From the November 2021 issue of HealthCare Business News magazine

The solution was developed, trained and validated on data from more than 30,000 low-dose CT images from a large data set from the National Lung Screening Trial (NLST), which has affirmed that early detection and treatment with low-dose CT can reduce lung cancer mortality. Its design included making sure it could filter out unwanted artifacts and noise and extract features needed for diagnosis. Researchers also validated it using another 2,085 NLST images.

Heart disease and cancer share common risk factors, including tobacco use, diet, blood pressure and obesity. A multi-institutional study from 2019 found cancer put patients at higher risk of dying from cardiovascular disease based on an analysis of three million cancer cases and 28 different types of malignancies.


Yan and his colleagues are planning more multi-vendor, multi-site studies to further validate the algorithm.

The findings were published in Natures Communications.

Researchers fuse MR and CT images using deep learning technique
Over the summer, researchers in China began touting a new deep learning-based process that “fuses” multi-modal scans to create a higher quality medical image that can improve clinical diagnosis and patient outcomes.

Known as image fusion, the technique automatically identifies and combines information of scans from different modalities to produce a single high-quality image. “Experimental results indicate that the proposed method achieves state-of-the-art performance in terms of both visual quality and quantitative evaluation metrics,” said author Yi Li, with Qingdao University’s College of Data Science and Software Engineering in Qingdao, China, in a statement.

Li and his colleagues used the technique to fuse MR, CT and SPECT images to build an image training database. The database was then used to fuse medical images in batches, with the newly fused images appearing more natural and with sharper edges and higher resolutions. Additionally, detailed information and features of interest were better preserved to some extent, while key information was clearly contrasted and the virtual shadow was effectively removed.

The end result, according to the researchers, is a single image that contains most of the information in the two multi-modal images of CT and MR or MR and SPECT, thereby making up for the deficiencies of each of these images alone.

“The increase in the amount of information undoubtedly brings changes to the improvement of medical imaging diagnoses. More valuable information can be used to support effective diagnosis. It also brings about possibilities for the study of 'automatic diagnosis' technology and conducts tentative research,” wrote the researchers in their study.

November 2021 Magazine

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