Swante Welander, Peter Rönnquist, and Hans Norberg

Image Processing
New processors bring greater speed to medical imaging industry
In personal computers, quad-core microprocessors and graphics processors allow the user to view graphics with extreme detail. The gaming industry has embraced this technology, and an entire generation of teenagers has driven the development of advanced computer games. Now, the medical imaging industry has discovered that it can leverage the high-speed computation technology to advance medical imaging.
June 11, 2009
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Top Stories
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MRI
Deep-learning technique boosts diagnostic ability of fast knee MRI
A new, fast deep learning-based MR imaging technique shows excellent performance for diagnosing knee injuries, researchers have reported.
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Molecular Imaging
PET scans identify people at early risk for cognitive decline
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Ultrasound
Most veterans adhere to post-EVAR surveillance imaging
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CT
Quantitative CT textures of the lung help assess COPD severity
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AI
Health data restrictions plague medtech AI developers
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Deep-learning technique boosts diagnostic ability of fast knee MRI
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A new, fast deep learning-based MR imaging technique shows excellent performance for diagnosing knee injuries, researchers have reported.
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Most veterans adhere to post-EVAR surveillance imaging
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Quantitative CT can capture lung inflammation patterns to assess the severity of chronic obstructive pulmonary disease (COPD).
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Medtech industry and health leaders from big tech have introduced a new AI Policy Roadmap.
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Images show lung ultrasound findings of major pleuro-pulmonary pathologies. (a) Pleural effusion; (b) Pneumonia; (c) Bronchopneumonia; (d) Acute pulmonary edema; (e) Chronic obstructive pulmonary disease exacerbation; (f) Pleuropulmonary tumors; (g) Acute respiratory distress syndrome.
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Figure: Improved image quality with a generative adversarial network architecture for low- to high-field-strength image translation, called LowGAN. (A–C) Top: High-field-strength (3-T) and low-field-strength (64-mT) images and LowGAN outputs for a single participant across (A) fluid-attenuated inversion recovery (FLAIR), (B) T1-weighted (T1w), and (C) T2-weighted (T2w) contrasts. Bottom: Graphs demonstrate the structural similarity index (SSIM), peak signal-to-noise ratio (PSNR), normalized cross-correlation (NCC), and feature similarity index (FSIM) between high field strength and low field strength (64-mT) and high field strength and LowGAN outputs. a.u. = arbitrary units.****p ≤ 0.0001. Images and caption courtesy of Radiology and the RSNA.
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Clinical and ultrasound features improve diagnostic accuracy for detecting endometrial malignancy.
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Ultrasound Gynecologist
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