CT diagnostic imaging is a major contributor to ionizing radiation exposure in the United States. Unfortunately, a reduction in radiation dose often results in degraded image quality. Automatic Exposure Control (AEC) is the most commonly used method to balance image quality and dose in x-ray CT, generally by modifying the scan’s tube current modulation (TCM) parameters. To allow current AEC techniques to be better personalized to the patient size, organ dose, and clinical task, our team previously proposed Scout-Dose and Scout-IQA to prospectively estimate dose and noise from frontal and lateral scouts, scan range, and TCM map. In this study, we evaluate for the first time the performance of our scout-based organ dose and noise predictions in an optimization framework to prospectively determine real-time, personalized TCM maps from a patient’s acquired scouts and scan ranges.
Automatic exposure control based on tube current modulation (TCM) can effectively reduce dose while maintaining image quality. Conventional TCM uses total exposure from the tube and noise in the center of CT slices as surrogates of dose and image quality, respectively. In this abstract, we present an automated method to optimize TCM at the organ level, offering increased flexibility and aligning with the concept of organ-specific radiation risk assessment. We applied our method to a retrospective CT dataset and incorporated automatic organ segmentation, Monte Carlo simulation for dose calculation, and an empirical model for noise estimation. This method was fully automated and readily scalable to massive clinical data, allowing the generation of ground-truth data for any data-driven approach to prospective planning, including methods utilizing scout images.
Numerous dual-energy CT (DECT) techniques have been developed in the past few decades. Dual-energy CT (DECT) statistical iterative reconstruction (SIR) has demonstrated its potential for reducing noise and increasing accuracy. Our lab proposed a joint statistical DECT algorithm for stopping power estimation and showed that it outperforms competing image-based material-decomposition methods. However, due to its slow convergence and the high computational cost of projections, the elapsed time of 3D DECT SIR is often not clinically acceptable. Therefore, to improve its convergence, we have embedded DECT SIR into a deep learning model-based unrolled network for 3D DECT reconstruction (MB-DECTNet) that can be trained in an end-to-end fashion. This deep learning-based method is trained to learn the shortcuts between the initial conditions and the stationary points of iterative algorithms while preserving the unbiased estimation property of model-based algorithms. MB-DECTNet is formed by stacking multiple update blocks, each of which consists of a data consistency layer (DC) and a spatial mixer layer, where the spatial mixer layer is the shrunken U-Net, and the DC layer is a one-step update of an arbitrary traditional iterative method. Although the proposed network can be combined with numerous iterative DECT algorithms, we demonstrate its performance with the dual-energy alternating minimization (DEAM). The qualitative result shows that MB-DECTNet with DEAM significantly reduces noise while increasing the resolution of the test image. The quantitative result shows that MB-DECTNet has the potential to estimate attenuation coefficients accurately as traditional statistical algorithms but with a much lower computational cost.
Accuracy in proton range prediction is critical in proton therapy to ensure conformal tumor dose. Our lab proposed a joint statistical image reconstruction method (JSIR) based on a basis vector model (BVM) for estimation of stopping power ratio maps and demonstrated that it outperforms competing Dual Energy CT (DECT) methods. However, no study has been performed on the clinical utility of our method. Here, we study the resulting dose prediction error, the difference between the dose delivered to tissue based on the more accurate JSIR-BVM method and the planned dose based on Single Energy CT (SECT).
Proton radiotherapy has the potential to provide clinically effective treatment and highly conformal dose delivery when the rapid dose falloff at the end of its proton-beam range is correctly aligned to the distal margin of the clinical target volume. However, in current clinical practice an additional 2-3.5% safety margin must be added to the proton range to account for uncertainties in the estimation of proton-beam range when using stopping-power ratios (SPRs) derived from single-energy CT scans. Several approaches have been proposed to estimate stopping power by using dual-energy CT (DECT) and have been shown through theoretical analysis to outperform single-energy CT (SECT) under the presence of tissue composition and density variations. Our lab previously proposed a joint statistical image reconstruction (JSIR) method built on a basis-vector model (BVM) tissue parameterization for SPR estimation, which was shown to perform comparatively better than other DECT image- and sinogram-domain decomposition approaches on simulated as well as experimental data. This comparison, however, assumed theoretical SPR values calculated from the samples’ known compositions and densities as ground truth and used the mean excitation energy and effective electron density from ICRU reports along with a simplified version of the Bethe-Bloch equation to determine SPR reference values. Furthermore, CT scans were acquired with an assumed ideal point source at a narrow beam collimation; thus, the signal formation assumed by our JSIR process neglected scatter and off-focal radiation. In this paper, we verify the accuracy of our method by comparing the SPR values derived from JSIR-BVM to direct measurements of relative SPR, as well as present a preliminary study on the impact of fan-beam scatter radiation on JSIR-BVM SPR prediction accuracy.
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