SABER: A Systems Approach to Blur Estimation and Reduction in X-Ray Imaging

Mohan, K. Aditya; Panas, Robert M.; Cuadra, Jefferson

doi:10.1109/tip.2020.3006339

Cited by 16 publications

(5 citation statements)

References 37 publications

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“…The measurement technique developed for this work, and described previously in Refs. [54] and [55], uses a forward propagation approach to predict images from known material distributions, then modifies the scale of the variation source PSFs to improve the match with the measured image.…”

Section: 24mentioning

confidence: 99%

A Systems Approach to Estimating the Uncertainty Limits of X-Ray Radiographic Metrology

Panas

Cuadra

Mohan

et al. 2021

Journal of Micro and Nano-Manufacturing

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Micro- and nano-manufacturing capabilities have rapidly expanded over the past decade to include complex 3d structure fabrication; however, the metrology required to accurately assess these processes via part inspection and characterization has struggled to keep pace. X-ray Computed Tomography (XCT) is considered an ideal candidate for providing the critically needed metrology on the smallest scales, especially internal features or inaccessible regions. XCT supporting micro- and nano-manufacturing often push against the poorly understood resolution and variation limits inherent to the machines which can distort or hide fine structures. We develop and experimentally verify a comprehensive analytical uncertainty propagation Signal Variation Flow Graph (SVFG) model for X-ray radiography in this work to better understand resolution and image variability limits on the small scale. The SVFG approach captures, quantifies and predicts variations occurring in the system that limit metrology capabilities, particularly in the micro/nano domain. This work is the first step to achieving full uncertainty modeling of computed tomography (CT) reconstructions and provides insight into improving X-ray attenuation imaging systems. The SVFG methodology framework is applied to generate a complete basis set of functions describing the major sources of variation in radiographs. Five models are identified, covering variation in Energy (0DE), Intensity (0DI), Length (1DL), Blur (1DB), and Position (2DP). Radiographic system experiments are defined to measure the parameters required by the SVFGs. Best practices are identified for these measurements. The SVFG models are confirmed via direct measurement of variation to predict variation within 30% on average.

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Section: 24mentioning

confidence: 99%

A Systems Approach to Estimating the Uncertainty Limits of X-Ray Radiographic Metrology

Panas

Cuadra

Mohan

et al. 2021

Journal of Micro and Nano-Manufacturing

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“…Often, noise, resolution and other aspects of image quality are measured using a known test-object, but are not modeled to explain the contributions of the x-ray system components or image reconstruction operations [6]- [10]. The detector blur has been modeled without considering cross-talk [11], [12]. Calculations of noise correlation in reconstructed images have been made without considering cross-talk [13], [14].…”

Section: Introductionmentioning

confidence: 99%

The Impact of Cross-Talk in a Flat Panel Detector on CT Image Quality

Karimi,

Hall,

Smith

et al. 2023

IEEE Open J. Instrum. Meas.

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Spatial resolution and image noise are two aspects of image quality of an x-ray computed tomography (CT) system, and are determined by the x-ray source, the detector, and mathematical operations for image reconstruction. In CT scanners with flat panel detectors, there is cross-talk (signal leakage) between detector pixels. The contribution of the cross-talk to spatial resolution and noise in reconstructed images has not been adequately modeled. Previously, we estimated cross-talk from autocovariance measurements in air, and modeled the impact of cross-talk on spatial resolution. We have extended that work to calculate the impact of cross-talk on signal-to-noise ratio in radiographs and to reconstructed image noise. We modeled the spatial resolution and noise of a CT scanner that uses a flat-panel detector with 0.2 mm pixels and a gadolinium oxysulfide scintillator, and a 450 kVp, dual-focus x-ray tube. Our noise model agrees with measurements from experimental data and simulations to within 10%. We show that cross-talk in flat panel detectors can reduce resolution by over 30%, reduce noise by approximately a factor of two, and introduce correlation in the noise, and therefore, cannot be disregarded when assessing CT image quality.

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“…Long scans also require sophisticated equipment for stabilizing and monitoring the sample during imaging. Non-orthogonal and inconsistent positioning leads to significant image artifacts, particularly in our setting of large-field, high-resolution micro CT [34].…”

Section: Introductionmentioning

confidence: 99%

Sinogram Domain Angular Upsampling of Sparse-View Micro-CT with Dense Residual Hierarchical Transformer and Noise-Aware Loss

Adishesha

Vanselow

Rivière

et al. 2023

Preprint

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Reduced angular sampling is a key strategy for increasing scanning efficiency of micron-scale computed tomography (micro-CT). Despite boosting throughput, this strategy introduces noise and artifacts due to undersampling. In this work, we present a solution to this issue, by proposing a novel Dense Residual Hierarchical Transformer (DRHT) network to recover high-quality sinograms from 2x, 4x and 8x undersampled scans. DRHT is trained to utilize limited information available from sparsely angular sampled scans and once trained, it can be applied to recover higher-resolution sinograms from shorter scan sessions. Our proposed DRHT model aggregates the benefits of a hierarchical- multi-scale structure along with the combination of local and global feature extraction through dense residual convolutional blocks and non-overlapping window transformer blocks respectively. We also propose a novel noise-aware loss function named KL-L1 to improve sinogram restoration to full resolution. KL-L1, a weighted combination of pixel-level and distribution-level cost functions, leverages inconsistencies in noise distribution and uses learnable spatial weights to improve the training of the DRHT model. We present ablation studies and evaluations of our method against other state-of-the-art (SOTA) models over multiple datasets. Our proposed DRHT network achieves an average increase in peak signal to noise ratio (PSNR) of 17.73dB and a structural similarity index (SSIM) of 0.161, for 8x upsampling, across the three unique datasets, compared to their respective Bicubic interpolated versions. This novel approach can be utilized to decrease radiation exposure to patients and reduce imaging time for large-scale CT imaging projects.

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SABER: A Systems Approach to Blur Estimation and Reduction in X-Ray Imaging

Cited by 16 publications

References 37 publications

A Systems Approach to Estimating the Uncertainty Limits of X-Ray Radiographic Metrology

A Systems Approach to Estimating the Uncertainty Limits of X-Ray Radiographic Metrology

The Impact of Cross-Talk in a Flat Panel Detector on CT Image Quality

Sinogram Domain Angular Upsampling of Sparse-View Micro-CT with Dense Residual Hierarchical Transformer and Noise-Aware Loss

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