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MultiBand SENSE is a software option intended for use on Achieva and Ingenia 1.5T & 3.OT MR Systems. It's indicated for use in magnetic resonance imaging of the brain for BOLD fMRI. MultiBand SENSE consists of an acquisition and reconstruction technique allowing simultaneous excitation of multiple volumes to accelerate imaging acquisition times or increasing coverage or resolution without increasing scan time.

The MultiBand SENSE technique enables simultaneous excitation and acquisition of multiple volumes or slices for the purpose of speeding up acquisition times or increasing coverage or resolution at constant scan time. The simultaneous excitation is done using a multi-band radiofrequency pulse. The unfolding of the simultaneously acquired volumes is done using the SENSE algorithm. The unfolding process (solving the linear equation of the SENSE algorithm) is improved by introducing a linear phase over k-space in the volume direction resulting in a spatial shift of the aliased pixels. The phase shift is applied by additional blip-gradients in the slice direction or switching between different RF pulses, and compensated for in reconstruction by a translation of the coil sensitivity data before the SENSE unfolding. The feature consists of:

• Modulated RF pulses exciting 2 or more slices
• Blip-gradients to introduce a phase shift for improved unfolding ●
• New parameterization of the SENSE calculations ●
• Shifting coil sensitivities in reconstruction to correct for linear phase shift. ●

MultiBand SENSE is supported on the following systems:

• . 3.0T Ingenia
• 3.0T Achieva ●
• 1.5T Ingenia ●
• . 1.5T Achieva

The functionality is supported on all available gradient performance levels. Optimized protocols will be provided for the different performance points. MultiBand SENSE is supported on the centralized data acquisition systems of the Achieva systems as well as the digitally networked data acquisition system of the Ingenia systems. The data acquisition system is fully transparent to the MultiBand SENSE pulse sequences and reconstructions.

The main functional units in the software are:

• Methods (acquisition of MR signals by means of MR pulse sequences) -
• Reconstruction (transforming the MR signals to images) -
• -Patient Administration (storing of the images in the database and providing access)
• Viewing (display of images) -

The technical impact of the feature MultiBand SENSE comprises:

• -Methods: Introduction of a modulated RF pulse. Apply blipped gradients. Provide new parameterization for SENSE reconstruction.
• Reconstruction: Read new parameterization of SENSE calculations. Shift coil sensitivities before SENSE calculations.

No off-the-shelf software is used for the feature MultiBand SENSE. The off-the-shelf software used in the basic MR system is cleared. MultiBand SENSE is not designed to be connected to an external network.

MultiBand SENSE does not require any change of the hardware platform. The extension introduced by Multiband SENSE, are in methods pulse sequence code, and in reconstruction only for a new parameterization of a cleared SENSE unfolding calculation. Those run on the host computer characteristics :

• Manufacturer: HP; Model: Z420; Processor clock: 3.5 GHz; RAM: 64 GB RAM; -Processors: six core with hyper threading
• Operating system: Windows 7, 64 bits -

The only new element for the operator of the Multiband SENSE feature in this clinical routine workflow is:

• Protocol selection: The operator selects an ExamCard with Multiband SENSE protocols -
• Planscan phase: Optionally the operator may want to change the predefined Multiband acceleration factor.

All other steps are not changed. The generated image types can be viewed, post-processed, printed and archived as any other image type.

This document is a 510(k) Summary for the Philips MultiBand SENSE device, a software option for MRI systems. It focuses on demonstrating substantial equivalence to a predicate device. The information provided is primarily related to verification and validation, rather than a detailed comparative effectiveness study with specific acceptance criteria and statistical analysis as might be found in a clinical trial report.

Here's an analysis of the provided text in relation to your questions, noting where information is explicitly stated, implied, or absent:

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

The document does not present a formal table of quantitative acceptance criteria and corresponding device performance metrics in the way a statistically powered study might. Instead, it describes general successful outcomes of verification and validation testing.

Detailed breakdown of other questions:

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

• Sample Size for Test Set: Not explicitly stated. The document refers to "the tests performed" and "fMRI scans" but doesn't quantify the number of cases or subjects used in either non-clinical verification or clinical validation.
• Data Provenance: Not explicitly stated. The document does not specify the country of origin of the data or whether the studies were retrospective or prospective. It describes testing scenarios but not the source of patient data.

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)

Not explicitly stated. The document mentions "clinical user needs are tested as part of validation" but does not detail how ground truth was established, who the experts were, or their qualifications. The focus is on the functional and performance aspects of the software, not diagnostic accuracy requiring expert consensus on images.

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

Not applicable/Not explicitly stated. Given that the "clinical validation" described focuses on functional aspects (scan time, number of slices, workflow) and safety ("no new hazards"), rather than diagnostic accuracy of images, an adjudication method for establishing ground truth on image interpretations is not mentioned or implied to be relevant to the described testing.

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 was specifically described as being performed. The document states "MultiBand SENSE is a software option intended for use on Achieva and Ingenia 1.5T & 3.OT MR Systems. It's indicated for use in magnetic resonance imaging of the brain for BOLD fMRI." The validation described confirmed functional improvements (shorter scan time, increased coverage/resolution) due to the technology itself, not an AI assistance to human readers for diagnostic tasks. Therefore, an effect size of human readers improving with AI assistance is not applicable to the described validation.

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

This refers to a standalone performance study, but the device is a technical feature of an MRI system, not an AI algorithm intended for diagnostic interpretation. The functionality of the MultiBand SENSE algorithm (simultaneous excitation, unfolding via SENSE algorithm, blip-gradients, etc.) was tested in a "standalone" sense in that it runs without direct human intervention in its moment-to-moment operation during a scan. The verification and validation activities described confirm the algorithm's proper functioning and impact on imaging parameters. Yes, the "nonclinical tests" and aspects of "clinical tests" essentially represent standalone testing of the device's functionality and performance as an algorithm within the MR system.

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

Not explicitly stated in terms of diagnostic ground truth. For the "clinical validation" mentioned, the ground truth was related to the functional performance of the device:

• Whether scan times were shorter when MultiBand SENSE was enabled.
• Whether the number of slices could be increased without increasing scan time.
• Whether the SNR deviation was within acceptable limits.
• Smoothness of workflow and absence of defects/hazards.
  This is a performance-based ground truth specific to the technical capabilities of the MRI sequence, not a diagnostic ground truth derived from pathology or expert consensus on image findings.

8. The sample size for the training set

The document describes the device as a "software option" and a "technique" that involves "Modulated RF pulses," "Blip-gradients," "New parameterization of the SENSE calculations," and "Shifting coil sensitivities in reconstruction." This sounds like an algorithmic and sequence-based enhancement, rather than a machine learning or AI model that typically requires a large training set of data. Therefore, the concept of a "training set" in the context of machine learning is not applicable to this device as described.

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

As the concept of a "training set" for a machine learning model is not applicable here, there is no information on how its ground truth would have been established.

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