The segmentation of glioma is crucial for early diagnosis, according to a World Health Organization (WHO) 2021 report. For glioma diagnosis, 3D multi-modal brain MRI/CT imaging has become an essential tool, offering detailed information. Nowadays, deep learning frameworks have been applied to various medical imaging problems, including brain glioma segmentation. Recently, foundation models like Segment Anything Model (SAM) have emerged as pivotal tools in computer vision tasks. These models are trained using large (real-world) datasets, offering a generalized understanding of visual data and semantic key features. Therefore, the effective utilization of foundation models in medical imaging is a significant area of current research. However, the differences in data distribution between multi-modal medical images and real-world images present challenges in directly applying foundation models to medical imaging. Additionally, utilizing multi-modal images to extract crucial information and its fusion poses further challenges. To address these issues, we propose a framework using foundation model and novel strategies for multi-modal fusion. Our fusion adapters effectively integrate the information from different modalities to enhance glioma segmentation in multi-modal MRI scans. Our method outperforms current state-of-the-art methods for accurate segmentation of the glioma using private and publicly available brain MRI datasets, proving the effectiveness of our approach across different datasets and imaging modalities.

Many pre-operative overall survival (OS) prediction methods have been proposed to assist personalized treatment of diffuse glioma for better prognosis. Most of them utilize pre-operative data, while post-operative data, which contains essential prognosis-related information (e.g., surgical outcomes and lesion evolution) is neglected, hindering prediction accuracy. However, incorporating post-operative data could make OS prediction inapplicable at pre-operative stage, affecting clinical utility. To address this contradiction, in this paper, we propose an effective framework that leverages longitudinal data (pre- and post-operative data) to enhance pre-operative OS prediction. Specifically, two OS prediction networks are built in a knowledge distillation framework. One is the teacher network trained with longitudinal data, and the other is the student network relying solely on pre-operative data. Distillation of deep features is conducted to align the performance of the student network with that of the teacher network. Moreover, mass effect and its distillation are adopted to incorporate lesion evolution information, further enhancing prediction performance. Based on our framework, the student network can leverage essential post-operative information without compromising its applicability at pre-operative stage. Experiments on both in-house and public datasets demonstrate that the student network outperforms all state-of-the-art methods under evaluation with statistical significance. Further ablation study reveals that distillation of mass effect and deep features play positive roles in OS prediction. Moreover, new prognosis-related factors are discovered by comparing the student network with and without distillation.

In this article, glioma segmentation in the glioma grading computer-aided diagnosis (CAD) system requires manual delineation from radiologists, adding substantially to their workload. Although automatic segmentation is powerful, it cannot fully delegate power to artificial intelligence. We propose an AI-powered radiomics algorithm based on slice pooling (AI-RASP). AI-RASP generated compress images by compressing the gray value of each magnetic resonance imaging slice for radiologists to segment manually. In addition, AI-RASP integrated radiomics models to verify the glioma grading effect and the availability of compressed images. AI-RASP significantly reduce the time of manual segmentation. Results reported on multicenter datasets reveal that our architecture is better than the traditional manual segmentation while being over five times faster. The radiomics model with slice pooling mechanism achieves an area under the curve values of 0.86, 086, and 0.83 in the validation cohorts. Radiologists and patients can benefit from a CAD system integrated with AI-RASP.

Use of synthetic data has provided a potential solution for addressing unavailable or insufficient training samples in deep learning-based magnetic resonance imaging (MRI). However, the challenge brought by domain gap between synthetic and real data is usually encountered, especially under complex experimental conditions. In this study, by combining Bloch simulation and general MRI models, we propose a framework for addressing the lack of training data in supervised learning scenarios, termed MOST-DL. A challenging application is demonstrated to verify the proposed framework and achieve motion-robust [Formula: see text] mapping using single-shot overlapping-echo acquisition. We decompose the process into two main steps: (1) calibrationless parallel reconstruction for ultra-fast pulse sequence and (2) intra-shot motion correction for [Formula: see text] mapping. To bridge the domain gap, realistic textures from a public database and various imperfection simulations were explored. The neural network was first trained with pure synthetic data and then evaluated with in vivo human brain. Both simulation and in vivo experiments show that the MOST-DL method significantly reduces ghosting and motion artifacts in [Formula: see text] maps in the presence of unpredictable subject movement and has the potential to be applied to motion-prone patients in the clinic. Our code is available at https://github.com/qinqinyang/MOST-DL.