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ADMIRE is a CT reconstruction software. The end user can choose to apply either ADMIRE or the weighted filter back-projection (WFBP) to the acquired raw data. Depending on the clinical task, patient size, anatomical location, and clinical practice, the use of ADMIRE can help to reduce radiation dose while maintaining pixel noise, low contrast detectability and high contrast resolution.

Phantom measurements showed that high contrast resolution is equivalent or improved and pixel noise is equivalent between full dose WFBP images and reduced dose ADMIRE images. Additionally, ADMIRE can reduce spiral artifacts by using iterations going back and forth between image space and raw data space.

Images reconstructed with ADMIRE are not intended to be evaluated with syngo Osteo CT or syngo Calcium Scoring.

ADMIRE is an iterative reconstruction option designed to be used on Siemens currently marketed and future CT devices. Its use has been previously cleared by FDA (K133646, clearance date June 20, 2014). No modifications were made to the algorithm or to the implementation of the feature. The changes proposed within this 510(k) only pertain to an extension of the claims associated with the feature as follows:

• 1. Compared to images reconstructed with WFBP, ADMIRE may simultaneously enable*
  • 80 to 85% dose reduction at the same image quality and .
  • 73 to 77% image noise reduction at the reduced dose and o
  • up to 42% improved high-contrast spatial resolution improvement at reduced o dose and reduced image noise.
• 2. Alternatively, ADMIRE may enable
  • up to 150% improved low contrast detectability (factor 2.5) at the same dose or .
  • up to 90% image noise reduction at constant dose or .
  • up to 87% improved high-contrast spatial resolution improvement at 85% . reduced dose and constant image noise or
  • up to 38% improved high-contrast resolution at 90% reduced image noise and . constant dose.

Furthermore, the following claims have previously been cleared by FDA as part of K133646 (clearance date June 20, 2014) and will be maintained with this clearance:

• 3. Additionally, ADMIRE
  • compared to SAFIRE potentially features a more "FBP-like" noise texture in . terms of the number of "outliers" in the noise texture, especially for higher strength settings of the algorithm and
  • can reduce spiral artifacts by using iterations going back and forth between image 0 space and raw data space and
  • has the potential to result in a higher noise reduction compared to SAFIRE when . reconstructing thick slices.

• Image quality as defined by low contrast detectability using a model observer method for evaluation. Equivalent low contrast detectability can be achieved with 80% to 85% less dose using ADMIRE at highest strength level for thin (0.6 mm) reconstruction slices in measured and simulated body and head phantoms for low contrast objects with different contrasts. See ADMIRE data sheet for further information.

In clinical practice, the use of ADMIRE may reduce CT patient dose depending on the clinical task, patient size, anatomical location, and clinical practice. A consultation with a radiologist and a physicist should be made to determine the appropriate dose to obtain diagnostic image quality for the particular clinical task.

Here's a breakdown of the requested information based on the provided text:

Acceptance Criteria and Device Performance Study for ADMIRE

1. Table of Acceptance Criteria and Reported Device Performance:

The document describes performance claims rather than explicit acceptance criteria with specific thresholds for FDA clearance in this section. However, based on the claims being extended and previous clearance, the reported performance is presented below. The "acceptance criteria" are implied by the performance metrics Siemens seeks to claim.

*Image quality defined by low contrast detectability using a model observer method for evaluation.

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

• Sample Size for Test Set: The document mentions "a large number of CT images" were generated for evaluation by using "computer-simulated as well as measured CT data." However, specific numerical sample sizes are not provided for either the simulated or measured data used in the testing.
• Data Provenance: The study utilized "PhantomLabs® CCT189 and CCT191 phantoms" for low-contrast detectability and a "Teflon edge phantom" for high-contrast spatial resolution. This indicates the testing was conducted using phantom data, not human patient data. There is no information on the country of origin of the data, but it is clear it is retrospective (in the sense of being pre-generated data/scans for evaluation) rather than prospective human trials.

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

• The document does not mention the involvement of experts for establishing ground truth in the context of the phantom-based performance evaluation. The evaluation methods described (model observer for LCD, standard deviation for noise, ImPACT CT evaluation group definition for spatial resolution) are quantitative, objective measurements from the phantom images rather than expert interpretation.

4. Adjudication Method for the Test Set:

• Not applicable. Since the ground truth for the test set was established through objective phantom measurements and model observer studies, there was no need for expert adjudication.

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

• No, an MRMC comparative effectiveness study was not done. The device's performance was evaluated using phantom measurements and model observer studies, focusing on quantitative image metrics rather than human reader performance with or without AI assistance.

6. Standalone (Algorithm Only Without Human-in-the-Loop Performance) Study:

• Yes, a standalone study was performed. The evaluations of low-contrast detectability, image noise, and high-contrast spatial resolution were conducted on images reconstructed by the ADMIRE algorithm, comparing its output to WFBP reconstructed images or SAFIRE, using objective metrics derived directly from the images. This did not involve a human-in-the-loop component for performance evaluation.

7. Type of Ground Truth Used:

• The ground truth used was phantom-based objective measurements. For low-contrast detectability, a "model observer method" was used. For image noise, "standard deviation across images at all pixel locations" was computed. For high-contrast spatial resolution, it was based on "measured CT data of a Teflon edge phantom as defined by the ImPACT CT evaluation group."

8. Sample Size for the Training Set:

• The document does not provide information on the sample size for the training set for the ADMIRE algorithm. It focuses on the validation of its performance.

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

• The document does not provide information on how the ground truth for the training set was established. Since ADMIRE is an iterative reconstruction algorithm, its training (if applicable, as some iterative methods are rule-based or optimized rather than "trained" in a deep learning sense) would likely involve various simulated or measured data with known properties to optimize the reconstruction parameters. However, the details are not explicitly stated in this summary.

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ADMIRE is a CT reconstruction software. The end user can choose to apply either ADMIRE or the weighted filter backprojection (WFBP) to the acquired raw data. Depending on the clinical task, patient size, anatomical location, and clinical practice, the use of ADMIRE can help to reduce radiation dose while maintaining pixel noise, low contrast detectability and high contrast resolution. Phantom measurements showed that high contrast resolution and pixel noise are equivalent between full dose WFBP images and reduced dose ADMIRE images. Additionally, ADMIRE can reduce spiral artifacts by using iterations going back and forth between image space and raw data space.

Images reconstructed with ADMIRE are not intended to be evaluated with syngo Osteo CT or syngo Calcium Scoring.

Siemens ADMIRE is an extension of the previously cleared Sinogram Affirmed Iterative Reconstruction (SAFIRE) reconstruction algorithm. ADMIRE is a software option for CT operating systems that provides an improved image quality or reciprocally can allow the physician to acquire scans with reduced radiation dose without reduction of image quality compared to today's standard.

ADMIRE is designed to improved reconstructed image quality through the integration of additional processing steps in image reconstruction. These additional steps result in the following improvements in image quality:

• . Higher pixel noise reduction
• A noise texture closer to filtered back projection (FBP) .
• . Improved resolution for high contrast edges

Here's an analysis of the provided text regarding the acceptance criteria and the study that proves the device meets them:

1. Table of Acceptance Criteria and Reported Device Performance

The document doesn't explicitly state quantitative acceptance criteria for device performance. Instead, it frames the performance improvements of ADMIRE relative to its predicate device (SAFIRE) and traditional methods (FBP/WFBP). The "acceptance criteria" are implied to be the successful demonstration of these improvements and equivalence for certain metrics.

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

• Sample Size: Not explicitly stated for specific tests. The document mentions "phantom testing" and "sample clinical images." No specific number of patients or imaging studies are provided.
• Data Provenance: The document does not specify the country of origin. It indicates that "phantom testing" was conducted and "sample clinical images were also provided within the submission." The general nature of the testing suggests it's likely internal Siemens data. The testing involves "simulated body and head phantoms."
• Retrospective/Prospective: The nature of the testing described (phantom testing, bench testing, verification/validation) suggests a controlled, likely retrospective analysis of specific data, or controlled prospective phantom acquisitions. There's no mention of a large-scale, prospective clinical trial with human subjects.

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

This information is not provided in the document. The evaluation primarily relies on phantom measurements and technical performance metrics (noise reduction, resolution, noise texture) rather than a subjective human reader assessment against a clinical ground truth.

4. Adjudication Method for the Test Set

This information is not provided. Given the focus on objective phantom measurements and technical image quality metrics, a formal human reader adjudication method like 2+1 or 3+1 is unlikely to have been used in the primary performance evaluations 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, a Multi-Reader Multi-Case (MRMC) comparative effectiveness study with human readers assessing AI-assisted vs. non-AI-assisted images is not mentioned or described in this 510(k) summary. The document focuses on the technical improvements of the reconstruction algorithm itself.

6. If a Standalone (i.e. algorithm only without human-in-the loop performance) Was Done

Yes, the performance described is primarily that of the standalone algorithm (ADMIRE). The improvements in pixel noise reduction, noise texture, high contrast resolution, and artifact reduction are inherent to the algorithm's processing of raw data. The statement "The end user can choose to apply either ADMIRE or the weighted filter back-projection (WFBP) to the acquired raw data" further reinforces its standalone nature as a reconstruction option.

7. The Type of Ground Truth Used

The ground truth used appears to be:

• Physical Phantom Measurements: For metrics like pixel noise, high contrast resolution, and low contrast detectability. This involves comparing the device's output against known physical properties or reference measurements from phantoms.
• Reference Reconstruction Methods: Comparisons are made against established methods like Filtered Back Projection (FBP) and Weighted Filtered Back Projection (WFBP) to demonstrate improvements or equivalence.
• Compliance with Standards: Verification against various international standards for medical devices and software (ISO, IEC, NEMA).

There is no mention of "expert consensus," "pathology," or "outcomes data" being used as ground truth for the performance evaluation described in this summary.

8. The Sample Size for the Training Set

This information is not provided. The document outlines changes to a reconstruction algorithm rather than a machine learning model that would typically have a distinct training set. While reconstruction algorithms are developed and refined using data, the document doesn't use the terminology of "training set" in the context of deep learning.

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

This information is not provided, and the concept of a "training set" and associated ground truth, as typically understood in machine learning, is not discussed in this summary. The development process likely involved engineering and optimization against known physical models and existing reconstruction results, rather than labeled training data for a learning algorithm.

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