Browsing by Keywords "Reconstruction"
Now showing items 1-15 of 15
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Adana Tepebağ Mahallesi tarihi
(Bilkent University, 2018)Bu makalenin konusu, Adana'nın ilk yerleşim yeri olan Tepebağ Mahallesi'nin geçmişten bugüne olan değişimini göstermektir. Tepebağ Mahallesi'nin ulaşabildiğimiz kayıtları M.ö 2000 yılına ait belgelerle başlar, 1360 yılında ... -
Adaptive reconstruction for vessel preservation in unenhanced MR angiography
(IEEE, 2016)The image quality of unenhanced magnetic resonance angiography, which images blood vessels without contrast agents, is limited by constraints related to scan time. To address this problem, techniques that undersample ... -
Cumulant-based parametric multichannel FIR system identification methods
(IEEE, 1993)In this paper, ''least squares'' and recursive methods for simultaneous identification of four nonminimum phase linear, time-invariant FIR systems are presented. The methods utilize the second- and fourth-order cumulants ... -
Deep learning for accelerated 3D MRI
(Bilkent University, 2021-08)Magnetic resonance imaging (MRI) offers the flexibility to image a given anatomic volume under a multitude of tissue contrasts. Yet, scan time considerations put stringent limits on the quality and diversity of MRI data. The ... -
Deep learning for accelerated MR imaging
(Bilkent University, 2021-02)Magnetic resonance imaging is a non-invasive imaging modality that enables multi-contrast acquisition of an underlying anatomy, thereby supplementing mul-titude of information for diagnosis. However, prolonged scan duration ... -
Deep unsupervised learning for accelerated MRI reconstruction
(Bilkent University, 2022-07)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 ... -
Federated learning of generative ımage priors for MRI reconstruction
(Institute of Electrical and Electronics Engineers Inc., 2022-11-09)Multi-institutional efforts can facilitate training of deep MRI reconstruction models, albeit privacy risks arise during cross-site sharing of imaging data. Federated learning (FL) has recently been introduced to address ... -
Learning-based reconstruction methods for magnetic particle imaging
(Bilkent University, 2023-01)Magnetic particle imaging (MPI) is a novel modality for imaging of magnetic nanoparticles (MNP) with high resolution, contrast and frame rate. An inverse problem is usually cased for reconstruction, which requires a ... -
PP-MPI: A deep plug-and-play prior for magnetic particle imaging reconstruction
(Springer Cham, 2022-09)Magnetic particle imaging (MPI) is a recent modality that enables high contrast and frame-rate imaging of the magnetic nanoparticle (MNP) distribution. Based on a measured system matrix, MPI reconstruction can be cast as ... -
Prior-Guided image reconstruction for accelerated multi-contrast MRI via generative adversarial networks
(IEEE, 2020)Multi-contrast MRI acquisitions of an anatomy enrich the magnitude of information available for diagnosis. Yet, excessive scan times associated with additional contrasts may be a limiting factor. Two mainstream frameworks ... -
Profile-encoding reconstruction for multiple-acquisition balanced steady-state free precession imaging
(John Wiley and Sons Inc., 2017)Purpose: The scan-efficiency in multiple-acquisition balanced steady-state free precession imaging can be maintained by accelerating and reconstructing each phase-cycled acquisition individually, but this strategy ignores ... -
Progressively volumetrized deep generative models for data-efficient contextual learning of MR image recovery
(Elsevier BV, 2022-05)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. ... -
Segmentation-aware MRI reconstruction
(Springer Cham, 2022-09-22)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 ... -
TranSMS: transformers for super-resolution calibration in magnetic particle imaging
(Institute of Electrical and Electronics Engineers Inc., 2022-07-11)Magnetic particle imaging (MPI) offers exceptional contrast for magnetic nanoparticles (MNP) at high spatio-temporal resolution. A common procedure in MPI starts with a calibration scan to measure the system matrix (SM), ... -
Unsupervised MRI reconstruction via zero-shot learned adversarial transformers
(Institute of Electrical and Electronics Engineers Inc., 2022-01-27)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 ...
