<p>Recent years, the utilization of deep learning formedical images has spread rapidly in areas such as organ segmentation, computerdiagnostic detection/diagnosis (CAD) and image noise reduction. Especially, application of deep learningreconstruction (DLR) for imagequality improvement has attracted attention. We have developed artificialintelligence (AI) solution invarious fields, such as Advanced intelligent Clear-IQ Engine: AiCE (DLR), Precise IQ Engine: PIQE (super resolution-DLR), SpectralImaging System of a new dual energy technology and Abierto Reading SupportSolution：Abierto RSS (CAD) for workflow improvement of radiologic interpretationefficiency. AiCE is a process that distinguishes between noise and signalcomponents, and achieves a significant noise reduction effect while maintainingspatial resolution, contributing to both high image quality and radiation dosereduction. In order to improve spatial resolution, PIQE uses target data fromSHR mode (0.25mm, 1792ch) of Aquilion Precision, and super resolution processing to improve spatialresolution of data scanned by conventional CT (AquilionONE). Additionally, significant noise reduction andgraininess maintenance effect can be obtained, therefore high resolution imagecan be obtained with lower radiation exposure. This paper describesreconstruction principles of AiCE and PIQE, and introduces their physicalproperties and clinical benefit.</p>