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GE HealthCare Launches LOGIQ Focus to Merge cSound Architecture with AI Workflow Automation

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GE HealthCare Launches LOGIQ Focus to Merge cSound Architecture with AI Workflow Automation

Medical technology and imaging giant GE HealthCare announced the commercial launch of LOGIQ Focus, a new general imaging ultrasound system that bridges high-end beamforming performance with AI-enabled workflow productivity tools.

Built on GE HealthCare’s proprietary cSound Architecture, it delivers advanced speckle-reduction imaging, an integrated High Definition Ultrasound (HDU) display for fine tissue characterization, and high-definition microvascular flow visualization.

Ultrasound-Guided Fat Fraction (UGFF) for quantitative steatosis assessment and 2D Shear Wave Elastography for liver tissue stiffness and fibrosis staging.

Rather than relying on legacy hardware beamformers that process acoustic data sequentially, LOGIQ Focus is built on GE HealthCare’s software-based cSound Architecture:

cSound Beamforming & Advanced Speckle Reduction: Ingests massive volumes of raw ultrasound data per channel, executing software-based signal processing to suppress acoustic noise, enhance tissue boundaries, and preserve subtle structural contrast in difficult-to-image patients.

High Definition Ultrasound (HDU) Display: Pairs the cSound processing pipeline with an HDU monitor engineered to render fine tissue differentiation, microvascular architecture, and crisp border definition for deep abdominal, musculoskeletal, and vascular structures.

Cable-Free Ergonomics via Vscan Air CL: Incorporates native compatibility with the wireless, handheld Vscan Air CL dual-probe. This allows sonographers and clinicians to undock a cable-free probe directly from the cart console, enabling rapid, flexible positioning around bed-bound patients without tether drag or repetitive shoulder strain.

Clinical Throughput Validation: In third-party comparative evaluations, 90 percent of clinical participants rated the overall image quality of LOGIQ Focus as excellent, while the system achieved a 26 percent reduction in exam completion time compared to its predicate platform.


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