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Adnane Gdihi

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#diffusion models Open access Sep 2026

spectraMR: A Multi-Paradigm Research Framework for MRI Reconstruction, Super-Resolution, and Generative Modelling

spectraMR is a research framework for MRI reconstruction, super-resolution and generative modelling. It spans GAN, diffusion (including cold and latent diffusion), VAE/VQ-VAE, masked-autoencoder and self-supervised pretraining, reconstruction-only, domain-adaptation, physics-driven, disentangled, sensitivity-estimation (PINN), cycle-Bloch, flow, state-space (Mamba), magnetic-resonance-fingerprinting, fMRI and active/learnable-acquisition paradigms behind one configuration schema. Complex k-space is handled through centred, orthonormally-scaled FFT operators rather than raw transforms; configuration is nested, frozen and loaded once; components resolve through registries and a dependency-injection container rather than dispatch chains. Heavy pipelines run on an accelerator or raise, with no silent CPU fallback. Not for clinical use. This is research software. It is not a medical device and has not been evaluated by any regulatory authority.

Adnane Gdihi · 0 citations
#diffusion models Open access Sep 2026

spectraMR: A Multi-Paradigm Research Framework for MRI Reconstruction, Super-Resolution, and Generative Modelling

spectraMR is a research framework for MRI reconstruction, super-resolution and generative modelling. It spans GAN, diffusion (including cold and latent diffusion), VAE/VQ-VAE, masked-autoencoder and self-supervised pretraining, reconstruction-only, domain-adaptation, physics-driven, disentangled, sensitivity-estimation (PINN), cycle-Bloch, flow, state-space (Mamba), magnetic-resonance-fingerprinting, fMRI and active/learnable-acquisition paradigms behind one configuration schema. Complex k-space is handled through centred, orthonormally-scaled FFT operators rather than raw transforms; configuration is nested, frozen and loaded once; components resolve through registries and a dependency-injection container rather than dispatch chains. Heavy pipelines run on an accelerator or raise, with no silent CPU fallback. Not for clinical use. This is research software. It is not a medical device and has not been evaluated by any regulatory authority.

Adnane Gdihi · 0 citations
#diffusion models Open access Sep 2026

spectraMR: A Multi-Paradigm Research Framework for MRI Reconstruction, Super-Resolution, and Generative Modelling

spectraMR is a research framework for MRI reconstruction, super-resolution and generative modelling. It spans GAN, diffusion (including cold and latent diffusion), VAE/VQ-VAE, masked-autoencoder and self-supervised pretraining, reconstruction-only, domain-adaptation, physics-driven, disentangled, sensitivity-estimation (PINN), cycle-Bloch, flow, state-space (Mamba), magnetic-resonance-fingerprinting, fMRI and active/learnable-acquisition paradigms behind one configuration schema. Complex k-space is handled through centred, orthonormally-scaled FFT operators rather than raw transforms; configuration is nested, frozen and loaded once; components resolve through registries and a dependency-injection container rather than dispatch chains. Heavy pipelines run on an accelerator or raise, with no silent CPU fallback. Not for clinical use. This is research software. It is not a medical device and has not been evaluated by any regulatory authority.

Adnane Gdihi · 0 citations
#diffusion models Open access Sep 2026

spectraMR: A Multi-Paradigm Research Framework for MRI Reconstruction, Super-Resolution, and Generative Modelling

spectraMR is a research framework for MRI reconstruction, super-resolution and generative modelling. It spans GAN, diffusion (including cold and latent diffusion), VAE/VQ-VAE, masked-autoencoder and self-supervised pretraining, reconstruction-only, domain-adaptation, physics-driven, disentangled, sensitivity-estimation (PINN), cycle-Bloch, flow, state-space (Mamba), magnetic-resonance-fingerprinting, fMRI and active/learnable-acquisition paradigms behind one configuration schema. Complex k-space is handled through centred, orthonormally-scaled FFT operators rather than raw transforms; configuration is nested, frozen and loaded once; components resolve through registries and a dependency-injection container rather than dispatch chains. Heavy pipelines run on an accelerator or raise, with no silent CPU fallback. Not for clinical use. This is research software. It is not a medical device and has not been evaluated by any regulatory authority.

Adnane Gdihi · 0 citations

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