Volume 4, Issue 2
The Use of Artificial Intelligence in Computed Tomography Image Reconstruction: A Systematic Review
PDF 489.52 KB
Current image reconstruction techniques in computed tomography (CT) such as filtered back-projection (FBP) and iterative reconstruction (IR) have limited use in low-dose CT imaging due to poor image quality and reconstruction times not fit for clinical implementation. Hence, with the increasing need for radiation dose reductions in CT, the use of artificial intelligence (AI) in image reconstruction has been an area of growing interest.
The aim of this review is to examine the use of AI in CT image reconstruction and its effectiveness in enabling further dose reductions through improvements in image quality of low-dose CT images.
A review of the literature from 2016 to 2020 was conducted using the databases Scopus, Ovid MEDLINE, and PubMed. A subsequent search of several well-known journals was performed to obtain additional information. After careful assessment, articles were excluded if they were not obtainable from the databases or not available in English.
This review found that deep learning-based algorithms demonstrate promising results in improving the image quality of low-dose images through noise suppression, artefact reduction, and structure preservation in addition to optimising IR methods.
In conclusion, with the two AI-based CT systems currently in clinical use showing favourable benefits, it is expected that AI algorithms will continue to proliferate and enable significant dose reductions in CT imaging.
Computed tomography (CT); Artificial Intelligence (AI); Image reconstruction (IN); Machine learning (ML); Deep learning (DL); Dose reduction.
AI: Artificial intelligence; CT: Computed tomography; ML: Machine learning; DL: Deep learning; FBP: Filtered back-projection; IR: Iterative reconstruction; MBIR: Model-based iterative reconstruction; LDCT: Low-dose computed tomography; FDA: U.S Food and Drug Administration; ANN: Artificial neural network; DNN: Deep neural network; CNN: Convolutional neural network; CNR: Contrast-to-noise ratio; SNR: Signal-to-noise ratio.
Study of Analysis the Sensitivity of the Computational Environment for Radiological Research Field Size Based on Two Dimensional Dose Distribution for Water Phantom Cases
PDF 656.66 KB
This study analysed the sensitivity of the field size from variations in the target volume dimensions, depth, and position. The variations in the target volume analysis were used to determine the width of the field size. Thus, the quality control of the radiation beam can be obtained.
Materials and Methods
The computed tomography (CT) image of the IBA Dose 1 type of water phantom consists of 350 slices. Variations in the dimension of the target volume were modelled in 10×10×10 cm3, 10×12×10 cm3, 10.2×10×10.2 cm3, and 15×15×15 cm3. Beam parameters use one beam of irradiation on the central axis 0°, 6 MV energy, 100 cm source-skin distance (SSD), beamlet delta x, and y set to 0.1 cm. Dose distribution in the form of the XZ isodose curve and dose profile was used to observe the field size.
In this study, the isodose curve was successfully displayed in the XZ isodose curve. The field size’s sensitivity has been successfully reviewed from variations of the target volume, depth, and position. The target X and Z direction analysis is used in determining the width and length of the field size.
The analysis related to the field size sensitivity study was obtained from a relatively valid calculation. The field size was evaluated with variations in depth of 1.5 cm, 5 cm, 10 cm, and variations in positions of 10 cm, 12 cm, 14 cm, 18 cm, and 20 cm. This study will be used as a reference to validate the distribution of computational environment for radiotherapy research (CERR) dose in the future. Thus, the accuracy of the dose calculation can be obtained.
2D Dose Distribution; Sensitivity; Quality control; Treatment planning system; Radiation therapy dosimetry.
Calculation of Photons Reaction Rate Resulting at 120 kVp X-ray Tube Voltage and 1 mAs as Function to Digital Imaging and Communications in Medicine Pixel Numbers Using Monte Carlo N-Particle Transport and a Voxel Model of a 29-Year-Old Patient
PDF 784.94 KB
To read the digital imaging and communications in medicine (DICOM) images of brain and extract intensity values and build a three dimensional model for Monte Carlo n-particle transport (MCNP) code input file in purpose to study the average particle flux and deposited energy of X-Ray photons resulting at 120 kVp and 1 mAs (form point source) as function to DICOM pixel numbers in the brain tissues for a 29-year-old female patient using MCNP code and Matlab program to read the DICOM images.
The matrix laboratory (MATLAB) program was used to read the DICOM images and extract the intensity values in each pixel of the DICOM image corresponding to certain slice of the brain. These color levels are characteristic of different tissue, and have been relied upon to create the specific material in each volume element in MCNP input file.
Values of the deposited energy at surface of skin are high, so it is always necessary to be cautious when performing the examination to obtain acceptable images from the first time and without having to repeat the imaging again for the same case unless there are necessities for it.
Computed tomography (CT); X-ray; Voxel phantom; MCNP Code; Average particle flux; Matlab.
Radio Histological Treatment of Endometrial Hyperplasia: A Case Report
PDF 358.33 KB
Endometrial hyperplasia is defined as the pathological condition caused by hyper plastic changes at the level of the glandular and stromal structures of the endometrium that are part of the lining of the endometrial cavity. Atypical endometrial hyperplasia can cause an essential problem because it is considered a precursor of endometrial cancer. The early diagnosis of precancerous endometrial
lesions and the exclusion of pre-existing endometrial carcinomas are necessary for patients’ optimal management. The following is a case of a 50-year-old Guatemalan patient with a three-day history of vaginal bleeding. The transvaginal ultrasound reports endometrial thickening suggestive of endometrial hyperplasia. The diagnosis was confirmed with histology. The treatment offered was surgery without indicating any medication.
Endometrium; Endometrial hyperplasia; Biopsy; Histological; Echography; World Health Organization (WHO).