Journal: IEEE transactions on medical imaging

Article Title: Augmentation of CBCT Reconstructed from Under-sampled Projections using Deep Learning

doi: 10.1109/TMI.2019.2912791

Figure Lengend Snippet: Results of noise study on XCAT. A free-shape region of interest (ROI) is highlighted in red contour in the vertebra body to evaluate our proposed SR-CNNhi robustness against noise. (a) is onboard ground truth CBCT image simulated with XCAT. (b-c) are the SSIM and PSNR of selected ROI as functions of noise level in 72 XCAT-simulated cone-beam half-fan projections. P in x-axis label indicates Poisson noise and N indicates Normal distribution noise. (d-h) are TV-minimization reconstructed images from projections of different noise levels (same order with (b) and (c)). (i-m) are the corresponding difference images between (d-h) and the ground truth. (n-r) are (d-h)sponding difference images between (d-h) and the ground tru corresponding difference images between (n-r) and the ground truth. ROIs are zoomed in and placed on the upper left corners of images. Red arrows indicate image details for visual inspection. Color bar in each column indicates the image display window/level.

Article Snippet: 3) Patient Study using Real CBCT Projections a) Evaluation of SR-CNN using Real 4D-CBCT Projections A subset containing 145 projections was extracted from one phase of a SPARE Varian 4D CBCT scanning, and was used for the under-sampled TV-regularized image reconstruction. shows the qualitative comparison between the referenced FDK-based, under-sampled TV-based and SR-CNN augmented images.

Techniques:

Journal: IEEE transactions on medical imaging

Article Title: Augmentation of CBCT Reconstructed from Under-sampled Projections using Deep Learning

doi: 10.1109/TMI.2019.2912791

Figure Lengend Snippet: Results of SR-CNN robustness study. A free-shape region of interest (ROI) is highlighted in transparent red mask to evaluate our proposed SR-CNNI) robustness against clinical projection number. (a) is ground truth CBCT image reconstructed from fully-sampled projections using a clinical CBCT constructor. (b) is the SSIM of selected ROI as function of projection number. (c-e) are TV based CBCT images reconstructed from 90, 120 and 180 projections, respectively. (f-h) are the corresponding difference images between (c-e) and the ground truth. (i-k) are corresponding SR-CNN augmented images. (l-n) are the corresponding difference images between (i-k) and the ground truth. ROIs are zoomed in and placed on the top corners of images. Red arrows indicate image details for visual inspection. Color bar in each column indicates the image display window/level.

Article Snippet: 3) Patient Study using Real CBCT Projections a) Evaluation of SR-CNN using Real 4D-CBCT Projections A subset containing 145 projections was extracted from one phase of a SPARE Varian 4D CBCT scanning, and was used for the under-sampled TV-regularized image reconstruction. shows the qualitative comparison between the referenced FDK-based, under-sampled TV-based and SR-CNN augmented images.

Techniques: