Research

Reducing the acquisition time of the Amide Proton Transfer (APT) technique is crucial for its widespread application. One way to reduce the scanning time is by decreasing the frequency offsets used during image acquisition, which might compromise the quantification of the APT effect. In our study, we develop a deep learning-based model that can reconstruct dense frequency offsets from sparse ones, potentially reducing scanning time. We propose using convLSTM to extract both short and long-range spatial and frequency features of APT imaging sequences. Our proposed model outperforms other seq2seq models, achieving superior reconstruction with a peak signal-to-noise ratio of 45.8 (95% confidence interval (CI)): [44.9 46.7]), and a structural similarity index of 0.989 (95% CI:[0.987 0.993]) for the tumor region. We have integrated a weighted layer into our model to evaluate the impact of individual frequency offsets on the reconstruction process. The weights assigned to the frequency offset at ± 6.5 ppm, 0 ppm, and 3.5 ppm demonstrate higher significance as learned by the model. Experimental results demonstrate that our proposed model effectively reconstructs dense APT imaging sequences (+7 to -7 with 0.5 ppm as interval) from data with 21 frequency offsets, reducing scanning time by 25%. This work presents a method for lowering the APT imaging time, offering potential guidance for parameter settings in physical devices during APT imaging and serving as a valuable reference for clinicians.

As a new magnetic resonance imaging technology with a unique contrast mechanism, Amide Proton Transfer Weighted (APTw) imaging has shown its potential in the diagnosis, treatment evaluation, and prognosis prediction of breast cancer. The signal contrast between the lesion and the surrounding glandular tissue is low on the anatomical images obtained using the APTw imaging sequence in comparison to conventional anatomical dynamic contrast-enhanced MR images, which leads to subjective and inconsistent identification of the lesion for clinicians. However, breast lesion regions and their surrounding glandular tissues exhibit different APT effects, which can be utilized to enhance the segmentation accuracy of breast lesion regions on acquired anatomical images of the APTw sequence. Therefore, this paper proposes a breast lesion region segmentation network based on the model that incorporates APTw parameter map fitting and pathological classification for automatic lesion region segmentation. In addition, this paper demonstrates the importance of each frequency in APTw imaging for clinical tasks. It improves the interpretability of the network, allowing clinicians to understand its functionality better. We conducted experiments on 164 cases of originally acquired images of the APTw sequence, with lesion regions being jointly labeled as ground truth by three senior radiologists. The results show that the proposed method performs well in lesion region segmentation on images of APT sequence. Compared with these advanced methods such as U-Net, SAM, and TransBTS, our method achieves higher accuracy. Additionally, the model's interpretable contribution to different frequency offsets aligns with clinical observations.

Magnetic Resonance Imaging (MRI) stands as a cornerstone in modern medical diagnostics, renowned for its unparalleled ability to provide exquisite soft tissue contrast. This advanced imaging modality has become an indispensable tool in patient screening processes. The versatility of MRI is underscored by its array of imaging sequences, including T1-weighted (T1w), T1-weighted contrast-enhanced (T1c), T2-weighted (T2W), T2-fluid attenuated inversion recovery (T2f), and Apparent Diffusion Coefficient (ADC), among others. Each sequence offers a unique perspective on the lesions within the body, providing crucial information that aids clinicians in formulating accurate diagnoses and treatment plans.

Despite its numerous advantages, the diversity of MRI sequences poses a substantial challenge for radiologists. They are often burdened with the arduous and time-intensive task of manually labeling multiple organs or cancerous regions in three-dimensional (3D) space, based on various MRI sequences. This process is not only labor-intensive but also prone to human error, which can impact patient care. Furthermore, existing automated segmentation methods are largely tailored to specific MRI sequences, organs, or types of cancer. This specialization results in a lack of generality, making these methods less adaptable to a broader range of medical imaging tasks. As the number of tasks for which these methods are trained increases, inference speeds tend to slow down, and the parameter counts, leading to increased computational complexity.

In light of these challenges, there is an urgent demand for the development of an automatic universal 3D segmentation network. Such a network would need to be capable of handling multiple MRI sequences, as well as segmenting various organs and types of cancer. This innovative approach would not only streamline the diagnostic process but also enhance the overall efficiency and accuracy of medical imaging analysis, ultimately contributing to improved patient outcomes.

This study proposes a multimodal cross global learnable attention network (MMCGLANet) for denoising magnetic resonance (MR) images, which addresses the limitations of existing methods in processing multimodal MR images with arbitrary modal loss and noise. The main innovations are as follows: (1) Cross global learnable attention mechanism: MMCGLANet uses cross global attention mechanism to achieve adaptive feature fusion between different modes and within the same mode. This method effectively utilizes the correlation between multimodal MR images and improves denoising performance. (2) Multimodal fusion capability: This model combines a multimodal cross nonlinear non activated temperature network (MMCNAFTN) and a multimodal cross global learnable attention module (MMCGLAM) to achieve robust multimodal fusion. It enhances generalization ability by using modal masking scheme to handle any missing modality. (3) Sequence codes for missing modalities: The introduction of sequence codes allows the model to label and handle missing modalities during training, validation, and testing, ensuring that the model can flexibly adapt to real-world scenarios with incomplete data. (4) ConvLSTM is used for intra modal correlation: ConvLSTM is used to capture the spatial similarity between different slices in the same mode, further improving the ability of the model to extract meaningful features. (5) Controllable denoising level: This model allows for adjusting the denoising intensity by using a noise level map, providing flexibility to meet different clinical requirements.

APTw imaging and segmentation

Analysis of the entire group

UniMRISegNet: Universal 3D Network for Various Organs and Cancers Segmentation on Multi-Sequence MRI

MR image denoising