Estimation of intracranial volume: A comparative study between synthetic MRI and FSL-brain extraction tool (BET)2

Utility of 1.5 Tesla MRI Scanner in the Management of Small Sample Sizes Driven from 3D Breast Cell Culture

The aim of this work was to use and optimize a 1.5 Tesla magnetic resonance imaging (MRI) system for three-dimensional (3D) images of small samples obtained from breast cell cultures in vitro. The basis of this study was to design MRI equipment to enable imaging of MCF-7 breast cancer cell cultures (about 1 million cells) in 1.5 and 2 mL glass tubes and/or bioreactors with an external diameter of less than 20 mm. Additionally, the development of software to calculate longitudinal and transverse relaxation times is described. Imaging tests were performed using a clinical MRI scanner OPTIMA 360 manufactured by GEMS. Due to the size of the tested objects, it was necessary to design additional receiving circuits allowing for the study of MCF-7 cell cultures placed in glass bioreactors. The examined sample's volume did not exceed 2.0 mL nor did the number of cells exceed 1 million. This work also included a modification of the sequence to allow for the analysis of T1 and T2 relaxation times. The analysis was performed using the MATLAB package (produced by MathWorks). The created application is based on medical MR images saved in the DICOM3.0 standard which ensures that the data analyzed are reliable and unchangeable in an unintentional manner that could affect the measurement results. The possibility of using 1.5 T MRI systems for cell culture research providing quantitative information from in vitro studies was realized. The scanning resolution for FOV = 5 cm and the matrix was achieved at a level of resolution of less than 0.1 mm/pixel. Receiving elements were built allowing for the acquisition of data for MRI image reconstruction confirmed by images of a phantom with a known structure and geometry. Magnetic resonance sequences were modified for the saturation recovery (SR) method, the purpose of which was to determine relaxation times. An application in MATLAB was developed that allows for the analysis of T1 and T2 relaxation times. The relaxation times of cell cultures were determined over a 6-week period. In the first week, the T1 time value was 1100 ± 40 ms, which decreased to 673 ± 59 ms by the sixth week. For T2, the results were 171 ± 10 ms and 128 ± 12 ms, respectively.

Evaluating T1, T2 Relaxation, and Proton Density in Normal Brain Using Synthetic MRI with Fast Imaging Protocol

Synthetic MRI is being increasingly used for the quantification of brain longitudinal relaxation time (T1), transverse relaxation time (T2), and proton density (PD) values. However, the effect of fast imaging protocols on these quantitative values has not been fully estimated. The purpose of this study was to investigate the effect of fast scan parameters on T1, T2, and PD measured with a multi-dynamic multi-echo (MDME) sequence of normal brain at 3.0T. Thirty-four volunteers were scanned using 3 MDME sequences with different scan times (named Fast, 2 min, 29 sec; Routine, 4 min, 07 sec; and Research, 7 min, 46 sec, respectively). The measured T1, T2, and PD in 18 volumes of interest (VOI) of brain were compared between the 3 sequences using rank sum test, t test, coefficients of variation (CVs) analysis, correlation analysis, and Bland-Altman analysis. We found that even though T1, T2, and PD were significantly different between the 3 sequences in most of the brain regions, the intersequence CVs were relatively low and linear correlation were high. Bland-Altman plots showed that most of the values fall within the 95% prediction limits. We concluded that fast imaging protocols of MDME sequence used in our study can potentially be used for quantitative evaluation of brain tissues. Since changing scan parameters can affect the measured T1, T2, and PD values, it is necessary to use consistent scan parameter for comparing or following up cases quantitatively.

Accuracy of skull stripping in a single-contrast convolutional neural network model using eight-contrast magnetic resonance images

In automated analyses of brain morphometry, skull stripping or brain extraction is a critical first step because it provides accurate spatial registration and signal-intensity normalization. Therefore, it is imperative to develop an ideal skull-stripping method in the field of brain image analysis. Previous reports have shown that convolutional neural network (CNN) method is better at skull stripping than non-CNN methods. We aimed to evaluate the accuracy of skull stripping in a single-contrast CNN model using eight-contrast magnetic resonance (MR) images. A total of 12 healthy participants and 12 patients with a clinical diagnosis of unilateral Sturge-Weber syndrome were included in our study. A 3-T MR imaging system and QRAPMASTER were used for data acquisition. We obtained eight-contrast images produced by post-processing T1, T2, and proton density (PD) maps. To evaluate the accuracy of skull stripping in our CNN method, gold-standard intracranial volume (ICVG) masks were used to train the CNN model. The ICVG masks were defined by experts using manual tracing. The accuracy of the intracranial volume obtained from the single-contrast CNN model (ICVE) was evaluated using the Dice similarity coefficient [= 2(ICVE ⋂ ICVG)/(ICVE + ICVG)]. Our study showed significantly higher accuracy in the PD-weighted image (WI), phase-sensitive inversion recovery (PSIR), and PD-short tau inversion recovery (STIR) compared to the other three contrast images (T1-WI, T2-fluid-attenuated inversion recovery [FLAIR], and T1-FLAIR). In conclusion, PD-WI, PSIR, and PD-STIR should be used instead of T1-WI for skull stripping in the CNN models.

Accurate Estimation of Total Intracranial Volume in MRI using a Multi-tasked Image-to-Image Translation Network

Total intracranial volume (TIV) is the volume enclosed inside the cranium, inclusive of the meninges and the brain. TIV is extensively used to correct variations in inter-subject head size for the evaluation of neurodegenerative diseases. In this work, we present an automatic method to generate a TIV mask from MR images while synthesizing a CT image to be used in subsequent analysis. In addition, we propose an alternative way to obtain ground truth TIV masks using a semi-manual approach, which results in significant time savings. We train a conditional generative adversarial network (cGAN) using 2D MR slices to realize our tasks. The quantitative evaluation showed that the model was able to synthesize CT and generate TIV masks that closely approximate the reference images. This study also provides a comparison of the described method against skull stripping tools that output a mask enclosing the cranial volume, using MRI scan. In particular, highlighting the deficiencies in using such tools to approximate the volume using MRI scan.