### Research Objectives and Expected Contributions  
In particular, for MRA images intended for the identification of intracranial aneurysms, automating preprocessing could reduce the time spent on laborious manual editing of MIP images. By training a suitable deep learning model--potentially one that combines multi-scale feature extraction with attention mechanisms to highlight vascular structures--critical vessels could be emphasized, while irrelevant tissues and overprojected structures are suppressed. Furthermore, the standardization that such an automated method would provide could mitigate inter-operator variability, leading to a more uniform level of image quality for diagnosis. Given that cerebral vasculature is inherently complex, and that smaller aneurysms can be difficult to discern in lower-resolution or obscured images, an effective preprocessing step can have direct implications for the sensitivity and specificity of aneurysm detection algorithms downstream. By integrating these preprocessing capabilities with advanced detection or segmentation approaches specifically targeting aneurysms, there is an opportunity to create a more comprehensive pipeline that seamlessly delivers diagnostic-ready images. This pipeline could then be integrated into existing picture archiving and communication systems (PACSs), reducing the turnaround time for radiologists and allowing for quicker therapeutic decision-making. Ultimately, automating image preprocessingfor MRA, while less high-profile than tasks like tumor segmentation, has the potential to bridge a major gap in current deep learning applications within neurosurgery. If reliably implemented, such automation would not only expedite the creation of accurate and easily interpretable MIP images but also lessen the chance of operator-induced errors during the editing process. It could, in turn, amplify the benefits already seen in other deep learning applications, such as better detection of small aneurysms and improved quantification of vascular pathologies. Previous studies have demonstrated the effectiveness of deep learning in cerebrovascular imaging, particularly in aneurysm detection [17; 18; 19; 20], vessel segmentation [21; 22; 23; 24], and MRA super-resolution [25]. However, most prior research has focused on downstream applications such as lesion detection rather than preprocessing tasks. By automating vascular preprocessing, our study aims to bridge this gap and improve the interpretability of MRA images. Accordingly, the goal of this study is to develop a software application that integrates deep learning-based algorithms into the MRA processing pipeline to generate diagnostic-quality images of cerebral vessels, thereby mitigating the necessity for extensive manual preprocessing. By incorporating cutting-edge deep learning architectures with careful consideration of clinically relevant training data, we aim to produce a robust tool capable of removing superfluous structures, highlighting subtle vascular anomalies and ensuring that crucial diagnostic cues--like the presence of a small aneurysm--remain readily discernible. Such a tool could not only streamline the diagnostic workflow but also elevate the overall standard of cerebrovascular assessments, offering a new perspective on the role deep learning can play in neurosurgical imaging beyond the more common tasks of segmentation and lesion detection. Through the development and validation of this software, we hope to demonstrate that automation in the preprocessing stage has significant potential to enhance the quality, speed, and reliability of MRA-based diagnostic procedures, contributing to better patient outcomes and more efficient healthcare delivery. In particular, while DeepLab v3+ is a proven semantic segmentation framework [26; 27; 28; 29; 30], our primary contribution lies in automating MRA preprocessing steps that have traditionally been carried out manually.