Browsing by Subject "Semi-supervised"

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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.
Open Access
Semi-supervised learning of MRI synthesis without fully-sampled ground truths
(IEEE, 2022-08-16) Yurt, Mahmut; Dalmaz, Onat; Dar, Salman; Özbey, Muzaffer; Tınaz, Berk; Oğuz, Kader; Çukur, Tolga
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 costs or time. To facilitate curation of training sets, here we introduce the first semi-supervised model for MRI contrast translation (ssGAN) that can be trained directly using undersampled k-space data. To enable semi-supervised learning on undersampled data, ssGAN introduces novel multi-coil losses in image, k-space, and adversarial domains. The multi-coil losses are selectively enforced on acquired k-space samples unlike traditional losses in single-coil synthesis models. Comprehensive experiments on retrospectively undersampled multi-contrast brain MRI datasets are provided. Our results demonstrate that ssGAN yields on par performance to a supervised model, while outperforming single-coil models trained on coil-combined magnitude images. It also outperforms cascaded reconstruction-synthesis models where a supervised synthesis model is trained following self-supervised reconstruction of undersampled data. Thus, ssGAN holds great promise to improve the feasibility of learning-based multi-contrast MRI synthesis.