Computed tomography with energy-resolved detection: a feasibility study

X-ray computed tomography (CT) imaging plays an essential role in disease diagnosis due to its noninvasive, painless mode and superior penetration depth. However, the resolution of the soft tissue ...

Diagnostic value of quantitative parameters for myocardial perfusion assessment in patients with suspected coronary artery disease by single- and dual-energy computed tomography myocardial perfusion imaging.

Four-dimensional CT scans for treatment planning in stereotactic radiotherapy for stage I lung cancer

Low-dose computed tomography (CT) is desirable for treatment planning and simulation in radiation therapy. Multiple rescanning and replanning during the treatment course with a smaller amount of dose than a single conventional full-dose CT simulation is a crucial step in adaptive radiation therapy. We developed a machine learning-based method to improve image quality of low-dose CT for radiation therapy treatment simulation. We used a residual block concept and a self-attention strategy with a cycle-consistent adversarial network framework. A fully convolution neural network with residual blocks and attention gates (AGs) was used in the generator to enable end-to-end transformation. We have collected CT images from 30 patients treated with frameless brain stereotactic radiosurgery (SRS) for this study. These full-dose images were used to generate projection data, which were then added with noise to simulate the low-mAs scanning scenario. Low-dose CT images were reconstructed from this noise-contaminated projection data and were fed into our network along with the original full-dose CT images for training. The performance of our network was evaluated by quantitatively comparing the high-quality CT images generated by our method with the original full-dose images. When mAs is reduced to 0.5% of the original CT scan, the mean square error of the CT images obtained by our method is , with respect to the original full-dose images. The proposed method successfully improved the noise, contract-to-noise ratio, and nonuniformity level to be close to those of full-dose CT images and outperforms a state-of-the-art iterative reconstruction method. Dosimetric studies show that the average differences of dose-volume histogram metrics are ( ). These quantitative results strongly indicate that the denoised low-dose CT images using our method maintains image accuracy and quality and are accurate enough for dose calculation in current CT simulation of brain SRS treatment. We also demonstrate the great potential for low-dose CT in the process of simulation and treatment planning.

COMPARISON OF 3D QUANTITATIVE OSTEOARTHRITIS IMAGING BIOMARKERS: A STUDY USING PAIRED CT AND MR IMAGES FROM THE IMI-APPROACH STUDY

Purpose: To analyze the error in contouring the brainstem for patients with head and neck cancer who underwent radiotherapy based on computed tomography (CT) and magnetic resonance (MR) images. Methods: 20 brain tumor and 17 nasopharyngeal cancer patients were randomly selected. Each patient underwent MR and CT scanning. For each patient, one observer contoured the brainstem on CT and MR images for 10 times, and 10 observers from five centers delineated the brainstem on CT and MR images only one time. The inter- and intra-observers volume and outline variations were compared. Results: The volumes of brainstem contoured by inter- and intra-observers on CT and MR images were similar (p>0.05). The reproducibility of contouring brainstem on MR images was better than that on CT images (p<0.05) for both inter- and intra-observer variability. The inter- and intra-observer for contouring on CT images reached mean values of 0.81±0.05 (p>0.05) and of 0.85±0.05 (p>0.05), respectively, while on MR images these respective values were 0.90±0.05 (p>0.05) and 0.92±0.04 (p>0.05). Conclusion: Contouring the brainstem on MR images was more accurate and reproducible than that on CT images. Precise information might be more helpful for protecting the brainstem radiation injury the patients whose lesion were closed to brainstem.

Investigation of the LabPET™ detector and electronics for photon-counting CT imaging

Lung cancer is the leading cause of cancer death worldwide. Patients with lung cancer have a very poor outcome compared to other common cancers. The aim of this project is to identify whether CT data accumulated by patients with Non-Small Cell Lung Cancer (NSCLC) provide an untapped resource that can improve their quality of care, by generating more information from existing imaging. Extracting additional data from imaging is termed radiomics. One way of extracting this data is using mathematical descriptions of an image or regions of interest within an image. This type of assessment is termed Textural Analysis (TA). The experimental work described in this thesis uses a second order TA technique to analyse CT (Computer Tomography) imaging data from patients with NSCLC. The software developed as part of this project uses a voxel by voxel analysis of CT imaging, by comparing grey levels using grey level co-occurrence matrices (GLCMs). Three approaches (themes 1-3) were explored. The first approach shows that TA can help differentiate between tumour and Radiation Induced Lung Injury (RILI) after Stereotactic Ablative Body Radiotherapy (SABR) in early stage lung cancer. The second approach suggests that assessing muscle loss on diagnostic imaging can help predict outcome in advanced NSCLC. The third and final approach uses TA to generate a functional assessment of lung function. TA is able to differentiate between patients who are fit or unfit for radical radiotherapy, based on TA of lung tissue on CT imaging, rather than formal lung function tests. TA technique described in this thesis is a novel intervention in gaining functional data from CT imaging. It is particularly attractive as the analysis is generated from routine oncological imaging. As a result these tools have the potential to be cost effective and could be integrated into a standard radiology workflow.

Comparative analysis of ResNet, ResNet-SE, and attention-based RaNet for hemorrhage classification in CT images using deep learning

The Prognostic Value of Residual FDG-Avidity Prior to Allogeneic Stem Cell Transplantation for Lymphoma.

To evaluate the accuracy of computed tomography (CT) and magnetic resonance imaging (MRI) in staging renal carcinoma. 33 renal carcinomas were preoperatively examined for tumour staging by CT and MR imaging and correlated with histopathological staging. CT imaging was performed at first as a non-contrast scan. Finally incremental images (10 mm) after intravenous contrast injection were obtained. In MR imaging we performed a transversal T1-weighted GE sequence (112/5) with and without GDTPA, a transversal fat-suppressed double-echo sequence (3900/22/90), a coronal T1-weighted GE sequence with and without GDTPA and a coronal T2-weighted TSE sequence (2800/128). In addition, dynamic T1-weighted GE imaging after GDTPA injection as well as TOF angiography in coronal direction were performed. Finally CT and MRI findings were correlated with surgical and histopathological staging results. CT and MRI staging was correct in 27 and 28 of 33 tumours. Sensitivity and specificity for tumour stage T3b to T4 was for MRI and CT 88.9% and 95.8%. With MRI 4 out of 7 thrombi were correctly diagnosed with high accuracy, but via CT none. In early stage renal carcinoma CT and MR imaging yielded similar staging accuracies. In advanced renal carcinoma MRI was superior to CT imaging, especially in diagnosing tumour thrombus. Consequently the extent of tumour thrombus may be assessed by MRI which may therefore replace conventional cavography.

MP25-01 CT VOLUMETRIC MEASUREMENTS ESTIMATE FUTURE SPLIT RENAL FUNCTION IN RENAL TRAUMA PATIENTS WITH SEGMENTAL VASCULAR INJURIES

Optimal Measurement Modality and Method for Evaluation of Responses to Transarterial Chemoembolization of Hepatocellular Carcinoma Based on Enhancement Criteria

Computed tomography (CT) plays an essential role in oncological imaging as the modality of screening for malignancies, mapping out the treatment strategy at staging, assessing response to the treatment, and following up patient outcome after the treatment. The advent of multidetector-row CT (MDCT) has brought about dramatic changes in clinical oncological imaging. The very superior temporal and spatial resolution of MDCT has transformed CT imaging from a transaxial cross-sectional technique into an isotropic volume-imaging technique. MDCT facilitates multiphasic contrast-enhanced study for a wide range of body scanning in a single examination, as well as providing flexibility of multidirectional reconstruction and high-quality three-dimensional imaging. With increases in the number of detector rows year by year, systems with 64-detector rows have become commercially available in 2006. The purpose of this article is to review the status of CT imaging in oncological imaging: (1) to outline the impact of MDCT, focusing on oncological imaging and (2) to review the clinical applications of oncological CT imaging with MDCT.

An automated system is developed for lung and mediastinum segmentation in lung CT (Computed Tomography) images for the purpose of using these segmentations not only in CT images but also in PET (Positron Emission Tomography) images to exploit the useful integration of the CT and PET images performed by the highly valuable oncological equipment PET/CT. Segmentation is the most crucial step in a CAD (Computer-Aided Diagnosis) system as lung borders delimit the region inside which pathological areas are searched for, while mediastinum borders identify the region containing the lymph nodes used for staging and restaging phases. Our method consists of an appropriate combination of image processing techniques. It was tested on CT images with different attributes such as resolution and slice thickness, containing 47 cancerous areas. We achieved almost 90% and 100% correct segmentation for lung and mediastinum, respectively.