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| Hauptseite > Publikationsdatenbank > A Deep Learning Framework for Transforming Image Reconstruction Into Pixel Classification > print |
| Hauptseite > Publikationsdatenbank > A Deep Learning Framework for Transforming Image Reconstruction Into Pixel Classification > print |
| 001 | 868442 | ||
| 005 | 20210130004220.0 | ||
| 024 | 7 | _ | |a 10.1109/ACCESS.2019.2959037 |2 doi |
| 024 | 7 | _ | |a 2128/23781 |2 Handle |
| 024 | 7 | _ | |a WOS:000509483800029 |2 WOS |
| 037 | _ | _ | |a FZJ-2020-00036 |
| 082 | _ | _ | |a 621.3 |
| 100 | 1 | _ | |a Pawar, Kamlesh |0 0000-0001-6199-2312 |b 0 |e Corresponding author |
| 245 | _ | _ | |a A Deep Learning Framework for Transforming Image Reconstruction Into Pixel Classification |
| 260 | _ | _ | |a New York, NY |c 2019 |b IEEE |
| 336 | 7 | _ | |a article |2 DRIVER |
| 336 | 7 | _ | |a Output Types/Journal article |2 DataCite |
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| 336 | 7 | _ | |a ARTICLE |2 BibTeX |
| 336 | 7 | _ | |a JOURNAL_ARTICLE |2 ORCID |
| 336 | 7 | _ | |a Journal Article |0 0 |2 EndNote |
| 520 | _ | _ | |a A deep learning framework is presented that transforms the task of MR image reconstruction from randomly undersampled k-space data into pixel classification. A DL network was trained to remove incoherent undersampling artifacts from MR images. The underlying, fully sampled, target image was represented as a discrete quantized image. The quantization step enables the design of a convolutional neural network (CNN) that can classify each pixel in the input image to a discrete quantized level. The reconstructed image quality of the proposed DL classification model was compared with conventional compressed sensing (CS) and a DL regression model. The reconstructed images using the DL classification model outperformed the state-of-the-art compressed sensing and DL regression models with a similar number of parameters assessed using quantitative measures. The experiments reveal that the proposed deep learning method is robust to noise and is able to reconstruct high-quality images in low SNR scenarios where conventional CS reconstructions and DL regression networks perform poorly. A generic design framework for transforming MR image reconstruction into pixel classification is developed. The proposed method can be easily incorporated into other DL-based image reconstruction methods. |
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| 588 | _ | _ | |a Dataset connected to CrossRef |
| 700 | 1 | _ | |a Chen, Zhaolin |0 0000-0002-0173-6090 |b 1 |
| 700 | 1 | _ | |a Shah, N. J. |0 P:(DE-Juel1)131794 |b 2 |u fzj |
| 700 | 1 | _ | |a Egan, Gary. F. |0 0000-0002-3186-4026 |b 3 |
| 773 | _ | _ | |a 10.1109/ACCESS.2019.2959037 |g Vol. 7, p. 177690 - 177702 |0 PERI:(DE-600)2687964-5 |p 177690 - 177702 |t IEEE access |v 7 |y 2019 |x 2169-3536 |
| 856 | 4 | _ | |y OpenAccess |u https://juser.fz-juelich.de/record/868442/files/08931762.pdf |
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