Medical Imaging

Physicians utilize medical imaging to see inside the body to diagnose and treat patients. This includes computed tomography (CT), magnetic resonance imaging (MRI), X-ray, ultrasound, fluoroscopy, angiography,  and the nuclear imaging modalities of PET and SPECT. 

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‘A transformation in care’: New treatment improves survival for advanced prostate cancer patients

Results of the international, phase 3 trial were presented recently during the American Society of Clinical Oncology Annual Meeting.

MLB ‘FEVER’ pitch: New MRI view changing the game for elbow injuries

Major league pitchers put a tremendous amount of stress on their arms and new imaging insights can give players a competitive advantage on their road to recovery.

knee x-ray

Support system helps technologists correct knee X-ray errors, reducing exam retake rates

Reperforming lateral knee radiographs is common practice but consumes unnecessary resources and exposes patients to added radiation, experts explained in Radiography.

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RSNA updates rules and regulations for 2021 meeting, including a new live scanning policy

The Oak Brook, Illinois-based society’s board of directors has approved a decision to expand live imaging beyond just ultrasound.

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How to discuss routine X-ray shielding with patients: 2 views

A pair of Houston-based imaging providers shared their experience and best practices for managing one of the field's most controversial questions in JACR.

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New imaging device may bypass ultrasound for thyroid cancer screening

The Laser and Ultrasound Co-analyzer for Thyroid Nodules, or LUCA, device was recently tested in a small group of patients and accurately classified 13 benign and four malignant nodules.

Cutting-edge MRI method reveals persistent COVID-19 lung damage missed by routine CT

Hyperpolarized xenon MRI enables sensitive, regional investigation of breathing and gas transfer into the blood stream, according to a new study in Radiology

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Ultrasound imaging and AI combine to ‘revolutionize’ fetal heart defect diagnosis

Human providers typically spot as few as 30% of these conditions before birth, but a new machine learning model from UCSF bumped the rate up to 95%.