Challenges and Practical Solutions to MRI and Histology Matching and Measurements Using Available ImageJ Software Tools

Revealing the MRI-Contrast in Optically Cleared Brains

The current consensus holds that optically-cleared specimens are unsuitable for Magnetic Resonance Imaging (MRI); exhibiting absence of contrast. Prior studies combined MRI with tissue-clearing techniques relying on the latter's ability to eliminate lipids, thereby fostering the assumption that lipids constitute the primary source of ex vivo MRI-contrast. Nevertheless, these findings contradict an extensive body of literature that underscores the contribution of other features to contrast. Furthermore, it remains unknown whether non-delipidating clearing methods can produce MRI-compatible specimens or whether MRI-contrast can be re-established. These limitations hinder the development of multimodal MRI-light-microscopy (LM) imaging approaches. This study assesses the relation between MRI-contrast, and delipidation in optically-cleared whole brains following different tissue-clearing approaches. It is demonstrated that uDISCO and ECi-brains are MRI-compatible upon tissue rehydration, despite both methods' substantial delipidating-nature. It is also demonstrated that, whereas Scale-clearing preserves most lipids, Scale-cleared brain lack MRI-contrast. Furthermore, MRI-contrast is restored to lipid-free CLARITY-brains without introducing lipids. Our results thereby dissociate between the essentiality of lipids to MRI-contrast. A tight association is found between tissue expansion, hyperhydration and loss of MRI-contrast. These findings then enabled us to develop a multimodal MRI-LM-imaging approach, opening new avenues to bridge between the micro- and mesoscale for biomedical research and clinical applications.

Effects of cage vs. net-floor mixed rearing system on goose spleen histomorphology and gene expression profiles

Due to the demands for both environmental protection and modernization of the goose industry in China, the traditional goose waterside rearing systems have been gradually transitioning to the modern intensive dryland rearing ones, such as the net-floor mixed rearing system (MRS) and cage rearing system (CRS). However, the goose immune responses to different dryland rearing systems remain poorly understood. This study aimed to investigate and compare the age-dependent effects of MRS and CRS on the splenic histomorphological characteristics and immune-related genes expression profiles among three economically important goose breeds, including Sichuan White goose (SW), Gang goose (GE), and Landes goose (LD). Morphological analysis revealed that the splenic weight and organ index of SW were higher under CRS than under MRS (p < 0.05). Histological observations showed that for SW and LD, the splenic corpuscle diameter and area as well as trabecular artery diameter were larger under MRS than under CRS at 30 or 43 weeks of age (p < 0.05), while the splenic red pulp area of GE was larger under CRS than under MRS at 43 weeks of age (p < 0.05). Besides, at 43 weeks of age, higher mRNA expression levels of NGF, SPI1, and VEGFA in spleens of SW were observed under MRS than under CRS (p < 0.05), while higher levels of HSPA2 and NGF in spleens of LD were observed under MRS than under CRS (p < 0.05). For GE, there were higher mRNA expression levels of MYD88 in spleens under CRS at 30 weeks of age (p < 0.05). Moreover, our correlation analysis showed that there appeared to be more pronounced positive associations between the splenic histological parameters and expression levels of several key immune-related genes under MRS than under CRS. Therefore, it is speculated that the geese reared under MRS might exhibit enhanced immune functions than those under CRS, particularly for SW and LD. Although these phenotypic differences are assumed to be associated with the age-dependent differential expression profiles of HSPA2, MYD88, NGF, SPI1, and VEGFA in the goose spleen, the underlying regulatory mechanisms await further investigations.

Correlation Attention Registration Based on Deep Learning from Histopathology to MRI of Prostate

Deep learning offers a promising methodology for the registration of prostate cancer images from histopathology to MRI. We explored how to effectively leverage key information from images to achieve improved end-to-end registration. We developed an approach based on a correlation attention registration framework to register segmentation labels of histopathology onto MRI. The network was trained using paired prostate datasets of histopathology and MRI from the Cancer Imaging Archive. We introduced An L2-Pearson correlation layer to enhance feature matching. Furthermore, our model employed an enhanced attention regression network to distinguish between key and nonkey features. For data analysis, we used the Kolmogorov-Smirnov test and a one-sample t-test, with the statistical significance level for the one-sample t-test set at 0.001. Compared with two other models (ProsRegNet and CNNGeo), our model exhibited improved performance in Dice coefficient, with increases of 9.893% and 2.753%, respectively. The Hausdorff distance was reduced by approximately 50% and 50%, while the average label error (ALE) was reduced by 0.389% and 15.021%. The proposed improved multimodal prostate registration framework demonstrated high performance in statistical analysis. The results indicate that our enhanced strategy significantly improves registration performance and enables faster registration of histopathological images of patients undergoing radical prostatectomy to preoperative MRI. More accurate registration can prevent over-diagnosing low-risk cancers and frequent false positives due to observer differences.