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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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An in-house step-wedge phantom for the calibration of pixel values in CT localizer radiographs for water-equivalent diameter measurement

Rif’ah Sitti M., Anam Choirul, Sutanto Heri, Asiah Rin H., Holid Heryani, Dougherty Geoff

Polish Journal of Medical Physics and Engineering

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2023

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Vol. 29, Iss. 1

50--57

EN

Introduction: To develop an in-house acrylic-based step-wedge phantom with several thickness configurations for calibrating computed tomography (CT) localizer radiographs in order to measure the water-equivalent diameter (Dw) and the size-specific dose estimate (SSDE). Method: We developed an in-house step-wedge phantom using 3 mm thick acrylic, filled with water. The phantom had five steps with thicknesses of 6, 12, 18, 24, and 30 cm. The phantom was scanned using a 64-slice Siemens Definition AS CT scanner with tube currents of 50, 100, 150, 200, and 250 mA. The relationship between pixel value (PV) and water-equivalent thickness (tw) was obtained for the different step thicknesses. This was used to calibrate the CT localizer radiographs in order to measure Dw and SSDE. The results of Dw and SSDE from the radiographs were compared with those calculated from axial CT images. Results: The relationship between PV and tw from CT localizer radiographs of the phantom step-wedge produced a linear relationship with R2 > 0.990. The linear relationships of the Dw and SSDE values obtained from CT localizer radiographs and axial CT images had R2 values > 0.94 with a statistical test of p-value > 0.05. The Dw difference between those from CT localizer radiographs and axial CT images was 3.7% and the SSDE difference between both was 4.3%. Conclusion: We have successfully developed a step-wedge phantom to calibrate the relationship between PV and tw. Our phantom can be easily used to calibrate CT localizer radiographs in order to measure Dw and SSDE.

3

Normal tissue objective (NTO) tool in Eclipse treatment planning system for dose distribution optimization

Indrayani Liza, Anam Choirul, Sutanto Heri, Subroto Rinarto, Dougherty Geoff

Polish Journal of Medical Physics and Engineering

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2022

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Vol. 28, Iss. 2

99--106

EN

Introduction: The purpose of this study was to determine the best normal tissue objective (NTO) values based on the dose distribution from brain tumor radiation therapy. Material and methods: The NTO is a constraint provided by Eclipse to limit the dose to normal tissues by steepening the dose gradient. The multitude of NTO setting combinations necessitates optimal NTO settings. The Eclipse supports manual and automatic NTOs. Fifteen patients were re-planned using NTO priorities of 1, 50, 100, 150, 200, and 500 in combination with dose fall-offs of 0.05, 0.1, 0.2, 0.3, 0.5, 1 and 5 mm-1. NTO distance to planning target volume (PTV), start dose, and end dose were 1 mm, 105%, and 60%, respectively, for all plans. In addition, planning without the NTO was arranged to find out its effect on planning. The prescription dose covered 95% of the PTV. Planning was evaluated using several indices: conformity index (CI), homogeneity index (HI), gradient index (GI), modified gradient index (mGI), comprehensive quality index (CQI), and monitor unit (MU). Differences among automatic NTO, manual NTO, and without NTO were evaluated using the Wilcoxon signed-rank test. Results: Comparisons obtained without and with manual NTO were: CI of 0.77 vs. 0.96 (p = 0.002), GI of 4.52 vs. 4.69 (p = 0.233), mGI of 4.93 vs. 3.95 (p = 0.001), HI of 1.10 vs. 1.10 (p = 0.330), and MU/cGy of 3.44 vs. 3.42 (p = 0.460). Planning without NTO produced a poor conformity index. Comparisons of automatic and manual NTOs were: CI of 0.92 vs. 0.96 (p = 0.035), GI of 5.25 vs. 4.69 (p = 0.253), mGI of 4.46 vs. 3.95 (p = 0.001), HI of 1.09 vs. 1.10 (p = 0.004), MU/cGy of 3.31 vs. 3.42 (p = 0.041). Conclusions: Based on these results, manual NTO with a priority of 100 and dose fall-off 0.5 mm-1 was optimal, as indicated by the high dose reduction in normal tissue.

4

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.

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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.

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Automatic validation of the gantry tilt in a computed tomography scanner using a head polymethyl methacrylate phantom

Noviliawati Rakhma, Anam Choirul, Sutanto Heri, Dougherty Geoff, Mak’ruf Muhammad Ridha

Polish Journal of Medical Physics and Engineering

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2021

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Vol. 27, Iss. 1

57--62

EN

The purpose of this study was to develop an automatic method for validating the computed tomography gantry tilt. A head polymethyl methacrylate phantom with a diameter of 16 cm was used. Gantry tilt angles were measured both manually and automatically. Manual measurements were performed by measuring the length of the anteroposterior and lateral diameters from acquired images using electronic calipers. Automatic measurements consisted of a number of steps: phantom segmentation, determination of the center of the phantom, measurement of the anteroposterior and lateral diameters, and computation of the gantry tilt angle. The method was implemented on the gantry angles from 0° to 15°. The proposed method of measuring gantry angles produced accurate gantry tilt angles. The differences with the angles displayed on the gantry were less than 1°. The results of the automatic method were the same as those of the manual method (R2 > 0.98).

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Automated determination of chest characteristics of Indonesians as the basis of chest dosimetrical phantom design

Heryani Heryani, Anam Choirul, Sutanto Heri, Reskianto Ari Dwi, Dougherty Geoff

Polish Journal of Medical Physics and Engineering

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2020

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Vol. 26, Iss. 4

263--268

EN

Purpose: The purpose of this study was to develop software to automatically measure the main areas of the chest, i.e. soft tissue, bone, and air and to implement it in Kraton Regional General Hospital for designing a specific dosimetrical phantom for chest digital radiography (DR) examination. Methods: This study was a retrospective study on all DR images from 2015 to 2019, and computed tomography (CT) images of 102 patients in Digital Imaging and Communications in Medicine (DICOM) format files scanned from January-December 2019 at the Kraton Regional General Hospital. We evaluated the number of basic DR chest examinations compared to all DR radiological examinations. We developed a MatLab graphical user interface (GUI) for automated measurement of the areas of the main chest components (soft tissue, bone, and air). We computed the areas of the main components of the chest in order to develop a specific chest phantom for DR in the hospital. In order to compute the areas of the main components, we used chest CT images of patients with clinical indications of chest tumors. Results: The basic DR chest examination comprised 59.5% of all DR examinations in the hospital during 2015-2019. The average areas of soft tissue, bone, and air within the chest in all patients were 331, 20, and 125 cm2, respectively, with values of 345, 23, and 139 cm2 for males, and 309, 15, and 103 cm2 for females. The areas were also dependent on age with values of 121, 10, 55 cm2 for patients aged 5-11 years, 371, 27, and 88 cm2 for patients aged 12-25 years, 322, 22, and 131 cm2 for patients aged 26-45 years, and 334, 19, and 126 cm2 for patients > 45 years old. Conclusion: A GUI for computing the main composition of the chest was successfully developed. The areas of chest male patients were greater than female patients. The areas of soft tissue, bone, and air were dependent on the patient's age. Therefore, the design of dosimetrical DR phantom must consider the gender and age of the patient.

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Automated MTF measurement in CT images with a simple wire phantom

Anam Choirul, Fujibuchi Toshioh, Haryanto Freddy, Budi Wahyu Setia, Sutanto Heri, Adi Kusworo, Muhlisin Zaenul, Dougherty Geoff

Polish Journal of Medical Physics and Engineering

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2019

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Vol. 25, Iss. 3

179--187

EN

This study developed a simple wire phantom and an algorithm to automatically measure the modulation transfer function (MTF) in computed tomography (CT) and implemented it to evaluate the effect of focal spot size and reconstruction filter type. The phantom consisted of a resin cylinder filled with water, with a tin wire of diameter 0.1 mm positioned along the center of the cylinder. The automated MTF algorithm used an axial image of the phantom and comprised several steps. The center position of a region of interest (ROI) was automatically determined at the center of the wire image. The pixels were then summed along the y-direction to obtain the profile of the pixel values at a point along the x-direction. Following this, both edges of the profile were made equal to zero. The profile curve was then normalized so that the total of all the data was equal to unity. The normalized profile curve is the line spread function (LSF), and the MTF curve was obtained by taking its Fourier transform. Our system (phantom and algorithm) is able to differentiate the MTFs of CT images from different focal sizes and reconstruction filter types.