Greg Freiherr[email protected]PACS/VNAViewers define issues underlying enterprise imagingIn the final installment of our series on the challenges of enterprising imaging, contributing writer Greg Freiherr takes a look at a key ingredient in any enterprise installation: viewers. They can either enable enterprise imaging or doom it to failure.April 14, 2016PACS/VNAImage sharing presents challenges to the enterpriseImage sharing touches all aspects of patient management, from the practical to the incongruous, but it still remains a problem for patients and physicians at many institutions, according to contributing writer Greg Freiherr, who offers this third installment on the challenges of enterprise imaging.March 22, 2016PACS/VNAPACS and EMR vendors aim to bridge workflow gapSurrounded by flying dice and one-armed bandits, the Healthcare Information and Management Systems Society conference in Las Vegas did its best to establish a middle ground for discussions about enterprise imaging. Among the many difficulties that accompany the integration of medical images into patient medical records, workflow stood out.March 10, 2016PACS/VNAHIMSS: Challenges take shape as enterprise imaging gains prominenceLAS VEGAS - At the just-concluded Healthcare Information and Management Systems Society (HIMSS) meeting, three radiology thought leaders described the challenges that threaten enterprise imaging and the forces that are compelling the medical community to embrace it.March 2, 2016Page 1 of 1Top StoriesMRIUnstable brain networks may drive multiple sclerosis fatigueFatigue affects between 36.5% and 78% of people with multiple sclerosis and can dramatically impair their daily lives.Womens ImagingAI-calculated BAC on mammograms predicts cardiovascular disease in womenCardiac ImagingECR: Cardiac imaging is at the 'heart' of precision medicineUltrasoundUltrasound MinnieCast, Episode 3: Focused ultrasound brings hope to patientsWomens ImagingECR: LLMs help patients understand breast imaging reports
![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)
