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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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The effective and water-equivalent diameters as geometrical size functions for estimating CT dose in the thoracic, abdominal, and pelvic regions

Dewi Winda Kusuma, Anam Choirul, Hidayanto Eko, Nitasari Arrum, Dougherty Geoff

Polish Journal of Medical Physics and Engineering

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2021

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Vol. 27, Iss. 3

213--222

EN

Purpose: The aim of this work was to establish the relationships of patient size in terms of effective diameter (Deff) and water-equivalent diameter (Dw) with lateral (LAT) and anterior-posterior (AP) dimensions in order to predict the specific patient dose for thoracic, abdominal, and pelvic computed tomography (CT) examinations. Methods: A total of 47 thoracic images, 79 abdominal images, and 50 pelvic images were analyzed in this study. The patient’s images were retrospectively collected from Dr. Kariadi and Kensaras Hospitals, Semarang, Indonesia. The slices measured were taken from the middle of the scan range. The calculations of patient sizes (LAT, AP, Deff, and Dwf) were automatically performed by IndoseCT 20b software. Deff and Dw were plotted as functions of LAT, AP, and AP+LAT. In addition, Dw was plotted as a function of Deff. Results: Strong correlations of Deff and Dw with LAT, AP, and AP+LAT were found. Stronger correlations were found in the Deff curves (R2 > 0.9) than in the Dw curves (R2 > 0.8). It was found that the average Deff was higher than the average Dw in the thoracic region, the average values were similar in the abdominal and pelvic regions. Conclusion: The current study extended the study of the relationships between Deff and Dw and the basic geometric diameter LAT, AP, and AP+LAT beyond those previously reported by AAPM. We evaluated the relationships for three regions, i.e. thoracic, abdominal, and pelvic regions. Based on our findings, it was possible to estimate Deff and Dw from only the LAT or AP dimension.