Titlebook: Biomedical Image Registration; 11th International W Marc Modat,Ivor Simpson,Tony C. W. Mok Conference proceedings 2024 The Editor(s) (if ap

Cultivate

MultiGradICON: A Foundation Model for?Multimodal Medical Image Registrationccuracy and are generally fast. However, deep learning (DL) approaches are, in contrast to conventional non-deep-learning-based approaches, anatomy-specific. Recently, a universal deep registration approach, uniGradICON, has been proposed. However, uniGradICON focuses on monomodal image registration

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XSynthMorph: Generative-Guided Deformation for?Unsupervised Ill-Posed Volumetric Recoveryred 3D volume of the patient. Such pre-capturing volume is readily available in many important medical procedures and previous methods already used such a volume. Earlier methods that work by deforming this volume to match the projections can fail when the number of projections is very low as the al

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Unleashing Registration: Diffusion Models for?Synthetic Paired 3D Training DataFor the task of image registration, this is especially relevant, since networks rely on image pairs for training. Augmenting the dataset with synthetic deformations does not suffice for anonymous publication, as patient-specific topologies are kept. To handle this, we propose to leverage vector quan

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Feedback Attention for?Unsupervised Cardiac Motion Estimation in?3D Echocardiographyliable performance with deep learning image registration (DLIR) is traditionally challenging due to intrinsic noise and fuzzy anatomic boundaries in echocardiography. It is further advantageous to achieve DLIR in 3D, as the cardiac anatomy has complex 3D structures and motions that are difficult to

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UNT

Mamba? Catch The Hype Or Rethink What Really Helps for?Image Registrationd this approach by replacing CNNs with Attention mechanisms, claiming enhanced performance. More recently, the rise of Mamba with selective state space models has led to MambaMorph, which substituted Attention with Mamba blocks, asserting superior registration. These developments prompt a critical q

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Assessing the?Robustness of?Image Registration Models Under Domain Shifts with?Learnable Input Imagecial to assess model robustness under such shifts, often accomplished using simulated domain shifts and expert annotations, e.g., landmarks. This work presents ProactiV-Reg, an annotation-free approach that utilizes a learnable image mapping: it iteratively adjusts a moving image to align with a fix

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凝結(jié)劑

Comparative Study on Co-registration Techniques for Diffusion-Weighted Breast MRI and Improved ADC Mon coefficient (ADC) maps which can supplement differentiation between malignant and benign breast lesions. However, artifacts in DWI are not infrequent, e.g., due to patient motion, pulsation or other sources, which can cause shifts between the different b-value acquisitions and affect the accuracy