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Articles by Suzie Bash
Roundtable Recap: Imaging Biomarkers for the Early Diagnosis of Alzheimer’s Disease
Image Gallery Roundtable Recap: Imaging Biomarkers for the Early Diagnosis of Alzheimer’s Disease. Appl Radiol.
Roundtable Recap: Future Directions of Neuroimaging in the Management of Dementia
Roundtable Recap: Future Directions of Neuroimaging in the Management of Dementia. Appl Radiol.
Roundtable Recap: The Role of MRI in Detecting Amyloid-related Imaging Abnormalities (ARIA)
Image Gallery Roundtable Recap: The Role of MRI in Detecting Amyloid-related Imaging Abnormalities (ARIA). Appl Radiol.
Treatment Brings Hope in Alzheimer Disease: Role of Imaging AI
Alzheimer disease (AD) is a devastating, lethal disease affecting 6 million in the US (and 20 million worldwide). The hallmarks of AD in the brain are beta-amyloid (Aβ) plaques, which are imaged with amyloid PET (Figure 1), and neurofibrillary tangles, which are imaged with tau PET. Figure 1. Amyloid PET. Amyloid PET-CT fusion with color overlay (A) and amyloid PET-MR fusion (axial T2W) with color overlay (B).
Treatment Brings Hope in Alzheimer Disease: Role of Imaging AI
Image Gallery Bash S, Tanenbaum LN. Treatment Brings Hope in Alzheimer Disease: Role of Imaging AI. Appl Radiol. 2022;51(1):25-30. By Suzie Bash, MD, and Lawrence N Tanenbaum, MD, FACR| January 06, 2022 Categories: Clinical Departments|Imaging Modalities|Section About the Author Suzie Bash, MD, and Lawrence N Tanenbaum, MD, FACR Affiliations: Dr Bash is a neuroradiologist at RadNet, Inc, Los Angeles, CA. She is also a member of the Editorial Advisory Board of Applied Radiology.
Natural Language Processing and Understanding in Clinical Practice
Tanenbaum LN, Bash S. Natural Language Processing and Understanding in Clinical Practice. Appl Radiol. 2021;50(6):32-33. By Lawrence N Tanenbaum, MD, FACR, and Suzie Bash, MD| November 06, 2021 Categories: Clinical Departments|Digital Portals|Imaging Informatics|Section
Quantitative Volumetry is Revolutionizing Neuroimaging
Artificial intelligence (AI) is impacting medical imaging in many ways, including enhancing value through quantitative volumetric neuroimaging (QNI). QNI offers gains in efficiency, accuracy, clinical relevance, reduced subjectivity, and even a modest return on investment and the potential to increase referrals.
Deep Learning: Promising to Revolutionize Image Reconstruction
Artificial intelligence-based image reconstruction tools are poised to revolutionize computed tomography (CT) and magnetic resonance (MR) procedures in 2021. Lower radiation dose, improved image quality, higher scanning efficiency, and positive impacts on workflow and patient experience are among the broad range of benefits deep learning is expected to deliver in the coming year.
Enhancing Neuroimaging with Artificial Intelligence
People traditionally think of artificial intelligence (AI) as a means of using computer-generated neural networks to mirror the intellectual thought processes of human beings. In reality, however, humans are not so interested in thought duplication; they are much more interested in thought augmentation. We want computers to elevate and augment human capacity and improve efficiency.
Beyond Reconstruction: The Next Frontiers in AI Imaging Acquisition
Deep learning reconstruction (DLR) is a transformative technology that is rapidly becoming the standard of care across clinical practices. Faster scans, lower noise, and sharper images are no longer a competitive edge, but a baseline expectation. The question now is no longer whether artificial intelligence (AI) can accelerate acquisition, but whether it can make acquisition itself smarter. The next frontier in acquisition AI is emerging across 3 principles: Acquire less. Acquire smarter.
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