Matlab version 2021b

Manufactured by MathWorks

Sourced in United States

MATLAB Version 2021b is a high-performance numerical computing environment and programming language. It provides a suite of tools and functions for data analysis, algorithm development, and visualization. MATLAB Version 2021b includes core features for matrix manipulation, data analysis, and visualization.

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Lab products found in correlation

Achieva dstream, by Philips (1 mentions)

4 protocols using matlab version 2021b

Functional MRI Acquisition and Analysis

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FMRI scanning took place on a 3T Philips Achieva dStream using a 32-channel head coil. A high resolution individual T1-weighted structural image was first collected in 192 sagittal slices with a voxel size of 1 mm3. Then, 290 whole-brain functional images were measured for each experimental run. An echo planar imaging (EPI) sequence parallel to the anterior-posterior commissure in axial planes was utilized. The sequence acquired 35 slices in ascending order with a spatial resolution of 3 mm³ (TR = 2 s, TE = 30 ms, FOV = 240 × 240, flip angle = 90).
Preprocessing and statistical analyses of the functional images was realized with the Statistical Parametric Mapping software (SPM12, Wellcome Trust Center for Neuroimaging, London) running on Matlab Version 2021b (the Mathworks Inc, MA). Preprocessing with SPM encompassed realignment to the first image in the respective run and co-registration of all images to the individual mean. Functional images were then spatially normalized to the anatomical T1 template provided by SPM (normalized voxel size of 2 mm3) and smoothed with a 6 mm FWHM istotropic Gaussian kernel. A highpass filter was applied at a cutoff of 1/128 Hz to remove low-frequency drifts.

Tegelbeckers J., Brechmann A., Breitling-Ziegler C., Bonath B., Flechtner H.H, & Krauel K. (2022). Neural Mechanisms Underlying the Effects of Novel Sounds on Task Performance in Children With and Without ADHD. Frontiers in Human Neuroscience, 16, 878994.

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Robust Radiotherapy Planning Optimization

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Analyses were performed in Matlab version 2021b (MathWorks Inc., Natick, MA, USA). Nominal treatment planning parameters, robust range and pass rates for standard resolution measurements were not normally distributed according to one-sample Kolmogorov-Smirnov tests. Two-sided paired Wilcoxon tests were therefore carried out to evaluate differences in these parameters between HS and non-HS treatment. Associations between pass rates for standard resolution measurements and patient age, CTV_WB volume, hippocampus volume, use of RS and measurement WED for both HS and non-HS measurements were investigated using Spearman’s rank correlation. Values of p < 0.05 were considered statistically significant.

Edvardsson A., Gorgisyan J., Andersson K.M., Vallhagen Dahlgren C., Dasu A., Gram D., Björk-Eriksson T, & Munck af Rosenschöld P. (2024). Robustness and dosimetric verification of hippocampal-sparing craniospinal pencil beam scanning proton plans for pediatric medulloblastoma. Physics and Imaging in Radiation Oncology, 29, 100555.

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Predicting COVID-19 Severity using PLS-LDA

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MATLAB version 2021b from MathWorks was used for the calculations. Robust principal components analysis (Robust PCA) was carried out using the LIBRA toolbox [9 ] and partial least square with linear discriminant analysis (PLS-LDA) was carried out using the plslda toolbox [10 (link)]. In a supervised method, such as PLS-LDA, the approach was to select several clinical variables for to make a mathematical model. This model can be used for the prediction of COVID-19 evolution.
We have used the plslda statistical toolbox [10 (link)] because it allows a very robust classification. This classification can be evaluated using the different statistical parameters provided by the classification algorithm (partial least squares linear discriminant analysis, PLSLDA). Within the statistical package used, we also have algorithms that allow select the most informative variables to build the classification model. We have used subwindow permutation analysis (SPA) for this purpose of variable selection. In our study, the classification model looks for the clinical variables that best help us to predict the severity of the disease.

Marhuenda-Egea F.C, & Narro-Serrano J. (2023). Evaluation of risk factors for COVID-19 severity or death and their relationship to metabolic pathways. Heliyon, 9(3), e14161.

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Quantifying Retinal Vascular Ridge Morphology

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OCT volumes were processed and presented in linear scale. A graphical user interface developed in-house using MATLAB version 2021B (MathWorks, Inc) was used to visualize the OCT en face. The peak of the ridgeline was then manually traced and segmented along the vascular-avascular junction on the vascular side (Figure 1). The user could toggle between mean and maximum intensity projections, adjust the number of adjacent B-scans registered and averaged, and adjust contrast for en face and B-scan images to identify the optimal tracing. Segmentation and manual ridge tracings were performed in a masked fashion relative to demographic and clinical information. On the B-scan, the area between the retinal pigment epithelium and the inner surface of the ridge (retinal or fibrovascular ridge vs vitreous interface) was segmented. Thickness was calculated pixelwise in the axial dimension and the top 10 maximum thickness measurements along the ridge were averaged to calculate maximum thickness for the ridge (presented as means with SDs). Ordinal stage was compared to maximum thickness using a generalized estimating equation to account for intereye and interexamination correlations. The association between continuous stage and maximum thickness was assessed via Spearman correlation.

Nguyen T.T., Ni S., Ostmo S., Rajagopalan A., Coyner A.S., Woodward M., Chiang M.F., Jia Y., Huang D., Campbell J.P, & Jian Y. (2022). Association of Optical Coherence Tomography–Measured Fibrovascular Ridge Thickness and Clinical Disease Stage in Retinopathy of Prematurity. JAMA Ophthalmology, 140(11), 1121-1127.

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