Aysel Sabuncu Brain Research Center (BAM)

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Browsing Aysel Sabuncu Brain Research Center (BAM) by Subject "Accelerated MRI"

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    Reconstruction by calibration over tensors for multi-coil multi-acquisition balanced SSFP imaging
    (John Wiley & Sons, 2018) Bıyık, Erdem; Ilıcak, Efe; Çukur, Tolga
    Purpose: To develop a rapid imaging framework for balanced steady-state free precession (bSSFP) that jointly reconstructs undersampled data (by a factor of R) across multiple coils (D) and multiple acquisitions (N). To devise a multi-acquisition coil compression technique for improved computational efficiency. Methods: The bSSFP image for a given coil and acquisition is modeled to be modulated by a coil sensitivity and a bSSFP profile. The proposed reconstruction by calibration over tensors (ReCat) recovers missing data by tensor interpolation over the coil and acquisition dimensions. Coil compression is achieved using a new method based on multilinear singular value decomposition (MLCC). ReCat is compared with iterative self-consistent parallel imaging (SPIRiT) and profile encoding (PE-SSFP) reconstructions. Results: Compared to parallel imaging or profile-encoding methods, ReCat attains sensitive depiction of high-spatial-frequency information even at higher R. In the brain, ReCat improves peak SNR (PSNR) by 1.1 ± 1.0 dB over SPIRiT and by 0.9 ± 0.3 dB over PE-SSFP (mean ± SD across subjects; average for N = 2-8, R = 8-16). Furthermore, reconstructions based on MLCC achieve 0.8 ± 0.6 dB higher PSNR compared to those based on geometric coil compression (GCC) (average for N = 2-8, R = 4-16). Conclusion: ReCat is a promising acceleration framework for banding-artifact-free bSSFP imaging with high image quality; and MLCC offers improved computational efficiency for tensor-based reconstructions. Magn Reson Med 79:2542-2554, 2018.
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    A transfer-learning approach for accelerated MRI using deep neural networks
    (Wiley, 2020) Dar, Salman Ul Hassan; Özbey, Muzaffer; Çatlı, Ahmet Burak; Çukur, Tolga
    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 practice, however, large datasets comprising hundreds of subjects scanned under a common protocol are rare. The goal of this study is to introduce a transfer‐learning approach to address the problem of data scarcity in training deep networks for accelerated MRI. Methods: Neural networks were trained on thousands (upto 4 thousand) of samples from public datasets of either natural images or brain MR images. The networks were then fine‐tuned using only tens of brain MR images in a distinct testing domain. Domain‐transferred networks were compared to networks trained directly in the testing domain. Network performance was evaluated for varying acceleration factors (4‐10), number of training samples (0.5‐4k), and number of fine‐tuning samples (0‐100). Results: The proposed approach achieves successful domain transfer between MR images acquired with different contrasts (T1‐ and T2‐weighted images) and between natural and MR images (ImageNet and T1‐ or T2‐weighted images). Networks obtained via transfer learning using only tens of images in the testing domain achieve nearly identical performance to networks trained directly in the testing domain using thousands (upto 4 thousand) of images. Conclusion: The proposed approach might facilitate the use of neural networks for MRI reconstruction without the need for collection of extensive imaging datasets.