Search Results

Vantage Orian 1.5T systems are indicated for use as a diagnostic imaging modality that produces cross-sectional transaxial, coronal, sagittal, and oblique images that display anatomic structures of the head or body. Additionally, this system is capable of non-contrast enhanced imaging, such as MRA.

MRI (magnetic resonance imaging) images correspond to the spatial distribution of protons (hydrogen nuclei) that exhibit nuclear magnetic resonance (NMR). The NMR properties of body tissues and fluids are:

·Proton density (PD) (also called hydrogen density)

• ·Spin-lattice relaxation time (T1)
• ·Spin-spin relaxation time (T2)

·Flow dynamics

·Chemical Shift

Depending on the region of interest, contrast agents may be used. When interpreted by a trained physician, these images yield information that can be useful in diagnosis.

The Vantage Orian (Model MRT-1550) is a 1.5 Tesla Magnetic Resonance Imaging (MRI) System. The Vantage Orian uses 1.4 m short and 3.8 tons light weight magnet. It includes the Pianissimo™ technology (scan noise reduction technology). The design of the gradient coil and the WB coil of the Vantage Orian 1.5T provides the maximum field of view of 55 x 50 cm. The Model MRT-1550/AC, AD, AG, AH includes the standard gradient system and Model MRT-1550/AK, AL, AO, AP includes the XGO gradient system.

This system is based upon the technology and materials of previously marketed Canon Medical Systems MRI systems and is intended to acquire and display cross-sectional transaxial, coronal, sagittal, and oblique images of anatomic structures of the head or body. The Vantage Orian MRI System is comparable to the current 1.5T Vantage Orian MRI System (K193097), cleared July 14th, 2020 with the following modifications.

The provided text describes a 510(k) premarket notification for a modified MRI device, the Vantage Orian 1.5T, MRT-1550, V7.0 with AiCE Reconstruction Processing Unit for MR. The submission primarily details software functionalities added to an already cleared predicate device (V6.0 version). While it mentions image quality testing, it does not present specific acceptance criteria or a detailed study proving the device meets those criteria in a format applicable to evaluating AI/algorithm performance.

Specifically, the document does not contain the detailed information required for the requested output about AI/algorithm acceptance criteria and performance study, such as:

• A table of acceptance criteria with numerical performance metrics (e.g., sensitivity, specificity, AUC).
• Sample sizes used for test sets specifically for AI performance evaluation.
• Data provenance (country of origin, retrospective/prospective) for AI testing.
• Number and qualifications of experts for ground truth establishment.
• Adjudication methods.
• MRMC comparative effectiveness study details.
• Standalone algorithm performance metrics.
• Type of ground truth (e.g., pathology, outcomes data).
• Training set sample size and ground truth establishment for the training set.

The document discusses imaging performance parameters and states "image quality testing was completed which demonstrated that the subject device meets predetermined acceptance criteria." However, it only provides a high-level summary of this testing:

• Testing Information Present:
  • "MR image quality metrics were performed, utilizing volunteer images, to assess 3D FAST sequences and 3D Compressed SPEEDER acceleration sequences."
  • "Representative images, reviewed by American Board Certified Radiologists and American Board Certified Cardiologists with MR certification, were obtained using the subject device."
  • "Reviewers provided detailed assessments of overall image noise, image sharpness, image degradation, image artifacts, diagnostic confidence, contrast, lesion/pathology conspicuity, and clinical utility."
  • "It was confirmed that 3D FAST and 3D Compressed SPEEDER images were of diagnostic quality."

This information focuses on the qualitative assessment of diagnostic image quality by experts for new imaging sequences or acceleration techniques (which are software functionalities, but not necessarily an AI diagnostic algorithm that outputs a decision or risk score). There is no mention of a specific AI algorithm for diagnosis or detection requiring the type of performance metrics typically associated with AI/algorithmic acceptance criteria (e.g., those found in a diagnostic AI device).

Therefore, I cannot fulfill the request with the provided input text as the necessary details for a robust AI performance study are absent. The document describes modifications to an MRI system itself and its imaging sequences, rather than the evaluation of a distinct AI diagnostic algorithm with specific performance targets.

Ask a specific question about this device