Browsing by Subject "MRI synthesis"

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Open Access
Bottleneck sharing generative adversarial networks for unified multi-contrast MR image synthesis
(IEEE, 2022-08-29) Dalmaz, Onat; Sağlam, Baturay; Gönç, Kaan; Dar, Salman Uh.; Çukur, Tolga
Magnetic Resonance Imaging (MRI) is the favored modality in multi-modal medical imaging due to its safety and ability to acquire various different contrasts of the anatomy. Availability of multiple contrasts accumulates diagnostic information and, therefore, can improve radiological observations. In some scenarios, acquiring all contrasts might be challenging due to reluctant patients and increased costs associated with additional scans. That said, synthetically obtaining missing MRI pulse sequences from the acquired sequences might prove to be useful for further analyses. Recently introduced Generative Adversarial Network (GAN) models offer state-of-the-art performance in learning MRI synthesis. However, the proposed generative approaches learn a distinct model for each conditional contrast to contrast mapping. Learning a distinct synthesis model for each individual task increases the time and memory demands due to the increased number of parameters and training time. To mitigate this issue, we propose a novel unified synthesis model, bottleneck sharing GAN (bsGAN), to consolidate learning of synthesis tasks in multi-contrast MRI. bsGAN comprises distinct convolutional encoders and decoders for each contrast to increase synthesis performance. A central information bottleneck is employed to distill hidden representations. The bottleneck, based on residual convolutional layers, is shared across contrasts to avoid introducing many learnable parameters. Qualitative and quantitative comparisons on a multi-contrast brain MRI dataset show the effectiveness of the proposed method against existing unified synthesis methods.
Open Access
Deep learning for multi-contrast MRI synthesis
(2021-07) Yurt, Mahmut
Magnetic resonance imaging (MRI) possesses the unique versatility to acquire images under a diverse array of distinct tissue contrasts. Multi-contrast images, in turn, better delineate tissues, accumulate diagnostic information, and enhance radiological analyses. Yet, prolonged, costly exams native to multi-contrast pro-tocols often impair the diversity, resulting in missing images from some contrasts. A promising remedy against this limitation arises as image synthesis that recovers missing target contrast images from available source contrast images. Learning-based models demonstrated remarkable success in this source-to-target mapping due to their prowess in solving even the most demanding inverse problems. Main-stream approaches proposed for synthetic MRI were typically subjected to a model training to perform either one-to-one or many-to-one mapping. One-to-one models manifest elevated sensitivity to detailed features of the given source, but they perform suboptimally when source-target images are poorly linked. Meanwhile, many-to-one counterparts pool information from multiple sources, yet this comes at the expense of losing detailed features uniquely present in cer-tain sources. Furthermore, regardless of the mapping, they both innately demand large training sets of high-quality source and target images Fourier-reconstructed from Nyquist-sampled acquisitions. However, time and cost considerations put signiﬁcant challenges in compiling such datasets. To address these limitations, here we ﬁrst propose a novel multi-stream model that task-adaptively fuses unique and shared image features from a hybrid of multiple one-to-one streams and a single many-to-one stream. We then introduce a novel semi-supervised learning framework based on selective tensor loss functions to learn high-quality image synthesis directly from a training dataset of undersampled acquisitions, bypass-ing the undesirable data requirements of deep learning. Demonstrations on brain MRI images from healthy subjects and glioma patients indicate the superiority of the proposed approaches against state-of-the-art baselines.