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Radiology is a major contributor to health care's impact on climate change, in part due to its reliance on energy-intensive equipment as well as its growing technological reliance. Delivering modern patient care requires a robust informatics team to move images from the imaging equipment to the workstations and the health care system. Radiology informatics is the field that manages medical imaging IT. This involves the acquisition, storage, retrieval, and use of imaging information in health care to improve access and quality, which includes PACS, cloud services, and artificial intelligence. However, the electricity consumption of computing and the life cycle of various computer components expands the carbon footprint of health care. The authors provide a general framework to understand the environmental impact of clinical radiology informatics, which includes using the international Greenhouse Gas Protocol to draft a definition of scopes of emissions pertinent to radiology informatics, as well as exploring existing tools to measure and account for these emissions. A novel standard ecolabel for radiology informatics tools, such as the Energy Star label for consumer devices or Leadership in Energy and Environmental Design certification for buildings, should be developed to promote awareness and guide radiologists and radiology informatics leaders in making environmentally conscious decisions for their clinical practice. At this critical climate juncture, the radiology community has a unique and pressing obligation to consider our shared environmental responsibility in innovating clinical technology for patient care.
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http://dx.doi.org/10.1016/j.jacr.2023.11.019 | DOI Listing |
J Appl Clin Med Phys
September 2025
Icon Cancer Centre Toowoomba, Toowoomba, Queensland, Australia.
Introduction: The role of imaging in radiotherapy is becoming increasingly important. Verification of imaging parameters prior to treatment planning is essential for safe and effective clinical practice.
Methods: This study described the development and clinical implementation of ImageCompliance, an automated, GUI-based script designed to verify and enforce correct CT and MRI parameters during radiotherapy planning.
J Appl Clin Med Phys
September 2025
Department of Radiation Oncology, University of Utah, Salt Lake City, Utah, USA.
Purpose: The development of on-board cone-beam computed tomography (CBCT) has led to improved target localization and evaluation of patient anatomical change throughout the course of radiation therapy. HyperSight, a newly developed on-board CBCT platform by Varian, has been shown to improve image quality and HU fidelity relative to conventional CBCT. The purpose of this study is to benchmark the dose calculation accuracy of Varian's HyperSight cone-beam computed tomography (CBCT) on the Halcyon platform relative to fan-beam CT-based dose calculations and to perform end-to-end testing of HyperSight CBCT-only based treatment planning.
View Article and Find Full Text PDFMol Psychiatry
September 2025
Center for Depression, Anxiety and Stress Research, McLean Hospital, Belmont, MA, USA.
Dysregulated dopaminergic signaling has been implicated in the pathophysiology of major depressive disorder (MDD) and childhood sexual abuse (CSA), but inconsistencies abound. In a multimodal PET-functional MRI study, harnessing the highly selective tracer [C]altropane, we investigated dopamine transporter availability (DAT) and resting-state functional connectivity (rsFC) within reward-related regions among 112 unmedicated individuals (MDD: n = 37, MDD/CSA: n = 18; CSA no MDD: n = 14; controls: n = 43). Striatal DAT and seed-based rsFC were assessed in the dorsal and ventral striatum and the ventral tegmental area.
View Article and Find Full Text PDFCardiovasc Intervent Radiol
September 2025
CardioVascular and Interventional Radiology, Vienna, Austria.
Radiology
September 2025
Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck St, Boston, MA 02115.
Despite the rapid growth of Food and Drug Administration-cleared artificial intelligence (AI)- and machine learning-enabled medical devices for use in radiology, current tools remain limited in scope, often focusing on narrow tasks and lacking the ability to comprehensively assist radiologists. These narrow AI solutions face limitations in financial sustainability, operational efficiency, and clinical utility, hindering widespread adoption and constraining their long-term value in radiology practice. Recent advances in generative and multimodal AI have expanded the scope of image interpretation, prompting discussions on the development of generalist medical AI.
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