Magnetic Resonance Angiography Vessel Research Articles

Segmenting Small Blood Vessels in MRA/MRI: A Machine Learning Approach with Bilateral Filtering Integration

The adoption of artificial intelligence (AI) in various sectors, including healthcare, has gained significant popularity due to its potential to improve services. In the medical field, misdiagnosis has been a major problem, leading to increased mortality rates. Accurate diagnosis is crucial for effective treatment and management of diseases. This research aims to develop a machine-learning model for segmenting small blood vessels in magnetic resonance angiography (MRA) and magnetic resonance imaging (MRI) datasets using bilateral filtering. The research identifies the limitations of existing machine learning models in blood vessel segmentation, particularly the loss of important edge information due to convolutions that blur images. To address this issue, a non-linear bilateral filter is introduced to enhance the segmentation of blood vessels in MRI images. The proposed framework aims to improve the accuracy of the segmentation algorithm by reducing image blurring and noise through bilateral filtering. The objectives of this research include training and testing a machine-learning prototype using bilateral filtering, exploring the weaknesses of existing models in blood vessel segmentation, and developing a machine-learning model specifically designed for segmenting small blood vessels using bilateral filtering. Various studies have proposed machine learning algorithms, such as convolutional neural networks, for blood vessel segmentation. The review emphasizes the importance of bilateral filtering in improving classification accuracy by reducing image blurring. In conclusion, this research aims to contribute to the field of medical image analysis by developing a framework that utilizes bilateral filtering to enhance the segmentation of small blood vessels in MRA and MRI datasets. The proposed machine learning model has the potential to improve the accuracy of blood vessel segmentation, enabling more accurate diagnoses and reducing misdiagnosis-related mortality rates.

First magnetic particle imaging angiography in human-sized organs by employing a multimodal ex vivo pig kidney perfusion system

Objective: Magnetic particle imaging (MPI) is a new, fast 3D imaging technique, which is considered promising for angiographies. As available MPI scanners suffer from restricted spatial resolution and are mostly constructed for small animal imaging, no vessels within one organ have been depicted by MPI, yet. The purpose of this study was to develop an ex vivo organ perfusion system to display vessels within one organ of human size by MPI and to compare the results to an established 3D imaging technique. Approach: An ex vivo porcine kidney perfusion system compatible with digital subtraction angiography (DSA), magnetic resonance tomography and MPI was developed. DSA was used to exemplarily prove intact vessel structures under ex vivo perfusion in two organs. Perfusion in nine organs was displayed by the 3D imaging techniques magnetic resonance angiography (MRA) and MPI angiography. All visible vessels in MRA and MPI were counted and their number compared between both techniques. Main results: The ex vivo organ perfusion system allowed us to perform angiographies by DSA, MRA and MPI. With it, organs of human size could be imaged in small animal scanners, which permitted us to depict vessels within one organ by MPI for the first time. In comparison to MRA, 33% of all vessels were visible in MPI, a difference probably caused by restricted spatial resolution in MPI. Significance: The presented ex vivo organ perfusion system can serve to practically evaluate MPI’s potential for angiography in human-sized organs. This is especially relevant as long as available, for angiography-suited MPI scanners still suffer from size and spatial resolution restrictions.

Improving the Otsu method for MRA image vessel extraction via resampling and ensemble learning.

Accurate extraction of vessels plays an important role in assisting diagnosis, treatment, and surgical planning. The Otsu method has been used for extracting vessels in medical images. However, blood vessels in magnetic resonance angiography (MRA) image are considered as a sparse distribution. Pixels on vessels in MRA image are considered as an imbalanced data in classification of vessels and non-vessel tissues. To extract vessels accurately, a novel method using resampling technique and ensemble learning is proposed for solving the imbalanced classification problem. Each pixel is sampled multiple times through multiple local patches within the image. Then, vessel or non-vessel tissue is determined by the ensemble voting mechanism via a p-tile algorithm. Experimental results show that the proposed method is able to outperform the traditional Otsu method by extracting vessels in MRA images more accurately.

Gray-scale skeletonization of small vessels in magnetic resonance angiography.

Interpretation of magnetic resonance angiography (MRA) is problematic due to complexities of vascular shape and to artifacts such as the partial volume effect. We present new methods to assist in the interpretation of MRA. These include methods for detection of vessel paths and for determination of branching patterns of vascular trees. They are based on the ordered region growing (ORG) algorithm that represents the image as an acyclic graph, which can be reduced to a skeleton by specifying vessel endpoints or by a pruning process. Ambiguities in the vessel branching due to vessel overlap are effectively resolved by heuristic methods that incorporate a priori knowledge of bifurcation spacing. Vessel paths are detected at interactive speeds on a 500-MHz processor using vessel endpoints. These methods apply best to smaller vessels where the image intensity peaks at the center of the lumen which, for the abdominal MRA, includes vessels whose diameter is less than 1 cm.