Dr. David Rozas Profile

Dr. David Rozas

at Analogic Corp

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Publications (2)

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Proceedings Article | 4 April 2022 Poster + Paper

3D residual convolutional neural network for low dose CT denoising

Alexander Zamyatin, Leiming Yu, David Rozas

Proceedings Volume 12031, 120312J (2022) https://doi.org/10.1117/12.2611575

KEYWORDS: Denoising, 3D image processing, X-ray computed tomography, Data modeling, Computed tomography, Image processing, Signal to noise ratio, RGB color model

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CT continues to be one of the most widely used medical imaging modalities. Concerns about long term effect of x-ray radiation on patients have led to efforts to reduce the x-ray dose imparted during CT exams. Lowering CT dose results in a lower signal to noise ratio in CT data which lowers CT Image Quality (IQ). Deep learning algorithms have shown competitive denoising results against the state-of-art image-based denoising approaches. Among these deep learning algorithms, deep residual networks have demonstrated effectiveness for edge-preserving noise reduction and imaging performance improvement compared to traditional edge-preserving filters. Previously published Residual Encoder- Decoder Convolutional Neural Network (RED-CNN) showed significant achievement for noise suppression, structural preservation, and lesion detection. However, its 2D architecture makes it unsuitable for thin slice and reformatted (sagittal, coronal) imaging. In this work, we present a novel 3D RED-CNN architecture, evaluate the effect of model parameters on performance and IQ, and show steps to improve optimization convergence. We use standard imaging metrics (SSIM, PSNR) to assess imaging performance and compare to previously published algorithms. Compared to 2D RED-CNN, our proposed 3D RED CNN produces higher quality 3D results, as shown by reformatted (sagittal, coronal) views, while maintaining all advantages of the original RED-CNN in axial imaging.

Proceedings Article | 9 March 2018 Paper

Optimal cardiac phase in prospectively gated axial cardiac CT scans

Alexander Zamyatin, Basak Ulker Karbeyaz, Charles Shaughnessy, David Rozas

Proceedings Volume 10573, 105733L (2018) https://doi.org/10.1117/12.2293862

KEYWORDS: Computed tomography, Reconstruction algorithms, Image restoration, Scanners, Collimation, Heart, CT reconstruction, Temporal resolution, Data acquisition

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To improve temporal resolution in prospectively gated axial cardiac CT scans, short scan (half-scan, partial scan) is used for image reconstruction. While some vendors offer scanners with 16cm collimation, capable of collecting entire heart data in a single rotation, the majority of routine cardiac scans are still done with 4cm collimation. In case of a prospective axial cardiac scanning, four or more axial acquisitions are performed at staggered patient table positions to cover the entire heart. At each acquisition, raw data is collected at the prescribed phase of cardiac R-R interval with the range of the x-ray source angles covering one or less than one rotation. If this angle range is greater than what is required for a short scan reconstruction, it allows some room for optimizing the reconstruction phase. Often, such optimization is done by manually reviewing images at slightly different reconstruction phase angles, and selecting the images with the least pronounced motion artifacts. Considering there are at least four acquisitions for each prospective cardiac scan, this may become a tedious time-consuming process. This paper proposes an automated process to select the best short-scan view range within full rotation acquisitions that minimizes motion artifacts at each table position. The proposed method was tested with a motion phantom which was connected to an ECG simulator and clinical cardiac data. Results show that the proposed method reliably provides reduction of motion artifact in reconstructed images.

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