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Akademický článek

Deep Learning-Based Reconstruction for Cardiac MRI: A Review

Autor: Julio A. Oscanoa, Matthew J. Middione, Cagan Alkan, Mahmut Yurt, Michael Loecher, Shreyas S. Vasanawala, Daniel B. Ennis

Publikováno v: Bioengineering, Vol 10, Iss 3, p 334 (2023)

Cardiac magnetic resonance (CMR) is an essential clinical tool for the assessment of cardiovascular disease. Deep learning (DL) has recently revolutionized the field through image reconstruction techniques that allow unprecedented data undersampling

Externí odkaz: https://doaj.org/article/b9f160beb01a4661a79fa61a5374cafc

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Semi-Supervised Learning of MRI Synthesis Without Fully-Sampled Ground Truths

Autor: Mahmut Yurt, Onat Dalmaz, Salman Dar, Muzaffer Ozbey, Berk Tinaz, Kader Oguz, Tolga Cukur

Publikováno v: IEEE Transactions on Medical Imaging

Learning-based translation between MRI contrasts involves supervised deep models trained using high-quality source- and target-contrast images derived from fully-sampled acquisitions, which might be difficult to collect under limitations on scan cost

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b0922315bec2c95768d4214b3331a2d9
https://doi.org/10.1109/tmi.2022.3199155

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Unsupervised MRI Reconstruction via Zero-Shot Learned Adversarial Transformers

Autor: Yilmaz Korkmaz, Salman U. H. Dar, Mahmut Yurt, Muzaffer Ozbey, Tolga Cukur

Publikováno v: IEEE Transactions on Medical Imaging

Supervised reconstruction models are characteristically trained on matched pairs of undersampled and fully-sampled data to capture an MRI prior, along with supervision regarding the imaging operator to enforce data consistency. To reduce supervision

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7d2d14e5f3b01645f0399a4544c11550
https://doi.org/10.1109/tmi.2022.3147426

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Deep Learning Initialized Compressed Sensing (Deli-CS) in Volumetric Spatio-Temporal Subspace Reconstruction

Autor: Siddharth S. Iyer, S. Sophie Schauman, Christopher M. Sandino, Mahmut Yurt, Xiaozhi Cao, Congyu Liao, Natthanan Ruengchaijatuporn, Itthi Chatnuntawech, Elizabeth Tong, Kawin Setsompop

Publikováno v: bioRxiv

IntroductionSpatio-temporal MRI methods enable whole-brain multi-parametric mapping at ultra-fast acquisition times through efficient k-space encoding, but can have very long reconstruction times, which limit their integration into clinical practice.

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::72a5a699f4f7969660e7c8f3ab9565ad
https://doi.org/10.1101/2023.03.28.534431

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ResViT: Residual Vision Transformers for Multimodal Medical Image Synthesis

Autor: Onat Dalmaz, Mahmut Yurt, Tolga Cukur

Publikováno v: IEEE Transactions on Medical Imaging

Generative adversarial models with convolutional neural network (CNN) backbones have recently been established as state-of-the-art in numerous medical image synthesis tasks. However, CNNs are designed to perform local processing with compact filters,

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9229a703899611cffc9e8a4c13c9493d
https://pubmed.ncbi.nlm.nih.gov/35436184

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GRJointNET: Synergistic Completion and Part Segmentation on 3D Incomplete Point Clouds

Autor: Mahmut Yurt, Melisa Taspinar, Sedat Ozer, Yigit Gurses

Publikováno v: SIU

Segmentation of three-dimensional (3D) point clouds is an important task for autonomous systems. However, success of segmentation algorithms depends greatly on the quality of the underlying point clouds (resolution, completeness etc.). In particular,

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::df05dbbbe655fc5ff5edca7db048e0e6
https://doi.org/10.1109/siu53274.2021.9477918

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Progressively volumetrized deep generative models for data-efficient contextual learning of MR image recovery

Autor: Mahmut Yurt, Muzaffer Özbey, Salman U.H. Dar, Berk Tinaz, Kader K. Oguz, Tolga Çukur

Publikováno v: Medical Image Analysis

Magnetic resonance imaging (MRI) offers the ﬂexibility to image a given anatomic volume under a multi- tude of tissue contrasts. Yet, scan time considerations put stringent limits on the quality and diversity of MRI data. The gold-standard approach

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f49d2b2f1eba0d8e91c5f00fb6a86cf7
https://pubmed.ncbi.nlm.nih.gov/35367713

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Deep MRI Reconstruction with Generative Vision Transformers

Autor: Tolga Çukur, Mahmut Yurt, Salman Ul Hassan Dar, Yilmaz Korkmaz, Muzaffer Özbey

Publikováno v: Machine Learning for Medical Image Reconstruction ISBN: 9783030885519
MLMIR@MICCAI
Lecture Notes in Computer Science

Conference Name: International Workshop on Machine Learning for Medical Image Reconstruction, MLMIR 2021 Date of Conference: 25 September 2021 Supervised training of deep network models for MRI reconstruction requires access to large databases of ful

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::32d70f52bf885d9f942bf4190de97b4b
https://doi.org/10.1007/978-3-030-88552-6_6

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mustGAN: multi-stream Generative Adversarial Networks for MR Image Synthesis

Autor: Erkut Erdem, Mahmut Yurt, Tolga Çukur, Aykut Erdem, Salman Ul Hassan Dar, Kader Karli Oguz

Publikováno v: Medical Image Analysis

Multi-contrast MRI protocols increase the level of morphological information available for diagnosis. Yet, the number and quality of contrasts are limited in practice by various factors including scan time and patient motion. Synthesis of missing or

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5b1a58c9c6761c3ab4de5c6f52354cd1
https://pubmed.ncbi.nlm.nih.gov/33690024

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