Fast Variance Prediction for Iteratively Reconstructed CT Images With Locally Quadratic Regularization

Schmitt, Stephan; Goodsitt, Mitchell M.; Fessler, Jeffrey A.

doi:10.1109/tmi.2016.2593259

Cited by 13 publications

(16 citation statements)

References 29 publications

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“…Inclusion of the non-prewhitening feature is important for this work because prewhitening observers are invariant to the amount of smoothing in the reconstructed image and would not yield an optimum for regularization design. Similarly, prewhitening observers can “undo” correlations introduced by the reconstruction process such that the optimal tube current modulation would assign all the fluence to the least attenuating view for the location of signal (Schmitt 2015). Ideally, the observer model should be one that perfectly matches the human visual response, including characteristics of slice scrolling, search, as well as the effect of non-uniform background.…”

Section: Discussionmentioning

confidence: 99%

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

2017

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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 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.

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Section: Discussionmentioning

confidence: 99%

“…In transmission tomography, Gang et al (Gang et al 2011) proposed methods for a spatially-varying penalty strength map to maximize task-based detectability index. Schmitt (Schmitt 2015) examined an exhaustive search method for jointly optimizing penalty strength and TCM parameterized by a sinusoid.…”

Section: A Introductionmentioning

confidence: 99%

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

2017

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“…This leads to a fluorescent Xray undercount rather than a spreading of the count [2]. Such processes might then be modeled in the form [1,2]…”

Section: Measurement Imperfectionsmentioning

confidence: 99%

“…Image quality (IQ) assessment is an important element of system design, optimization, and quality control [1,2]. A complete assessment evaluates the entire imaging chain including both data acquisition and image reconstruction stages.…”

Section: Introductionmentioning

confidence: 99%

Fluorescent X-ray Scan Image Quality Prediction

Weichman

Lavely

2019

J Hardw Syst Secur

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This paper summarizes approaches to image quality prediction in support of an effort under the IARPA RAVEN program to demonstrate a non-destructive, tabletop X-ray microscope for high-resolution 3D imaging of integrated circuits (ICs). The fluorescent X-rays are generated by scanning an electron beam along an appropriately patterned target layer placed in front of the sample and are then detected after passing through the sample by a high-resolution (in both solid angle and energy) backside sensor array. The images are created by way of a model-based tomographic inversion algorithm, with image resolution depending critically on the electron beam scan density and diversity of sample orientations. We derive image quality metrics that quantify the image point spread function and noise sensitivity for any proposed experiment design. Application of these metrics will guide final system design when physical data are not yet available.

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“…12 The theories of noise propagation from raw projection data to an iteratively reconstructed CT image have been previously developed, and can be divided into two main categories: propagation-based methods [13][14][15] and fixed-point based methods. [16][17][18] However, most existing methods are applied on iterative CT reconstruction algorithms with quadratic regularization terms. As numerous nonquadratic regularization terms with better reconstruction properties have been proposed to improve iterative CT reconstruction, [19][20][21] it is of great importance to develop a practical method of computing the pixel-wise noise statistics for general iterative reconstruction methods.…”

Section: Introductionmentioning

confidence: 99%

Pixel-wise estimation of noise statistics on iterative CT reconstruction from a single scan

Wang

Zhu

2017

Med. Phys.

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Purpose As iterative CT reconstruction continues to advance, the spatial distribution of noise standard deviation (STD) and accurate noise power spectrum (NPS) on the reconstructed CT images become important for method evaluation as well as optimization of algorithm parameters. Using a single CT scan, we propose a practical method for pixel‐wise calculation of noise statistics on an iteratively reconstructed CT image, which enables accurate calculation of noise STD for each pixel and NPS. Method We first derive the noise propagation from measured projections to an iteratively reconstructed CT image provided that the projection noise is known. We then show that the model of noise propagation remains approximately unchanged for extra simulated noise added on the measured projections. To compute the noise STD map and the NPS map on an iteratively reconstructed CT image from a single scan, we first iteratively reconstruct the CT image from the measured projections using an existing reconstruction algorithm. The same measured projections are added by different sets (a total of 32 sets in our implementation) of projection noise simulated from an estimated projection noise model, and are then used to iteratively reconstruct different CT images. The calculations of the noise STD map and the NPS map are finally performed on the entire stack of these different reconstruction images. Results We evaluate our method on an anthropomorphic head phantom, and demonstrate the clinical utility on a set of head and neck patient CT data, using two iterative CT reconstruction algorithms: the penalized weighted least‐square (PWLS) algorithm and the total‐variation (TV) regularization. In the head phantom case, repeated scans are acquired to generate the ground truths of noise STD and NPS maps. Using only one single scan, the proposed method accurately calculates the noise STD maps with a root‐mean‐square error (RMSE) of less than 5HU. In the NPS map estimation, we compare the result of our proposed method with that of the conventional method which calculates the NPS maps on a uniform region of interest on one CT image. Our method outperforms the conventional method on the NPS map estimation with RMSE reduced by 92%. The implementation of the proposed method on the patient data successfully provides the noise STD values around complex structures and a high‐quality NPS map. Conclusion The proposed method accurately calculates noise STD for each pixel and NPS on an iteratively reconstructed CT image, with no requirement of repeated CT scans. It provides a detailed evaluation of imaging performance of different iterative reconstruction methods on the same CT dataset.

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Fast Variance Prediction for Iteratively Reconstructed CT Images With Locally Quadratic Regularization

Cited by 13 publications

References 29 publications

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

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

Fluorescent X-ray Scan Image Quality Prediction

Pixel-wise estimation of noise statistics on iterative CT reconstruction from a single scan

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