DTI is an established method to study cerebral white-matter microstructure. Two established measures of DTI are fractional anisotropy (FA) and mean diffusivity (MD) and both differ for bilingual and monolingual speakers. Less is known about differences in two other measures called radial (RD) and axial diffusivity (AD). We report differences in mean RD and AD-values in the right superior longitudinal fasciculus (SLF) and forceps minor between bilingual (Hindi–English) and monolingual (English) speakers as well as differences in mean FA-values in the anterior thalamic radiation, right inferior fronto-occipital and inferior longitudinal fasciculus (ILF) and mean MD-values in forceps minor and bilateral SLF. Noteworthy, a positive correlation between L2 proficiency and mean RD-values in the right SLF was observed. We suggest that changes in the geometry of white matter tracts reflect regular bilingual language experience and contend that neuroplasticity in right SLF results from demands on cognitive control for bilingual speakers.

Non-invasive characterization of pancreatic masses aids in the management of pancreatic lesions. Intravoxel incoherent motion-diffusion kurtosis imaging (IVIM-DKI) and machine learning-based texture analysis was used to differentiate pancreatic masses such as pancreatic ductal adenocarcinoma (PDAC), pancreatic neuroendocrine tumor (pNET), solid pseudopapillary epithelial neoplasm (SPEN), and mass-forming chronic pancreatitis (MFCP). A total of forty-eight biopsy-proven patients with pancreatic masses were recruited and classified into pNET (n = 13), MFCP (n = 6), SPEN (n = 4), and PDAC (n = 25) groups. All patients were scanned for IVIM-DKI sequences acquired with 14 b-values (0 to 2500 s/mm²) on a 1.5T MRI. An IVIM-DKI model with a 3D total variation (TV) penalty function was implemented to estimate the precise IVIM-DKI parametric maps. Texture analysis (TA) of the apparent diffusion coefficient (ADC) and IVIM-DKI parametric map was performed and reduced using the chi-square test. These features were fed to an artificial neural network (ANN) for characterization of pancreatic mass subtypes and validated by 5-fold cross-validation. Receiver operator characteristics (ROC) analyses were used to compute the area under curve (AUC). Perfusion fraction (f) was significantly higher (p < 0.05) in pNET than PDAC. The f showed better diagnostic performance for PDAC vs. MFCP with AUC:0.77. Both pseudo-diffusion coefficient (D*) and f for PDAC vs. pNET showed an AUC of 0.73. ADC and diffusion coefficient (D) showed good diagnostic performance for pNET vs. MFCP with AUC: 0.79 and 0.76, respectively. In the TA of PDAC vs. non-PDAC, f and combined IVIM-DKI parameters showed high accuracy ≥ 84.3% and AUC ≥ 0.84. Mean f and combined IVIM-DKI parameters estimated that the IVIM-DKI model with TV texture features has the potential to be helpful in characterizing pancreatic masses.