Self-Attention U-Net with Residual Bottleneck for High-Quality CT to MRI Image Translation

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F26%3A0199903" target="_blank" >RIV/00216305:26220/26:0199903 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1109/icumt67815.2025.11268631" target="_blank" >http://dx.doi.org/10.1109/icumt67815.2025.11268631</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/icumt67815.2025.11268631" target="_blank" >10.1109/icumt67815.2025.11268631</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Self-Attention U-Net with Residual Bottleneck for High-Quality CT to MRI Image Translation

Popis výsledku v původním jazyce

The challenge of high-quality CT to MRI image translation remains a critical issue in medical imaging, as MRI scans are expensive and not always accessible, especially in resource-limited settings. This work addresses this problem by presenting a novel Self-Attention U-Net with Residual Bottleneck architecture for generating high-quality MRI images from low-cost CT scans. The model incorporates self-attention mechanisms to capture long-range dependencies, while the residual bottleneck blocks enable efficient feature propagation and preserve fine details during translation. A spectral-normalized Patch Discriminator is employed to improve the realism of generated images, ensuring accurate synthesis of MRI-like structures. Trained on a dataset of 389 paired CT and MRI images, the model is evaluated using Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM), achieving an average PSNR of 23.57 and an average SSIM of 0.6435. These results indicate the model’s ability to generate high-fidelity MRI images from CT scans with competitive perceptual quality. The proposed approach not only shows potential in reducing the reliance on expensive MRI scans but also provides a cost-effective solution for medical imaging, especially in underserved regions. This method could enhance diagnostic capabilities and patient care by enabling the synthesis of MRI images from readily available CT scans, particularly where MRI accessibility is limited.

Název v anglickém jazyce

Self-Attention U-Net with Residual Bottleneck for High-Quality CT to MRI Image Translation

Popis výsledku anglicky

The challenge of high-quality CT to MRI image translation remains a critical issue in medical imaging, as MRI scans are expensive and not always accessible, especially in resource-limited settings. This work addresses this problem by presenting a novel Self-Attention U-Net with Residual Bottleneck architecture for generating high-quality MRI images from low-cost CT scans. The model incorporates self-attention mechanisms to capture long-range dependencies, while the residual bottleneck blocks enable efficient feature propagation and preserve fine details during translation. A spectral-normalized Patch Discriminator is employed to improve the realism of generated images, ensuring accurate synthesis of MRI-like structures. Trained on a dataset of 389 paired CT and MRI images, the model is evaluated using Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM), achieving an average PSNR of 23.57 and an average SSIM of 0.6435. These results indicate the model’s ability to generate high-fidelity MRI images from CT scans with competitive perceptual quality. The proposed approach not only shows potential in reducing the reliance on expensive MRI scans but also provides a cost-effective solution for medical imaging, especially in underserved regions. This method could enhance diagnostic capabilities and patient care by enabling the synthesis of MRI images from readily available CT scans, particularly where MRI accessibility is limited.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20203 - Telecommunications

Projekt

<a href="/cs/project/EH23_021%2F0008829" target="_blank" >EH23_021/0008829: Nové technologie pro digitální zdravotnictví</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

2025 17th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT)

ISBN

979-8-3315-7675-2

ISSN

—

e-ISSN

—

Počet stran výsledku

6

Strana od-do

246-251

Název nakladatele

IEEE

Místo vydání

Italy

Místo konání akce

Florence, Italy

Datum konání akce

3. 11. 2025

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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