NAS-optimized topology-preserving transfer learning for differentiating cortical folding patterns.

Increasing evidence suggests that cortical folding patterns of human cerebral cortex manifest overt structural and functional differences. However, for interpretability, few studies leverage advanced techniques (e.g., deep learning) to investigate the difference among cortical folds, resulting in more differences yet to be extensively explored. To this end, we proposed an effective topology-preserving transfer learning framework to differentiate cortical fMRI time series extracted from cortical folds. Our framework consists of three main parts: (1) Neural architecture search (NAS), which is used to devise a well-performing network structure based on an initialized hand-designed super-graph in an image dataset; (2) Topology-preserving transfer, which takes the model searched by NAS as the source network, keeping the topological connectivity in the network unchanged, while transforming all 2D operations including convolution and pooling into 1D, therefore resulting in a topology-preserving network, named TPNAS-Net; (3) Classification and correlation analysis, which involves using the TPNAS-Net to classify 1D cortical fMRI time series for each individual brain, and performing a group difference analysis between autism spectrum disorder (ASD) and healthy control (HC) and correlation analysis with clinical information (i.e., age). Extensive experiments on two ASD datasets obtain consistent results, demonstrating that the TPNAS-Net not only discriminates cortical folding patterns at high classification accuracy, but also captures subtle differences between ASD and HC (p-value = 0.042). In addition, we discover that there is a positive correlation between the classification accuracy and age in ASD (r = 0.39, p-value = 0.04). These findings together suggest that structural and functional differences in cortical folding patterns between ASD and HC may provide a potentially useful biomarker for the diagnosis of ASD.

Core-Satellite Polydopamine-Gadolinium-Metallofullerene Nanotheranostics for Multimodal Imaging Guided Combination Cancer Therapy.

Integration of magnetic resonance imaging (MRI) and other imaging modalities is promising to furnish complementary information for accurate cancer diagnosis and imaging-guided therapy. However, most gadolinium (Gd)-chelator MR contrast agents are limited by their relatively low relaxivity and high risk of released-Gd-ions-associated toxicity. Herein, a radionuclide-64 Cu-labeled doxorubicin-loaded polydopamine (PDA)-gadolinium-metallofullerene core-satellite nanotheranostic agent (denoted as CDPGM) is developed for MR/photoacoustic (PA)/positron emission tomography (PET) multimodal imaging-guided combination cancer therapy. In this system, the near-infrared (NIR)-absorbing PDA acts as a platform for the assembly of different moieties; Gd3 N@C80 , a kind of gadolinium metallofullerene with three Gd ions in one carbon cage, acts as a satellite anchoring on the surface of PDA. The as-prepared CDPGM NPs show good biocompatibility, strong NIR absorption, high relaxivity (r 1 = 14.06 mM-1 s-1 ), low risk of release of Gd ions, and NIR-triggered drug release. In vivo MR/PA/PET multimodal imaging confirms effective tumor accumulation of the CDPGM NPs. Moreover, upon NIR laser irradiation, the tumor is completely eliminated with combined chemo-photothermal therapy. These results suggest that the CDPGM NPs hold great promise for cancer theranostics.

The role of viscosity estimation for oil-in-gelatin phantom in shear wave based ultrasound elastography.

Shear wave based ultrasound elastography utilizes mechanical excitation or acoustic radiation force to induce shear waves in deep tissue. The tissue response is monitored to obtain elasticity information about the tissue. During the past two decades, tissue elasticity has been extensively studied and has been used in clinical disease diagnosis. However, biological soft tissues are viscoelastic in nature. Therefore, they should be simultaneously characterized in terms of elasticity and viscosity. In this study, two shear wave-based elasticity imaging methods, shear wave dispersion ultrasound vibrometry (SDUV) and acoustic radiation force impulsive (ARFI) imaging, were compared. The discrepancy between the measurements obtained by the two methods was analyzed, and the role of viscosity was investigated. To this end, four types of gelatin phantoms containing 0%, 20%, 30% and 40% castor oil were fabricated to mimic different viscosities of soft tissue. For the SDUV method, the shear elasticity µ1 was 3.90 ± 0.27 kPa, 4.49 ± 0.16 kPa, 2.41 ± 0.33 kPa and 1.31 ± 0.09 kPa; and the shear viscosity µ2 was 1.82 ± 0.31 Pa•s, 2.41 ± 0.35 Pa•s, 2.65 ± 0.13 Pa•s and 2.89 ± 0.14 Pa•s for 0%, 20%, 30% and 40% oil, respectively in both cases. For the ARFI measurements, the shear elasticity µ was 7.30 ± 0.20 kPa, 8.20 ± 0.31 kPa, 7.42 ± 0.21 kPa and 5.90 ± 0.36 kPa for 0%, 20%, 30% and 40% oil, respectively. The SDUV results demonstrated that the elasticity first increased from 0% to 20% oil and then decreased for the 30% and 40% oil. The viscosity decreased consistently as the concentration of castor oil increased from 0% to 40%. The elasticity measured by ARFI showed the same trend as that of the SDUV but exceeded the results measured by SDUV. To clearly validate the impact of viscosity on the elasticity estimation, an independent measurement of the elasticity and viscosity by dynamic mechanical analysis (DMA) was conducted on these four types of gelatin phantoms and then compared with SDUV and ARFI results. The shear elasticities obtained by DMA (3.44 ± 0.31 kPa, 4.29 ± 0.13 kPa, 2.05 ± 0.29 kPa and 1.06 ± 0.18 kPa for 0%, 20%, 30% and 40% oil, respectively) were lower than those by SDUV, whereas the shear viscosities obtained by DMA (2.52 ± 0.32 Pa·s, 3.18 ± 0.12 Pa·s, 3.98 ± 0.19 Pa·s and 4.90 ± 0.20 Pa·s for 0%, 20%, 30% and 40% oil, respectively) were greater than those obtained by SDUV. However, the DMA results showed that the trend in the elasticity and viscosity data was the same as that obtained from the SDUV and ARFI. The SDUV results demonstrated that adding castor oil changed the viscoelastic properties of the phantoms and resulted in increased dispersion of the shear waves. Viscosity can provide important and independent information about the inner state of the phantoms, in addition to the elasticity. Because the ARFI method ignores the dispersion of the shear waves, namely viscosity, it may bias the estimation of the true elasticity. This study sheds further light on the significance of the viscosity measurements in shear wave based elasticity imaging methods.