Secret History: Return of the Black Death Channel 4, 7-8pm In 1348 the Black Death swept through London, killing people within days of the appearance of their first symptoms. Exactly how many died, and why, has long been a mystery. This Secret History documentary follows experts as they pick through the evidence and reveal why the plague killed on such a scale. And they ask, what might be coming next?

Medscape

“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

IEEE Transactions on Pattern Analysis and Machine Intelligence

X-ray computer tomography (CT) is fast becoming an accepted tool within the materials science community for the acquisition of 3D images. Here the authors review the current state of the art as CT transforms from a qualitative diagnostic tool to a quantitative one. Our review considers first the image acquisition process, including the use of iterative reconstruction strategies suited to specific segmentation tasks and emerging methods that provide more insight (e.g. fast and high resolution imaging, crystallite (grain) imaging) than conventional attenuation based tomography. Methods and shortcomings of CT are examined for the quantification of 3D volumetric data to extract key topological parameters such as phase fractions, phase contiguity, and damage levels as well as density variations. As a non-destructive technique, CT is an ideal means of following structural development over time via time lapse sequences of 3D images (sometimes called 3D movies or 4D imaging). This includes information nee...

https://www.tandfonline.com/doi/pdf/10.1179/1743280413Y.0000000023

Quantitative X-ray tomography

In order to reduce the potential radiation risk, low-dose CT has attracted an increasing attention. However, simply lowering the radiation dose will significantly degrade the image quality. In this paper, we propose a new noise reduction method for low-dose CT via deep learning without accessing original projection data. A deep convolutional neural network is here used to map low-dose CT images towards its corresponding normal-dose counterparts in a patch-by-patch fashion. Qualitative results demonstrate a great potential of the proposed method on artifact reduction and structure preservation. In terms of the quantitative metrics, the proposed method has showed a substantial improvement on PSNR, RMSE and SSIM than the competing state-of-art methods. Furthermore, the speed of our method is one order of magnitude faster than the iterative reconstruction and patch-based image denoising methods.

Low-dose CT via convolutional neural network


Background: The study was aimed at investigating the mathematical relationship between the aortic valve and aortic root through CTA imaging-based reconstruction.

Methods: We selected 121 healthy participants and analyzed the measurements of aortic root dimensions, including the sinotubular junction (SJT), ventriculo-arterial junction (VAJ), maximum sinus diameter (SD), sinus height (SH), effective height (eH) and coaptation height (cH). We also reconstructed 3-D aortic valve cusps using CTA imaging to calculate the aortic cusp surface areas. Data were collected to analyze the ratios and the correlation between aortic valve and aortic root dimensions.

Results: Among healthy participants, the STJ was approximately 10% larger than the VAJ, and the SD was 1.375 times larger than the VAJ. The average eH and cH were 8.94 mm and 3.62 mm, respectively. The aortic cusp surface areas were larger in men than women. Regardless of sex, the non-coronary cusp was found to be largest, and was followed by the right coronary cusp and the left coronary cusp. Although the aortic root dimensions were also significantly larger in in men than women, the STJ to VAJ, SD to VAJ, and SH to VAJ ratios did not significantly differ by sex. The mathematical relationship between the aortic cusp surface areas and VAJ orifice area was calculated as aortic cusp surface areas 


Conclusions: The aortic root has specific geometric ratios. The mathematical relationship between the aortic valve and aortic root might be used to guide aortic valve repair.

https://www.scienceopen.com/document_file/d7e70b46-bdd1-4cd1-986d-fc1ed633ad49/ScienceOpen/CVIA.2023.0044.pdf

Investigation of the Mathematical Relationship between the Aortic Valve and Aortic Root: Implications for Precise Guidance in Aortic Valve Repair

Objective. Plan-of-the-day (PoD) adaptive radiation therapy (ART) is based on a library of treatment plans, among which, at each treatment fraction, the PoD is selected using daily images. However, this strategy is limited by PoD selection uncertainties. This work aimed to propose and evaluate a workflow to automatically and quantitatively identify the PoD for cervix cancer ART based on daily CBCT images. Approach. The quantification was based on the segmentation of the main structures of interest in the CBCT images (clinical target volume [CTV], rectum, bladder, and bowel bag) using a deep learning model. Then, the PoD was selected from the treatment plan library according to the geometrical coverage of the CTV. For the evaluation, the resulting PoD was compared to the one obtained considering reference CBCT delineations. Main results. In experiments on a database of 23 patients with 272 CBCT images, the proposed method obtained an agreement between the reference PoD and the automatically identified PoD for 91.5% of treatment fractions (99.6% when considering a 5% margin on CTV coverage). Significance. The proposed automatic workflow automatically selected PoD for ART using deep-learning methods. The results showed the ability of the proposed process to identify the optimal PoD in a treatment plan library.

/pdf/automatic-segmentation-for-plan-of-the-day-selection-in-cbct-29wkpd88.pdf

Automatic segmentation for plan-of-the-day selection in CBCT-guided adaptive radiation therapy of cervical cancer

Prediction of brain functional activity is of great significance for neuroscience research. The brain functional activities at different regions are highly related, and their relationships can be captured with functional connectivity and structural connectivity. The existing works are challenging to integrate two connectivity information for functional activity prediction. In this paper, we propose a Temporal Cross-Graph Network (TCGN) for predicting brain functional activity, which can comprehensively exploit multi-modal spatial dependence and temporal patterns. In particular, a novel cross-graph convolution module is developed to capture the spatial features of brain structural and functional connectivity. A temporal fusion module is designed to learn the pattern of dynamic functional connectivity to guide the prediction. Specially, a multi-task loss function is proposed to incorporate functional activity and dynamic functional connectivity. Extensive experiments on the Human Connectome Project dataset demonstrate the effectiveness of the proposed framework.

Temporal Cross-Graph Network for Brain Functional Activity Prediction

The foundation models based on pre-training technology have significantly advanced artificial intelligence from theoretical to practical applications. These models have facilitated the feasibility of computer-aided diagnosis for widespread use. Medical contrastive vision-language pre-training, which does not require human annotations, is an effective approach for guiding representation learning using description information in diagnostic reports. However, the effectiveness of pre-training is limited by the large-scale semantic overlap and shifting problems in medical field. To address these issues, we propose the Knowledge-Boosting Contrastive Vision-Language Pre-training framework (KoBo), which integrates clinical knowledge into the learning of vision-language semantic consistency. The framework uses an unbiased, open-set sample-wise knowledge representation to measure negative sample noise and supplement the correspondence between vision-language mutual information and clinical knowledge. Extensive experiments validate the effect of our framework on eight tasks including classification, segmentation, retrieval, and semantic relatedness, achieving comparable or better performance with the zero-shot or few-shot settings. Our code is open on https://github.com/ChenXiaoFei-CS/KoBo.

Guanyu Yang

Papers

Investigation of the Mathematical Relationship between the Aortic Valve and Aortic Root: Implications for Precise Guidance in Aortic Valve Repair

Automatic segmentation for plan-of-the-day selection in CBCT-guided adaptive radiation therapy of cervical cancer

Temporal Cross-Graph Network for Brain Functional Activity Prediction

Knowledge Boosting: Rethinking Medical Contrastive Vision-Language Pre-Training