Task-driven image acquisition and reconstruction in cone-beam CT

Task-based image quality assessment of an intraoperative CBCT for spine surgery compared with conventional CT

PURPOSE

To analyze the image quality of a novel, state-of-the art platform for CBCT image-guided spine surgery, focusing particularly on the dose-effectiveness compared with conventional CT (the gold standard for postoperative assessment).

METHODS

The ClarifEye platform (Philips Healthcare) with integrated augmented-reality surgical navigation, has been compared with a GE Revolution CT (GE Healthcare). The 3D spatial resolution (TTF) and noise (NPS) were evaluated considering relevant feature contrasts (200-900 HU) and background noise for differently sized patients (200-300 mm water-equivalent diameter). These measures were used to determine the noise equivalent quanta (NEQ) and observer model detectability.

RESULTS

The CBCT system exhibited a linear response with 50% TTF at 5.7 cycles/cm (10% TTF at 9.2 cycles/cm), and the axial noise power peaking at about 3.6 cycles/cm (average frequency of 4.1 cycles/cm). The noise magnitude and texture differed markedly compared to iteratively reconstructed CT images (GE ASiR-V). The CBCT system had 26% lower detectability for a high-frequency task (related to edge detection) compared with CT images reconstructed using the Bone kernel combined with ASiR-V 50%. Likewise, it had 18% lower detectability for low- and mid-frequency tasks compared with CT images reconstructed using the Standard kernel. This difference translates to 50%-80% higher CBCT imaging doses required to match the CT image quality.

CONCLUSIONS

The ClarifEye platform demonstrates intraoperative CBCT-imaging capabilities that under certain circumstances are comparable with conventional CT. However, due to limited dose-effectiveness, a trade-off between timeliness and radiation exposure must be considered if end-of-procedure CBCT is to replace postoperative CT.

A nonconvex model-based combined geometric calibration scheme for micro cone-beam CT with irregular trajectories

BACKGROUND

Many dedicated cone-beam CT (CBCT) systems have irregular scanning trajectories. Compared with the standard CBCT calibration, accurate calibration for CBCT systems with irregular trajectories is a more complex task, since the geometric parameters for each scanning view are variable. Most of the existing calibration methods assume that the intrinsic geometric relationship of the fiducials in the phantom is precisely known, and rarely delve deeper into the issue of whether the phantom accuracy is adapted to the calibration model.

PURPOSE

A high-precision phantom and a highly robust calibration model are interdependent and mutually supportive, and they are both important for calibration accuracy, especially for the high-resolution CBCT. Therefore, we propose a calibration scheme that considers both accurate phantom measurement and robust geometric calibration.

METHODS

Our proposed scheme consists of two parts: (1) introducing a measurement model to acquire the accurate intrinsic geometric relationship of the fiducials in the phantom; (2) developing a highly noise-robust nonconvex model-based calibration method. The measurement model in the first part is achieved by extending our previous high-precision geometric calibration model suitable for CBCT with circular trajectories. In the second part, a novel iterative method with optimization constraints based on a back-projection model is developed to solve the geometric parameters of each view.

RESULTS

The simulations and real experiments show that the measurement errors of the fiducial ball bearings (BBs) are within the subpixel level. With the help of the geometric relationship of the BBs obtained by our measurement method, the classic calibration method can achieve good calibration based on far fewer BBs. All metrics obtained in simulated experiments as well as in real experiments on Micro CT systems with resolutions of 9 and 4.5 μm show that the proposed calibration method has higher calibration accuracy than the competing classic method. It is particularly worth noting that although our measurement model proves to be very accurate, the classic calibration method based on this measurement model can only achieve good calibration results when the resolution of the measurement system is close to that of the system to be calibrated, but our calibration scheme enables high-accuracy calibration even when the resolution of the system to be calibrated is twice that of the measurement system.

CONCLUSIONS

The proposed combined geometrical calibration scheme does not rely on a phantom with an intricate pattern of fiducials, so it is applicable in Micro CT with high resolution. The two parts of the scheme, the "measurement model" and the "calibration model," prove to be of high accuracy. The combination of these two models can effectively improve the calibration accuracy, especially in some extreme cases.

Source-detector trajectory optimization in cone-beam computed tomography: a comprehensive review on today’s state-of-the-art

Cone-beam computed tomography (CBCT) imaging is becoming increasingly important for a wide range of applications such as image-guided surgery, image-guided radiation therapy as well as diagnostic imaging such as breast and orthopaedic imaging. The potential benefits of non-circular source-detector trajectories was recognized in early work to improve the completeness of CBCT sampling and extend the field of view (FOV). Another important feature of interventional imaging is that prior knowledge of patient anatomy such as a preoperative CBCT or prior CT is commonly available. This provides the opportunity to integrate such prior information into the image acquisition process by customized CBCT source-detector trajectories. Such customized trajectories can be designed in order to optimize task-specific imaging performance, providing intervention or patient-specific imaging settings. The recently developed robotic CBCT C-arms as well as novel multi-source CBCT imaging systems with additional degrees of freedom provide the possibility to largely expand the scanning geometries beyond the conventional circular source-detector trajectory. This recent development has inspired the research community to innovate enhanced image quality by modifying image geometry, as opposed to hardware or algorithms. The recently proposed techniques in this field facilitate image quality improvement, FOV extension, radiation dose reduction, metal artifact reduction as well as 3D imaging under kinematic constraints. Because of the great practical value and the increasing importance of CBCT imaging in image-guided therapy for clinical and preclinical applications as well as in industry, this paper focuses on the review and discussion of the available literature in the CBCT trajectory optimization field. To the best of our knowledge, this paper is the first study that provides an exhaustive literature review regarding customized CBCT algorithms and tries to update the community with the clarification of in-depth information on the current progress and future trends.

Toward on-the-fly trajectory optimization for C-arm CBCT under strong kinematic constraints

Cone beam computed tomography (CBCT) has become a vital tool in interventional radiology. Usually, a circular source-detector trajectory is used to acquire a three-dimensional (3D) image. Kinematic constraints due to the patient size or additional medical equipment often cause collisions with the imager while performing a full circular rotation. In a previous study, we developed a framework to design collision-free, patient-specific trajectories for the cases in which circular CBCT is not feasible. Our proposed trajectories included enough information to appropriately reconstruct a particular volume of interest (VOI), but the constraints had to be defined before the intervention. As most collisions are unpredictable, performing an on-the-fly trajectory optimization is desirable. In this study, we propose a search strategy that explores a set of trajectories that cover the whole collision-free area and subsequently performs a search locally in the areas with the highest image quality. Selecting the best trajectories is performed using simulations on a prior diagnostic CT volume which serves as a digital phantom for simulations. In our simulations, the Feature SIMilarity Index (FSIM) is used as the objective function to evaluate the imaging quality provided by different trajectories. We investigated the performance of our methods using three different anatomical targets inside the Alderson-Rando phantom. We used FSIM and Universal Quality Image (UQI) to evaluate the final reconstruction results. Our experiments showed that our proposed trajectories could achieve a comparable image quality in the VOI compared to the standard C-arm circular CBCT. We achieved a relative deviation less than 10% for both FSIM and UQI metrics between the reconstructed images from the optimized trajectories and the standard C-arm CBCT for all three targets. The whole trajectory optimization took approximately three to four minutes.

A CT Denoising Neural Network with Image Properties Parameterization and Control

A wide range of dose reduction strategies for x-ray computed tomography (CT) have been investigated. Recently, denoising strategies based on machine learning have been widely applied, often with impressive results, and breaking free from traditional noise-resolution trade-offs. However, since typical machine learning strategies provide a single denoised image volume, there is no user-tunable control of a particular trade-off between noise reduction and image properties (biases) of the denoised image. This is in contrast to traditional filtering and model-based processing that permits tuning of parameters for a level of noise control appropriate for the specific diagnostic task. In this work, we propose a novel neural network that includes a spatial-resolution parameter as additional input permits explicit control of the noise-bias trade-off. Preliminary results show the ability to control image properties through such parameterization as well as the possibility to tune such parameters for increased detectability in task-based evaluation.

Optimization for customized trajectories in cone beam computed tomography

Purpose

We developed a target‐based cone beam computed tomography (CBCT) imaging framework for optimizing an unconstrained three dimensional (3D) source‐detector trajectory by incorporating prior image information. Our main aim is to enable a CBCT system to provide topical information about the target using a limited angle noncircular scan orbit with a minimal number of projections. Such a customized trajectory should include enough information to sufficiently reconstruct a particular volume of interest (VOI) under kinematic constraints, which may result from the patient size or additional surgical or radiation therapy‐related equipment.

Methods

A patient‐specific model from a prior diagnostic computed tomography (CT) volume is used as a digital phantom for CBCT trajectory simulations. Selection of the best projection views is accomplished through maximizing an objective function fed by the imaging quality provided by different x‐ray positions on the digital phantom data. The final optimized trajectory includes a limited angular range and a minimal number of projections which can be applied to a C‐arm device capable of general source‐detector positioning. The performance of the proposed framework is investigated in experiments involving an in‐house‐built box phantom including spherical targets as well as an Alderson‐Rando head phantom. In order to quantify the image quality of the reconstructed image, we use the average full‐width‐half‐maximum (FWHM_avg) for the spherical target and feature similarity index (FSIM), universal quality index (UQI), and contrast‐to‐noise ratio (CNR) for an anatomical target.

Results

Our experiments based on both the box and head phantom showed that optimized trajectories could achieve a comparable image quality in the VOI with respect to the standard C‐arm circular CBCT while using approximately one quarter of projections. We achieved a relative deviation <7% for FWHM_avg between the reconstructed images from the optimized trajectories and the standard C‐arm CBCT for all spherical targets. Furthermore, for the anatomical target, the relative deviation of FSIM, UQI, and CNR between the reconstructed image related to the proposed trajectory and the standard C‐arm circular CBCT was found to be 5.06%, 6.89%, and 8.64%, respectively. We also compared our proposed trajectories to circular trajectories with equivalent angular sampling as the optimized trajectories. Our results show that optimized trajectories can outperform simple partial circular trajectories in the VOI in term of image quality. Typically, an angular range between 116° and 152° was used for the optimized trajectories.

Conclusion

We demonstrated that applying limited angle noncircular trajectories with optimized orientations in 3D space can provide a suitable image quality for particular image targets and has a potential for limited angle and low‐dose CBCT‐based interventions under strong spatial constraints.

Known-component 3D image reconstruction for improved intraoperative imaging in spine surgery: A clinical pilot study

PURPOSE

Intraoperative imaging plays an increased role in support of surgical guidance and quality assurance for interventional approaches. However, image quality sufficient to detect complications and provide quantitative assessment of the surgical product is often confounded by image noise and artifacts. In this work, we translated a three-dimensional model-based image reconstruction (referred to as "Known-Component Reconstruction," KC-Recon) for the first time to clinical studies with the aim of resolving both limitations.

METHODS

KC-Recon builds upon a penalized weighted least-squares (PWLS) method by incorporating models of surgical instrumentation ("known components") within a joint image registration-reconstruction process to improve image quality. Under IRB approval, a clinical pilot study was conducted with 17 spine surgery patients imaged under informed consent using the O-arm cone-beam CT system (Medtronic, Littleton MA) before and after spinal instrumentation. Volumetric images were generated for each patient using KC-Recon in comparison to conventional filtered backprojection (FBP). Imaging performance prior to instrumentation ("preinstrumentation") was evaluated in terms of soft-tissue contrast-to-noise ratio (CNR) and spatial resolution. The quality of images obtained after the instrumentation ("postinstrumentation") was assessed by quantifying the magnitude of metal artifacts (blooming and streaks) arising from pedicle screws. The potential low-dose advantages of the algorithm were tested by simulating low-dose data (down to one-tenth of the dose of standard protocols) from images acquired at normal dose.

RESULTS

Preinstrumentation images (at normal clinical dose and matched resolution) exhibited an average 24.0% increase in soft-tissue CNR with KC-Recon compared to FBP (N = 16, P = 0.02), improving visualization of paraspinal muscles, major vessels, and other soft-tissues about the spine and abdomen. For a total of 72 screws in postinstrumentation images, KC-Recon yielded a significant reduction in metal artifacts: 66.3% reduction in overestimation of screw shaft width due to blooming (P < 0.0001) and reduction in streaks at the screw tip (65.8% increase in attenuation accuracy, P < 0.0001), enabling clearer depiction of the screw within the pedicle and vertebral body for an assessment of breach. Depending on the imaging task, dose reduction up to an order of magnitude appeared feasible while maintaining soft-tissue visibility and metal artifact reduction.

CONCLUSIONS

KC-Recon offers a promising means to improve visualization in the presence of surgical instrumentation and reduce patient dose in image-guided procedures. The improved soft-tissue visibility could facilitate the use of cone-beam CT to soft-tissue surgeries, and the ability to precisely quantify and visualize instrument placement could provide a valuable check against complications in the operating room (cf., postoperative CT).

Task-driven source-detector trajectories in cone-beam computed tomography: II. Application to neuroradiology

We apply the methodology detailed in "Task-driven source-detector trajectories in cone-beam computed tomography: I. Theory and methods" by Stayman et al. for task-driven optimization of source-detector orbits in cone-beam computed tomography (CBCT) to scenarios emulating imaging tasks in interventional neuroradiology. The task-driven imaging framework is used to optimize the CBCT source-detector trajectory by maximizing the detectability index, d ' . The approach was applied to simulated cases of endovascular embolization of an aneurysm and arteriovenous malformation and was translated to real data first using a CBCT test bench followed by implementation on an interventional robotic C-arm. Task-driven trajectories were found to generally favor higher fidelity (i.e., less noisy) views, with an average increase in d ' ranging from 7% to 28%. Visually, this resulted in improved conspicuity of particular stimuli by reducing the noise and altering the noise correlation to a form distinct from the spatial frequencies associated with the imaging task. The improvements in detectability and the demonstration of the task-driven workflow using a real interventional imaging system show the potential of the task-driven imaging framework to improve imaging performance on motorized, multiaxis C-arms in neuroradiology.

Reconstruction-Aware Imaging System Ranking by Use of a Sparsity-Driven Numerical Observer Enabled by Variational Bayesian Inference

It is widely accepted that optimization of imaging system performance should be guided by task-based measures of image quality. It has been advocated that imaging hardware or data-acquisition designs should be optimized by use of an ideal observer that exploits full statistical knowledge of the measurement noise and class of objects to be imaged, without consideration of the reconstruction method. In practice, accurate and tractable models of the complete object statistics are often difficult to determine. Moreover, in imaging systems that employ compressive sensing concepts, imaging hardware and sparse image reconstruction are innately coupled technologies. In this paper, a sparsity-driven observer (SDO) that can be employed to optimize hardware by use of a stochastic object model describing object sparsity is described and investigated. The SDO and sparse reconstruction method can, therefore, be "matched" in the sense that they both utilize the same statistical information regarding the class of objects to be imaged. To efficiently compute the SDO test statistic, computational tools developed recently for variational Bayesian inference with sparse linear models are adopted. The use of the SDO to rank data-acquisition designs in a stylized example as motivated by magnetic resonance imaging is demonstrated. This paper reveals that the SDO can produce rankings that are consistent with visual assessments of the reconstructed images but different from those produced by use of the traditionally employed Hotelling observer.

Personalization of X-Ray Tube Motion in Digital Breast Tomosynthesis Using Virtual Defrise Phantoms

In digital breast tomosynthesis (DBT), projection images are acquired as the x-ray tube rotates in the plane of the chest wall. We constructed a prototype next-generation tomosynthesis (NGT) system that has an additional component of tube motion in the perpendicular direction (i.e., posteroanterior motion). Our previous work demonstrated the advantages of the NGT system using the Defrise phantom. The reconstruction shows higher contrast and fewer blurring artifacts. To expand upon that work, this paper analyzes how image quality can be further improved by customizing the motion path of the x-ray tube based on the object being imaged. In simulations, phantoms are created with realistic 3D breast outlines based on an established model of the breast under compression. The phantoms are given an internal structure similar to a Defrise phantom. Two tissue types (fibroglandular and adipose) are arranged in a square-wave pattern. The reconstruction is analyzed as a binary classification task using thresholding to segment the two tissue types. At various thresholds, the classification of each voxel in the reconstruction is compared against the phantom, and receiver operating characteristic (ROC) curves are calculated. It is shown that the area under the ROC curve (AUC) is dependent on the x-ray tube trajectory. The trajectory that maximizes AUC differs between phantoms. In conclusion, this paper demonstrates that the acquisition geometry in DBT should be personalized to the object being imaged in order to optimize the image quality.

CT automated exposure control using a generalized detectability index

PURPOSE

Identifying an appropriate tube current setting can be challenging when using iterative reconstruction due to the varying relationship between spatial resolution, contrast, noise, and dose across different algorithms. This study developed and investigated the application of a generalized detectability index ( d gen ' ) to determine the noise parameter to input to existing automated exposure control (AEC) systems to provide consistent image quality (IQ) across different reconstruction approaches.

METHODS

This study proposes a task-based automated exposure control (AEC) method using a generalized detectability index ( d gen ' ). The proposed method leverages existing AEC methods that are based on a prescribed noise level. The generalized d gen ' metric is calculated using lookup tables of task-based modulation transfer function (MTF) and noise power spectrum (NPS). To generate the lookup tables, the American College of Radiology CT accreditation phantom was scanned on a multidetector CT scanner (Revolution CT, GE Healthcare) at 120 kV and tube current varied manually from 20 to 240 mAs. Images were reconstructed using a reference reconstruction algorithm and four levels of an in-house iterative reconstruction algorithm with different regularization strengths (IR1-IR4). The task-based MTF and NPS were estimated from the measured images to create lookup tables of scaling factors that convert between d gen ' and noise standard deviation. The performance of the proposed d gen ' -AEC method in providing a desired IQ level over a range of iterative reconstruction algorithms was evaluated using the American College of Radiology (ACR) phantom with elliptical shell and using a human reader evaluation on anthropomorphic phantom images.

RESULTS

The study of the ACR phantom with elliptical shell demonstrated reasonable agreement between the d gen ' predicted by the lookup table and d ' measured in the images, with a mean absolute error of 15% across all dose levels and maximum error of 45% at the lowest dose level with the elliptical shell. For the anthropomorphic phantom study, the mean reader scores for images resulting from the d gen ' -AEC method were 3.3 (reference image), 3.5 (IR1), 3.6 (IR2), 3.5 (IR3), and 2.2 (IR4). When using the d gen ' -AEC method, the observers' IQ scores for the reference reconstruction were statistical equivalent to the scores for IR1, IR2, and IR3 iterative reconstructions (P > 0.35). The d gen ' -AEC method achieved this equivalent IQ at lower dose for the IR scans compared to the reference scans.

CONCLUSIONS

A novel AEC method, based on a generalized detectability index, was investigated. The proposed method can be used with some existing AEC systems to derive the tube current profile for iterative reconstruction algorithms. The results provide preliminary evidence that the proposed d gen ' -AEC can produce similar IQ across different iterative reconstruction approaches at different dose levels.

Tomosynthesis implementation with adaptive online calibration on clinical C-arm systems

PURPOSE

Cone beam computed tomography (CBCT) systems offer physicians crucial 3D and 2D imaging capabilities during interventions. However, certain medical applications only require very specific information from the CBCTs (e.g., determination of the position of high-contrast objects). In diagnostics, tomosynthesis techniques can be used in these cases to minimize dose exposure. Therefore, integrating such techniques on CBCT systems could also be beneficial for interventions. In this paper, we investigate the performance of our implementation of circular tomosynthesis on a CBCT device.

METHODS

The tomosynthesis scan trajectory is realized with step-and-shoot on a clinical C-arm device. The online calibration algorithm uses conventionally acquired 3D CBCT of the scanned object as prior knowledge to correct the imaging geometries. The online calibration algorithm was compared to an offline calibration to test its performance. A ball bearing phantom was used to evaluate the reconstructions with respect to geometric distortions. The evaluation was done for three different scenarios to test the robustness of our tomosynthesis implementation against object deviations (e.g., pen) and different object positioning.

RESULTS

The circular tomosynthesis was tested on a ball bearing and an anthropomorphic phantom. The results show that the calibration is robust against isocenter shifts and object deviations in the CBCT. All reconstructions used 100 projections and displayed limited angle artifacts. The accuracy of the positions and shapes of high-contrast objects were, however, determined precisely. (The maximal center position deviation is 0.31 mm.) CONCLUSION: For medical procedures that primarily determine the precise position of high-contrast objects, circular tomosynthesis could offer an approach to reduce dose exposure.

A fast 4D cone beam CT reconstruction method based on the OSC-TV algorithm

BACKGROUND

Four-dimensional cone beam computed tomography allows for temporally resolved imaging with useful applications in radiotherapy, but raises particular challenges in terms of image quality and computation time.

OBJECTIVE

The purpose of this work is to develop a fast and accurate 4D algorithm by adapting a GPU-accelerated ordered subsets convex algorithm (OSC), combined with the total variation minimization regularization technique (TV).

METHODS

Different initialization schemes were studied to adapt the OSC-TV algorithm to 4D reconstruction: each respiratory phase was initialized either with a 3D reconstruction or a blank image. Reconstruction algorithms were tested on a dynamic numerical phantom and on a clinical dataset. 4D iterations were implemented for a cluster of 8 GPUs.

RESULTS

All developed methods allowed for an adequate visualization of the respiratory movement and compared favorably to the McKinnon-Bates and adaptive steepest descent projection onto convex sets algorithms, while the 4D reconstructions initialized from a prior 3D reconstruction led to better overall image quality.

CONCLUSION

The most suitable adaptation of OSC-TV to 4D CBCT was found to be a combination of a prior FDK reconstruction and a 4D OSC-TV reconstruction with a reconstruction time of 4.5 minutes. This relatively short reconstruction time could facilitate a clinical use.

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.

Modified ideal observer model (MIOM) for high-contrast and high-spatial resolution CT imaging tasks

PURPOSE

Although a variety of mathematical observer models have been developed to predict human observer performance for low contrast lesion detection tasks, their predictive power for high contrast and high spatial resolution discrimination imaging tasks, including those in CT bone imaging, could be limited. The purpose of this work was to develop a modified observer model that has improved correlation with human observer performance for these tasks.

METHODS

The proposed observer model, referred to as the modified ideal observer model (MIOM), uses a weight function to penalize components in the task function that have less contribution to the actual human observer performance for high contrast and high spatial resolution discrimination tasks. To validate MIOM, both human observer and observer model studies were performed, each using exactly the same CT imaging task [discrimination of a connected component in a high contrast (1000 HU) high spatial resolution bone fracture model (0.3 mm)] and experimental CT image data. For the human observer studies, three physicist observers rated the connectivity of the fracture model using a five-point Likert scale; for the observer model studies, a total of five observer models, including both conventional models and the proposed MIOM, were used to calculate the discrimination capability of the CT images in resolving the connected component. Images used in the studies encompassed nine different reconstruction kernels. Correlation between human and observer model performance for these kernels were quantified using the Spearman rank correlation coefficient (ρ). After the validation study, an example application of MIOM was presented, in which the observer model was used to select the optimal reconstruction kernel for a High-Resolution (Hi-Res, GE Healthcare) CT scan technique.

RESULTS

The performance of the proposed MIOM correlated well with that of the human observers with a Spearman rank correlation coefficient ρ of 0.88 (P = 0.003). In comparison, the value of ρ was 0.05 (P = 0.904) for the ideal observer, 0.05 (P = 0.904) for the non-prewhitening observer, -0.18 (P = 0.634) for the non-prewhitening observer with eye filter and internal noise, and 0.30 (P = 0.427) for the prewhitening observer with eye filter and internal noise. Using the validated MIOM, the optimal reconstruction kernel for the Hi-Res mode to perform high spatial resolution and high contrast discrimination imaging tasks was determined to be the HD Ultra kernel at the center of the scan field of view (SFOV), or the Lung kernel at the peripheral region of the SFOV. This result was consistent with visual observations of nasal CT images of an in vivo canine subject.

CONCLUSION

Compared with other observer models, the proposed modified ideal observer model provides significantly improved correlation with human observers for high contrast and high spatial resolution CT imaging tasks.

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.

Task-Driven Orbit Design and Implementation on a Robotic C-Arm System for Cone-Beam CT

PURPOSE

This work applies task-driven optimization to the design of non-circular orbits that maximize imaging performance for a particular imaging task. First implementation of task-driven imaging on a clinical robotic C-arm system is demonstrated, and a framework for orbit calculation is described and evaluated.

METHODS

We implemented a task-driven imaging framework to optimize orbit parameters that maximize detectability index d'. This framework utilizes a specified Fourier domain task function and an analytical model for system spatial resolution and noise. Two experiments were conducted to test the framework. First, a simple task was considered consisting of frequencies lying entirely on the f_z-axis (e.g., discrimination of structures oriented parallel to the central axial plane), and a "circle + arc" orbit was incorporated into the framework as a means to improve sampling of these frequencies, and thereby increase task-based detectability. The orbit was implemented on a robotic C-arm (Artis Zeego, Siemens Healthcare). A second task considered visualization of a cochlear implant simulated within a head phantom, with spatial frequency response emphasizing high-frequency content in the (f_y , f_z ) plane of the cochlea. An optimal orbit was computed using the task-driven framework, and the resulting image was compared to that for a circular orbit.

RESULTS

For the f_z -axis task, the circle + arc orbit was shown to increase d' by a factor of 1.20, with an improvement of 0.71 mm in a 3D edge-spread measurement for edges located far from the central plane and a decrease in streak artifacts compared to a circular orbit. For the cochlear implant task, the resulting orbit favored complementary views of high tilt angles in a 360° orbit, and d' was increased by a factor of 1.83.

CONCLUSIONS

This work shows that a prospective definition of imaging task can be used to optimize source-detector orbit and improve imaging performance. The method was implemented for execution of non-circular, task-driven orbits on a clinical robotic C-arm system. The framework is sufficiently general to include both acquisition parameters (e.g., orbit, kV, and mA selection) and reconstruction parameters (e.g., a spatially varying regularizer).

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.

Task-Based Design of Fluence Field Modulation in CT for Model-Based Iterative Reconstruction

A task-driven imaging framework for prospective fluence field modulation (FFM) is developed in this paper. The design approach uses a system model that includes a parameterized FFM acquisition and model-based iterative reconstruction (MBIR) for image formation. Using prior anatomical knowledge (e.g. from a low-dose 3D scout image), accurate predictions of spatial resolution and noise as a function of FFM are integrated into a task-based objective function. Specifically, detectability index (d'), a common metric for task-based image quality assessment, is computed for a specific formulation of the imaging task. To optimize imaging performance in across an image volume, a maximin objective function was adopted to maximize the minimum detectability index for many locations sampled throughout the volume. To reduce the dimensionality, FFM patterns were represented using wavelet bases, the coefficients of which were optimized using the covariance matrix adaptation evolutionary strategy (CMA-ES) algorithm. The optimization was performed for a mid-frequency discrimination task involving a cluster of micro-calcifications in an abdomen phantom. The task-driven design yielded FFM patterns that were significantly different from traditional strategies proposed for FBP reconstruction. In addition to a higher minimum d' consistent with the objective function, the task-driven approach also improved d' to a greater extent over a larger area of the phantom. Results from this work suggests that FFM strategies suitable for FBP reconstruction need to be reevaluated in the context of MBIR and that a task-driven imaging framework provides a promising approach for such optimization.

Task-Driven Tube Current Modulation and Regularization Design in Computed Tomography with Penalized-Likelihood Reconstruction

PURPOSE

This work applies task-driven optimization to design CT tube current modulation and directional regularization in penalized-likelihood (PL) reconstruction. The relative performance of modulation schemes commonly adopted for filtered-backprojection (FBP) reconstruction were also evaluated for PL in comparison.

METHODS

We adopt a task-driven imaging framework that utilizes a patient-specific anatomical model and information of the imaging task to optimize imaging performance in terms of detectability index (d'). This framework leverages a theoretical model based on implicit function theorem and Fourier approximations to predict local spatial resolution and noise characteristics of PL reconstruction as a function of the imaging parameters to be optimized. Tube current modulation was parameterized as a linear combination of Gaussian basis functions, and regularization was based on the design of (directional) pairwise penalty weights for the 8 in-plane neighboring voxels. Detectability was optimized using a covariance matrix adaptation evolutionary strategy algorithm. Task-driven designs were compared to conventional tube current modulation strategies for a Gaussian detection task in an abdomen phantom.

RESULTS

The task-driven design yielded the best performance, improving d' by ~20% over an unmodulated acquisition. Contrary to FBP, PL reconstruction using automatic exposure control and modulation based on minimum variance (in FBP) performed worse than the unmodulated case, decreasing d' by 16% and 9%, respectively.

CONCLUSIONS

This work shows that conventional tube current modulation schemes suitable for FBP can be suboptimal for PL reconstruction. Thus, the proposed task-driven optimization provides additional opportunities for improved imaging performance and dose reduction beyond that achievable with conventional acquisition and reconstruction.

Fourier Analysis of Noise Characteristics in Cone-Beam Microtomography Laboratory Scanners

GOAL

We investigate the signal and noise performance of an x-ray microtomography system that incorporates a complementary metal-oxide-semiconductor flat-panel detector as a projection image receptor.

METHODS

Signal and noise performance is analyzed in the Fourier domain using modulation-transfer function (MTF), noise-power spectrum (NPS), and noise-equivalent number of quanta (NEQ) with respect to magnification and different convolution kernels for image reconstruction.

RESULTS

Higher magnification provides lower NPS, and thus, higher NEQ performance in the transaxial planes from microtomography. A window function capable of smoothing the ramp filter edge to below one-half of the Nyquist limit results in better performance in terms of NPS and NEQ. The characteristics of convolution kernels do not affect signal and noise performance in longitudinal planes; hence, MTF performance mainly dominates the NEQ performance. The signal and noise performances investigated in this study are demonstrated with images obtained from the contrast phantom and postmortem mouse.

CONCLUSION

The results of our study could be helpful in developing x-ray microtomography systems based on flat-panel detectors.

Task-driven imaging in cone-beam computed tomography

PURPOSE

Conventional workflow in interventional imaging often ignores a wealth of prior information of the patient anatomy and the imaging task. This work introduces a task-driven imaging framework that utilizes such information to prospectively design acquisition and reconstruction techniques for cone-beam CT (CBCT) in a manner that maximizes task-based performance in subsequent imaging procedures.

METHODS

The framework is employed in jointly optimizing tube current modulation, orbital tilt, and reconstruction parameters in filtered backprojection reconstruction for interventional imaging. Theoretical predictors of noise and resolution relates acquisition and reconstruction parameters to task-based detectability. Given a patient-specific prior image and specification of the imaging task, an optimization algorithm prospectively identifies the combination of imaging parameters that maximizes task-based detectability. Initial investigations were performed for a variety of imaging tasks in an elliptical phantom and an anthropomorphic head phantom.

RESULTS

Optimization of tube current modulation and view-dependent reconstruction kernel was shown to have greatest benefits for a directional task (e.g., identification of device or tissue orientation). The task-driven approach yielded techniques in which the dose and sharp kernels were concentrated in views contributing the most to the signal power associated with the imaging task. For example, detectability of a line pair detection task was improved by at least three fold compared to conventional approaches. For radially symmetric tasks, the task-driven strategy yielded results similar to a minimum variance strategy in the absence of kernel modulation. Optimization of the orbital tilt successfully avoided highly attenuating structures that can confound the imaging task by introducing noise correlations masquerading at spatial frequencies of interest.

CONCLUSIONS

This work demonstrated the potential of a task-driven imaging framework to improve image quality and reduce dose beyond that achievable with conventional imaging approaches.