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The local environment of a barium monofluoride (BaF) molecule embedded in an argon matrix is calculated. A substitution of a BaF molecule for four Ar atoms is found to be strongly favoured compared to substitutions for other numbers of Ar atoms. The equilibrium positions of the BaF molecule and its nearby Ar neighbours are found by minimising the total energy. The potential barrier that prevents the migration of the BaF molecule within the solid and the barrier that prevents its rotation are calculated. At the cryogenic temperatures used by the EDM 3 collaboration, these barriers are sufficiently large to fix the position and orientation of the molecule. Knowledge of the local environment of matrix-isolated BaF molecules is essential for the EDM 3 collaboration, which is using them in a precision measurement of the electron electric dipole moment. [ABSTRACT FROM AUTHOR]

The infrared absorption spectra of the CO monomer isolated in solid N2 have been recorded at various temperatures between 4.5 and 30 K. The absorption features of the fundamental stretching mode show its linewidth and matrix-induced frequency shift to be weakly temperature-dependent. As the temperature of the matrix was raised, an increase in the linewidth together with a redshift in the central frequency was observed. These observations were explained in terms of the quenching of the CO rotational states by the N2 matrix into closely-lying librational states. A quantitative model was then used to calculate the energy difference between these librational states. Results show that they can be thermally populated through the absorption of matrix phonons. [ABSTRACT FROM AUTHOR]

• Deep-learning based models help physician make radiotherapy planning. • Most dose prediction methods only use binary masks as inputs without distance information. • An explicit distance transformation is performed before the network. • This work demonstrated the effectiveness and generalizability of our distance transformation. Geometric information such as distance information is essential for dose calculations in radiotherapy. However, state-of-the-art dose prediction methods use only binary masks without distance information. This study aims to develop a dose prediction deep learning method for nasopharyngeal carcinoma radiotherapy by taking advantage of the distance information as well as the mask information. A novel transformation method based on boundary distance was proposed to facilitate the prediction of dose distributions. Radiotherapy datasets of 161 nasopharyngeal carcinoma patients were retrospectively collected, including binary masks of organs-at-risk (OARs) and targets, planning CT, and clinical plans. The patients were randomly divided into 130, 11 and 20 cases for training, validating, and testing the models, respectively. Furthermore, 40 patients from an external cohort were used to test the generalizability of the models. The proposed method shows superior performance. The predicted dose error and dose–volume histogram (DVH) error of our method were 7.51% and 11.6% lower than the mask-based method, respectively. For the inverse planning, compared with mask-based methods, our method provided similar performances on the GTVnx and OARs and outperformed on the GTVnd and the CTV, the pass rates of which increased from 89.490% and 90.016% to 96.694% and 91.189%, respectively. The preliminary results on nasopharyngeal carcinoma radiotherapy cases showed that our proposed distance-guided method for dose prediction achieved better performance than mask-based methods. Further studies with more patients and on other cancer sites are warranted to fully validate the proposed method. [ABSTRACT FROM AUTHOR]

Anatomical resection (AR) based on anatomical sub-regions is a promising method of precise surgical resection, which has been proven to improve long-term survival by reducing local recurrence. The fine-grained segmentation of an organ's surgical anatomy (FGS-OSA), i.e., segmenting an organ into multiple anatomic regions, is critical for localizing tumors in AR surgical planning. However, automatically obtaining FGS-OSA results in computer-aided methods faces the challenges of appearance ambiguities among sub-regions (i.e., inter-sub-region appearance ambiguities) caused by similar HU distributions in different sub-regions of an organ's surgical anatomy, invisible boundaries, and similarities between anatomical landmarks and other anatomical information. In this paper, we propose a novel fine-grained segmentation framework termed the "anatomic relation reasoning graph convolutional network" (ARR-GCN), which incorporates prior anatomic relations into the framework learning. In ARR-GCN, a graph is constructed based on the sub-regions to model the class and their relations. Further, to obtain discriminative initial node representations of graph space, a sub-region center module is designed. Most importantly, to explicitly learn the anatomic relations, the prior anatomic-relations among the sub-regions are encoded in the form of an adjacency matrix and embedded into the intermediate node representations to guide framework learning. The ARR-GCN was validated on two FGS-OSA tasks: i) liver segments segmentation, and ii) lung lobes segmentation. Experimental results on both tasks outperformed other state-of-the-art segmentation methods and yielded promising performances by ARR-GCN for suppressing ambiguities among sub-regions.

Precise classification of histopathological images is crucial to computer-aided diagnosis in clinical practice. Magnification-based learning networks have attracted considerable attention for their ability to improve performance in histopathological classification. However, the fusion of pyramids of histopathological images at different magnifications is an under-explored area. In this paper, we proposed a novel deep multi-magnification similarity learning (DSML) approach that can be useful for the interpretation of multi-magnification learning framework and easy to visualize feature representation from low-dimension (e.g., cell-level) to high-dimension (e.g., tissue-level), which has overcome the difficulty of understanding cross-magnification information propagation. It uses a similarity cross entropy loss function designation to simultaneously learn the similarity of the information among cross-magnifications. In order to verify the effectiveness of DMSL, experiments with different network backbones and different magnification combinations were designed, and its ability to interpret was also investigated through visualization. Our experiments were performed on two different histopathological datasets: a clinical nasopharyngeal carcinoma and a public breast cancer BCSS2021 dataset. The results show that our method achieved outstanding performance in classification with a higher value of area under curve, accuracy, and F-score than other comparable methods. Moreover, the reasons behind multi-magnification effectiveness were discussed.

Background: In the radiotherapy of nasopharyngeal carcinoma (NPC), magnetic resonance imaging (MRI) is widely used to delineate tumor area more accurately. While MRI offers the higher soft tissue contrast, patient positioning and couch correction based on bony image fusion of computed tomography (CT) is also necessary. There is thus an urgent need to obtain a high image contrast between bone and soft tissue to facilitate target delineation and patient positioning for NPC radiotherapy. In this paper, our aim is to develop a novel image conversion between the CT and MRI modalities to obtain clear bone and soft tissue images simultaneously, here called bone-enhanced MRI (BeMRI)., Methods: Thirty-five patients were retrospectively selected for this study. All patients underwent clinical CT simulation and 1.5T MRI within the same week in Shenzhen Second People's Hospital. To synthesize BeMRI, two deep learning networks, U-Net and CycleGAN, were constructed to transform MRI to synthetic CT (sCT) images. Each network used 28 patients' images as the training set, while the remaining 7 patients were used as the test set (~1/5 of all datasets). The bone structure from the sCT was then extracted by the threshold-based method and embedded in the corresponding part of the MRI image to generate the BeMRI image. To evaluate the performance of these networks, the following metrics were applied: mean absolute error (MAE), structural similarity index (SSIM), and peak signal-to-noise ratio (PSNR)., Results: In our experiments, both deep learning models achieved good performance and were able to effectively extract bone structure from MRI. Specifically, the supervised U-Net model achieved the best results with the lowest overall average MAE of 125.55 (P<0.05) and produced the highest SSIM of 0.89 and PSNR of 23.84. These results indicate that BeMRI can display bone structure in higher contrast than conventional MRI., Conclusions: A new image modality BeMRI, which is a composite image of CT and MRI, was proposed. With high image contrast of both bone structure and soft tissues, BeMRI will facilitate tumor localization and patient positioning and eliminate the need to frequently check between separate MRI and CT images during NPC radiotherapy., Competing Interests: Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://dx.doi.org/10.21037/qims-20-1239). The special issue “Artificial Intelligence for Image-guided Radiation Therapy” was commissioned by the editorial office without any funding or sponsorship. The authors have no other conflicts of interest to declare., (2021 Quantitative Imaging in Medicine and Surgery. All rights reserved.)