Technical Note: Free<scp>CT</scp>_<scp>ICD</scp>: An open‐source implementation of a model‐based iterative reconstruction method using coordinate descent optimization for <scp>CT</scp> imaging investigations

Hoffman, John M.; Noo, Frédéric; Young, Stefano; Hsieh, Scott S.; McNitt‐Gray, Michael F.

doi:10.1002/mp.13026

Cited by 5 publications

(15 citation statements)

References 29 publications

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“…While previous investigations into the impacts of quantitative imaging have been performed they have not been comprehensive enough to establish robust confidence for widespread clinical use. One potential application of the pipeline is the exhaustive search of clinical parameters to establish “acceptable” conditions under which a given quantitative test can be performed reliably.…”

Section: Resultsmentioning

confidence: 99%

Technical Note: Design and implementation of a high‐throughput pipeline for reconstruction and quantitative analysis of CT image data

et al. 2019

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Purpose With recent substantial improvements in modern computing, interest in quantitative imaging with CT has seen a dramatic increase. As a result, the need to both create and analyze large, high‐quality datasets of clinical studies has increased as well. At present, no efficient, widely available method exists to accomplish this. The purpose of this technical note is to describe an open‐source high‐throughput computational pipeline framework for the reconstruction and analysis of diagnostic CT imaging data to conduct large‐scale quantitative imaging studies and to accelerate and improve quantitative imaging research. Methods The pipeline consists of two, primary “blocks”: reconstruction and analysis. Reconstruction is carried out via a graphics processing unit (GPU) queuing framework developed specifically for the pipeline that allows a dataset to be reconstructed using a variety of different parameter configurations such as slice thickness, reconstruction kernel, and simulated acquisition dose. The analysis portion then automatically analyzes the output of the reconstruction using “modules” that can be combined in various ways to conduct different experiments. Acceleration of analysis is achieved using cluster processing. Efficiency and performance of the pipeline are demonstrated using an example 142 subject lung screening cohort reconstructed 36 different ways and analyzed using quantitative emphysema scoring techniques. Results The pipeline reconstructed and analyzed the 5112 reconstructed datasets in approximately 10 days, a roughly 72× speedup over previous efforts using the scanner for reconstructions. Tightly coupled pipeline quality assurance software ensured proper performance of analysis modules with regard to segmentation and emphysema scoring. Conclusions The pipeline greatly reduced the time from experiment conception to quantitative results. The modular design of the pipeline allows the high‐throughput framework to be utilized for other future experiments into different quantitative imaging techniques. Future applications of the pipeline being explored are robustness testing of quantitative imaging metrics, data generation for deep learning, and use as a test platform for image‐processing techniques to improve clinical quantitative imaging.

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Section: Resultsmentioning

confidence: 99%

Technical Note: Design and implementation of a high‐throughput pipeline for reconstruction and quantitative analysis of CT image data

et al. 2019

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“…Our algorithm inherits some limitations from the current FreeCT_ICD reference code. The direct output of this code is in a rotating frame of reference and requires derotation prior to storage, leading to possible resolution loss, but encouraging results in this aspect were previously shown . As with FreeCT_ICD, run times are proportional to the SSM size.…”

Section: Discussionmentioning

confidence: 99%

“…applied it to helical CT with a three‐dimensional regularizer and described the process in detail. Its adaptation in the FreeCT package has previously been described …”

Section: Methodsmentioning

confidence: 99%

“…Within the xy‐plane, various strategies have been proposed for selecting the voxel update ordering, including lexicographic ordering (left‐to‐right, top‐to‐bottom), random updates, and a probabilistic update criteria that favors voxels likely to change . In this work, we use a random update strategy where we create a traversal order that visits each voxel once but in random order.…”

Section: Methodsmentioning

confidence: 99%

“…Our group has recently released FreeCT_ICD, an open‐source reconstruction package that implements ICD on diagnostic CT data. Together with efforts to release a repository of open‐source sinogram data, the FreeCT packages could enable reproducible, extensible community development of reconstruction software. However, FreeCT_ICD currently requires several hours to converge to a solution, hampering further development efforts as well as the long‐term viability of using FreeCT_ICD as a tool for CT imaging research.…”

Section: Introductionmentioning

confidence: 99%

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Accelerating iterative coordinate descent using a stored system matrix

2019

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Purpose The computational burden associated with model‐based iterative reconstruction (MBIR) is still a practical limitation. Iterative coordinate descent (ICD) is an optimization approach for MBIR that has sometimes been thought to be incompatible with modern computing architectures, especially graphics processing units (GPUs). The purpose of this work is to accelerate the previously released open‐source FreeCT_ICD to include GPU acceleration and to demonstrate computational performance with ICD that is comparable with simultaneous update approaches. Methods FreeCT_ICD uses a stored system matrix (SSM), which precalculates the forward projector in the form of a sparse matrix and then reconstructs on a rotating coordinate grid to exploit helical symmetry. In our GPU ICD implementation, we shuffle the sinogram memory ordering such that data access in the sinogram coalesce into fewer transactions. We also update NS voxels in the xy‐plane simultaneously to improve occupancy. Conventional ICD updates voxels sequentially (NS = 1). Using NS > 1 eliminates existing convergence guarantees. Convergence behavior in a clinical dataset was therefore studied empirically. Results On a pediatric dataset with sinogram size of 736 × 16 × 13860 reconstructed to a matrix size of 512 × 512 × 128, our code requires about 20 s per iteration on a single GPU compared to 2300 s per iteration for a 6‐core CPU using FreeCT_ICD. After 400 iterations, the proposed and reference codes converge within 2 HU RMS difference (RMSD). Using a wFBP initialization, convergence within 10 HU RMSD is achieved within 4 min. Convergence is similar with NS values between 1 and 256, and NS = 16 was sufficient to achieve maximum performance. Divergence was not observed until NS > 1024. Conclusions With appropriate modifications, ICD may be able to achieve computational performance competitive with simultaneous update algorithms currently used for MBIR.

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Impact of the non‐negativity constraint in model‐based iterative reconstruction from CT data

et al. 2019

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Purpose Model‐based iterative reconstruction is a promising approach to achieve dose reduction without affecting image quality in diagnostic x‐ray computed tomography (CT). In the problem formulation, it is common to enforce non‐negative values to accommodate the physical non‐negativity of x‐ray attenuation. Using this a priori information is believed to be beneficial in terms of image quality and convergence speed. However, enforcing non‐negativity imposes limitations on the problem formulation and the choice of optimization algorithm. For these reasons, it is critical to understand the value of the non‐negativity constraint. In this work, we present an investigation that sheds light on the impact of this constraint. Methods We primarily focus our investigation on the examination of properties of the converged solution. To avoid any possibly confounding bias, the reconstructions are all performed using a provably converging algorithm started from a zero volume. To keep the computational cost manageable, an axial CT scanning geometry with narrow collimation is employed. The investigation is divided into five experimental studies that challenge the non‐negativity constraint in various ways, including noise, beam hardening, parametric choices, truncation, and photon starvation. These studies are complemented by a sixth one that examines the effect of using ordered subsets to obtain a satisfactory approximate result within 50 iterations. All studies are based on real data, which come from three phantom scans and one clinical patient scan. The reconstructions with and without the non‐negativity constraint are compared in terms of image similarity and convergence speed. In select cases, the image similarity evaluation is augmented with quantitative image quality metrics such as the noise power spectrum and closeness to a known ground truth. Results For cases with moderate inconsistencies in the data, associated with noise and bone‐induced beam hardening, our results show that the non‐negativity constraint offers little benefit. By varying the regularization parameters in one of the studies, we observed that sufficient edge‐preserving regularization tends to dilute the value of the constraint. For cases with strong data inconsistencies, the results are mixed: the constraint can be both beneficial and deleterious; in either case, however, the difference between using the constraint or not is small relative to the overall level of error in the image. The results with ordered subsets are encouraging in that they show similar observations. In terms of convergence speed, we only observed one major effect, in the study with data truncation; this effect favored the use of the constraint, but had no impact on our ability to obtain the converged solution without constraint. Conclusions Our results did not highlight the non‐negativity constraint as being strongly beneficial for diagnostic CT imaging. Altogether, we thus conclude that in some imaging scenarios, the non‐negativity constraint could be disregarded to simplify the op...

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Technical Note: FreeCT_ICD: An open‐source implementation of a model‐based iterative reconstruction method using coordinate descent optimization for CT imaging investigations

Cited by 5 publications

References 29 publications

Technical Note: Design and implementation of a high‐throughput pipeline for reconstruction and quantitative analysis of CT image data

Technical Note: Design and implementation of a high‐throughput pipeline for reconstruction and quantitative analysis of CT image data

Accelerating iterative coordinate descent using a stored system matrix

Impact of the non‐negativity constraint in model‐based iterative reconstruction from CT data

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