Source: Kolenbrander , I D , Maspero , M , Hendriksen , A A , Pollitt , R , van der Voort van Zyp , J R N , van den Berg , C A T , Pluim , J P W & van Eijnatten , M A J M 2024 , ' Deep-learning-based joint

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DeepFLAIR:A neural network approach to mitigate signal and contrast loss in temporal lobes at 7 Tesla FLAIR images

Authors : Uher, Daniel; Drenthen, Gerhard S.; Poser, Benedikt A.

Subjects: Artifact removal; FLAIR; Machine learning

Source: Uher , D , Drenthen , G S , Poser , B A , Hofman , P A M , Wagner , L G , van Lanen , R H G J , Hoeberigs , C M , Colon , A J , Schijns , O E M G , Jansen , J F A & Backes , W H 2024 , ' DeepFLAIR : A

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Contrastive Learning vs. Self-Learning vs. Deformable Data Augmentation in Semantic Segmentation of Medical Images

Authors : Arabi, Hossein; Zaidi, Habib

Subjects: Brain Neoplasms/diagnostic imaging; Deep Learning; Hippocampus/diagnostic imaging

Source: Arabi , H & Zaidi , H 2024 , ' Contrastive Learning vs. Self-Learning vs. Deformable Data Augmentation in Semantic Segmentation of Medical Images ' , Journal of Imaging

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Impact of harmonization on the reproducibility of MRI radiomic features when using different scanners, acquisition parameters, and image pre-processing techniques:a phantom study

Authors : Hajianfar, Ghasem; Hosseini, Seyyed Ali; Bagherieh, Sara

Subjects: Harmonization; MRI; Pre-processing

Source: Hajianfar , G , Hosseini , S A , Bagherieh , S , Oveisi , M , Shiri , I & Zaidi , H 2024 , ' Impact of harmonization on the reproducibility of MRI radiomic features when using different scanners,

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Patient-independent, MHD-robust R-peak detection for retrospective gating in cardiac MRI imaging

Authors : Ganassin, Sara; Galli, Alessandra; Ouzounov, Sotir

Subjects: cardiac gating; cardiac magnetic resonance imaging; electrocardiogram

Source: Ganassin , S , Galli , A , Ouzounov , S & Narduzzi , C 2024 , ' Patient-independent, MHD-robust R-peak detection for retrospective gating in cardiac MRI imaging ' ,

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Fast refacing of MR images with a generative neural network lowers re-identification risk and preserves volumetric consistency.

Authors : Molchanova, N.; Maréchal, B.; Thiran, J.P. ...

Subjects: Humans; Magnetic Resonance Imaging/methods; Adult

Source: Human brain mapping, vol. 45, no. 9, pp. e26721

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The beating heart: artificial intelligence for cardiovascular application in the clinic.

Authors : Villegas-Martinez, M.; de Villedon de Naide, V.; Muthurangu, V.

Subjects: Humans; Artificial Intelligence; Magnetic Resonance Imaging/methods

Source: Magma, vol. 37, no. 3, pp. 369-382

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AI-based motion artifact severity estimation in undersampled MRI allowing for selection of appropriate reconstruction models

Authors : Beljaards, Laurens; Pezzotti, Nicola; Rao, Chinmay

Subjects: accelerated MRI; deep learning; motion artifact severity estimation

Source: Beljaards , L , Pezzotti , N , Rao , C , Doneva , M , van Osch , M J P & Staring , M 2024 , ' AI-based motion artifact severity estimation in undersampled MRI allowing for selection of appropriate

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فلترة النتائج

Structural-based uncertainty in deep learning across anatomical scales: Analysis in white matter lesion segmentation.

Self-gated free-running 5D whole-heart MRI using blind source separation for automated cardiac motion extraction.

Deep-learning-based joint rigid and deformable contour propagation for magnetic resonance imaging-guided prostate radiotherapy

DeepFLAIR:A neural network approach to mitigate signal and contrast loss in temporal lobes at 7 Tesla FLAIR images

Contrastive Learning vs. Self-Learning vs. Deformable Data Augmentation in Semantic Segmentation of Medical Images

Impact of harmonization on the reproducibility of MRI radiomic features when using different scanners, acquisition parameters, and image pre-processing techniques:a phantom study

Patient-independent, MHD-robust R-peak detection for retrospective gating in cardiac MRI imaging

Fast refacing of MR images with a generative neural network lowers re-identification risk and preserves volumetric consistency.

The beating heart: artificial intelligence for cardiovascular application in the clinic.

AI-based motion artifact severity estimation in undersampled MRI allowing for selection of appropriate reconstruction models

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