Cubic-Spline Interpolation for Sparse-View CT Image Reconstruction With Filtered Backprojection in Dynamic Myocardial Perfusion Imaging

Enjilela, Esmaeil; Lee, Ting‐Yim; Wisenberg, Gerald; Teefy, Patrick; Bagur, Rodrigo; Islam, Ali; Hsieh, Jiang

doi:10.18383/j.tom.2019.00013

Cited by 8 publications

(8 citation statements)

References 24 publications

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“…GECT n can be used for SVCT reconstruction. The columns (b) to (f) of Figure 11 present the CT images reconstructed by FBP, simultaneous iterative reconstruction technique (SIRT) [9], FBP with cubic‐spline interpolation (FBP‐CI) [42], GECT 3000 , and GECT 12000 , respectively, where the number of projection views of top and bottom rows were 60 and 30, respectively. Table 8 presents the performance evaluation.…”

Section: Experiments and Resultsmentioning

confidence: 99%

Image representation and reconstruction by compositing Gaussian ellipses

Cheng

2023

IET Image Processing

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In this paper, a method of stroke‐based rendering is proposed for image representation and reconstruction. The proposed method involves compositing a set of ellipses that greatly vary in location, size, rotation angle, color, and opacity. This study proves that alpha compositing is differentiable if a two‐dimensional Gaussian function is used to draw a solid ellipse. The gradient method can then be employed for automatically identifying the parameters of each ellipse such that the difference between the input image and the composited image is minimized. Experimental results indicate that the proposed method can represent various types of images including portraits, landscapes, buildings, street scenes, artificial objects, medical images etc. The proposed method can particularly render the most details and significant features of an input image with fewer strokes compared to other stroke‐based rendering algorithms. This study also demonstrates that the proposed method can be applied in painting style simulation and sparse‐view computed tomography imaging.

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Section: Experiments and Resultsmentioning

confidence: 99%

Image representation and reconstruction by compositing Gaussian ellipses

Cheng

2023

IET Image Processing

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“…9 SVCT can also be achieved by compensating for missing projections, such as by inpainting sinograms 10 or interpolating sinograms by using sine waves 11 or cubic splines. 12 Since 2017, many researchers have proposed deep learning-based SVCT methods. [13][14][15][16][17][18] Several of these methods are based on U-Net, 19 a convolutional neural network (CNN) comprising an encoder and decoder for processing two-dimensional (2D) arrays.…”

Section: Introductionmentioning

confidence: 99%

Sparse-view tomographic reconstruction using residual u-net with attention gates

Cheng

2024

Medical Imaging 2024: Image Processing

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Reconstructing a tomographic image with sparse-view sampling is a major challenge in low-dose computed tomography. Recently, several studies have reported that deep-learning-based methods can reconstruct images of 512 × 512 pixels from 60-view X-ray projections without large artifacts. In this study, a U-Net variant with residual connections and attention gates is proposed for sparse-view computed tomography. A pair of the proposed U-Nets with a loss function based on the structural similarity index measure can be applied to synthesize sparse-view sampling sinograms and denoise reconstructed images. The experimental results indicate the performance of the proposed method is superior to those of other U-Net-based methods for fewer than 60 projection views. Experiments on a public data set of chest tomographic images validated that the proposed method can be used for COVID-19 identification.

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“…However, their performance is highly sample dependent due to the sparsity requirement. Previously, continuous image representation has been developed to reduce memory constraints while preserving the image resolution using spline interpolations [49][50][51][52]. While splines are expressive, fitting their parameters to complex signals has not been demonstrated at the scale possible with modern neural fields.…”

Section: Introductionmentioning

confidence: 99%

ONIX: An X‐ray deep‐learning tool for 3D reconstructions from sparse views

et al. 2023

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Time‐resolved three‐dimensional (3D) X‐ray imaging techniques rely on obtaining 3D information for each time point and are crucial for materials‐science applications in academia and industry. Standard 3D X‐ray imaging techniques like tomography and confocal microscopy access 3D information by scanning the sample with respect to the X‐ray source. However, the scanning process limits the temporal resolution when studying dynamics and is not feasible for many materials‐science applications, such as cell‐wall rupture of metallic foams. Alternatives to obtaining 3D information when scanning is not possible are X‐ray stereoscopy and multi‐projection imaging, but these approaches suffer from limited volumetric information as they only acquire a very small number of views or projections compared to traditional 3D scanning techniques. Here, we present optimized neural implicit X‐ray imaging (ONIX), a deep‐learning algorithm capable of retrieving a continuous 3D object representation from only a small and limited set of sparse projections. ONIX is based on an accurate differentiable model of the physics of X‐ray propagation. It generalizes across different instances of similar samples to overcome the limited volumetric information provided by limited sparse views. We demonstrate the capabilities of ONIX compared to state‐of‐the‐art tomographic reconstruction algorithms by applying it to simulated and experimental datasets, where a maximum of eight projections are acquired. ONIX, although it does not have access to any volumetric information, outperforms unsupervised reconstruction algorithms, which reconstruct using single instances without generalization over different instances. We anticipate that ONIX will become a crucial tool for the X‐ray community by (i) enabling the study of fast dynamics not possible today when implemented together with X‐ray multi‐projection imaging and (ii) enhancing the volumetric information and capabilities of X‐ray stereoscopic imaging.

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Cubic-Spline Interpolation for Sparse-View CT Image Reconstruction With Filtered Backprojection in Dynamic Myocardial Perfusion Imaging

Cited by 8 publications

References 24 publications

Image representation and reconstruction by compositing Gaussian ellipses

Image representation and reconstruction by compositing Gaussian ellipses

Sparse-view tomographic reconstruction using residual u-net with attention gates

ONIX: An X‐ray deep‐learning tool for 3D reconstructions from sparse views

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