Search Results

Ezra Flash is an image processing software used for image enhancement of MR images. It can be used to reduce image noise in images acquired as part of non-contrast MRI exams on 1.5-Tesla and 3-Tesla Siemens and GE scanners for patients > 18 years of age:

• · Sagittal T1, Axial T2 and Axial Flair sequences within the head region
• · Axial T2, Coronal T2 within the Abdomen region.
• · Sagittal T2, Axial T2, Coronal T2 within the Pelvis region

Ezra Flash is a Software as a Medical Device (SaMD) consisting of a software algorithm that enhances images of the head, abdomen, and pelvis regions taken by MRI scanners. As it only processes images for the end user, the device has no interface. It is intended to be used by radiologists in an imaging center, clinic, or hospital. The software can be used with MR images acquired as part of MRI exams on 1.5-Tesla and 3-Tesla scanners from Siemens and GE.

The outputs are images with enhanced image quality. Both the original non-enhanced studies and the Ezra Flash-enhanced studies are available to the end user.

Ezra Flash receives DICOM-compliant non-contrast MR image inputs acquired on 1.5-Tesla and 3-Tesla scanners within the head, abdomen and pelvis regions. The software uses a convolutional neural network-based algorithm to improve image quality by reducing noise. The device outputs a DICOM-compliant copy of the images with improved image quality.

Ezra Flash is tested for performance on Sagittal T1, Axial T2, Axial T2 Flair images of the head, Coronal T2, Axial T2 images of the abdomen, Sagittal T2, Axial T2, and Coronal T2 images of the pelvis.

The provided text describes the Ezra Flash, an image processing software for MRI image enhancement. Here's a breakdown of the acceptance criteria and the study proving the device meets them:

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

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

• Sample Size for Test Set: Not explicitly stated. The document mentions "each test dataset" but does not provide the total number of images or patients included in the test set.
• Data Provenance: Not explicitly stated. There is no information regarding the country of origin of the data or whether it was retrospective or prospective.

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

• This information is not provided in the document. The document describes performance testing for objective metrics (SNR, CNR) and a subjective perceived noise assessment using a Likert scale, but it does not detail how the ground truth for these assessments was established or how many experts were involved.

4. Adjudication method for the test set:

• This information is not provided in the document. The text mentions "mean Likert results" but does not specify any adjudication method (e.g., 2+1, 3+1, none) used for subjective assessments or for establishing ground truth if it involved multiple readers.

5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done, and the effect size of how much human readers improve with AI vs without AI assistance:

• A MRMC comparative effectiveness study is not explicitly mentioned or described in the document. The performance testing focuses on the device's ability to improve image quality through objective metrics (SNR, CNR) and perceived noise, not on its impact on human reader performance or diagnostic accuracy.

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

• Yes, the performance testing described appears to be a standalone (algorithm only) performance evaluation. The criteria focus on the intrinsic image quality improvements achieved by the Ezra Flash software itself (SNR, CNR, perceived noise comparison between raw and enhanced images), rather than its performance in conjunction with a human reader for diagnostic tasks.

7. The type of ground truth used:

• The ground truth for the objective metrics (SNR, CNR) is inherently tied to the raw MR-acquired images as the baseline for comparison.
• For the subjective "Image Quality Perceived Noise" criterion, the ground truth appears to be based on mean Likert scale results, which represent expert assessment of noise rather than a definitive pathological or outcomes-based ground truth.

8. The sample size for the training set:

• The sample size for the training set is not provided in the document.

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

• How the ground truth for the training set was established is not provided in the document. The document mentions that the algorithm uses a "convolutional neural network-based algorithm" and that "The parameters of the filters were obtained through an image-guided optimization process," which implies a training process, but details on the ground truth used for this training are absent.

Ask a specific question about this device

Ezra Flash is an image processing software used for image enhancement of MR images. It can be used to reduce image noise in images acquired as part of non-contrast MRI exams on 3-Tesla Siemens for Sagittal T1, Axial T2 and Axial Flair sequences within the head region for patients > 18 years of age.

Ezra Flash is a Software as a Medical Device (SaMD) consisting of a software algorithm that enhances images of the head region taken by MRI scanners. As it only processes images for the end user, the device has no interface. It is intended to be used by radiologists in an imaging center, clinic, or hospital. The software can be used with MR images acquired as part of MRI exams on 3-Tesla Siemens and GE scanners. The outputs are images with enhanced image quality. Both the original non-enhanced studies and the Ezra Flash-enhanced studies are available to the end user. Ezra Flash receives DICOM-compliant non-contrast MR image inputs acquired on 3-Tesla scanners for Sagittal T1, Axial T2 and Axial Flair sequences within the head region. The software uses a convolutional neural network-based algorithm to improve image quality by reducing noise. The device outputs a DICOM-compliant copy of the images with improved image quality.

Ezra Flash Acceptance Criteria and Performance Study

The Ezra Flash device for image enhancement of MR images (K230264) underwent performance testing to demonstrate its safety and effectiveness. The study focused on quantitative metrics (SNR, CNR) and a subjective image quality assessment by experts (Perceived Noise).

1. Table of Acceptance Criteria and Reported Device Performance

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

The document does not explicitly state the sample size for the test set or the data provenance (e.g., country of origin, retrospective or prospective). It mentions "each test dataset" for SNR and CNR, suggesting multiple datasets were used.

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

The document mentions "Likert results for the Ezra Flash-enhanced images compared to the original MR-acquired images (raw)," which implies human expert assessment of image quality, likely for perceived noise. However, it does not specify:

• The number of experts used.
• The qualifications of those experts (e.g., specific years of experience, board certification).

4. Adjudication Method for the Test Set

The document does not describe any specific adjudication method (e.g., 2+1, 3+1) for the expert ratings on the test set. It only refers to "mean Likert results."

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

The provided text does not indicate that a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was done. The performance testing description focuses on objective image metrics (SNR, CNR) and a subjective image quality assessment, but not on human reader performance with and without AI assistance. Therefore, no effect size for human reader improvement is provided.

6. Standalone (Algorithm Only) Performance Study

Yes, a standalone performance study was done for the Ezra Flash algorithm. The described testing focuses on the algorithm's direct impact on image quality metrics (SNR, CNR, perceived noise) by processing raw MR images and comparing them to the enhanced outputs. The device "only processes images for the end user" and "has no interface," with outputs viewed on existing PACS workstations. This indicates evaluation of the algorithm's performance in isolation from a human-in-the-loop workflow.

7. The Type of Ground Truth Used

The ground truth for the quantitative metrics (SNR, CNR) is inherently derived from the raw MR-acquired images themselves, serving as the baseline for improvement. For the "Image Quality Perceived Noise" metric, the ground truth is based on expert consensus (or at least expert scores) derived from Likert scale ratings comparing enhanced images to raw images. This is a form of subjective expert evaluation.

8. The Sample Size for the Training Set

The document does not specify the sample size for the training set. It mentions that the device uses a "convolutional neural network-based algorithm" whose "parameters of the filters were obtained through an image-guided optimization process," implying a training phase. However, the specific number of images or cases used for training is not disclosed.

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

The document states that the "parameters of the filters were obtained through an image-guided optimization process." While this indicates that the model was trained, it does not explicitly describe how the ground truth for the training set was established. Typically, for image enhancement, training ground truth might involve:

• Paired low-quality/high-quality images.
• Synthetic noise addition to high-quality images.
• Expert-annotated "ideal" images or metrics.

However, the provided text does not elaborate on this aspect.

Ask a specific question about this device