TOGS: Gaussian Splatting with Temporal Opacity Offset for Real-Time 4D DSA Rendering

Four-dimensional Digital Subtraction Angiography (4D DSA) is a medical imaging technique that provides a series of 2D images captured at different stages and angles during the process of contrast agent filling blood vessels. It plays a significant role in the diagnosis of cerebrovascular diseases. Improving the rendering quality and speed under sparse sampling is important for observing the status and location of lesions. The current methods exhibit inadequate rendering quality in sparse views and suffer from slow rendering speed. To overcome these limitations, we propose TOGS, a Gaussian splatting method with opacity offset over time, which can effectively improve the rendering quality and speed of 4D DSA. We introduce an opacity offset table for each Gaussian to model the temporal variations in the radiance of the contrast agent. By interpolating the opacity offset table, the opacity variation of the Gaussian at different time points can be determined. This enables us to render the 2D DSA image at that specific moment. Additionally, we introduced a Smooth loss term in the loss function to mitigate overfitting issues that may arise in the model when dealing with sparse view scenarios. During the training phase, we randomly prune Gaussians, thereby reducing the storage overhead of the model. The experimental results demonstrate that compared to previous methods, this model achieves state-of-the-art reconstruction quality under the same number of training views. Additionally, it enables real-time rendering while maintaining low storage overhead. The code will be publicly available.

ConvFormer: Plug-and-Play CNN-Style Transformers for Improving Medical Image Segmentation

Transformers have been extensively studied in medical image segmentation to build pairwise long-range dependence. Yet, relatively limited well-annotated medical image data makes transformers struggle to extract diverse global features, resulting in attention collapse where attention maps become similar or even identical. Comparatively, convolutional neural networks (CNNs) have better convergence properties on small-scale training data but suffer from limited receptive fields. Existing works are dedicated to exploring the combinations of CNN and transformers while ignoring attention collapse, leaving the potential of transformers under-explored. In this paper, we propose to build CNN-style Transformers (ConvFormer) to promote better attention convergence and thus better segmentation performance. Specifically, ConvFormer consists of pooling, CNN-style self-attention (CSA), and convolutional feed-forward network (CFFN) corresponding to tokenization, self-attention, and feed-forward network in vanilla vision transformers. In contrast to positional embedding and tokenization, ConvFormer adopts 2D convolution and max-pooling for both position information preservation and feature size reduction. In this way, CSA takes 2D feature maps as inputs and establishes long-range dependency by constructing self-attention matrices as convolution kernels with adaptive sizes. Following CSA, 2D convolution is utilized for feature refinement through CFFN. Experimental results on multiple datasets demonstrate the effectiveness of ConvFormer working as a plug-and-play module for consistent performance improvement of transformer-based frameworks. Code is available at https://github.com/xianlin7/ConvFormer.

TiAVox: Time-aware Attenuation Voxels for Sparse-view 4D DSA Reconstruction

Four-dimensional Digital Subtraction Angiography (4D DSA) plays a critical role in the diagnosis of many medical diseases, such as Arteriovenous Malformations (AVM) and Arteriovenous Fistulas (AVF). Despite its significant application value, the reconstruction of 4D DSA demands numerous views to effectively model the intricate vessels and radiocontrast flow, thereby implying a significant radiation dose. To address this high radiation issue, we propose a Time-aware Attenuation Voxel (TiAVox) approach for sparse-view 4D DSA reconstruction, which paves the way for high-quality 4D imaging. Additionally, 2D and 3D DSA imaging results can be generated from the reconstructed 4D DSA images. TiAVox introduces 4D attenuation voxel grids, which reflect attenuation properties from both spatial and temporal dimensions. It is optimized by minimizing discrepancies between the rendered images and sparse 2D DSA images. Without any neural network involved, TiAVox enjoys specific physical interpretability. The parameters of each learnable voxel represent the attenuation coefficients. We validated the TiAVox approach on both clinical and simulated datasets, achieving a 31.23 Peak Signal-to-Noise Ratio (PSNR) for novel view synthesis using only 30 views on the clinically sourced dataset, whereas traditional Feldkamp-Davis-Kress methods required 133 views. Similarly, with merely 10 views from the synthetic dataset, TiAVox yielded a PSNR of 34.32 for novel view synthesis and 41.40 for 3D reconstruction. We also executed ablation studies to corroborate the essential components of TiAVox. The code will be publically available.