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Medical University of Vienna

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Selected work

Representative Papers

Synthesizing Post-Acetazolamide Cerebral Blood Flow Maps from Baseline MRI in Moyamoya Using 3D Generative AI

Aug 14, 2026

This study addresses the challenge of assessing cerebrovascular reserve in Moyamoya disease patients with acetazolamide contraindications by proposing CAE3D, a three-dimensional conditional autoencoder that non-invasively synthesizes post-challenge cerebral blood flow maps from baseline ASL images. This work represents the first validation of retrospectively generating stress perfusion data from baseline MRI using a deterministic architecture integrating perfusion imaging with deep learning. Experimental results demonstrate superior performance over various 3D generative and foundation models, achieving a mean absolute error of 0.066, a structural similarity index of 0.80, and near-zero whole-brain bias. Consequently, this method establishes a novel paradigm for precise hemodynamic assessment in contraindicated patients, effectively bridging advanced deep learning techniques with clinical neuroimaging requirements to overcome diagnostic limitations associated with pharmacological vasodilators.

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LoRCA: LoRA Cycle Adaptation for Histology to HiP-CT Translation with DINOv3

Aug 07, 2026

This work addresses the challenge of nonlinear registration between histological sections and HiP-CT volumetric data, which arises from significant modality discrepancies. To tackle this without requiring paired training data, the authors propose a structure-preserving cross-modal image translation method. Leveraging a frozen DINOv2 backbone as a semantic structural anchor and modality-specific LoRA adapters, the approach enables efficient and generalizable cross-modal representation learning within a cycle-consistent adversarial training framework. This design effectively mitigates content drift while enhancing structural consistency. Experimental results demonstrate that the proposed method outperforms CycleGAN in terms of Fréchet Inception Distance (FID), mutual information, and edge preservation metrics, and substantially improves feature correspondence in downstream registration tasks.

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Recent publications

Latest Papers

Synthesizing Post-Acetazolamide Cerebral Blood Flow Maps from Baseline MRI in Moyamoya Using 3D Generative AI

Aug 14, 2026

This study addresses the challenge of assessing cerebrovascular reserve in Moyamoya disease patients with acetazolamide contraindications by proposing CAE3D, a three-dimensional conditional autoencoder that non-invasively synthesizes post-challenge cerebral blood flow maps from baseline ASL images. This work represents the first validation of retrospectively generating stress perfusion data from baseline MRI using a deterministic architecture integrating perfusion imaging with deep learning. Experimental results demonstrate superior performance over various 3D generative and foundation models, achieving a mean absolute error of 0.066, a structural similarity index of 0.80, and near-zero whole-brain bias. Consequently, this method establishes a novel paradigm for precise hemodynamic assessment in contraindicated patients, effectively bridging advanced deep learning techniques with clinical neuroimaging requirements to overcome diagnostic limitations associated with pharmacological vasodilators.

0 citationsRead paper

LoRCA: LoRA Cycle Adaptation for Histology to HiP-CT Translation with DINOv3

Aug 07, 2026

This work addresses the challenge of nonlinear registration between histological sections and HiP-CT volumetric data, which arises from significant modality discrepancies. To tackle this without requiring paired training data, the authors propose a structure-preserving cross-modal image translation method. Leveraging a frozen DINOv2 backbone as a semantic structural anchor and modality-specific LoRA adapters, the approach enables efficient and generalizable cross-modal representation learning within a cycle-consistent adversarial training framework. This design effectively mitigates content drift while enhancing structural consistency. Experimental results demonstrate that the proposed method outperforms CycleGAN in terms of Fréchet Inception Distance (FID), mutual information, and edge preservation metrics, and substantially improves feature correspondence in downstream registration tasks.

0 citationsRead paper