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ZOOM Image Enhancement System is an image processing software that can be used for image enhancement in MRI images. Enhanced images will be sent to PACS server and exist in conjunction to the original images.

ZOOM Image Enhancement System is an image processing software that can be used for image enhancement in MRI images. ZOOM image enhancement software implements a noise reduction algorithm using wavelets and image guided filtering. Original images are decomposed into different wavelet sub bands and noise in each band is soft threshold. De-noised images are reconstructed from softthresholded images using inverse wavelet transform. The software, which is installed on a remote computer, receives DICOM images from MRI host computer, automatically processes the received images and sends the enhanced images to a PACS server. Enhanced images exist in conjunction to the original images.

Here's a breakdown of the acceptance criteria and the study that proves the device meets them, based on the provided text:

Acceptance Criteria and Device Performance

Study Information

1. Sample size used for the test set and the data provenance:
* Sample Size: A total of 64 data sets were acquired using an ACR MRI phantom.
* Data Provenance: The data was acquired using various MRI systems: GE 1.5T Excite, Siemens Avanto 1.5T, Philips Intera 1.5T, and Toshiba Titan 1.5T. The data appears to be prospectively collected as part of a validation study for the device. The country of origin is not explicitly stated, but the use of the ACR (American College of Radiology) phantom suggests a North American context.

2. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:
* The document does not mention human experts or their qualifications for establishing ground truth in this performance study. The ground truth appears to be based on objective phantom measurements (Signal-to-Noise Ratio and contrast resolution) as defined by the ACR quality control protocols.

3. Adjudication method (e.g., 2+1, 3+1, none) for the test set:
* None. The performance test relies on objective measurements from the ACR MRI phantom using defined protocols, not expert adjudication of images.

4. 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 MRMC comparative effectiveness study involving human readers and AI assistance was not conducted or described in this document. The study focuses solely on the objective performance of the image enhancement software itself.

5. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done:
* Yes. The described study is a standalone performance test of the ZOOM software (algorithm only). It evaluates the software's ability to enhance SNR and maintain resolution based on phantom data, without human interaction in the evaluation process.

6. The type of ground truth used (expert consensus, pathology, outcomes data, etc.):
* The ground truth is based on objective measurements derived from an ACR MRI phantom protocol. Specifically, it uses ACR quality control guidelines to measure signal-to-noise ratio (SNR) and high/low contrast resolution.

7. The sample size for the training set:
* The document does not specify the sample size used for the training set of the ZOOM software. It only describes the validation/test set.

8. 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.

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Z-DOSE29 Dose Check system is intended to provide dose check features to Computed Tomography systems. Z-DOSE29 software extracts CTDI values from CT host computer's screen, compares it to predefined thresholds, and sends notification and alert messages to the CT scanner display monitor prior to scanning.

Z-DOSE29 Dose Check provides the Dose Check feature to CT scanners as specified by the NEMA XR-25 standard and the Dose Check feature of NEMA XR-29*. This includes both Dose Notifications (for individual series) and Dose Alerts (for accumulated dose).

The Z-DOSE29 Dose Check software, which is installed on a remote computer that resides on the same local area network as the CT host computer, receives DICOM images from the CT host computer. The software captures CTDI values for each prescribed CT series, compares the values to AAPM dose check guideline (version 1.0) values and displays notification message(s) on the CT scanner operator's console. The Z-DOSE29 Dose Check software also adds the CTDI values from the individual series and displays alert message(s) on the CT scanner operator's console if the accumulated CTDI value exceeds a predefined ALERT threshold.

*NEMA XR-29 incorporates NEMA XR-25 Dose Check feature

In the event of an Alert, and in addition to displaying the alert message to the CT operator, Z-DOSE29 utilizes a relay to disable scanning until a correct password is inputted by the CT operator.

The Z-DOSE29 Dose Check software also records CTDI values and CT exam information into a local a database. Information in the database can be accessed by authorized users via a web browser.

The Z-DOSE29 Dose Check software runs on a Windows operating system 10 with a .net framework 4.0 or higher version.

Here's an analysis of the acceptance criteria and study details for the Z-DOSE29 device, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria are derived from the "Expected outcome" column, and the reported device performance is from the "Observed outcome" column in Table 2: Z-DOSE29 system level verification and validation summary.

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

• Test Set (for CTDI extraction accuracy): Over 2400 image data sets were used to test the Off-the-Shelf (OTS) software component responsible for extracting CTDI information.
• Test Set (for system-level SRS requirements): The system-level verification and validation (Table 2) were performed on 4 different CT scanners from different manufacturers (GE Light Speed 32, Siemens Sensation 16, Philips Brilliance 64, Toshiba Aquilion 64). Lower-level requirements were verified on 9 different systems.
• Data Provenance: The document does not specify the country of origin of the data or whether it was retrospective or prospective. It refers to "image data sets" for OTS testing and mentions specific CT scanner models from various manufacturers, suggesting a diverse, though undefined, source for testing the system.

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

The document does not explicitly state the number of experts used or their specific qualifications for establishing ground truth for the test set. However, the ground truth for dose thresholds is stated to be based on "AAPM Dose Check Guidelines suggested threshold value (Default Value)." The AAPM (American Association of Physicists in Medicine) is a professional organization, implying expert consensus in the guidelines used, but not individual experts for this specific study.

4. Adjudication Method for the Test Set

The document does not describe any specific adjudication method (e.g., 2+1, 3+1) for the test set. The performance testing appears to be a direct comparison of the device's output against predefined guidelines or system requirements.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done

No, an MRMC comparative effectiveness study was not done. The device, Z-DOSE29, is a "Dose Check system" that automates dose monitoring and alerts. It does not involve human readers interpreting images, but rather the device extracting CTDI values and comparing them to thresholds. Therefore, a study to measure human reader improvement with or without AI assistance is not applicable to this device's function.

6. If a Standalone (Algorithm Only Without Human-in-the-Loop Performance) Was Done

Yes, a standalone performance assessment was done. The entire performance testing described (SRS.001 to SRS.004) focuses on the software's ability to automatically capture, compare, alert, disable scanning, record, and report CTDI values based on predefined guidelines. The only human intervention mentioned is the ability for the CT operator to manually input CTDI values if the software misreads them and to input a password to re-enable scanning after an alert. The core functionality and its evaluation are standalone. The "OTS" software for CTDI extraction was tested separately for its accuracy (99%).

7. The Type of Ground Truth Used

The ground truth used for the dose notification and alert features (SRS.001 and SRS.002) is based on AAPM Dose Check Guidelines suggested threshold values. For requirements SRS.003 (disabling system) and SRS.004 (record and report), the ground truth is against the System Requirement Specifications (SRS) themselves, observing if the device performs as expected.

8. The Sample Size for the Training Set

The document mentions that the OTS software was tested using "more than 2400 image data sets." However, this refers to testing the OTS component, not training the Z-DOSE29 system itself. The document does not specify a separate training set size for the Z-DOSE29 software or any machine learning components within it (other than the OTS which it utilizes). Given the description of the device's function (extracting CTDI values from a screen via optical character recognition (OCR) and comparing them to static thresholds), it is likely more rule-based than machine learning dependent, potentially reducing the need for a traditional "training set" for the core logic, beyond perhaps any underlying OCR model.

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

As no specific training set for the Z-DOSE29 software (excluding the OTS component it uses) is mentioned, the method for establishing its ground truth is also not detailed. For the OTS component's accuracy in CTDI extraction, its ground truth would likely involve manually verified CTDI values from those 2400+ image data sets.

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Zia™ Image Enhancement System is an image processing software that can be used for reducing noise in CT images. Enhanced images will be uploaded back to host/PACS systems and exist in conjunction to the original images. Zia™ is not intended for mammography applications. The device processing is not effective for lesion, mass, or abnormalities of sizes less than 2mm.

Zia™ Image Enhancement System is an image processing software that can be used for reducing noise in CT images. Zia™ image enhancement software is based on a core noise reduction algorithm that reduces noise in flat regions via a regularization process while keeping the edges via data fidelity constrains. The software, which is installed on a remote computer, receives DICOM images from CT host computer (Zia DICOM node needs to be configured on the scanner), automatically processes the received images and uploads the post processed images back on to the host computer and/or other PACS systems. Enhanced images exist in conjunction to the original images.

Here's a breakdown of the acceptance criteria and study details for the Zia™ Image Enhancement System based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance:

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

• Sample Size: A total of 81 datasets were processed and analyzed.
• Data Provenance: The data was generated using an ACR CT PHANTOM (Model 464) on three different CT scanners: GE BrightSpeed 4-Slice, Siemens Sensation 16-Slice, and Philips Brilliance 64-Slice. The images were acquired following specific protocols (Head 120KV, Head 80KV, and Body 120KV) with varying mAs (150-350mAs) and slice thicknesses (1.25-5mm). This indicates a controlled, simulated environment using a phantom, not retrospective or prospective patient data from a specific country of origin.

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

The document does not mention the use of human experts to establish ground truth for the test set. The ground truth appears to be based on physical measurements of the ACR CT phantom.

4. Adjudication Method for the Test Set:

Not applicable, as human experts were not used for establishing ground truth or evaluating the test set. The performance was measured quantitatively using the phantom.

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

No, a multi-reader multi-case (MRMC) comparative effectiveness study was not performed. The study focuses on the technical performance of the image processing software itself, not its impact on human reader performance.

6. Standalone Performance Study:

Yes, a standalone study was performed. The device's performance (noise reduction, CT# accuracy, contrast resolution) was evaluated directly by analyzing the processed images from the CT phantom, without human-in-the-loop.

7. Type of Ground Truth Used:

The ground truth used was based on the physical characteristics and known properties of the ACR CT PHANTOM (Model 464). Measurements of CT# and noise were obtained from specific regions of interest (ROIs) within the phantom, and resolution was assessed based on the phantom's design.

8. Sample Size for the Training Set:

The document does not specify a separate training set or its sample size. The description of the device's core algorithm as reducing noise in flat regions while keeping edges suggests it's a rule-based or model-based algorithm, rather than a machine learning algorithm requiring a separate, large training set with annotated data.

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

Not explicitly stated. Given the description of the algorithm, it likely relies on mathematical principles and image processing techniques. If there was any "training" in a general sense, it would have involved developing and refining these algorithms based on general image characteristics rather than a labeled training dataset with a specific ground truth.

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