Now showing items 1-6 of 6

    • Category-selective top-down modulation in the fusiform face area of the human brain during visual search 

      Dar, Salman Ul Hassan; Çukur, Tolga (IEEE, 2017)
      Several regions in the ventral-temporal cortex of the human brain are thought to have representations of specific categories of objects. Furthermore, a distributed network of frontal and parietal brain regions is implicated ...
    • Deep learning for accelerated MR imaging 

      Dar, Salman Ul Hassan (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 MRI reconstruction with generative vision transformer 

      Korkmaz, Yılmaz; Yurt, Mahmut; Dar, Salman Ul Hassan; Özbey, Muzaffer; Çukur, Tolga (Springer, 2021)
      Supervised training of deep network models for MRI reconstruction requires access to large databases of fully-sampled MRI acquisitions. To alleviate dependency on costly databases, unsupervised learning strategies have ...
    • Factorized sensitivity estimation for artifact suppression in phase‐cycled bSSFP MRI 

      Bıyık, Erdem; Keskin, Kübra; Dar, Salman Ul Hassan; Koç, Aykut; Çukur, Tolga (Wiley, 2020)
      Objective: Balanced steady‐state free precession (bSSFP) imaging suffers from banding artifacts in the presence of magnetic field inhomogeneity. The purpose of this study is to identify an efficient strategy to reconstruct ...
    • Spatially informed voxelwise modeling for naturalistic fMRI experiments 

      Çelik, Emin; Dar, Salman Ul Hassan; Yılmaz, Özgür; Keleş, Ümit; Çukur, Tolga (Elsevier, 2019)
      Voxelwise modeling (VM) is a powerful framework to predict single voxel responses evoked by a rich set of stimulus features present in complex natural stimuli. However, because VM disregards correlations across neighboring ...
    • A transfer-learning approach for accelerated MRI using deep neural networks 

      Dar, Salman Ul Hassan; Özbey, Muzaffer; Çatlı, Ahmet Burak; Çukur, Tolga (Wiley, 2020)
      Purpose: Neural networks have received recent interest for reconstruction of undersampled MR acquisitions. Ideally, network performance should be optimized by drawing the training and testing data from the same domain. In ...