A new viewer developed at Fujita Health University in Japan promises to streamline the process of reviewing images produced by multidetector CT scanners. Although specialized viewers for MDCT images exist, their operation tends to be complicated,
A new viewer developed at Fujita Health University in Japan promises to streamline the process of reviewing images produced by multidetector CT scanners.
Although specialized viewers for MDCT images exist, their operation tends to be complicated, distracting radiologists from concentrating fully on image interpretation, said Dr. Hidekazu Hattori of the radiology department at Fujita.
He helped develop a simple DICOM image viewer especially for MDCT images. A special control device enables users to handle MDCT images by scrolling, window adjustment, multiplanar reformatting, and synchronized comparison.
"Most viewers sort image series by series number, which is arbitrarily determined based on the order of image generation," Hattori said. "The series number is not the order of image acquisition, however, so the order is not appropriate for the diagnostic process."
Instead, Hattori's viewer can group series by acquisition time and sort by thickness, field-of-view, and reconstruction parameters. By grouping series by acquisition time and then sorting them by reconstruction parameters, radiologists can see how many scans were performed at a glance.
"In the diagnostic process, radiologists should know the outline of the study," he said.
Then they can view thick slice images to screen the study. When a lesion is found, thinner slice images can be reviewed.
Hattori said his viewer, which can also be used for MR, arranges series for smoother diagnostic interpretation.
The special control device offers several advanced features:
? The paging speed can be changed three steps forward or back with a specially designed switch.
? Page-by-page view displays run at a maximum speed of 60 images per second.
? A jog dial allows easy adjustment selection of the preset window values. Preset keys allow quick selection of preset window values.
? Any DICOM image created by other workstations, such as 3D volume rendering, can be viewed as well as the original CT images.
What is the Best Use of AI in CT Lung Cancer Screening?
April 18th 2025In comparison to radiologist assessment, the use of AI to pre-screen patients with low-dose CT lung cancer screening provided a 12 percent reduction in mean interpretation time with a slight increase in specificity and a slight decrease in the recall rate, according to new research.
The Reading Room: Racial and Ethnic Minorities, Cancer Screenings, and COVID-19
November 3rd 2020In this podcast episode, Dr. Shalom Kalnicki, from Montefiore and Albert Einstein College of Medicine, discusses the disparities minority patients face with cancer screenings and what can be done to increase access during the pandemic.
Can CT-Based AI Radiomics Enhance Prediction of Recurrence-Free Survival for Non-Metastatic ccRCC?
April 14th 2025In comparison to a model based on clinicopathological risk factors, a CT radiomics-based machine learning model offered greater than a 10 percent higher AUC for predicting five-year recurrence-free survival in patients with non-metastatic clear cell renal cell carcinoma (ccRCC).
Could Lymph Node Distribution Patterns on CT Improve Staging for Colon Cancer?
April 11th 2025For patients with microsatellite instability-high colon cancer, distribution-based clinical lymph node staging (dCN) with computed tomography (CT) offered nearly double the accuracy rate of clinical lymph node staging in a recent study.