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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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PERFSCAPE allows the post-processing and display of dynamically acquired Magnetic Resonance datasets to evaluate image intensity variations over time. PERFSCAPE retrieves and accepts data from existing MRI systems. Based on these data, PERFSCAPE performs quality control checks, displays Diffusion Weighted Images and generates parametric perfusion maps such as Relative Cerebral Blood Volume (rCBV), Relative Cerebral Blood Flow (rCBF), Relative Mean Transit Time (rMTT), Time to Peak (TTP) and Impulse Response Time to Peak (TMAX). These images when interpreted by a trained physician may yield information useful in clinical applications.

PERFSCAPE is a software application designed to analyze dynamically acquired datasets. Using well-established algorithms, parametric perfusion maps can be generated such as Relative Cerebral Blood Volume (rCBV), Relative Cerebral Blood Flow (CBF), Relative Mean Transit Time (MTT), Time to Peak (TTP) and impulse response time to peak (TMAX). The system includes critical features such as:

• Enables rapid creation of a complete array of critical perfusion . parameter maps of rCBV, rCBF, rMTT, TTP, TMAX;
• Automated brain mask generation: .
• View dynamic signal time course on a per-voxel basis; .
• . Interactive Arterial Input Function (AIF) selection:
• Export computed perfusion map to the NEUROSCAPE PACS system. .

PERFSCAPE also allows the user to view the computed perfusion maps using the NEUROSCAPE software. NEUROSCAPE displays the original study series for FLAIR, ADC, B0 and B1000 and PERFSCAPE computed series for TTP, CBV, CBF, MTT and TMAX.

The PERFSCAPE device is a software application intended for the post-processing and display of dynamically acquired Magnetic Resonance datasets to evaluate image intensity variations over time. It retrieves data from existing MRI systems to perform quality control checks, display Diffusion Weighted Images, and generate parametric perfusion maps (rCBV, rCBF, rMTT, TTP, TMAX). These images, when interpreted by a trained physician, are intended to provide information useful in clinical applications.

The provided document does not explicitly state specific quantitative acceptance criteria for the device's performance in terms of accuracy, sensitivity, or specificity for a particular clinical task. Instead, the focus of the 510(k) submission and the "Testing" section is on demonstrating substantial equivalence to a predicate device and confirming the software's functionality and reliability.

However, based on the provided text, we can infer the primary "acceptance criterion" as functional equivalence and reliability with the predicate device for its intended use as a post-processing and display tool for MR perfusion images.

Here's an analysis of the provided information:

1. Table of Acceptance Criteria and Reported Device Performance

As no explicit quantitative acceptance criteria (e.g., sensitivity, specificity, accuracy thresholds) are stated in the document, this table will reflect the inferred criteria of functional equivalence and software reliability.

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

The document states, "OLEA Medical has conducted extensive validation testing of the PERFSCAPE system..." but does not specify the sample size of the test set (number of cases/patients) or the data provenance (e.g., country of origin, retrospective or prospective). The testing is described as focused on the software's functional capabilities and reliability rather than a diagnostic performance study on a patient cohort.

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

The document does not mention the use of experts to establish ground truth for a test set, nor does it describe any such ground truth establishment process. The testing appears to be centered on software validation rather than clinical diagnostic accuracy against a ground truth.

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

As there's no mention of a test set with ground truth established by experts, there is no adjudication method 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 comparative effectiveness study is mentioned in the provided text. The device is a post-processing tool, and its submission focuses on substantial equivalence based on technical characteristics and intended use, not on reader performance improvement in a clinical setting.

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

The document describes the device as performing post-processing and displaying parametric maps, which are then "interpreted by a trained physician." This implies it is an algorithm-only (standalone) component that assists human interpretation rather than a system that provides a final diagnostic output. The validation described is of the software's ability to generate these maps and its functionality, which is a standalone performance aspect. However, it's not a standalone diagnostic performance in the sense of providing a definitive diagnosis without human oversight.

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

No specific type of ground truth is mentioned for the validation of the PERFSCAPE system, as the testing described focuses on software functionality, reliability, and substantial equivalence, not the diagnostic accuracy of the generated maps against external clinical truth. The "ground truth" for the software's performance seems to be its ability to correctly generate the specified parametric maps based on established algorithms and display them reliably.

8. The sample size for the training set

The document does not provide any information regarding a training set sample size. This is common for devices of this nature (post-processing software using established algorithms), as explicit "training" in the context of machine learning (where training set sizes are critical) might not be applicable or explicitly called out if the algorithms are deterministic and well-established.

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

Since no training set is described or implied in the context of machine learning, there is no information on how ground truth for a training set was established. The device "Using well-established algorithms" suggests that the underlying principles are known and validated, rather than learned from a labeled training set in a machine learning context.

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