The goal of this study is to understand how the normalized glandular dose coefficient (π·πππΆπ) varies with projection angle in dedicated cone-beam breast computed tomography (CBBCT). Seventy-five CBBCT clinical datasets from a research database were used for this study. All samples were segmented into skin, adipose and fibroglandular tissues. The segmented volumes were used in a Monte Carlo simulation package (GATE 8.0) to estimate the radiation dose at 10 angles in a full scan. An analytical model is proposed, and this model predicted that the angular π·πππΆπ follows a sine wave and the maximum is related to the center of geometry of the fibroglandular tissue (COGf ). The angular π·πππΆπ from Monte Carlo simulations was consistent with our model and follows a sine wave with amplitude of 0.0376. The maximum of the wave occurs when the x-ray source is approximately at head position, which is consistent with our model. Our results indicate that the higher angular π·πππΆπ occurs when the x-ray source is superior to the breast. This suggests using a x-ray source trajectory inferior to the breast for short-scan CBBCT design.
Maintaining or even improving image quality while lowering patient dose is always the desire in clinical computed tomography (CT) imaging. Iterative reconstruction (IR) algorithms have been designed to allow for a reduced dose while maintaining or even improving an image. However, we have previously shown that the dose-saving capabilities allowed with IR are different for different clinical tasks. The channelized scanning linear observer (CSLO) was applied to study clinical tasks that combine detection and estimation when assessing CT image data. The purpose of this work is to illustrate the importance of task complexity when assessing dose savings and to move toward more realistic tasks when performing these types of studies. Human-observer validation of these methods will take place in a future publication. Low-contrast objects embedded in body-size phantoms were imaged multiple times and reconstructed by filtered back projection (FBP) and an IR algorithm. The task was to detect, localize, and estimate the size and contrast of low-contrast objects in the phantom. Independent signal-present and signal-absent regions of interest cropped from images were channelized by the dense-difference of Gauss channels for CSLO training and testing. Estimation receiver operating characteristic (EROC) curves and the areas under EROC curves (EAUC) were calculated by CSLO as the figure of merit. The one-shot method was used to compute the variance of the EAUC values. Results suggest that the IR algorithm studied in this work could efficiently reduce the dose by βΌ50% while maintaining an image quality comparable to conventional FBP reconstruction warranting further investigation using real patient data.
The use of a channelization mechanism on model observers not only makes mimicking human visual behavior possible, but also reduces the amount of image data needed to estimate the model observer parameters. The channelized Hotelling observer (CHO) and channelized scanning linear observer (CSLO) have recently been used to assess CT image quality for detection tasks and combined detection/estimation tasks, respectively. Although the use of channels substantially reduces the amount of data required to compute image quality, the number of scans required for CT imaging is still not practical for routine use. It is our desire to further reduce the number of scans required to make CHO or CSLO an image quality tool for routine and frequent system validations and evaluations. This work explores different data-reduction schemes and designs an approach that requires only a few CT scans. Three different kinds of approaches are included in this study: a conventional CHO/CSLO technique with a large sample size, a conventional CHO/CSLO technique with fewer samples, and an approach that we will show requires fewer samples to mimic conventional performance with a large sample size. The mean value and standard deviation of areas under ROC/EROC curve were estimated using the well-validated shuffle approach. The results indicate that an 80% data reduction can be achieved without loss of accuracy. This substantial data reduction is a step toward a practical tool for routine-task-based QA/QC CT system assessment.
Maintaining image quality in the presence of motion is always desirable and challenging in clinical Cardiac CT imaging. Different image-reconstruction algorithms are available on current commercial CT systems that attempt to achieve this goal. It is widely accepted that image-quality assessment should be task-based and involve specific tasks, observers, and associated figures of merits. In this work, we developed an observer model that performed the task of estimating the percentage of plaque in a vessel from CT images. We compared task performance of Cardiac CT image data reconstructed using a conventional FBP reconstruction algorithm and the SnapShot Freeze (SSF) algorithm, each at default and optimal reconstruction cardiac phases. The purpose of this work is to design an approach for quantitative image-quality evaluation of temporal resolution for Cardiac CT systems. To simulate heart motion, a moving coronary type phantom synchronized with an ECG signal was used. Three different percentage plaques embedded in a 3 mm vessel phantom were imaged multiple times under motion free, 60 bpm, and 80 bpm heart rates. Static (motion free) images of this phantom were taken as reference images for image template generation. Independent ROIs from the 60 bpm and 80 bpm images were generated by vessel tracking. The observer performed estimation tasks using these ROIs. Ensemble mean square error (EMSE) was used as the figure of merit. Results suggest that the quality of SSF images is superior to the quality of FBP images in higher heart-rate scans.
Maintaining or even improving image quality while lowering patient dose is always the desire in clinical CT imaging. Iterative reconstruction (IR) algorithms have been designed to help reduce dose and/or provide better image quality. In this work, the channelized scanning linear observer (CSLO) is applied to study the combination of detection and estimation task performance using CT image data. The purpose of this work is to design a task-Ββbased approach to quantitatively evaluate image-Ββquality for different reconstruction algorithms. Low-Ββcontrast objects embedded in head-Ββsize and body-Ββsize phantoms are imaged multiple times and reconstructed by FBP and an IR algorithm for this study. Independent signal present and absent ROIs cropped from images are channelized by Difference of Gauss channels for CSLO training and testing. Estimation receiver operating characteristic (EROC) curves and the area under EROC curve (EAUC) are calculated by CSLO as the figure of merit. The One-Ββ Shot method is used to compute the variance of the EAUC values. Results suggest that the IR algorithm studied in this work could efficiently reduce the dose approximately 54% to achieve an image quality comparable to conventional FBP reconstruction for the combined detection and estimation tasks.
KEYWORDS: Computed tomography, Image quality, Imaging systems, Reconstruction algorithms, Statistical analysis, Signal to noise ratio, Matrices, Data analysis, Medical imaging, Systems modeling
The channelized Hotelling observer (CHO) is a powerful method for quantitative image quality evaluations of CT systems and their image reconstruction algorithms. It has recently been used to validate the dose reduction capability of iterative image-reconstruction algorithms implemented on CT imaging systems. The use of the CHO for routine and frequent system evaluations is desirable both for quality assurance evaluations as well as further system optimizations. The use of channels substantially reduces the amount of data required to achieve accurate estimates of observer performance. However, the number of scans required is still large even with the use of channels. This work explores different data reduction schemes and designs a new approach that requires only a few CT scans of a phantom. For this work, the leave-one-out likelihood (LOOL) method developed by Hoffbeck and Landgrebe is studied as an efficient method of estimating the covariance matrices needed to compute CHO performance. Three different kinds of approaches are included in the study: a conventional CHO estimation technique with a large sample size, a conventional technique with fewer samples, and the new LOOL-based approach with fewer samples. The mean value and standard deviation of area under ROC curve (AUC) is estimated by shuffle method. Both simulation and real data results indicate that an 80% data reduction can be achieved without loss of accuracy. This data reduction makes the proposed approach a practical tool for routine CT system assessment.
Radiation dose on patient has become a major concern today for Computed Tomography (CT) imaging in clinical practice. Various hardware and algorithm solutions have been designed to reduce dose. Among them, iterative reconstruction (IR) has been widely expected to be an effective dose reduction approach for CT. However, there is no clear understanding on the exact amount of dose saving an IR approach can offer for various clinical applications. We know that quantitative image quality assessment should be task-based. This work applied mathematical model observers to study detectability performance of CT scan data reconstructed using an advanced IR approach as well as the conventional filtered back-projection (FBP) approach. The purpose of this work is to establish a practical and robust approach for CT IR detectability image quality evaluation and to assess the dose saving capability of the IR method under study. Low contrast (LC) objects imbedded in head size and body size phantoms were imaged multiple times with different dose levels. Independent signal present and absent pairs were generated for model observer study training and testing. Receiver Operating Characteristic (ROC) curves for location known exact and location ROC (LROC) curves for location unknown as well as their corresponding the area under the curve (AUC) values were calculated. Results showed approximately 3 times dose reduction has been achieved using the IR method under study.
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