• The Journal of the Korea Contents Association
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• v.9 no.12
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• pp.742-749
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• 2009

The use of cone-beam computed tomography(CBCT) has been proposed for guiding the delivery of radiation therapy. A kilovoltage imaging system capable of radiography, fluoroscopy, and cone-beam computed tomography(CT) has been integrated with a medical linear accelerator. A standard clinical linear accelerator, operating in arc therapy mode, and an amorphous-silicon (a-Si) with an on-board electronic portal imager can be used to treat palliative patient and verify the patient's position prior to treatment. On-board CBCT images are used to generate patient geometric models to assist patient setup. The image data can also, potentially, be used for dose reconstruction in combination with the fluence maps from treatment plan. In this study, the accuracy of Hounsfield Units of CBCT images as well as the accuracy of dose calculations based on CBCT images of a phantom and compared the results with those of using CT simulator images. Phantom and patient studies were carried out to evaluate the achievable accuracy in using CBCT and CT stimulator for dose calculation. Relative electron density as a function of HU was obtained for both planning CT stimulator and CBCT using a Catphan-600 (The Phantom Laboratory, USA) calibration phantom. A clinical treatment planning system was employed for CT stimulator and CBCT based dose calculations and subsequent comparisons. The dosimetric consequence as the result of HU variation in CBCT was evaluated by comparing MU/cCy. The differences were about 2.7% (3-4MU/100cGy) in phantom and 2.5% (1-3MU/100cGy) in patients. The difference in HU values in Catphan was small. However, the magnitude of scatter and artifacts in CBCT images are affected by limitation of detector's FOV and patient's involuntary motions. CBCT images included scatters and artifacts due to In addition to guide the patient setup process, CBCT data acquired prior to the treatment be used to recalculate or verify the treatment plan based on the patient anatomy of the treatment area. And the CBCT has potential to become a very useful tool for on-line ART.)

• Proceedings of the Korea Contents Association Conference
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• 2009.05a
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• pp.1159-1166
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• 2009

The use of cone-beam computed tomography(CBCT) has been proposed for guiding the delivery of radiation therapy. A kilovoltage imaging system capable of radiography, fluoroscopy, and cone-beam computed tomography(CT) has been integrated with a medical linear accelerator. A standard clinical linear accelerator, operating in arc therapy mode, and an amorphous-silicon (a-Si) with an on-board electronic portal imager can be used to treat palliative patient and verify the patient's position prior to treatment. On-board CBCT images are used to generate patient geometric models to assist patient setup. The image data can also, potentially, be used for dose reconstruction in combination with the fluence maps from treatment plan. In this study, the accuracy of Hounsfield Units of CBCT images as well as the accuracy of dose calculations based on CBCT images of a phantom and compared the results with those of using CT simulator images. Phantom and patient studies were carried out to evaluate the achievable accuracy in using CBCT and CT stimulator for dose calculation. Relative electron density as a function of HU was obtained for both planning CT stimulator and CBCT using a Catphan-600 (The Phantom Laboratory, USA) calibration phantom. A clinical treatment planning system was employed for CT stimulator and CBCT based dose calculations and subsequent comparisons. The dosimetric consequence as the result of HU variation in CBCT was evaluated by comparing MU/cCy. The differences were about 2.7% (3-4MU/100cGy) in phantom and 2.5% (1-3MU/100cGy) in patients. The difference in HU values in Catphan was small. However, the magnitude of scatter and artifacts in CBCT images are affected by limitation of detector's FOV and patient's involuntary motions. CBCT images included scatters and artifacts due to In addition to guide the patient setup process, CBCT data acquired prior to the treatment be used to recalculate or verify the treatment plan based on the patient anatomy of the treatment area. And the CBCT has potential to become a very useful tool for on-line ART.)

• The Journal of Korean Society for Radiation Therapy
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• v.29 no.1
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• pp.77-84
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• 2017

Purpose: To evaluate the image quality improvement and dosimetric effects on virtual monochromatic images of a Dual Source-Dual Energy CT(DS-DECT) for radiotherapy planning. Materials and Methods: Dual energy(80/Sn 140 kVp) and single energy(120 kVp) scans were obtained with dual source CT scanner. Virtual monochromatic images were reconstructed at 40-140 keV for the catphan phantom study. The solid water-equivalent phantom for dosimetry performs an analytical calculation, which is implemented in TPS, of a 10 MV, $10{\times}10cm^2$ photon beam incident into the solid phantom with the existence of stainless steel. The dose profiles along the central axis at depths were discussed. The dosimetric consequences in computed treatment plans were evaluated based on polychromatic images at 120 kVp. Results: The magnitude of differences was large at lower monochromatic energy levels. The measurements at over 70 keV shows stable HU for polystyrene, acrylic. For CT to ED conversion curve, the shape of the curve at 120 kVp was close to that at 80 keV. 105 keV virtual monochromatic images were more successful than other energies at reducing streak artifacts, which some residual artifacts remained in the corrected image. The dose-calculation variations in radiotherapy treatment planning do not exceed ${\pm}0.7%$. Conclusion: Radiation doses with dual energy CT imaging can be lower than those with single energy CT imaging. The virtual monochromatic images were useful for the revision of CT number, which can be improved for target coverage and electron densities distribution.

• Journal of Biomedical Engineering Research
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• v.25 no.2
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• pp.89-95
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• 2004

Intensity-modulated radiation therapy (IMRT) often uses small beam segments. The heterogeneity effect is well known for relatively large field sizes used in the conventional radiation treatments. However, this effect is not known in small fields such as the beamlets used in IMRT. There are many factors that can cause errors in the small field i.e. electronic disequilibrium and multiple electron scattering. This study prepared geometrically regular heterogeneous phantoms, and compared the measurements with the calculations using the Convolution/Superposition algorithm and Monte Carlo method for small beams. This study used the BEAM00/EGS4 code to simulate the head of a Varian 2300C/D. The commissioning of a 6MV photon beam were performed from two points of view, the beam profiles and depth doses. The calculated voxel size was 1${\times}$1${\times}$2$\textrm{cm}^2$ with field sizes of 1${\times}$1$\textrm{cm}^2$, 2${\times}$2$\textrm{cm}^2$, and 5${\times}$5$\textrm{cm}^2$. The XiOTM TPS (Treatment Planning System) was used for the calculation using the Convolution/Superposition algorithm. The 6MV photon beam was irradiated to homogeneous (water equivalent) and heterogeneous phantoms (water equivalent ＋ air cavity, water equivalent ＋ bone equivalent). The beam profiles were well matched within :t1 mm and the depth doses were within ${\pm}$2%. In conclusion, the dose calculations of the Convolution/Superposition and Monte Carlo simulations showed good agreement with the film measurements in the small field.

• Journal of the Korean Society of Radiology
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• v.18 no.1
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• pp.37-44
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• 2024

Even though MR can reveal excellent soft-tissue contrast and functional information, CT is also required for electron density information for accurate dose calculation in Radiotherapy. For the fusion of MRI and CT images in RT treatment planning workflow, patients are normally scanned on both MRI and CT imaging modalities. Recently deep-learning-based generations of CT images from MR images became possible owing to machine learning technology. This eliminated CT scanning work. This study implemented a CycleGan deep-learning-based CT image generation from MR images. Three CT generators whose learning is based on T1- , T2- , or T1-&T2-weighted MR images were created, respectively. We found that the T1-weighted MR image-based generator can generate better than other CT generators when T1-weighted MR images are input. In contrast, a T2-weighted MR image-based generator can generate better than other CT generators do when T2-weighted MR images are input. The results say that the CT generator from MR images is just outside the practical clinics and the specific weight MR image-based machine-learning generator can generate better CT images than other sequence MR image-based generators do.