Fluence-Field Modulated X-ray CT using Multiple Aperture Devices

Design Optimization of Spatial-Spectral Filters for Cone-Beam CT Material Decomposition

Spectral CT has shown promise for high-sensitivity quantitative imaging and material decomposition. This work presents a new device called a spatial-spectral filter (SSF) which consists of a tiled array of filter materials positioned near the x-ray source that is used to modulate the spectral shape of the x-ray beam. The filter is moved to obtain projection data that is sparse in each spectral channel. To process this sparse data, we employ a one-step direct model-based material decomposition (MBMD) to reconstruct basis material density images directly from the SSF CT data. To evaluate different possible SSF designs, we define a new Fisher-information-based predictive image quality metric called separability index which characterizes the ability of a spectral CT system to distinguish between the signals from two or more materials. This spectral CT performance metric can be used to optimize spectral CT system design. We conducted simulation-based design optimization study to find optimized combinations of filter materials, filter thicknesses, filter widths, and source settings. Finally, we present MBMD results using simulated SSF CT measurements from the optimized designs to demonstrate the ability to reconstruct basis material density images and to show the benefits of the optimized designs. Our results indicate that optimizing SSF CT for separability leads to high-performance at material discrimination tasks.

Reconstruction of three-dimensional tomographic patient models for radiation dose modulation in CT from two scout views using deep learning

BACKGROUND

A tomographic patient model is essential for radiation dose modulation in x-ray computed tomography (CT). Currently, two-view scout images (also known as topograms) are used to estimate patient models with relatively uniform attenuation coefficients. These patient models do not account for the detailed anatomical variations of human subjects, and thus, may limit the accuracy of intraview or organ-specific dose modulations in emerging CT technologies.

PURPOSE

The purpose of this work was to show that 3D tomographic patient models can be generated from two-view scout images using deep learning strategies, and the reconstructed 3D patient models indeed enable accurate prescriptions of fluence-field modulated or organ-specific dose delivery in the subsequent CT scans.

METHODS

CT images and the corresponding two-view scout images were retrospectively collected from 4214 individual CT exams. The collected data were curated for the training of a deep neural network architecture termed ScoutCT-NET to generate 3D tomographic attenuation models from two-view scout images. The trained network was validated using a cohort of 55 136 images from 212 individual patients. To evaluate the accuracy of the reconstructed 3D patient models, radiation delivery plans were generated using ScoutCT-NET 3D patient models and compared with plans prescribed based on true CT images (gold standard) for both fluence-field-modulated CT and organ-specific CT. Radiation dose distributions were estimated using Monte Carlo simulations and were quantitatively evaluated using the Gamma analysis method. Modulated dose profiles were compared against state-of-the-art tube current modulation schemes. Impacts of ScoutCT-NET patient model-based dose modulation schemes on universal-purpose CT acquisitions and organ-specific acquisitions were also compared in terms of overall image appearance, noise magnitude, and noise uniformity.

RESULTS

The results demonstrate that (1) The end-to-end trained ScoutCT-NET can be used to generate 3D patient attenuation models and demonstrate empirical generalizability. (2) The 3D patient models can be used to accurately estimate the spatial distribution of radiation dose delivered by standard helical CTs prior to the actual CT acquisition; compared to the gold-standard dose distribution, 95.0% of the voxels in the ScoutCT-NET based dose maps have acceptable gamma values for 5 mm distance-to-agreement and 10% dose difference. (3) The 3D patient models also enabled accurate prescription of fluence-field modulated CT to generate a more uniform noise distribution across the patient body compared to tube current-modulated CT. (4) ScoutCT-NET 3D patient models enabled accurate prescription of organ-specific CT to boost image quality for a given body region-of-interest under a given radiation dose constraint.

CONCLUSION

3D tomographic attenuation models generated by ScoutCT-NET from two-view scout images can be used to prescribe fluence-field-modulated or organ-specific CT scans with high accuracy for the overall objective of radiation dose reduction or image quality improvement for a given imaging task.

The z-sbDBA, a new concept for a dynamic sheet-based fluence field modulator in x-ray CT

PURPOSE

We present a new concept for dynamic fluence field modulation (FFM) in x-ray computed tomography (CT). The so-called z-aligned sheet-based dynamic beam attenuator (z-sbDBA) is developed to dynamically compensate variations in patient attenuation across the fan beam and the projection angle. The goal is to enhance image quality and to reduce patient radiation dose.

METHODS

The z-sbDBA consists of an array of attenuation sheets aligned along the z direction. In neutral position, the array is focused toward the focal spot. Tilting the z-sbDBA defocuses the sheets, thus reducing the transmission for larger fan beam angles. The structure of the z-sbDBA significantly differs from the previous sheet-based dynamic beam attenuator (sbDBA) in two features: (a) The sheets of the z-sbDBA are aligned parallel to the detector rows, and (b) the height of the sheets increases from the center toward larger fan beam angles. We built a motor actuated prototype of the z-sbDBA integrated into a clinical CT scanner. In experiments, we investigated its feasibility for FFM. We compared the z-sbDBA to common CT bowtie filters in terms of the spectral dependency of the transmission and possible image variance distribution in reconstructed phantom images. Additionally, the potential radiation dose saving using z-sbDBA for region-of-interest (ROI) imaging was studied.

RESULTS

Our experimental results confirm that the z-sbDBA can realize variable transmission profiles of the radiation fluence by only small tilts. Compared to the sbDBA, the z-sbDBA can mitigate some practical and mechanical issues. In comparison to bowtie filters, the spectral dependency is considerably reduced when using the z-sbDBA. Likewise, more homogeneous image variance distributions can be attained in reconstructed phantom images. The z-sbDBA allows controlling the spatial image variance distribution which makes it suitable for ROI imaging. Our comparison on ROI imaging reveals skin dose reductions of up to 35% at equal ROI image quality by using the z-sbDBA.

CONCLUSION

Our new concept for FFM in x-ray CT, the z-sbDBA, was experimentally validated on a clinical CT scanner. It facilitates dynamic FFM by realizing variable transmission profiles across the fan beam angle on a projection-wise basis. This key feature allows for substantial improvements in image quality, a reduction in patient radiation dose, and additionally provides a technical solution for ROI imaging.

Dynamic fluence field modulation in computed tomography using multiple aperture devices

A novel beam filter consisting of multiple aperture devices (MADs) has been developed for dynamic fluence field modulation (FFM) in CT. Each MAD achieves spatial modulation of x-ray through fine-scale, highly attenuating tungsten bars of varying widths and spacings. Moiré patterns produced by relative motions between two MADs provide versatile classes of modulation profiles. The dual-MAD filter can be designed to achieve specific classes of target profiles. The designed filter was manufactured through a laser-sintering process and integrated to an experimental imaging system that enables linear actuation of the MADs. Dynamic FFM was achieved through a combination of beam shape modulation (by relative MAD motion) and amplitude modulation (by view-dependent mAs). To correct for gains associated with the MADs, we developed an algorithm to account for possible focal spot changes during/between scans and spectral effects introduced by the MADs. We performed FFM designs for phantoms following two imaging objectives: (1) to achieve minimum mean variance in filtered backprojection (FBP) reconstruction, and (2) to flatten the fluence behind the phantom. Comparisons with conventional FFM strategies involving a static bowtie and pulse width modulation were performed. The dual-MAD filter produced modulation profiles closely matched with the design target, providing varying beam widths not achievable by the static bowtie. The entire range of modulation profiles was achieved by 0.373 mm of MAD displacement. The correction algorithm effectively alleviated ring artifacts as a result of MADs while preserving phantom details such as wires and tissue boundaries. Dynamic FFM enabled by the MADs were effective in achieving the imaging objectives and demonstrated superior FFM capabilities compared to the static bowtie. In an ellipse phantom, the FFM of objective 1 achieved the lowest mean variance in all cases investigated. The FFM of objective 2 produce nearly isotropic local noise power spectrum and homogeneous noise magnitude. The dual-MAD filter provides an effective tool for fluence control in CT to overcome limitations of conventional static bowties and to further enable patient-specific FFM studies for a wide range of dose and image quality objectives.

Volume-of-interest Imaging Using Multiple Aperture Devices

Volume-of-interest (VOI) imaging is a promising strategy for dose reduction in computed tomography (CT) while retaining image quality. However, implementation of VOI-CT has been challenged by the lack of adequate hardware and the interior tomography reconstruction problem. Multiple aperture devices (MAD) are a novel filtration scheme that can achieve x-ray fluence field modulation in a compact design with small translations. In this work, we propose a general approach for VOI imaging using MADs. MAD trajectories are designed to dynamically tailor the fluence for prescribed VOI. A penalized-likelihood reconstruction algorithm is proposed for fully truncated projections extended with scout views. Physical experiments were conducted to verify the feasibility for non-centered elliptic VOIs. Image quality and dose were estimated and compared with standard fullfield protocols. The ability of MAD-based VOI imaging to retain high image quality while significantly decreasing the total dose is demonstrated, suggesting the potential for dose reduction in clinical CT applications.

Fluid-filled dynamic bowtie filter: Description and comparison with other modulators

PURPOSE

A dynamic bowtie filter can modulate flux along both fan and view angles for reduced patient dose, scatter, and required photon flux, which is especially important for photon counting detectors (PCDs). Among the proposed dynamic bowtie designs, the piecewise-linear attenuator (Hsieh and Pelc, Med Phys. 2013;40:031910) offers more flexibility than conventional filters, but relies on analog positioning of a limited number of wedges. In this work, we study our previously proposed dynamic attenuator design, the fluid-filled dynamic bowtie filter (FDBF) that has digital control. Specifically, we use computer simulations to study fluence modulation, reconstructed image noise, and radiation dose and to compare it to other attenuators. FDBF is an array of small channels each of which, if it can be filled with dense fluid or emptied quickly, has a binary effect on the flux. The cumulative attenuation from each channel along the x-ray path contributes to the FDBF total attenuation.

METHODS

An algorithm is proposed for selecting which FDBF channels should be filled. Two optimization metrics are considered: minimizing the maximum-count-rate for PCDs and minimizing peak-variance for energy-integrating detectors (EIDs) at fixed radiation dose (for optimizing dose efficiency). Using simulated chest, abdomen, and shoulder data, the performance is compared with a conventional bowtie and a piecewise-linear attenuator. For minimizing peak-variance, a perfect-attenuator (hypothetical filter capable of adjusting the fluence of each ray individually) and flat-variance attenuator are also included in the comparison. Two possible fluids, solutions of zinc bromide and gadolinium chloride, were tested.

RESULTS

To obtain the same SNR as routine clinical protocols, the proposed FDBF reduces the maximum-count-rate (across projection data, averaged over the test objects) of PCDs to 1.2 Mcps/mm² , which is 55.8 and 3.3 times lower than the max-count-rate of the conventional bowtie and the piecewise-linear bowtie, respectively. (Averaged across objects for FDBF, the max-count-rate without object and FDBF is 2063.5 Mcps/mm² , and the max-count-rate with object without FDBF is 749.8 Mcps/mm² .) Moreover, for the peak-variance analysis, the FDBF can reduce entrance-energy-fluence (sum of energy incident on objects, used as a surrogate for dose) to 34% of the entrance-energy-fluence from the conventional filter on average while achieving the same peak noise level. Its entrance-energy-fluence reduction performance is only 7% worse than the perfect-attenuator on average and is 13% better than the piecewise-linear filter for chest and shoulder. Furthermore, the noise-map in reconstructed image domain from the FDBF is more uniform than the piecewise-linear filter, with 3 times less variation across the object. For the dose reduction task, the zinc bromide solution performed slightly poorer than stainless steel but was better than the gadolinium chloride solution.

CONCLUSIONS

The FDBF allows finer control over flux distribution compared to piecewise-linear and conventional bowtie filters. It can reduce the required maximum-count-rate for PCDs to a level achievable by current detector designs and offers a high dose reduction factor.

Two-dimensional noise reconstruction in proton computed tomography using distance-driven filtered back-projection of simulated projections

We present a formalism for two-dimensional (2D) noise reconstruction in proton computed tomography (pCT). This is necessary for the application of fluence modulated pCT (FMpCT) since it permits image noise prescription and the corresponding proton fuence optimization. We aimed at extending previously published formalisms to account for the impact of multiple Coulomb scattering (MCS) on projection noise, and the use of filtered back projection (FBP) reconstruction along curved paths with distance driven binning (DDB). 2D noise reconstruction for a beam of protons with parallel initial momentum vectors, and for projections binned both at the rear tracker and with DDB, was established. Monte Carlo (MC) simulations of pCT scans of a water cylinder were employed to generate pCT projections and to calculate their noise for use in 2D noise reconstruction. These were compared to results from an analytical model accounting for MCS for rear tracker binning as well as against the previously published central pixel model which ignores MCS. Image noise reconstructed with the formalism for rear tracker binning and DDB were compared to MC results using annular regions of interest (ROIs). Agreement better than 8% was obtained between the noise of projections calculated with MC simulation and our model. Noise from annular ROIs agreed with our noise reconstructions for rear tracker binning and DDB. The central pixel model ignoring MCS underestimated projection and thus image noise by up to 40% towards the object's edge. The use of DDB decreased the image noise towards the object's edge when compared to rear tracker binning and yielded more uniform noise throughout the image. MCS should not be neglected when predicting image noise for pixels away from the center of an object in a pCT scan due to the increasing influence of the gradient of the object's hull closer to the edges.

Dynamic fluence field modulation for miscentered patients in computed tomography

Traditional CT image acquisition uses bowtie filters to reduce dose, x-ray scatter, and detector dynamic range requirements. However, accurate patient centering within the bore of the CT scanner takes time and is often difficult to achieve precisely. Patient miscentering combined with a static bowtie filter can result in significant increases in dose, reconstruction noise, and CT number variations, and consequently raise overall exposure requirements. Approaches to estimate the patient position from scout scans and perform dynamic spatial beam filtration during acquisition are developed and applied in physical experiments on a CT test bench using different beam filtration strategies. While various dynamic beam modulation strategies have been developed, we focus on two approaches: (1) a simple approach using attenuation-based beam modulation using a translating bowtie filter and (2) dynamic beam modulation using multiple aperture devices (MADs)-an emerging beam filtration strategy based on binary filtration of the x-ray beam using variable width slits in a high-density beam blocker. Improved dose utilization and more consistent image performance with respect to an unmodulated baseline (static filter) are demonstrated for miscentered objects and dynamic beam filtration in physical experiments. For a homogeneous object miscentered by 4 cm, the dynamic filter reduced the maximum regional noise and dose penalties (compared with a centered object) from 173% to 16% and 42% to 14%, respectively, for a traditional bowtie, 29% to 8% and 24% to 15%, respectively, for a single MAD, and 275% to 11% and 56% to 18%, respectively, for a dual-MAD filter. The proposed methodology has the potential to relax patient centering requirements within the scanner, reduce setup time, and facilitate additional CT dose reduction.

Regularization strategies in statistical image reconstruction of low-dose x-ray CT: A review

Statistical image reconstruction (SIR) methods have shown potential to substantially improve the image quality of low-dose x-ray computed tomography (CT) as compared to the conventional filtered back-projection (FBP) method. According to the maximum a posteriori (MAP) estimation, the SIR methods are typically formulated by an objective function consisting of two terms: (a) a data-fidelity term that models imaging geometry and physical detection processes in projection data acquisition, and (b) a regularization term that reflects prior knowledge or expectations of the characteristics of the to-be-reconstructed image. SIR desires accurate system modeling of data acquisition, while the regularization term also has a strong influence on the quality of reconstructed images. A variety of regularization strategies have been proposed for SIR in the past decades, based on different assumptions, models, and prior knowledge. In this paper, we review the conceptual and mathematical bases of these regularization strategies and briefly illustrate their efficacies in SIR of low-dose CT.

Quantitative accuracy of CT numbers: Theoretical analyses and experimental studies

PURPOSE

The CT number accuracy, that is, CT number bias, plays an important role in clinical diagnosis. When strategies to reduce radiation dose are discussed, it is important to make sure that the CT number bias is controlled within an acceptable range. The purpose of this paper was to investigate the dependence of CT number bias on radiation dose level and on image contrast (i.e., the difference in CT number between the ROI and the background) in Computed Tomography (CT).

METHODS

A lesion-background model was introduced to theoretically study how the CT number bias changes with radiation exposure level and with CT number contrast when a simple linear reconstruction algorithm such as filtered backprojection (FBP) is used. The theoretical results were validated with experimental studies using a benchtop CT system equipped with a photon-counting detector (XC-HYDRA FX50, XCounter AB, Sweden) and a clinical diagnostic MDCT scanner (Discovery CT750 HD, GE Healthcare, Waukesha, WI, USA) equipped with an energy-integrating detector. The Catphan phantom (Catphan 600, the Phantom Laboratory, Salem, NY, USA) was scanned at different mAs levels and 50 scans were performed for each mAs. The bias of CT number was evaluated for each combination of mAs and ROIs with different contrast levels. An anthropomorphic phantom (ATOM 10-year-old phantom, Model 706, CIRS Inc. Norfolk, VA, USA) with much more heterogeneous object content was used to test the applicability of the theory to the more general image object cases.

RESULTS

Both theoretical and experimental studies showed that the CT number bias is inversely proportional to the radiation exposure level yet linearly dependent on the CT number contrast between the lesion and the background, that is, Bias ( μ ^ 1 FBP ) = α mAs ( 1 + β Δ H U ) .

CONCLUSIONS

The quantitative accuracy of CT numbers can be problematic and thus needs some extra attention when radiation dose is reduced. In this work, we showed that the bias of the FBP reconstruction increases as mAs is reduced; both positive and negative bias can be observed depending on the contrast difference between a targeted ROI and its surrounding background tissues.

Fixed, object-specific intensity compensation for cone beam optical CT radiation dosimetry

Optical cone beam computed tomography (CT) scanning of radiochromic gel dosimeters, using a CCD camera and a low stray light convergent source, provides fast, truly 3D radiation dosimetry with high accuracy. However, a key limiting factor in radiochromic gel dosimetry at large (⩾10 cm diameter) volumes is the initial attenuation of the dosimeters. It is not unusual to observe a 5-10×  difference in signal intensity through the dosimeter center versus through the surrounding medium in pre-irradiation images. Thus, all dosimetric information in a typical experiment is measured within the lower 10%-20% of the camera sensor's range, and re-use of gels is often not possible due to a lack of transmission. To counteract this, in this note we describe a simple method to create source compensators by printing on transparent films. This technique, which is easily implemented and inexpensive, is an optical analogue to the bowtie filter in x-ray CT. We present transmission images and solution phantom reconstructions to demonstrate that (1) placing compensators beyond the focal zone of the imaging lens prevents high spatial frequency features of the printed films from generating reconstruction artifacts, and (2) object-specific compensation considerably reduces the range of intensities measured in projection images. This will improve the measurable dose range in optical CT dosimetry, and will enable imaging of larger gel volumes (∼15 cm diameter). Additionally, it should enable re-use of dosimeters by printing a new compensator for a second experiment.

Dynamic beam filtering for miscentered patients

Purpose

Accurate centering of the patient within the bore of a CT scanner takes time and is often difficult to achieve precisely. Patient miscentering can result in significant dose and image noise penalties with the use of traditional bowtie filters. This work describes a system to dynamically position an x-ray beam filter during image acquisition to enable more consistent image performance and potentially lower dose needed for CT imaging.

Methods

We propose a new approach in which two orthogonal low-dose scout images are used to estimate a parametric model of the object describing its shape, size, and location within the field of view (FOV). This model is then used to compute an optimal filter motion profile by minimizing the variance of the expected detector fluence for each projection. Dynamic filtration was implemented on a cone-beam CT (CBCT) test bench using two different physical filters: 1) an aluminum bowtie and 2) a structured binary filter called a multiple aperture device (MAD). Dynamic filtration performance was compared to a static filter in studies of dose and reconstruction noise as a function of the degree of miscentering of a homogeneous water phantom.

Results

Estimated filter trajectories were found to be largely sinusoidal with an amplitude proportional to the amount of miscentering. Dynamic filtration demonstrated an improved ability to keep the spatial distribution of dose and reconstruction noise at baseline levels across varying levels of miscentering, reducing the maximum noise and dose deviation from 53% to 15% and 42% to 14% respectively for the bowtie filter, and 25% to 8% and 24% to 15% respectively for the MAD filter.

Conclusion

Dynamic positioning of beam filters during acquisition improves dose utilization and image quality over static filters for miscentered patients. Such dynamic filters relax positioning requirements and have the potential to reduce set-up time and lower dose requirements.

Joint Optimization of Fluence Field Modulation and Regularization for Multi-Task Objectives

This work investigates task-driven optimization of fluence field modulation (FFM) and regularization for model-based iterative reconstruction (MBIR) when different imaging tasks are presented by different organs. Example applications of the design framework were demonstrated in an abdomen phantom where the task of interest in the liver is a low-contrast, low-frequency detection task while that in the kidney is a high-contrast, high-frequency discrimination task. The global performance objective is based on maximizing local detectability index (d') at a discrete set of locations. Two objective functions were formulated based on different imaging needs: 1) a maxi-min objective where all tasks are equally important, and 2) a region-of-interest (ROI) objective to maximize imaging performance in an ROI while maintaining a minimum level of performance elsewhere. The FFM pattern for the maxi-min objective is determined by the most challenging task in the liver where both angular and spatial modulation resulted in a ~35% improvement in d' compared to an unmodulated case. The FFM for the ROI objective prescribes the most fluence to the organs of interest, boosting d' by ~59%, but manages to achieve the minimum d' target elsewhere. A spatially varying regularization was found to be important when tasks of different frequency content are present in different parts of the image - the optimal regularization strength for the two studied tasks differed by two orders of magnitude. Initial investigations in this work demonstrated that a multi-task objective is potentially important in shaping the optimal FFM and MBIR regularization, and that these tools may help to generalize task-based acquisition and reconstruction design for more complex diagnostic scenarios.

Grating Oriented Line-Wise Filtration (GOLF) for Dual-Energy X-ray CT

In medical X-ray Computed Tomography (CT), the use of two distinct X-ray source spectra (energies) allows dose-reduction and material discrimination relative to that achieved with only one source spectrum. Existing dual-energy CT methods include source kVp-switching, double-layer detection, dual-source gantry, and two-pass scanning. Each method suffers either from strong spectral correlation or patient-motion artifacts. To simultaneously address these problems, we propose to improve CT data acquisition with the Grating Oriented Line-wise Filtration (GOLF) method, a novel X-ray filter that is placed between the source and patient. GOLF uses a combination of absorption and filtering gratings that are moved relative to each other and in synchronization with the X-ray tube kVp-switching process and/or the detector view-sampling process. Simulation results show that GOLF can improve the spectral performance of kVp-switching to match that of dual-source CT while avoiding patient motion artifacts and dual imaging chains. Although significant flux is absorbed by this pre-patient filter, the proposed GOLF method is a novel path for cost-effectively extracting dual-energy or multi-energy data and reducing radiation dose with or without kVp switching.

Task-Driven Optimization of Fluence Field and Regularization for Model-Based Iterative Reconstruction in Computed Tomography

This paper presents a joint optimization of dynamic fluence field modulation (FFM) and regularization in quadratic penalized-likelihood reconstruction that maximizes a task-based imaging performance metric. We adopted a task-driven imaging framework for prospective designs of the imaging parameters. A maxi-min objective function was adopted to maximize the minimum detectability index ( ) throughout the image. The optimization algorithm alternates between FFM (represented by low-dimensional basis functions) and local regularization (including the regularization strength and directional penalty weights). The task-driven approach was compared with three FFM strategies commonly proposed for FBP reconstruction (as well as a task-driven TCM strategy) for a discrimination task in an abdomen phantom. The task-driven FFM assigned more fluence to less attenuating anteroposterior views and yielded approximately constant fluence behind the object. The optimal regularization was almost uniform throughout image. Furthermore, the task-driven FFM strategy redistribute fluence across detector elements in order to prescribe more fluence to the more attenuating central region of the phantom. Compared with all strategies, the task-driven FFM strategy not only improved minimum by at least 17.8%, but yielded higher over a large area inside the object. The optimal FFM was highly dependent on the amount of regularization, indicating the importance of a joint optimization. Sample reconstructions of simulated data generally support the performance estimates based on computed . The improvements in detectability show the potential of the task-driven imaging framework to improve imaging performance at a fixed dose, or, equivalently, to provide a similar level of performance at reduced dose.

Task-driven optimization of CT tube current modulation and regularization in model-based iterative reconstruction

Tube current modulation (TCM) is routinely adopted on diagnostic CT scanners for dose reduction. Conventional TCM strategies are generally designed for filtered-backprojection (FBP) reconstruction to satisfy simple image quality requirements based on noise. This work investigates TCM designs for model-based iterative reconstruction (MBIR) to achieve optimal imaging performance as determined by a task-based image quality metric. Additionally, regularization is an important aspect of MBIR that is jointly optimized with TCM, and includes both the regularization strength that controls overall smoothness as well as directional weights that permits control of the isotropy/anisotropy of the local noise and resolution properties. Initial investigations focus on a known imaging task at a single location in the image volume. The framework adopts Fourier and analytical approximations for fast estimation of the local noise power spectrum (NPS) and modulation transfer function (MTF)-each carrying dependencies on TCM and regularization. For the single location optimization, the local detectability index (d') of the specific task was directly adopted as the objective function. A covariance matrix adaptation evolution strategy (CMA-ES) algorithm was employed to identify the optimal combination of imaging parameters. Evaluations of both conventional and task-driven approaches were performed in an abdomen phantom for a mid-frequency discrimination task in the kidney. Among the conventional strategies, the TCM pattern optimal for FBP using a minimum variance criterion yielded a worse task-based performance compared to an unmodulated strategy when applied to MBIR. Moreover, task-driven TCM designs for MBIR were found to have the opposite behavior from conventional designs for FBP, with greater fluence assigned to the less attenuating views of the abdomen and less fluence to the more attenuating lateral views. Such TCM patterns exaggerate the intrinsic anisotropy of the MTF and NPS as a result of the data weighting in MBIR. Directional penalty design was found to reinforce the same trend. The task-driven approaches outperform conventional approaches, with the maximum improvement in d' of 13% given by the joint optimization of TCM and regularization. This work demonstrates that the TCM optimal for MBIR is distinct from conventional strategies proposed for FBP reconstruction and strategies optimal for FBP are suboptimal and may even reduce performance when applied to MBIR. The task-driven imaging framework offers a promising approach for optimizing acquisition and reconstruction for MBIR that can improve imaging performance and/or dose utilization beyond conventional imaging strategies.

Task-driven optimization of CT tube current modulation and regularization in model-based iterative reconstruction

Tube current modulation (TCM) is routinely adopted on diagnostic CT scanners for dose reduction. Conventional TCM strategies are generally designed for filtered-backprojection (FBP) reconstruction to satisfy simple image quality requirements based on noise. This work investigates TCM designs for model-based iterative reconstruction (MBIR) to achieve optimal imaging performance as determined by a task-based image quality metric. Additionally, regularization is an important aspect of MBIR that is jointly optimized with TCM, and includes both the regularization strength that controls overall smoothness as well as directional weights that permits control of the isotropy/anisotropy of the local noise and resolution properties. Initial investigations focus on a known imaging task at a single location in the image volume. The framework adopts Fourier and analytical approximations for fast estimation of the local noise power spectrum (NPS) and modulation transfer function (MTF)-each carrying dependencies on TCM and regularization. For the single location optimization, the local detectability index (d') of the specific task was directly adopted as the objective function. A covariance matrix adaptation evolution strategy (CMA-ES) algorithm was employed to identify the optimal combination of imaging parameters. Evaluations of both conventional and task-driven approaches were performed in an abdomen phantom for a mid-frequency discrimination task in the kidney. Among the conventional strategies, the TCM pattern optimal for FBP using a minimum variance criterion yielded a worse task-based performance compared to an unmodulated strategy when applied to MBIR. Moreover, task-driven TCM designs for MBIR were found to have the opposite behavior from conventional designs for FBP, with greater fluence assigned to the less attenuating views of the abdomen and less fluence to the more attenuating lateral views. Such TCM patterns exaggerate the intrinsic anisotropy of the MTF and NPS as a result of the data weighting in MBIR. Directional penalty design was found to reinforce the same trend. The task-driven approaches outperform conventional approaches, with the maximum improvement in d' of 13% given by the joint optimization of TCM and regularization. This work demonstrates that the TCM optimal for MBIR is distinct from conventional strategies proposed for FBP reconstruction and strategies optimal for FBP are suboptimal and may even reduce performance when applied to MBIR. The task-driven imaging framework offers a promising approach for optimizing acquisition and reconstruction for MBIR that can improve imaging performance and/or dose utilization beyond conventional imaging strategies.

Joint Optimization of Fluence Field Modulation and Regularization in Task-Driven Computed Tomography

PURPOSE

This work presents a task-driven joint optimization of fluence field modulation (FFM) and regularization in quadratic penalized-likelihood (PL) reconstruction. Conventional FFM strategies proposed for filtered-backprojection (FBP) are evaluated in the context of PL reconstruction for comparison.

METHODS

We present a task-driven framework that leverages prior knowledge of the patient anatomy and imaging task to identify FFM and regularization. We adopted a maxi-min objective that ensures a minimum level of detectability index (d') across sample locations in the image volume. The FFM designs were parameterized by 2D Gaussian basis functions to reduce dimensionality of the optimization and basis function coefficients were estimated using the covariance matrix adaptation evolutionary strategy (CMA-ES) algorithm. The FFM was jointly optimized with both space-invariant and spatially-varying regularization strength (β) - the former via an exhaustive search through discrete values and the latter using an alternating optimization where β was exhaustively optimized locally and interpolated to form a spatially-varying map.

RESULTS

The optimal FFM inverts as β increases, demonstrating the importance of a joint optimization. For the task and object investigated, the optimal FFM assigns more fluence through less attenuating views, counter to conventional FFM schemes proposed for FBP. The maxi-min objective homogenizes detectability throughout the image and achieves a higher minimum detectability than conventional FFM strategies.

CONCLUSIONS

The task-driven FFM designs found in this work are counter to conventional patterns for FBP and yield better performance in terms of the maxi-min objective, suggesting opportunities for improved image quality and/or dose reduction when model-based reconstructions are applied in conjunction with FFM.

An Investigation of Low-Dose 3D Scout Scans for Computed Tomography

PURPOSE

Commonly 2D scouts or topograms are used prior to CT scan acquisition. However, low-dose 3D scouts could potentially provide additional information for more effective patient positioning and selection of acquisition protocols. We propose using model-based iterative reconstruction to reconstruct low exposure tomographic data to maintain image quality in both low-dose 3D scouts and reprojected topograms based on those 3D scouts.

METHODS

We performed tomographic acquisitions on a CBCT test-bench using a range of exposure settings from 16.6 to 231.9 total mAs. Both an anthropomorphic phantom and a 32 cm CTDI phantom were scanned. The penalized-likelihood reconstructions were made using Matlab and CUDA libraries and reconstruction parameters were tuned to determine the best regularization strength and delta parameter. RMS error between reconstructions and the highest exposure reconstruction were computed, and CTDI_W values were reported for each exposure setting. RMS error for reprojected topograms were also computed.

RESULTS

We find that we are able to produce low-dose (0.417 mGy) 3D scouts that show high-contrast and large anatomical features while maintaining the ability to produce traditional topograms.

CONCLUSIONS

We demonstrated that iterative reconstruction can mitigate noise in very low exposure CT acquisitions to enable 3D CT scout. Such additional 3D information may lead to improved protocols for patient positioning and acquisition refinements as well as a number of advanced dose reduction strategies that require localization of anatomical features and quantities that are not provided by simple 2D topograms.

Experimental evaluation of dual Multiple Aperture Devices for Fluence Field Modulated X-Ray Computed Tomography

Acquisition of CT images with comparable diagnostic power can potentially be achieved with lower radiation exposure than the current standard of care through the adoption of hardware-based fluence-field modulation (e.g. dynamic bowtie filters). While modern CT scanners employ elements such as static bowtie filters and tube-current modulation, such solutions are limited in the fluence patterns that they can achieve, and thus are limited in their ability to adapt to broad classes of patient morphology. Fluence-field modulation also enables new applications such as region-of-interest imaging, task specific imaging, reducing measurement noise or improving image quality. The work presented in this paper leverages a novel fluence modulation strategy that uses "Multiple Aperture Devices" (MADs) which are, in essence, binary filters, blocking or passing x-rays on a fine scale. Utilizing two MAD devices in series provides the capability of generating a large number of fluence patterns via small relative motions between the MAD filters. We present the first experimental evaluation of fluence-field modulation using a dual-MAD system, and demonstrate the efficacy of this technique with a characterization of achievable fluence patterns and an investigation of experimental projection data.

Evaluation of detector readout gain mode and bowtie filters for cone-beam CT imaging of the head

The effects of detector readout gain mode and bowtie filters on cone-beam CT (CBCT) image quality and dose were characterized for a new CBCT system developed for point-of-care imaging of the head, with potential application to diagnosis of traumatic brain injury, intracranial hemorrhage (ICH), and stroke. A detector performance model was extended to include the effects of detector readout gain on electronic digitization noise. The noise performance for high-gain (HG), low-gain (LG), and dual-gain (DG) detector readout was evaluated, and the benefit associated with HG mode in regions free from detector saturation was quantified. Such benefit could be realized (without detector saturation) either via DG mode or by incorporation of a bowtie filter. Therefore, three bowtie filters were investigated that varied in thickness and curvature. A polyenergetic gain correction method was developed to equalize the detector response between the flood-field and projection data in the presence of a bowtie. The effect of bowtie filters on dose, scatter-to-primary ratio, contrast, and noise was quantified in phantom studies, and results were compared to a high-speed Monte Carlo (MC) simulation to characterize x-ray scatter and dose distributions in the head. Imaging in DG mode improved the contrast-to-noise ratio (CNR) by ~15% compared to LG mode at a dose (D 0, measured at the center of a 16 cm CTDI phantom) of 19 mGy. MC dose calculations agreed with CTDI measurements and showed that bowtie filters reduce peripheral dose by as much as 50% at the same central dose. Bowtie filters were found to increase the CNR per unit square-root dose near the center of the image by ~5-20% depending on bowtie thickness, but reduced CNR in the periphery by ~10-40%. Images acquired at equal CTDIw with and without a bowtie demonstrated a 24% increase in CNR at the center of an anthropomorphic head phantom. Combining a thick bowtie filter with a short arc (180°  +  fan angle) scan centered on the posterior of the head reduced dose to the eye lens by up to 90%. Acquisition in DG mode (without a bowtie filter) was beneficial to the detection of small, low contrast lesions (e.g. subtle ICH) in CBCT. While bowtie filters were found to reduce dose, mitigate sensor saturation at the periphery in HG mode, and improve CNR at the center of the image, the image quality at the periphery was slightly reduced compared to DG mode, and the use of a bowtie required careful implementation of the polyenergetic flood-field correction to avoid artifacts.

Design of dual multiple aperture devices for dynamical fluence field modulated CT

A Multiple Aperture Device (MAD) is a novel x-ray beam modulator that uses binary filtration on a fine scale to spatially modulate an x-ray beam. Using two MADs in series enables a large variety of fluence profiles by shifting the MADS relative to each other. This work details the design and control of dual MADs for a specific class of desired fluence patterns. Specifically, models of MAD operation are integrated into a best fit objective followed by CMA-ES optimization. To illustrate this framework we demonstrate the design process for an abdominal phantom with the goal of uniform detected signal. Achievable fluence profiles show good agreement with target fluence profiles, and the ability to flatten projections when a phantom is scanned is demonstrated. Simulated data reconstruction using traditional tube current modulation (TCM) and MAD filtering with TCM are investigated with the dual MAD system demonstrating more uniformity in noise and illustrating the potential for dose reduction under a maximum noise level constraint.