Uncertainty Estimation and Out-of-Distribution Detection for Deep Learning-Based Image Reconstruction using the Local Lipschitz

no code implementations • 12 May 2023 • Danyal F. Bhutto, Bo Zhu, Jeremiah Z. Liu, Neha Koonjoo, Hongwei B. Li, Bruce R. Rosen, Matthew S. Rosen

We compare our proposed approach with baseline methods: Monte-Carlo dropout and deep ensembles, and further analysis included MRI denoising and Computed Tomography (CT) sparse-to-full view reconstruction using UNET architectures.

Computed Tomography (CT) Data Augmentation +3

Synthetic Low-Field MRI Super-Resolution Via Nested U-Net Architecture

no code implementations • 28 Nov 2022 • Aryan Kalluvila, Neha Koonjoo, Danyal Bhutto, Marcio Rockenbach, Matthew S. Rosen

To address this issue, we propose a Nested U-Net neural network architecture super-resolution algorithm that outperforms previously suggested deep learning methods with an average PSNR of 78. 83 and SSIM of 0. 9551.

Image Super-Resolution SSIM

On Real-time Image Reconstruction with Neural Networks for MRI-guided Radiotherapy

no code implementations • 10 Feb 2022 • David E. J. Waddington, Nicholas Hindley, Neha Koonjoo, Christopher Chiu, Tess Reynolds, Paul Z. Y. Liu, Bo Zhu, Danyal Bhutto, Chiara Paganelli, Paul J. Keall, Matthew S. Rosen

The gold-standard for reconstruction of undersampled MR data is compressed sensing (CS) which is computationally slow and limits the rate that images can be available for real-time adaptation.

Image Reconstruction