Search Results

MRCAT imaging is intended to provide the operator with information of tissue properties for radiation attenuation estimation purposes in photon external beam radiotherapy treatment planning.

Indications for use:

MRCAT Brain is indicated for radiotherapy treatment planning for primary and metastatic brain tumor patients.

MRCAT brain is a software application to Ingenia, Ingenia Ambition, and Ingenia Elition MR systems. MRCAT brain is available to the customer as an option to Ingenia MR-RT package, which is a set of accessories for Ingenia systems.

Automated generation of MRCAT images takes place at the MR console of Ingenia. The embedded image post-processing runs in the background parallel to image acquisition. MRCAT algorithm enables automatic tissue characterization: Bones are segmented from mDixon in-phase and water images using machine learning based segmentation. Body outline is segmented using in-phase and water images. Tissues are then assigned a continuum of HU values depending on the fat and water intensities of the voxels. The HU assignment provides MRCAT images with CT-like density information.

The document provides information on the Philips Medical Systems MR Finland MRCAT Brain device, which is a software add-on for MR systems intended for radiotherapy treatment planning for primary and metastatic brain tumor patients.

Here's an analysis of the acceptance criteria and supporting studies based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

• Simulated dose based on MRCAT Brain images shall not differ in 95% of the indicated patients (gamma analysis criterion 2%/2mm realized in 98% of voxels within the PTV or exceeding 75% of the maximum dose) when compared with CT-based plan.
• The average simulated dose based on MRCAT Brain shall not deviate more than 5% or 1 Gy, whichever is greater, in 99% of the indicated patients in the volume of sensitive organs when compared with CT based plan. | The robustness of the MRCAT brain algorithm for producing equivalent dose plans to CT using gamma analysis with criterion of 1%/1mm is shown by post-processing MRCAT images from patients, and calculating dose using the MRCAT images. (Though the reported criterion is 1%/1mm, the acceptance criterion specifically mentioned 2%/2mm, implying the device met or exceeded this with the 1%/1mm demonstration). The document states that MRCAT brain met the acceptance criteria and is adequate for its intended use. |
  | Geometric Accuracy:
• MRCAT accuracy: ± 1 mm accuracy for a 200 mm diameter sphere.
• MRCAT accuracy: ± 5 mm accuracy for a 500 mm diameter sphere (limited in the bore direction by +/- 160 mm from the z=0 mm plane). | The document states "No significant difference" in geometric accuracy when comparing MRCAT Brain to MRCAT Pelvis, which has the same geometric accuracy criteria. It also generally states that "Non Clinical verification and or validation test results demonstrate that the MRCAT brain... Meets the acceptance criteria and is adequate for its intended use." |
  | Compliance with Standards:
• ANSI/AAMI ES60601-1: 2012
• IEC 60601-1-6:2010
• IEC 60601-2-33:2015
• IEC 62304:2016
• IEC 62366-1:2015
• ISO 14971:2007 (A comprehensive list of international and FDA-recognized consensus standards for medical electrical equipment, usability, safety of MR equipment, medical device software lifecycle processes, usability engineering, and risk management). | "The MRCAT brain complies with the following international and FDA-recognized consensus standards." and "Non Clinical verification and or validation test results demonstrate that the MRCAT brain: Complies with the aforementioned international and FDA-recognized consensus standards." |
  | MRCAT image generation correctness:
• Sanity checks to ensure imaging field of view is correctly positioned.
• Sanity checks to ensure MRCAT body outline matches that of the MR. | "The generated MRCAT images are checked for correctness to ensure validity of the generated MRCAT for radiation treatment. The sanity checks ensure that the imaging field of view has been positioned correctly and that the MRCAT body outline matches that of the MR." The overall conclusion on non-clinical tests also states: "met the acceptance criteria and is adequate for this intended use." |
  | HU value calibration:
• The HU values for the MRCAT Brain are calibrated using registered CT images from several sites. | "The HU values for the MRCAT brain are calibrated using registered CT images." and "The overall conclusion on non-clinical tests also states: "met the acceptance criteria and is adequate for this intended use." |

The Study Proving Device Meets Acceptance Criteria:

The document describes a "Summary of Non-Clinical Performance Data" and a "Summary of Clinical Data" to support the device's substantial equivalence and adherence to acceptance criteria.

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

The document mentions that the robustness of the MRCAT brain algorithm for producing equivalent dose plans to CT using gamma analysis was shown by post-processing MRCAT images from patients. However, it does not specify the exact sample size used for this patient data (test set) or the country of origin/provenance (retrospective or prospective) of this patient data.

3. Number of Experts Used to Establish Ground Truth and Qualifications:

The document does not provide information regarding the number of experts used to establish ground truth or their specific qualifications for the test set.

4. Adjudication Method:

The document does not specify any adjudication method (e.g., 2+1, 3+1, none) used for establishing ground truth in the test set.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study:

The document makes no mention of a Multi-Reader Multi-Case (MRMC) comparative effectiveness study being done. The focus is on the device's technical performance in generating comparable dose plans to CT, not on human reader performance with or without AI assistance.

6. Standalone (Algorithm Only) Performance:

Yes, a standalone performance was done. The dose accuracy and geometric accuracy criteria, as well as the comparison to CT-based plans, are direct assessments of the algorithm's performance without human intervention in the primary image generation and initial dose calculation. The "robustness of the MRCAT brain algorithm" and its ability to produce "equivalent dose plans to CT" describe standalone performance.

7. Type of Ground Truth Used:

The primary ground truth used for assessing the device's performance, particularly dose accuracy and HU value calibration, is registered CT images and CT-based treatment plans. The comparisons are made against these CT data, which are considered the established standard for radiation attenuation estimation in radiotherapy planning.

8. Sample Size for the Training Set:

The document states that the Convolutional Neural Network (CNN) used in MRCAT image generation is "trained using matched pairs of CT and MRCAT source images." However, the exact sample size used for the training set is not specified.

9. How the Ground Truth for the Training Set Was Established:

The ground truth for the training set was established using "matched pairs of CT and MRCAT source images." This implies that CT images served as the reference or ground truth against which the MRCAT source images were processed and the CNN was trained to generate CT-like density information (HU values).

Ask a specific question about this device

MRCAT Pelvis is a software add-on for Ingenia 1.5T and 3.0T MR systems.

Intended Use:

MRCAT imaging is intended to provide the operator with information of tissue properties for radiation estimation purposes in photon external beam radiotherapy treatment planning.

Indications for use:

MRCAT Pelvis is indicated for radiotherapy treatment planning of soft tissue cancers in the pelvic region.

MRCAT Pelvis is a software application to Ingenia 1.5T and 3T MR systems. MRCAT Pelvis is available to the customer as an option to Ingenia MR-RT package, which is a set of accessories for Ingenia systems.

Automated generation of MRCAT images takes place at the MR console of Ingenia. The embedded image post-processing runs in the background parallel to image acquisition. MRCAT algorithms enable automatic tissue characterization of five tissue types; air, fat, waterrich tissue, spongy bone and compact bone. Subsequent density assignment provides MRCAT images with CT-like density information.

The provided text is a 510(k) summary for the Philips MRCAT Pelvis device. While it describes the device's intended use and general testing, it does not contain a detailed study demonstrating how the device meets specific acceptance criteria with performance metrics, sample sizes, ground truth establishment, or expert qualifications.

Therefore, many of the requested details cannot be extracted directly from this document.

Here's a breakdown of what can be inferred or is explicitly stated, and what is missing:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly present a table of acceptance criteria with corresponding performance metrics like sensitivity, specificity, accuracy, etc. It only states that the robustness of the algorithm was shown by "producing equivalent dose plans to CT using gamma analysis with criterion of 3%/3mm." This implies an acceptance criterion related to dosimetric accuracy, but no precise performance numbers (e.g., % of plans meeting 3%/3mm gamma) are given.

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

• Sample size: Not specified. The document only mentions "post-processing MRCAT images from patients." It does not provide the number of patients or cases.
• Data provenance: Not specified (e.g., country of origin). It also doesn't explicitly state whether the data was retrospective or prospective, though "post-processing MRCAT images from patients" suggests retrospective analysis of data acquired for other purposes or as part of a study.

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

Not specified. The primary ground truth reference appears to be CT scans for dosimetric comparison, rather than human expert interpretation of the MRCAT images themselves for diagnostic purposes.

4. Adjudication method for the test set

Not applicable/specified. The primary comparison is between MRCAT-derived dose plans and CT-derived dose plans using gamma analysis, not subjective interpretation requiring adjudication among experts.

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 described. The device is a "software add-on" that generates CT-like density information from MR images for radiation treatment planning. It's not designed for human readers to interpret MR images directly for diagnosis with AI assistance, but rather to provide input for a treatment planning system. Therefore, an MRMC study in the traditional sense of human reader performance with/without AI assistance is not relevant to this device's function as described.

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

Yes, a standalone evaluation was performed. The robustness of the "MRCAT Pelvis algorithm" was evaluated by comparing its output (dose plans from MRCAT images) against a reference (dose plans from CT) using gamma analysis. This is an algorithm-only performance evaluation.

7. The type of ground truth used

The ground truth for evaluating dosimetric accuracy appears to be CT imaging and subsequent dose plan calculations based on CT data. The MRCAT device aims to provide "CT-like density information" and "equivalent dose plans to CT."

8. The sample size for the training set

Not specified. Information about the training set size or methodology for the MRCAT algorithm is not provided in this document.

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

Not specified. How the ground truth for any training data (if machine learning was used extensively to develop the underlying algorithm) was established is not mentioned in this document.

Ask a specific question about this device

MRCAT is used with Ingenia 1.5T and 3.0T MR systems.

Intended Use:

MRCAT imaging is intended to provide the operator with information of tissue properties for radiation attenuation estimation purposes in photon external beam radiotherapy treatment planning.

Indications for use:

MRCAT is indicated for radiotherapy treatment planning for prostate cancer patients.

MRCAT is a software application to Ingenia 1.5T and 3T MR systems. MRCAT is available to the customer as an option to Ingenia MR-RT package, which is a set of accessories for Ingenia systems.

Automated generation of MRCAT images takes place at the MR console of Ingenia. The embedded image post-processing runs in the background parallel to image acquisition. MRCAT algorithms enable automatic tissue characterization of five tissue types; air, fat, water-rich tissue, spongy bone and compact bone. Subsequent bulk-density assignment provides MRCAT images with CT-based density information.

The provided text does not contain detailed acceptance criteria and the comprehensive study that proves the device meets them in the format requested. However, it offers some insights into the validation process and the claims made.

Here's an attempt to extract and infer information based on the provided document:

1. A table of acceptance criteria and the reported device performance

The document mentions gamma analysis with criterion of 3%/3mm as a method to show the robustness of the MRCAT algorithm for producing equivalent dose plans to CT using MRCAT images. This implies that the acceptance criterion is that the dose plans generated using MRCAT images should be equivalent to those generated using CT, with a 3%/3mm gamma pass rate.

Additional acceptance criteria related to software development and validation were mentioned in the "Summary of Non-Clinical Tests," but these are general quality assurance measures rather than specific performance metrics for the final output:

• Risk Analysis
• Testing on unit level (Subsystem verification)
• Integration testing (System verification)
• Final acceptance testing (Validation)
• Performance testing (Verification)
• Safety testing (Verification)

The document states, "All the tests performed for MRCAT software were successful. All defects have been analyzed and are confirmed that they are not safety defects and will not cause any hazardous situation on using this application."

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

The document states, "The robustness of the MRCAT algorithm... is shown by post-processing MRCAT images from prostate cancer patients."

• Sample Size: The exact number of prostate cancer patients used in the clinical test is not specified.
• Data Provenance: The document does not specify the country of origin or whether the data was retrospective or prospective.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)

This information is not provided in the document. The ground truth for dose plan comparison is implicitly assumed to be CT-based dose plans, but the expert involvement in establishing this ground truth or comparing the MRCAT-derived plans is not detailed.

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

This information is not provided in the document.

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

• MRMC Study: No MRMC comparative effectiveness study involving human readers and AI assistance is described. The device's primary function is to generate images for treatment planning, not to assist human interpretation directly in a diagnostic workflow. The "clinical test" described focuses on the comparison of dose plans generated by the algorithm (MRCAT) against CT, not human performance.

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

Yes, the clinical test described appears to be a standalone performance evaluation of the MRCAT algorithm. The "robustness of the MRCAT algorithm for producing equivalent dose plans to CT" was shown by "post-processing MRCAT images... and calculating dose using the MRCAT images." This implies an algorithm-to-algorithm comparison (MRCAT-generated dose plans vs. CT-generated dose plans).

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

The ground truth for comparison in the clinical test was CT-based dose plans. The intent is for MRCAT images to provide "radiation attenuation estimation purposes in photon external beam radiotherapy treatment planning," implying that the standard for attenuation estimation is conventional CT. The gamma analysis compares the dose distributions calculated from MRCAT images to those calculated from CT images.

8. The sample size for the training set

This information is not provided in the document. The description focuses on the clinical evaluation of the algorithm's output rather than its development or training data.

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

This information is not provided in the document.

Ask a specific question about this device

The HFO Shoulder Coil is indicated for use in the following anatomic regions and with the designated nuclei:

Anatomic Region: Shoulder and adjacent regions
Nuclei Excited: Hydrogen

The addition of the HFO Shoulder Coil does not change the existing indications for use of the cleared High Field Open (1.0T) Panorama system, as defined below.

The High Field Open (1.0T) Panorama system is indicated for use as a NMR device that produces images that: (1) correspond to the distribution of protons exhibiting NMR, (2) depend upon the NMR parameters (proton density, flow velocity, spin-lattice relaxation time (T1), and spin-spin relaxation time (T2), and (3) display the soft tissue structure of the head, extremities and whole body. When interpreted by a trained physician, these images yield information that can be useful in the determination of a diagnosis.

The HFO Shoulder Coil consists of a cup-shaped, plastic enclosure containing three coil elements for receiving of RF signals from the shoulder and adjacent region. The enclosure is placed on patient's shoulder for imaging. The enclosure contains tuning and decoupling electronics circuitry and preamplifiers. The coil enclosure has a cable attached to it and the cable connector is plugged into the system connector on the patient table. The cable provides the coil with supply and control voltages and transfers the received RF signals to the system. The cable connector contains coil interface circuitry for the system.

Here's an analysis of the provided 510(k) submission for the Philips Medical Systems HFO Shoulder Coil, based on the information available:

The provided document is a 510(k) summary for a Magnetic Resonance (MR) specialty coil, not a diagnostic AI device or software. Therefore, many of the requested sections related to AI performance criteria, ground truth, and clinical study designs (like MRMC or standalone performance) are not applicable to this type of submission.

This submission focuses on demonstrating substantial equivalence to a predicate device for hardware components used in an existing MR system. The "performance" in this context refers to the coil's ability to produce quality MR images within the existing safety and efficacy profile of the MRI system it's used with.

Acceptance Criteria and Study to Prove Device Meets Acceptance Criteria

1. Table of Acceptance Criteria and Reported Device Performance

As this is a hardware accessory (MR coil) and not an AI or diagnostic software, explicit "acceptance criteria" in terms of clinical performance metrics like sensitivity, specificity, or AUC are not presented in this document. The primary acceptance criteria for such a device would revolve around:

• Safety: Meeting established safety standards for MRI coils (e.g., SAR, static field, time-varying magnetic fields, acoustic noise) when integrated with the specific MRI system.
• Functionality: Correct operation, successful image acquisition.
• Image Quality: Producing images comparable to or better than previously cleared coils for the same anatomical region, enabling diagnostic interpretation.
• Substantial Equivalence: Alignment with the design, materials, and intended use of a legally marketed predicate device.

Study Proving Acceptance Criteria:

The document explicitly states: "The use of the HFO Shoulder Coil does not result in any changes to the safety specifications for the safety parameters (i.e., static field, time-varying magnetic fields, SAR, or acoustic noise) of the Philips HFO (1.0T) Panorama system." and "The use of this device does not result in additional potential hazards when compared to currently marketed, receive-only coils." This implies that testing (likely in-house engineering and phantom testing, and possibly animal or human subject scanning for image quality assessment, though not detailed in this summary) was conducted to verify that the coil operates within the established safety envelope of the MRI system and produces diagnostically acceptable images. The primary "study" here is the comparison to the predicate device and verification against established MRI safety standards.

2. Sample Size Used for the Test Set and Data Provenance

• Sample Size: Not specified in the provided summary. For an MR coil, "test set" would typically refer to phantom data, cadaver data, and/or small numbers of healthy volunteer or patient scans. This document does not provide details on the number of subjects or phantom scans.
• Data Provenance: Not specified. Testing would likely be conducted by the manufacturer, Philips Medical Systems, in Finland (where the coil is manufactured) or at a Philips testing facility.

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

• Not Applicable: For an MR coil, the concept of "ground truth" for diagnostic accuracy is not directly applied in the same way as for AI software. The "truth" is established by the accepted physics and engineering principles of MRI, and the output is images that trained radiologists use for diagnosis, not a primary diagnostic output from the device itself. While image quality would be assessed, it's typically done by trained MR physicists or radiologists ensuring the images are suitable for interpretation, rather than establishing a specific "ground truth" for a diagnostic outcome.

4. Adjudication Method for the Test Set

• Not Applicable: As there's no clinical "ground truth" being established in the traditional sense for diagnostic performance, an adjudication method isn't relevant here.

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, Not Applicable: This device is an MR coil, not an AI or diagnostic software. Therefore, an MRMC study comparing human reader performance with or without AI assistance is not relevant to this submission.

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

• No, Not Applicable: This is an MR coil, not an algorithm, so a standalone performance study as described is not relevant.

7. The Type of Ground Truth Used

• Implicit Engineering and Clinical Acceptability: The "ground truth" for an MR coil is its ability to:
  • Adhere to safety specifications (e.g., quantifiable SAR measurements, electromagnetic compatibility).
  • Produce images with expected signal-to-noise ratio, uniformity, and artifact levels (often assessed against phantom standards and clinical expert opinion on diagnostic utility).
  • Maintain the diagnostic capability of the overall MRI system.
• Not Applicable in the sense of expert consensus, pathology, or outcomes data for a specific diagnosis, as the coil itself does not provide a diagnosis.

8. The Sample Size for the Training Set

• Not Applicable: This is a hardware component. There is no "training set" in the context of machine learning or AI. Hardware design and development involve extensive engineering, simulation, and testing, but not training data for an algorithm.

9. How the Ground Truth for the Training Set was Established

• Not Applicable: No training set as described for AI/ML.

Ask a specific question about this device