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PERFSCAPE V2.0 is a PACS system that allows the display, analysis and postprocessing of dynamically acquired Magnetic Resonance (MRI) and Computed Tomography (CT) datasets to evaluate image intensity variations over time.

PERFSCAPE V2.0 retrieves and accepts data from existing MRI and CT systems. Based on these data, PERFSCAPE V2.0 performs quality control checks, displays Diffusion Weighted Images (MRI only) and generates parametric maps such as Relative Blood Volume, Relative Blood Flow, Relative Mean Transit Time, Time to Peak, Impulse Response Time to Peak, permeability and leakage between intravascular and extracellular space (MRI only), and temporal Maximum Intensity Projection (CT only). PERFSCAPE V2.0 also generates Diffusion Weighted Images and/or Diffusion Tensor Images (MRI only).

These images, when interpreted by a trained physician, may yield clinically useful information.

PERFSCAPE V2.0 is compliant with the DICOM standard allowing the system to visualize medical images. The system is a multiplatform software running on Windows, Mac and Linux operating systems.

Lossy compressed mammographic images and digitized film screen images must not be reviewed for primary image interpretations.

PERFSCAPE V2.0 is a software application designed to analyze dynamically acquired datasets. The software provides a wide range of basic image processing and manipulation functions applied to MRI or CT datasets.

Using well-established algorithms, parametric maps can be generated such as Relative Blood Volume, Relative Blood Flow. Relative Mean Transit Time. Time to Peak, impulse response time to peak, permeability and leakage between intravascular and extracellular space (MRI only), temporal Maximum Intensity Projection (CT only). PERFSCAPE V2.0 also generates Diffusion Weighted Images and/or Diffusion Tensor Images (DWI and DTI, MRI only).

The PERFSCAPE V2.0 device includes critical features such as:

• Enables the computation of DWI and DTI maps from co-registered . Bxxx images (eg B0 B500 B1000);
• . Enables rapid creation of a complete array of critical parameter maps;
• Automated organ mask generation; .
• . View dynamic signal time course on a per-voxel basis;
• Integrated motion correction ; .
• Automatic and Interactive Arterial Input Function (AIF) selection; ●
• Automatic and Interactive Venous Output Function (VOF) selection; .
• Export computed perfusion map to PACS or to DICOM files in . filesystem.

PERFSCAPE V2.0 also allows the user to view the computed maps using the NEUROSCAPE software (K083491).

Based on these common functionalities, Perfscape is divided into three modules named:

• MRI module, ●
• . CT module, and
• MRI-LC module. .

Each module is designed to address useful subsets of images.

The provided 510(k) summary for PERFSCAPE V2.0 does not contain the acceptance criteria or a study demonstrating that the device meets explicit acceptance criteria in the manner typically found in a clinical performance study.

This document is a 510(k) premarket notification summary, which focuses on demonstrating substantial equivalence to a legally marketed predicate device, not on proving clinical performance or meeting specific acceptance criteria through a formal study with statistical endpoints. The information provided relates to the device's description, intended use, and comparative characteristics to a predicate device.

Therefore, many of the requested fields cannot be extracted or are not applicable based on the provided text.

Here's an breakdown of what can and cannot be answered:

1. Table of acceptance criteria and reported device performance:

• Acceptance Criteria: Not explicitly stated in the document. 510(k) summaries for PACS systems typically focus on functional equivalence rather than clinical performance metrics with pre-defined acceptance thresholds.
• Reported Device Performance: The document describes the device's capabilities (e.g., generates parametric maps, performs quality control checks, displays DWI/DTI), but it does not report quantitative performance metrics against specific acceptance criteria. It mentions "well-established algorithms" but provides no performance data on them.

2. Sample size used for the test set and the data provenance:

• Sample Size: Not mentioned. No test set is described for formal performance evaluation against acceptance criteria.
• Data Provenance: Not mentioned.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

• Not mentioned. No test set or ground truth establishment process is described.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set:

• Not mentioned. No test set is described.

5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance:

• No MRMC study is mentioned. The device is a PACS system that provides analysis and post-processing, and its output is to be "interpreted by a trained physician," implying it's a tool, but not an AI-assisted diagnostic aid for which an MRMC study would typically be performed.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:

• No standalone performance study is mentioned. The device's output is intended for interpretation by a physician.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.):

• Not mentioned. No ground truth is described for performance evaluation.

8. The sample size for the training set:

• Not mentioned. This document describes a software application that uses "well-established algorithms" rather than a machine learning model that would typically require a training set.

9. How the ground truth for the training set was established:

• Not applicable/Not mentioned, as no training set is described.

In summary:

This 510(k) submission focuses on demonstrating substantial equivalence to a predicate device (Nordic Image Control and Evaluation (nICE) Software, K090546) based on indications for use, performance, and technological characteristics. For a PACS system that processes and displays medical images, the "performance" typically refers to its ability to correctly execute its defined functions (e.g., generate specific parametric maps, handle DICOM data, perform motion correction) rather than clinical diagnostic accuracy per se. There is no mention of a clinical study or performance evaluation with acceptance criteria to prove diagnostic efficacy or accuracy.

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