In X-Ray CT, there is always a desire to maintain the image quality while reducing the radiation dose. Recently several dose reduction approaches in both software and hardware have been developed to achieve the goal of making radiation as low as possible. Thus, the assessment of image quality becomes an important factor for routine quality control of medical X-Ray devices. In this work, task-based image quality measurements using model observers were used to evaluate the performance of X-Ray CT systems. To evaluate the dose reduction ability, detection tasks as well as combined detection and estimation tasks were considered. In detection tasks and combined detection and estimation tasks, the channelized Hotelling observer (CHO) and channelized scanning linear observer (CSLO) (with Dense Difference of Gauss channels) were employed respectively. They were used to evaluate the dose reduction capability of the iterative reconstruction algorithm developed by GE compared to the traditional reconstruction algorithm, filtered backprojection (FBP). Additionally, CHO and CSLO were also used for optimization of CT protocols. Our methods were also applied to Cardiac CT systems for temporal resolution evaluations. Two reconstruction algorithms, FBP and the motion correction algorithm, Snapshot Freeze (SSF), operated at two heart-beating rates with two reconstruction windows were quantitatively evaluated using task-based measurements. Finally, due to the huge demand of data acquisitions in the conventional channelized model observers, a proposed High-Dose-Signal-LOOL CHO/CSLO (HL-CHO/CSLO) that could efficiently reduce the data requirement has also been investigated in the pure detection, and combined detection and estimation task. In all studies, the practicality and the use of real data is emphasized. The results of all these studies demonstrate the usefulness of the task-based measurements of image quality in X-Ray CT imaging.

Improving imaging hardware, computational power, and algorithmic design are driving advances in interventional medical imaging. We lay the groundwork here for more effective use of machine learning and image registration in clinical electrophysiology. To achieve identification of atrial fibrosis using image data, we registered the electroanatomic map (EAM) data of atrial fibrillation (AF) patients undergoing pulmonary vein isolation (PVI) with MR (n = 16) or CT (n = 18) images. The relationship between image features and bipolar voltage was evaluated using single-parameter regression and random forest models. Random forest performed significantly better than regression, identifying fibrosis with area under the receiver operating characteristic curve (AUC) 0.746 (MR) and 0.977 (CT). This is the first evaluation of voltage prediction using image data. Next, we compared the character of native atrial fibrosis with ablation scar in MR images. Fourteen AF patients undergoing repeat PVI were recruited. EAM data from their first PVI was registered to the MR images acquired before the first PVI (‘pre-operative’) and before the second PVI ('post-operative' with respect to the first PVI). Non-ablation map points had similar characteristics in the two images, while ablation points exhibited higher intensity and more heterogeneity in post-operative images. Ablation scar is more strongly enhancing and more heterogeneous than native fibrosis. Finally, we addressed myocardial measurement in 3-D echocardiograms. The circular Hough transform was modified with a feature asymmetry filter, epicardial edges, and a search constraint. Manual and Hough measurements were compared in 5641 slices from 3-D images. The enhanced Hough algorithm was more accurate than the unmodified version (Dice coefficient 0.77 vs. 0.58). This method promises utility in segmentation-assisted cross-modality registration. By improving the information that can be extracted from medical images and the ease with which that information can be accessed, this progress will contribute to the advancing integration of imaging in electrophysiology.