Kyle Henson[email protected]PACS/VNADo hospitals really need (radiology) PACS?Are radiology PACS still necessary for hospitals? Imaging informatics expert Kyle Henson thinks that the era of hospital-based PACS networks for radiology images may be over. Find out why in this new column.May 5, 2019PACS/VNAThe true cost of downtime in radiologyNobody likes downtime in radiology. But in addition to causing annoyance and disruption, system outages can cost the average hospital hundreds of thousands of dollars a year, according to Kyle Henson. That means it's more important than ever to perform proactive monitoring to ensure your system doesn't go down in the first place.January 17, 2019AIBig data for sale: A solution to spur AI researchThe development of artificial intelligence (AI) has been slowed by the need to test AI algorithms against large volumes of real-world imaging studies. But such databases are rare, and data transfer bottlenecks make it hard to move data from clinical sites to algorithm developers. In this article, Kyle Henson offers a solution.May 1, 2018Page 1 of 1Top StoriesCTDL segmentation of chest muscle volume shows promise for COPD patientsDeep learning (DL)-based muscle volume assessment from routine CT scans could help identify COPD patients with comorbid sarcopenia.CareersHPI: Workforce attrition higher in radiology subspecialtiesAssociationsVideo from ECR: Minerva Becker shares congress highlightsCTECR: 'Black swan events' demand informed imaging infrastructurePractice ManagementECR: Integrating actionable reporting into the imaging workflow
![Images show the pectoralis muscles of a healthy male individual who never smoked (age, 66 years; height, 178 cm; body mass index [BMI, calculated as weight in kilograms divided by height in meters squared], 28.4; number of cigarette pack-years, 0; forced expiratory volume in 1 second [FEV1], 97.6% predicted; FEV1: forced vital capacity [FVC] ratio, 0.71; pectoralis muscle area [PMA], 59.4 cm2; pectoralis muscle volume [PMV], 764 cm3) and a male individual with a smoking history and chronic obstructive pulmonary disorder (COPD) (age, 66 years; height, 178 cm; BMI, 27.5; number of cigarette pack-years, 43.2, FEV1, 48% predicted; FEV1:FVC, 0.56; PMA, 35 cm2; PMV, 480.8 cm3) from the Canadian Cohort Obstructive Lung Disease (i.e., CanCOLD) study. The CT image is shown in the axial plane. The PMV is automatically extracted using the developed deep learning model and overlayed onto the lungs for visual clarity.](https://img.auntminnie.com/mindful/smg/workspaces/default/uploads/2026/03/genkin.25LqljVF0y.jpg?auto=format%2Ccompress&crop=focalpoint&fit=crop&h=400&q=70&w=600)
![Images show the pectoralis muscles of a healthy male individual who never smoked (age, 66 years; height, 178 cm; body mass index [BMI, calculated as weight in kilograms divided by height in meters squared], 28.4; number of cigarette pack-years, 0; forced expiratory volume in 1 second [FEV1], 97.6% predicted; FEV1: forced vital capacity [FVC] ratio, 0.71; pectoralis muscle area [PMA], 59.4 cm2; pectoralis muscle volume [PMV], 764 cm3) and a male individual with a smoking history and chronic obstructive pulmonary disorder (COPD) (age, 66 years; height, 178 cm; BMI, 27.5; number of cigarette pack-years, 43.2, FEV1, 48% predicted; FEV1:FVC, 0.56; PMA, 35 cm2; PMV, 480.8 cm3) from the Canadian Cohort Obstructive Lung Disease (i.e., CanCOLD) study. The CT image is shown in the axial plane. The PMV is automatically extracted using the developed deep learning model and overlayed onto the lungs for visual clarity.](https://img.auntminnie.com/mindful/smg/workspaces/default/uploads/2026/03/genkin.25LqljVF0y.jpg?auto=format%2Ccompress&crop=focalpoint&dpr=2&fit=crop&h=167&q=70&w=250)
