A Sparse Volume Reconstruction Method for Fetal Brain MRI Using Adaptive Kernel Regression

Development of fetal magnetic resonance imaging (MRI) phantom

Background

Anthropomorphic phantoms play an important role in routine clinical practice. They can be used to calibrate magnetic resonance imaging (MRI) scanners, control the diagnostic equipment quality, and reduce the acquisition time. The latter is especially critical for diagnosing fetal anomalies, which requires optimal image quality within the shortest possible time. This paper aims to develop an MRI fetal phantom and determine the materials that best mimic the magnetic resonance (MR) characteristics of its internal organs. Future phantom features will include simulations of fetal limb movements.

Methods

A single MRI study of a pregnant woman at 20 weeks 3 days of gestation was used as a reference and for image segmentation. Anonymized Digital Imaging and Communication in Medicine (DICOM) files were imported into 3D Slicer v. 5.2.1 for segmentation of the uterus, fetus, and internal organs. Based on the performed segmentation, a three-dimensional model was obtained for printing on a 3D printer. The mold was 3D printed on an Anycubic Photon M3 Max printer. The paper showcases the selection and manufacturing of compositions to simulate the relaxation times of the fetal organs. Formulations for emulsions and carrageenan- and agar-based hydrogels are presented. The selected compositions were used to fill the 3D printed model.

Results

Statistical analysis showed no significant differences in absolute and relative signal values obtained from scans of a pregnant woman at 20 weeks and 3 days and a fetal phantom.

Conclusions

During the study, an anthropomorphic fetal phantom was constructed, filled with compositions with relaxation times T1 and T2 similar to the control values of the corresponding tissues. The phantom can be used to set up and optimize fetal MRI protocols, train and educate medical students, residents, graduate students, and X-ray technicians, as well as to timely control image quality and equipment serviceability.

GPU-Based Parallel Processing Techniques for Enhanced Brain Magnetic Resonance Imaging Analysis: A Review of Recent Advances

The approach of using more than one processor to compute in order to overcome the complexity of different medical imaging methods that make up an overall job is known as GPU (graphic processing unit)-based parallel processing. It is extremely important for several medical imaging techniques such as image classification, object detection, image segmentation, registration, and content-based image retrieval, since the GPU-based parallel processing approach allows for time-efficient computation by a software, allowing multiple computations to be completed at once. On the other hand, a non-invasive imaging technology that may depict the shape of an anatomy and the biological advancements of the human body is known as magnetic resonance imaging (MRI). Implementing GPU-based parallel processing approaches in brain MRI analysis with medical imaging techniques might be helpful in achieving immediate and timely image capture. Therefore, this extended review (the extension of the IWBBIO2023 conference paper) offers a thorough overview of the literature with an emphasis on the expanding use of GPU-based parallel processing methods for the medical analysis of brain MRIs with the imaging techniques mentioned above, given the need for quicker computation to acquire early and real-time feedback in medicine. Between 2019 and 2023, we examined the articles in the literature matrix that include the tasks, techniques, MRI sequences, and processing results. As a result, the methods discussed in this review demonstrate the advancements achieved until now in minimizing computing runtime as well as the obstacles and problems still to be solved in the future.