Dose in ct renal angiography5 answersIn renal CT angiography, various studies have explored the use of low-dose protocols to reduce radiation exposure and contrast material volume while maintaining diagnostic image quality. Different approaches include utilizing low iodine concentration contrast material, adaptive statistical iterative reconstruction (ASiR), and patient-tailored contrast media formulas. These studies have shown significant reductions in radiation dose, with effective doses ranging from 2.46 to 9.5 mSv. Additionally, the use of lower tube voltages such as 80 kVp or 100 kVp, along with iterative model reconstruction algorithms, has been found to improve image quality and reduce radiation dose further. Overall, these approaches demonstrate the feasibility of achieving high-quality renal CT angiography images with lower radiation doses and contrast material volumes, enhancing patient safety during the imaging procedure.
How does the choice of irradiation field size impact the radiation dose received by patients during CT scans?4 answersThe choice of irradiation field size significantly affects the radiation dose received by patients during CT scans. Different field of view (FOV) sizes in cone-beam computed tomography (CBCT) units have been shown to impact the contrast-to-noise ratio (CNR). Moreover, variations in exposure parameters and field sizes in imaging protocols can lead to differences in size-specific effective dose (SED) received by patients, with smaller patients potentially receiving higher doses compared to average-sized individuals. It is crucial to optimize CT scan protocols to ensure that diagnostic reference levels (DRLs) are met, taking into account factors such as body size, kVp, mAs, and pitch to regulate the volume CT dose index (CTDIvol) and minimize radiation exposure. Proper selection of irradiation field size is essential to balance diagnostic image quality with minimizing radiation risks for patients undergoing CT scans.
Can advances in Ct technology improve the accuracy and sensitivity of brain tumor detection and diagnosis?4 answersAdvances in CT technology can improve the accuracy and sensitivity of brain tumor detection and diagnosis. Deep learning-based approaches utilizing multimodal imaging, such as T1-weighted, T2-weighted, and diffusion-weighted MRI scans, have shown improved sensitivity and specificity in tumor detection compared to single-modality approaches. The use of ResNet-101 architecture in the core network, along with transfer learning, has also been effective in improving model performance and achieving high accuracy in classifying tumors. Additionally, the use of fusion images and deep learning techniques has demonstrated superior results in accurately detecting tumor types. Furthermore, the analysis of eigenvalues and the reduction of image noise using the MSVD algorithm have shown increased accuracy and effectiveness in tumor segmentation and diagnosis. These advancements highlight the potential of CT technology in enhancing brain tumor detection and diagnosis.
What are DRR techniques for X-Ray rendering from CT Scan ?5 answersDeep learning techniques have been proposed to address the challenge of reconstructing a universal image from one filtered backprojection (FBP) image in CT scans. Wavelet-based compression algorithms such as Embedded Zero-Tree Wavelet (EZW), Set Partitioning in Hierarchical Trees (SPIHT), and Wavelet Difference Reduction (WDR) have been used to compress X-ray and CT images. Sparse-angle tomography is another approach for obtaining 3D reconstructions from limited data in CT scans. Techniques such as adaptive sampling, sparse casting, regula falsi method, Monte-Carlo ambient occlusion estimation, screen-space ambient occlusion, and depth of field have been used to improve the rendering quality of large volumetric data sets obtained from CT or MRI scans.
How can neural networks be used for ct-scans?5 answersNeural networks can be used for CT scans in various ways. One application is in the detection and classification of lung cancer using deep learning techniques. Different pre-trained models such as AlexNet, ResNet18, Googlenet, and ResNet50 have been modified and used to analyze CT images for lung cancer detection and stage classification. Another application is in the detection of COVID-19 using transfer learning. VGG-19, a CNN-based model, has been used with the COVID-CT dataset to achieve high accuracy, precision, recall, and F1-Score in identifying COVID-19 cases. Deep learning models have also been developed to detect COVID-19 and pneumonia from CT scans. Relief-based feature selection and patient-specific Class Activation Maps (CAMs) have been used to improve accuracy and highlight immunopathogenic differences. Additionally, neural networks have been employed for lesion detection in CT scans, aiding radiologists in identifying suspicious lesions for further assessment. Finally, neural networks have been used for stroke prediction by training CT image datasets, providing efficient and accurate classification.
How can patient management be improved in CT brain?5 answersPatient management in CT brain can be improved by using techniques such as REBOA as a bridge to CT scan in complex head and torso trauma, allowing for a rapid brain CT scan in traumatic brain injury. This is crucial as the duration of cerebral herniation is associated with worse outcomes and increased mortality. Additionally, CT dose management is important in ensuring patient safety and optimizing radiation doses. Furthermore, the technical improvement of CT and MRI, along with the development of new imaging techniques, has greatly improved the detection and characterization of brain tumors, allowing for optimal therapeutic management. It is also important to consider the radiation exposure from CT, PET, and SPECT investigations, and to develop guidelines for referring physicians to carefully weigh the benefits against potential risks.