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| 001 | 301775 | ||
| 005 | 20250803021820.0 | ||
| 024 | 7 | _ | |a 10.1002/mp.17910 |2 doi |
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| 100 | 1 | _ | |a Kabelac, Anton Fritz |0 P:(DE-He78)f3e579ec71a1981ca9c53351c460460b |b 0 |e First author |u dkfz |
| 245 | _ | _ | |a Latent space reconstruction for missing data problems in CT. |
| 260 | _ | _ | |a Hoboken, NJ |c 2025 |b Wiley |
| 336 | 7 | _ | |a article |2 DRIVER |
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| 500 | _ | _ | |a #EA:E025#LA:E025# / 2025 Jul;52(7):e17910 |
| 520 | _ | _ | |a The reconstruction of a computed tomography (CT) image can be compromised by artifacts, which, in many cases, reduce the diagnostic value of the image. These artifacts often result from missing or corrupt regions in the projection data, for example, by truncation, metal, or limited angle acquisitions.In this work, we introduce a novel deep learning-based framework, latent space reconstruction (LSR), which enables correction of various types of artifacts arising from missing or corrupted data.First, we train a generative neural network on uncorrupted CT images. After training, we iteratively search for the point in the latent space of this network that best matches the compromised projection data we measured. Once an optimal point is found, forward-projection of the generated CT image can be used to inpaint the corrupted or incomplete regions of the measured raw data.We used LSR to correct for truncation and metal artifacts. For the truncation artifact correction, images corrected by LSR show effective artifact suppression within the field of measurement (FOM), alongside a substantial high-quality extension of the FOM compared to other methods. For the metal artifact correction, images corrected by LSR demonstrate effective artifact reduction, providing a clearer view of the surrounding tissues and anatomical details.The results indicate that LSR is effective in correcting metal and truncation artifacts. Furthermore, the versatility of LSR allows its application to various other types of artifacts resulting from missing or corrupt data. |
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| 650 | _ | 7 | |a computed tomography |2 Other |
| 650 | _ | 7 | |a deep learning |2 Other |
| 650 | _ | 7 | |a missing data |2 Other |
| 700 | 1 | _ | |a Eulig, Elias |0 P:(DE-He78)9f72962d56560b57e5630885c8f8b31d |b 1 |u dkfz |
| 700 | 1 | _ | |a Maier, Joscha |0 P:(DE-He78)3c462b1378ce0906e7320c94e514abfa |b 2 |u dkfz |
| 700 | 1 | _ | |a Hammermann, Maximilian |0 P:(DE-He78)cabbfdc2fdfe17c047e5ec8d90438e18 |b 3 |u dkfz |
| 700 | 1 | _ | |a Knaup, Michael |0 P:(DE-He78)1795257b60b20a0d76c90e1d886faa5c |b 4 |u dkfz |
| 700 | 1 | _ | |a Kachelriess, Marc |0 P:(DE-He78)f288a8f92f092ddb41d52b1aeb915323 |b 5 |e Last author |u dkfz |
| 773 | _ | _ | |a 10.1002/mp.17910 |g p. mp.17910 |0 PERI:(DE-600)1466421-5 |n 7 |p e17910 |t Medical physics |v 52 |y 2025 |x 0094-2405 |
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