Search Results

The SIGNA Architect system is a whole body magnetic resonance scanner designed to support high signal-to-noise ratio, and short scan times. It is indicated for use as a diagnostic imaging device to produce axial, sagittal, coronal, and oblique images, spectroscopic images, parametric maps, and/or spectra, dynamic images of the structures and/or functions of the entire body, including, but not limited to, head, neck, TMJ, spine, breat, abdomen, pelvis, joints, prostate, blood vessels, and musculoskelatal regions of the region of interest being imaged, contrast agents may be used.

The images produced by the SIGNA Architect system reflect the spatial distribution or molecular environment of nuclei exhibiting magnetic resonance. These images and/or spectra when interpreted by a trained physician vield information that may assist in diagnosis.

SIGNA Architect is a whole body magnetic resonance scanner designed to support high resolution, high signal-to-noise ratio, and short scan times. The system features a superconducting magnet. The data acquisition system accommodates up to 128 independent receive channels in various increments and multiple independent coil elements per channel during a single acquisition series. Each system uses a combination of time varying magnetic fields (gradients) and RF transmissions to obtain information regarding the density and position of elements exhibiting magnetic resonance. Each system can imaqe in the saqittal, coronal, axial, oblique, and double oblique planes, using various pulse sequences and reconstruction algorithms.

This submission is prompted by the introduction of two new software features called HyperSense 2.0 and Star onto SIGNA Architect. HyperSense 2.0 is an acceleration technique based on sparse data compressibility allowing scan time reduction while maintaining SNR efficiency. Star is a motion-robust, free-breathing imaging technique. HyperSense 2.0 is a modification to the previously cleared HyperSense, while Star is a technique that can be used with the previously cleared DISCO feature. Both HyperSense and DISCO are listed above as reference devices along with their associated 510(k) submission numbers.

The addition of both the HyperSense 2.0 and Star features involved modifications to the SIGNA Architect system software. There were no changes from either of these features that were related to the system's hardware components.

The provided text describes a 510(k) premarket notification for a medical device, the SIGNA Architect, a Magnetic Resonance (MR) system with new software features (HyperSense 2.0 and Star). Here's a breakdown of the acceptance criteria and study proving the device meets them, based on the information provided:

Disclaimer: The provided document is a 510(k) summary, which is a high-level overview. It does not contain detailed information about the specific acceptance criteria, statistical methodologies, or all aspects of the studies that would be present in the full submission. Therefore, some sections below will indicate "Not explicitly stated in the provided document."

Acceptance Criteria and Device Performance

The core acceptance criterion for a 510(k) submission is Substantial Equivalence (SE) to a legally marketed predicate device. This means the new device is as safe and effective as the predicate, and does not raise new questions of safety and effectiveness.

The document indicates that studies were performed to demonstrate that the new features (HyperSense 2.0 and Star) do not negatively impact image quality or diagnostic utility compared to the predicate/existing techniques.

Table of Acceptance Criteria and Reported Device Performance:

Study Details

The document mentions two main types of studies: non-clinical and clinical. The clinical evaluation focuses on the new features: HyperSense 2.0 and Star.

1. Sample Size Used for the Test Set and Data Provenance:

• HyperSense 2.0: "The images involved were generated using 3 different reconstruction techniques across different anatomies."
  • Sample Size: Not explicitly stated (e.g., number of patients/cases, number of images).
  • Data Provenance: Not explicitly stated (e.g., country of origin, retrospective or prospective).
• Star: "Images from the assessment are provided."
  • Sample Size: Not explicitly stated (e.g., number of patients/cases, number of images).
  • Data Provenance: Not explicitly stated (e.g., country of origin, retrospective or prospective).

2. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications:

• HyperSense 2.0: "Radiologists were asked to evaluate side-by-side image quality of the HyperSense 2.0 images compared to the predicate."
  • Number of Experts: "Radiologists" (plural), but specific number not stated.
  • Qualifications: Not explicitly stated (e.g., years of experience, subspecialty).
• Star: "Images produced by Star were judged to be of sufficient quality for diagnostic use by a a U.S. Board Certified radiologist."
  • Number of Experts: "a U.S. Board Certified radiologist" (singular).
  • Qualifications: "U.S. Board Certified radiologist." (Years of experience or subspecialty not stated).

3. Adjudication Method (for the test set):

• HyperSense 2.0: "Radiologists were asked to evaluate side-by-side image quality of the HyperSense 2.0 images compared to the predicate." This suggests individual evaluation rather than a formal adjudication process between multiple readers.
  • Method: Not explicitly stated beyond individual reader evaluation of side-by-side images. No mention of 2+1, 3+1, or consensus.
• Star: "Images produced by Star were judged to be of sufficient quality for diagnostic use by a U.S. Board Certified radiologist."
  • Method: Single reader evaluation. No adjudication described.

4. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done:

• HyperSense 2.0: The description "An external reader evaluation study was performed" and "Radiologists were asked to evaluate side-by-side image quality" suggests a multi-reader study, but it's not explicitly labeled as a formal MRMC study. The details provided are insufficient to confirm the rigor of a full MRMC design (e.g., statistical analysis of reader performance differences).
  • Effect Size of Human Readers Improve with AI vs. without AI assistance: This specific metric is not applicable here as the described studies focus on image quality assessment of a new image acquisition/reconstruction technique, not directly on AI assisting human readers in a diagnostic task for a specific condition. The "AI" implied (HyperSense 2.0 and Star) are image processing algorithms, not diagnostic AI systems assisting in interpretations.

5. If a Standalone (i.e. algorithm only, without human-in-the-loop performance) was done:

• The non-clinical testing for both features would implicitly include standalone performance evaluation of the algorithms (e.g., technical measures of SNR, resolution, artifact reduction), but the document does not elaborate on these specific "standalone" metrics or a formal standalone study results. The clinical evaluations do involve human assessment of the images produced by the algorithms.

6. The Type of Ground Truth Used:

• The "ground truth" for these studies appears to be expert consensus/opinion on image quality and diagnostic sufficiency.
  • For HyperSense 2.0, the radiologists' evaluation of "overall image quality and uniformity" served as the basis for acceptance.
  • For Star, the "U.S. Board Certified radiologist's" judgment of "sufficient quality for diagnostic use" served as the basis for acceptance.
  • There is no mention of pathology, long-term outcomes data, or other objective ground truths beyond expert interpretation of the images themselves.

7. The Sample Size for the Training Set:

• This information is Not explicitly stated in the provided document. The document details the testing of the software features, but not the development or training set size (if algorithms involved machine learning).

8. How the Ground Truth for the Training Set was Established:

• This information is Not explicitly stated in the provided document. As the training set size isn't mentioned, neither is its ground truth establishment.

Ask a specific question about this device