Browsing by Author "Acar, Mert"

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Open Access
Segmentation informed deep learning algorithms for cardiac MRI reconstruction
(2023-08) Acar, Mert
Deep learning methods have produced impressive results in accelerated magnetic resonance imaging (MRI) reconstruction from under-sampled k-space acquisitions. However, existing MRI reconstruction models are commonly trained with loss functions that uniformly weigh contributions from separate voxels across the field-of-view (FOV), without attributing focus on relatively important regions within the FOV. Furthermore common frameworks for model training rely on availability of large sets of fully-sampled MRI data to construct a ground-truth for the network output. This heavy reliance is undesirable as it is challenging to collect such large datasets in many applications, and even impossible for high spatiotemporal-resolution protocols. In this thesis, we first introduce a self-supervised learning methodology for dynamic cardiac MRI that trains the network to reconstruct acquisitions in the absence of fully-sampled data. We then introduce a segmentation-aware reconstruction framework which implicitly guides the reconstruction process around an ROI with the segmentation error signal. Lastly, we introduce RATNet, a reconstruction framework augmented with attention capabilities which explicitly carries spatial information into the reconstruction process to focus around regions of interest. Self-supervision reduces the excessive demand on fully-sampled data whereas the segmentation-aware re-construction framework backpropagates the spatial information signal in to the reconstruction network. Lastly, RATNet incorporates the attention layers into reconstruction which are sensitive to focusing information supplied by the spatial information network. We demonstrate recovering fully-sampled images from under-sampled acquisitions in cardiac MRI and show their state-of-the-art performance in medical image reconstruction.
Open Access
Segmentation-aware MRI reconstruction
(Springer Cham, 2022-09-22) Acar, Mert; Çukur, Tolga; Öksüz, İ.
Deep learning models have been broadly adopted for accelerating MRI acquisitions in recent years. A common approach is to train deep models based on loss functions that place equal emphasis on reconstruction errors across the field-of-view. This homogeneous weighting of loss contributions might be undesirable in cases where the diagnostic focus is on tissues in a specific subregion of the image. In this paper, we propose a framework for segmentation-aware reconstruction based on segmentation as a proxy task. We leverage an end-to-end model comprising reconstruction and segmentation networks; and leverage backpropagation of segmentation error to devise a pseudo-attention effect to focus the reconstruction network. We introduce a novel stabilization method to prevent convergence onto a local minima with unacceptably poor reconstruction or segmentation performance. Our stabilization approach initiates learning on fully-sampled acquisitions, and gradually increases the undersampling rate assumed in the training set to its desired value. We validate our approach for cardiac MR reconstruction on the publicly available OCMR dataset. Segmentation-aware reconstruction significantly outperforms vanilla reconstruction for cardiac imaging.
Open Access
Self-supervised dynamic MRI reconstruction
(Springer, 2021-09-25) Acar, Mert; Çukur, Tolga; Öksüz, İlkay
Deep learning techniques have recently been adopted for accelerating dynamic MRI acquisitions. Yet, common frameworks for model training rely on availability of large sets of fully-sampled MRI data to construct a ground-truth for the network output. This heavy reliance is undesirable as it is challenging to collect such large datasets in many applications, and even impossible for high spatiotemporal-resolution protocols. In this paper, we introduce self-supervised training to deep neural architectures for dynamic reconstruction of cardiac MRI. We hypothesize that, in the absence of ground-truth data, elevating complexity in self-supervised models can instead constrain model performance due to the deficiencies in training data. To test this working hypothesis, we adopt self-supervised learning on recent state-of-the-art deep models for dynamic MRI, with varying degrees of model complexity. Comparison of supervised and self-supervised variants of deep reconstruction models reveals that compact models have a remarkable advantage in reliability against performance loss in self-supervised settings.
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Türkiye elektrikleniyor
(Bilkent University, 2018) Acar, Mert; Özer, Batuhan; Akçin, Barış; Adil, Adilhan; Usluel, Yağız