Unlabelled: We have evaluated the impact of increased body mass on the quality of myocardial perfusion imaging using a latest-generation γ-camera with cadmium-zinc-telluride semiconductor detectors in patients with high (≥40 kg/m(2)) or very high (≥45 kg/m(2)) body mass index (BMI).
Methods: We enrolled 81 patients, including 18 with no obesity (BMI < 30 kg/m(2)), 17 in World Health Organization obese class I (BMI, 30-34.9 kg/m(2)), 15 in class II (BMI, 35-39.9 kg/m(2)), and 31 in class III (BMI ≥ 40 kg/m(2)), including 15 with BMI ≥ 45 kg/m(2). Image quality was scored as poor (1), moderate (2), good (3), or excellent (4). Patients with BMI ≥ 45 kg/m(2) and nondiagnostic image quality (≤2) were rescanned after repositioning to better center the heart in the field of view. Receiver-operating-curve analysis was applied to determine the BMI cutoff required to obtain diagnostic image quality (≥3).
Results: Receiver-operating-curve analysis resulted in a cutoff BMI of 39 kg/m(2) (P < 0.001) for diagnostic image quality. In patients with BMI ≥ 40 kg/m(2), image quality was nondiagnostic in 81%; after CT-based attenuation correction this decreased to 55%. Repositioning further improved image quality. Rescanning on a conventional SPECT camera resulted in diagnostic image quality in all patients with BMI ≥ 45 kg/m(2).
Conclusion: Patients with BMI ≥ 40 kg/m(2) should be scheduled for myocardial perfusion imaging on a conventional SPECT camera, as it is difficult to obtain diagnostic image quality on a cadmium-zinc-telluride camera.
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http://dx.doi.org/10.2967/jnumed.111.102434 | DOI Listing |
J Imaging Inform Med
January 2025
Leiden University Medical Center (LUMC), Leiden, the Netherlands.
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January 2025
Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, China.
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J Gen Intern Med
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Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA.
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Objective: We sought to better understand urologist, primary care providers (PCPs), and patient experiences with AS care delivery to identify opportunities to improve adherence.
Int J Cardiovasc Imaging
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Department of Radiology, Massachusetts General Hospital, Boston, MA, USA.
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Eye Institute and Department of Ophthalmology, Eye & ENT Hospital, Fudan University, Shanghai, China.
Chatbot-based multimodal AI holds promise for collecting medical histories and diagnosing ophthalmic diseases using textual and imaging data. This study developed and evaluated the ChatGPT-powered Intelligent Ophthalmic Multimodal Interactive Diagnostic System (IOMIDS) to enable patient self-diagnosis and self-triage. IOMIDS included a text model and three multimodal models (text + slit-lamp, text + smartphone, text + slit-lamp + smartphone).
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