Two-View Topogram-Based Anatomy-Guided CT Reconstruction for Prospective Risk Minimization

no code implementations • 23 Jan 2024 • Chang Liu, Laura Klein, Yixing Huang, Edith Baader, Michael Lell, Marc Kachelrieß, Andreas Maier

The average organ dice of the proposed method is 0. 71 compared with 0. 63 in baseline model, indicating the enhancement of anatomical structures.

Anatomy Generative Adversarial Network +3

Robustness Investigation on Deep Learning CT Reconstruction for Real-Time Dose Optimization

no code implementations • 7 Dec 2020 • Chang Liu, Yixing Huang, Joscha Maier, Laura Klein, Marc Kachelrieß, Andreas Maier

For organ-specific AEC, a preliminary CT reconstruction is necessary to estimate organ shapes for dose optimization, where only a few projections are allowed for real-time reconstruction.

Computed Tomography (CT) Image Reconstruction

Neural Networks-based Regularization for Large-Scale Medical Image Reconstruction

no code implementations • 19 Dec 2019 • Andreas Kofler, Markus Haltmeier, Tobias Schaeffter, Marc Kachelrieß, Marc Dewey, Christian Wald, Christoph Kolbitsch

In this paper we present a generalized Deep Learning-based approach for solving ill-posed large-scale inverse problems occuring in medical image reconstruction.

Image Reconstruction SSIM

Towards Automatic Abdominal Multi-Organ Segmentation in Dual Energy CT using Cascaded 3D Fully Convolutional Network

no code implementations • 15 Oct 2017 • Shuqing Chen, Holger Roth, Sabrina Dorn, Matthias May, Alexander Cavallaro, Michael M. Lell, Marc Kachelrieß, Hirohisa ODA, Kensaku MORI, Andreas Maier

In this paper, we proposed a 3D FCN based method for automatic multi-organ segmentation in DECT.

Organ Segmentation