Zero-Shot Self-Supervised Learning for MRI Reconstruction
Authors: Burhaneddin Yaman, Seyed Amir Hossein Hosseini, Mehmet Akcakaya
ICLR 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We performed experiments on publicly available fully-sampled multi-coil knee and brain MRI from fast MRI database (Knoll et al., 2020a). Figure 3a and b show reconstruction results for Cor-PD knee and Ax-FLAIR brain MRI datasets in this setting. Table 1 shows the average SSIM and PSNR values on 30 test slices. |
| Researcher Affiliation | Academia | Department of Electrical&Computer Engineering, University of Minnesota Center for Magnetic Resonance Research, University of Minnesota {yaman013, hosse049, akcakaya}@umn.edu |
| Pseudocode | No | The paper describes the methodology using text and equations but does not include any explicit pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain an explicit statement about the release of its source code or provide a link to a code repository. |
| Open Datasets | Yes | We performed experiments on publicly available fully-sampled multi-coil knee and brain MRI from fast MRI database (Knoll et al., 2020a). |
| Dataset Splits | Yes | The proposed approach partitions the available measurements from a single scan into three disjoint sets. Two of these sets are used to enforce data consistency and define loss during training for selfsupervision, while the last set serves to self-validate, establishing an early stopping criterion. The k-space self-validation set Γ was selected from the acquired measurements Ωusing a uniformly random selection with |Γ|/|Ω| = 0.2. |
| Hardware Specification | Yes | The computation times were measured on the machines equipped with 4 NVIDIA V100 GPUs (each with 32 GB memory). |
| Software Dependencies | No | The paper mentions specific methods and tools like "Res Net" and "ESPIRi T" with citations, but does not specify versions for general software dependencies or programming languages. |
| Experiment Setup | Yes | All PG-DLR approaches were trained end-to-end using 10 unrolled iterations. End-to-end training was performed with a normalized ℓ1-ℓ2 loss (Adam optimizer, LR = 5 10 4, batch size = 1) (Yaman et al., 2020). |