iC-Clear™

Exceptional imaging technology

Real-time image processing allows you exceptional image clarity and procedural confidence.

Whale’s iC-Clear™ technology uses proprietary algorithms to provide richer detail and lower noise without significant loss of data. This provides you with a level of image clarity beyond traditional systems capability. Importantly, we are able to achieve this with less power and lower dose for the safety of operators and patients.

Our high end platform allows anatomy to be visualized clearly for greater diagnostic confidence and speed. A lower cumulative dose requirement eliminates the need for generators with excessive power ratings.

Motion Compensated Temporal Recursive Noise Filtering (MCTF) – This reduces noise with less motion blur than the simple frame averaging used by other systems. The final image has a higher signal to noise (S/N) ratio, enabling the surgeon to use less dose and lower power.

Automated Adaptive Image Optimization (AAIO) – These algorithms preserve the essential statistic property of the original image while optimizing the image brightness and contrast. This maintains the uniformity of the original image. Others typically use methods which image degradation and increase noise or graininess.

Adaptive Nonlinear Edge Enhancement (ANEE) – Traditional linear edge enhancement algorithms simply filter image data, but this amplifies noise, thereby reducing the S/N ratio. ANEE uses a unique two stage process to enhance only edges and leave uniform regions with their original data quality. The result is better defined edges without loss of detail.

Multi-Point Image Optimization (MPIO) – Each image is divided into 9 sub-images which are each optimized before being incorporated back into a single image. MPIO applies different filters to post-process the images without changing X-ray dose. The results in optimization across the whole image.

 

iClear

MCTF 1
Motion Compensated Temporal Recursive Noise Filtering (MCTF) – This reduces noise with less motion blur than the simple frame averaging used by other systems. The final image has a higher signal to noise (S/N) ratio, enabling the surgeon to use less dose and lower power.
AAIO
Automated Adaptive Image Optimization (AAIO) – These algorithms preserve the essential statistic property of the original image while optimizing the image brightness and contrast. This maintains the uniformity of the original image. Others typically use methods which image degradation and increase noise or graininess.
Adaptive Nonlinear Edge Enhancement (ANEE) - Traditional linear edge enhancement algorithms simply filter image data, but this amplifies noise, thereby reducing the S/N ratio. ANEE uses a unique two stage process to enhance only edges and leave uniform regions with their original data quality. The result is better defined edges without loss of detail.
Adaptive Nonlinear Edge Enhancement (ANEE) – Traditional linear edge enhancement algorithms simply filter image data, but this amplifies noise, thereby reducing the S/N ratio. ANEE uses a unique two stage process to enhance only edges and leave uniform regions with their original data quality. The result is better defined edges without loss of detail.
Multi-Point Image Optimization (MPIO) – Each image is divided into 9 sub-images which are each optimized before being incorporated back into a single image. This brings image you would expect in a cath lab into a mobile device. MPIO applies different filters to post-process the images without changing X-ray dose.
Multi-Point Image Optimization (MPIO) – Each image is divided into 9 sub-images which are each optimized before being incorporated back into a single image. This brings image you would expect in a cath lab into a mobile device. MPIO applies different filters to post-process the images without changing X-ray dose.
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