Purpose: The current study proposes a method for automatically measuring slice thickness using a non-rotational method on the middle stair object of the AAPM CT performance phantom image. Method: The AAPM CT performance phantom was scanned by a GE Healthcare 128-slice CT scanner with nominal slice thicknesses of 0.625, 1.25, 2.5, 3.75, 5, 7.5 and 10 mm. The automated slice thickness was measured as the full width at half maximum (FWHM) of the profile of the middle stair object using a non-rotational method. The non-rotational method avoided rotating the image of the phantom. Instead, the lines to make the profiles were automatically rotated to confirm the stair’s location and rotation. The results of this non-rotational method were compared with those from a previous rotational method. Results: The slice thicknesses from the non-rotational method were 1.55, 1.86, 3.27, 4.86, 6.58, 7.57, and 9.66 mm for nominal slice thicknesses of 0.625, 1.25, 2.4, 3.75, 5, 7.5, and 10 mm, respectively. By comparison, the slice thicknesses from the rotational method were 1.53, 1.87, 3.32, 4.98, 6.77, 7.75, and 9.80 mm, respectively. The results of the non- rotational method were slightly lower (i.e. 0.25%) than the results of the rotational method for each nominal slice thickness, except for the smallest slice thickness. Conclusions: An alternative algorithm using a non-rotational method to measure the slice thickness of the middle stair object in the AAPM CT performance phantom was successfully implemented. The slice thicknesses from the non- rotational method results were slightly lower than the rotational method results for each nominal slice thickness, except at the smallest nominal slice thickness (0.625 mm).

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Automated patient centering of computed tomography images and its implementation to evaluate clinical practices in three hospitals in Indonesia

Anam Choirul, Amilia Riska, Naufal Ariij, Adi Kusworo, Sutanto Heri, Budi Wahyu S., Arifin Zaenal, Dougherty Geoff

Polish Journal of Medical Physics and Engineering

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2022

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Vol. 28, Iss. 4

207--214

EN

Purpose: This study aims to develop a software tool for investigating patient centering profiles of axial CT images and to implement it to evaluate practices in three hospitals in Indonesia. Methods: The evaluation of patient centering accuracy was conducted by comparing the center coordinate of the patient’s image to the center coordinates of the axial CT image. This process was iterated for all slices to yield an average patient mis-centering in both the x- and y-axis. We implemented the software to evaluate the profile of centering on 268 patient images from the head, thorax, and abdomen examinations taken from three hospitals. Results: We found that 82% of patients were mis-centered in the y-axis (i.e., placed more than 5 mm from the iso-center), with 49% of patients placed 10–35 mm from the iso-center. Most of the patients had a tendency to be placed below the iso-centers. In head examinations, patients were more precisely positioned than in the other examinations. We did not find any significant difference in mis-centering between males and females. We found that there was a slight difference between mis-centering in adult and pediatric patients. Conclusion: Software for automated patient centering was successfully developed. Patients in three hospitals in Indonesia had a tendency to be placed under the iso-center of the gantry.