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The system is intended to produce cross-sectional images of the body by computer reconstruction of x-ray transmission projection data from the same axial plane taken at different angles. The system may acquire data using Axial, Cine, Helical, Cardiac, and Gated CT scan techniques from patients of all ages. These images may be obtained either with or without contrast. This device may include signal analysis and display equipment, patient and equipment supports, components and accessories.

This device may include data and image processing to produce images in a variety of trans-axial and reformatted planes. Further, the images can be post processed to produce additional imaging planes or analysis results.

The system is indicated for head, whole body, cardiac, and vascular X-ray Computed Tomography applications.

The device output is a valuable medical tool for the diagnosis of disease, trauma, or abnormality and for planning, guiding, and monitoring therapy.

If the spectral imaging option is included on the system, the system can acquire CT images using different kV levels of the same anatomical region of a patient in a single rotation from a single source. The differences in the energy dependence of the attenuation coefficient of the different materials provide information about the chemical composition of body materials. This approach enables images to be generated at energies selected from the available spectrum to visualize and analyze information about anatomical and pathological structures.

GSI provides information of the chemical composition of renal calculi by calculation and graphical display of the spectrum of effective atomic number. GSI Kidney stone characterization provides additional information to aid in the characterization of uric acid versus non-uric acid stones. It is intended to be used as an adjunct to current standard methods for evaluating stone etiology and composition.

The CT system is indicated for low dose CT for lung cancer screening. The screening must be performed within the established inclusion criteria of programs/ protocols that have been approved and published by either a governmental body or professional medical society.

This proposed device Revolution Vibe is a general purpose, premium multi-slice CT Scanning system consisting of a gantry, table, system cabinet, scanner desktop, power distribution unit, and associated accessories. It has been optimized for cardiac performance while still delivering exceptional imaging quality across the entire body.

Revolution Vibe is a modified dual energy CT system based on its predicate device Revolution Apex Elite (K213715). Compared to the predicate, the most notable change in Revolution Vibe is the modified detector design together with corresponding software changes which is optimized for cardiac imaging providing capability to image the whole heart in one single rotation same as the predicate.

Revolution Vibe offers an accessible whole heart coverage, full cardiac capability CT scanner which can deliver outstanding routine head and body imaging capabilities. The detector of Revolution Vibe uses the same GEHC's Gemstone scintillator with 256 x 0.625 mm row providing up to 16 cm of coverage in Z direction within 32 cm scan field of view, and 64 x 0.625 mm row providing up to 4 cm of coverage in Z direction within 50 cm scan field of view. The available gantry rotation speeds are 0.23, 0.28, 0.35, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, and 1.0 seconds per rotation.

Revolution Vibe inherits virtually all of the key technologies from the predicate such as: high tube current (mA) output, 80 cm bore size with Whisper Drive, Deep Learning Image Reconstruction for noise reduction (DLIR K183202/K213999, GSI DLIR K201745), ASIR-V iterative recon, enhanced Extended Field of View (EFOV) reconstruction MaxFOV 2 (K203617), fast rotation speed as fast as 0.23 second/rot (K213715), and spectral imaging capability enabled by ultrafast kilovoltage(kv) switching (K163213), as well as ECG-less cardiac (K233750). It also includes the Auto ROI enabled by AI which is integrated within the existing SmartPrep workflow for predicting Baseline and monitoring ROI automatically. As such, the Revolution Vibe carries over virtually all features and functionalities of the predicate device Revolution Apex Elite (K213715).

This CT system can be used for low dose lung cancer screening in high risk populations*.

The provided FDA 510(k) clearance letter and summary for the Revolution Vibe CT system does not include detailed acceptance criteria or a comprehensive study report to fully characterize the device's performance against specific metrics. The information focuses more on the equivalence to a predicate device and general safety/effectiveness.

However, based on the text, we can infer some aspects related to the Auto ROI feature, which is the only part of the device described with specific performance testing details.

Here's an attempt to extract and describe the available information, with clear indications of what is not provided in the document.

Acceptance Criteria and Device Performance for Auto ROI

The document mentions specific performance testing for the "Auto ROI" feature, which utilizes AI. For other aspects of the Revolution Vibe CT system, the submission relies on demonstrating substantial equivalence to the predicate device (Revolution Apex Elite) through engineering design V&V, bench testing, and a clinical reader study focused on overall image utility, rather than specific quantitative performance metrics meeting predefined acceptance criteria for the entire system.

1. Table of Acceptance Criteria and Reported Device Performance (Specific to Auto ROI)

Note: The document does not provide the explicit numerical value for the "pre-established acceptance criteria" or the actual "success rate" achieved for the Auto ROI feature.

2. Sample Size and Data Provenance for the Test Set (Specific to Auto ROI)

• Sample Size: 1341 clinical images
• Data Provenance: "real clinical practice" (Specific country of origin not mentioned). The images were used for "Auto ROI performance" testing, which implies retrospective analysis of existing clinical data.

3. Number of Experts and Qualifications to Establish Ground Truth (Specific to Auto ROI)

• Number of Experts: Not specified for the Auto ROI ground truth establishment.
• Qualifications of Experts: Not specified for the Auto ROI ground truth establishment.

Note: The document mentions 3 readers for the overall clinical reader study (see point 5), but this is for evaluating the diagnostic utility and image quality of the CT system and not explicitly for establishing ground truth for the Auto ROI feature.

4. Adjudication Method for the Test Set (Specific to Auto ROI)

• Adjudication Method: Not specified for the Auto ROI test set.

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

• Was an MRMC study done? Yes, a "clinical reader study of sample clinical data" was carried out. It is described as a "blinded, retrospective clinical reader study."

• Effect Size of Human Readers Improvement with AI vs. without AI assistance: The document states the purpose of this reader study was to validate that "Revolution Vibe are of diagnostic utility and is safe and effective for its intended use." It does not report an effect size or direct comparison of human readers' performance with and without AI assistance (specifically for the Auto ROI feature within the context of reader performance). The study seemed to evaluate the CT system's overall image quality and clinical utility, possibly implying that the Auto ROI is integrated into this overall evaluation, but a comparative effectiveness study of the AI's impact on human performance is not described.

  • Details of MRMC Study:
    • Number of Cases: 30 CT cardiac exams
    • Number of Readers: 3
    • Reader Qualifications: US board-certified in Radiology with more than 5 years' experience in CT cardiac imaging.
    • Exams Covered: "wide range of cardiac clinical scenarios."
    • Reader Task: "Readers were asked to provide evaluation of image quality and the clinical utility."

6. Standalone (Algorithm Only) Performance

• Was a standalone study done? Yes, for the "Auto ROI" feature, performance was tested "using 1341 clinical images from real clinical practice," and "the tests results in success rates exceeding the pre-established acceptance criteria." This implies an algorithm-only evaluation of the Auto ROI's ability to successfully identify and monitor ROI.

7. Type of Ground Truth Used (Specific to Auto ROI)

• Type of Ground Truth: Not explicitly stated for the Auto ROI. Given the "success rates" metric, it likely involved a comparison against a predefined "true" ROI determined by human experts or a gold standard method. It's plausible that this was established by expert consensus or reference standards.

8. Sample Size for the Training Set

• Sample Size: Not provided in the document.

9. How Ground Truth for the Training Set Was Established

• Ground Truth Establishment: Not provided in the document.

In summary, the provided documentation focuses on demonstrating substantial equivalence of the Revolution Vibe CT system to its predicate, Revolution Apex Elite, rather than providing detailed, quantitative performance metrics against specific acceptance criteria for all features. The "Auto ROI" feature is the only component where specific performance testing (standalone) is briefly mentioned, but key details like numerical acceptance criteria, actual success rates, and ground truth methodology for training datasets are not disclosed. The human reader study was for general validation of diagnostic utility, not a comparative effectiveness study of AI assistance.

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The Deep Learning Image Reconstruction for Gemstone Spectral Imaging option is a deep learning based CT reconstruction method intended to produce cross-sectional images by computer reconstruction of dual energy X-ray transmission data acquired with Gemstone Spectral Imaging, for all ages. Deep Learning Image Reconstruction for Gemstone Spectral Imaging can be used for whole body, vascular, and contrast enhanced head CT applications.

Deep Learning Image Reconstruction for Gemstone Spectral Imaging (DLIR-GSI) is the next step in CT reconstruction advancement. Like its predicate device (DLIR), DLIR-GSI is an image reconstruction method that uses a dedicated Convolution Neural Network (CNN) that has been designed and trained specifically to reconstruct CT GSI Images to give an image appearance similar to traditional FBP images while maintaining or improving the performance of ASiR-V. The DLIR-GSI can generate monochromatic images (MC), material decomposition images (MD), and virtual unenhanced images (VUE). Multiple MD images such as lodine, Water, Calcium, Hydroxyapatite (HAP), Fat, Uric Acid can be prescribed by the user and generated by the subject device. DLIR-GSI demonstrates same or better Imaging performance as compared to ASiR-V in the following areas: low contrast detectability (LCD), image noise, contrast to noise ratio (CNR), high contrast spatial resolution, CT number accuracy, MD quantification accuracy and metal artifact reduction. Reconstruction times with DLIR-GSI support a normal throughput for routine CT.

The device is marketed as Deep Learning Image Reconstruction for Gemstone Spectral Imaging and the images produced are branded as "TrueFidelity™ CT Images".

Deep Learning Image Reconstruction for Gemstone Spectral Imaging is compatible with dual energy scan modes using the standard kernel and was trained specifically on the Revolution CT family of systems (K163213, K133705, K19177). The deep learning technology is integrated into the scanner's existing raw data-based image reconstruction chain to produce DICOM compatible "TrueFidelity™ CT Images".

The system allows user selection of three strengths of DLR-GSI: Low, Medium, or High. The strength selection will vary with individual users' preference for the specific clinical need.

As compared to the predicate device, the intended use of Deep Learning Image Reconstruction for Gemstone Spectral Imaging does not change (head and whole-body CT image reconstruction). Both algorithms are designed to produce cross-sectional images of the head and body by computer reconstruction of X-ray transmission data, for all ages.

Acceptance Criteria and Study Details for Deep Learning Image Reconstruction for Gemstone Spectral Imaging (DLIR-GSI)

1. Table of Acceptance Criteria and Reported Device Performance:

The study concluding that the device meets the acceptance criteria is based on:

• Bench Testing: Performed on the identical raw datasets obtained on GE's Revolution CT family of systems, applying both DLIR-GSI and ASiR-V reconstructions for comparison.
• Clinical Reader Study: A retrospective study involving radiologists evaluating images reconstructed with both ASiR-V and DLIR-GSI.

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

• Test Set Sample Size:
  • Main Reader Study: 40 retrospectively collected cases.
  • Additional Clinical Evaluation: 7 additional retrospectively collected clinically challenging cases.
• Data Provenance: Retrospectively collected clinical cases. The country of origin is not specified in the provided text.

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

• Main Reader Study: 5 board-certified radiologists.
  • Qualifications: Expertise in the specialty areas that align with the anatomical region of each case. (e.g., three readers for body/extremity, three for contrast-enhanced head/neck, one qualified for both). Specific years of experience are not mentioned.
• Additional Clinical Evaluation: 1 board-certified radiologist.
  • Qualifications: Expertise in the specialty area that aligns with all cases containing small, low-contrast objects. Specific years of experience are not mentioned.

4. Adjudication Method for the Test Set:

• Main Reader Study: Each image was read by 3 different radiologists. The radiologists provided an assessment of image quality using a 5-point Likert scale.
  • Adjudication Method: Implicitly, a consensus or agreement among the 3 readers would have been used for the assessment of diagnostic quality and noise texture preference. The document states, "The result of this reader study confirmed that the DLIR-GSI (the subject device) produce diagnostic quality images and have preferred noise texture than the reference device ASiR-V," suggesting that the collective findings of the readers led to this confirmation. Explicit details of a 2+1 or 3+1 adjudication are not provided.
• Additional Clinical Evaluation: A single board-certified radiologist evaluated the 7 challenging cases. No adjudication method was applicable as there was only one reader.

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

• Yes, an MRMC study was performed (the "Clinical Testing" described).
• Effect Size (Improvement with AI vs. without AI assistance): The document states that readers confirmed DLIR-GSI produced "diagnostic quality images" and had "preferred noise texture" compared to ASiR-V (considered without AI assistance in this context, or a lesser AI-powered reconstruction). The additional evaluation confirmed "adequately visualized for diagnostic use" in challenging cases. However, specific quantitative effect sizes (e.g., a percentage improvement in diagnostic accuracy, a specific change in AUC, or a numerical metric of improvement in reader performance) are not provided in the given text.

6. Standalone (Algorithm Only) Performance Study:

• Yes, a standalone performance study was done through engineering bench testing. This testing compared DLIR-GSI (algorithm only) against ASiR-V (reference/control algorithm) using identical raw datasets. Metrics like LCD, image noise, CNR, spatial resolution, CT number accuracy, material decomposition accuracy, iodine detection, and metal artifact reduction were evaluated directly from the reconstructed images without human interpretation.

7. Type of Ground Truth Used:

• Bench Testing: The ground truth for metrics like LCD, image noise, spatial resolution, CT number accuracy, etc., would have been based on physical phantom measurements and known parameters of the phantoms used in the engineering tests.
• Clinical Reader Study: The ground truth for image quality and diagnostic usability was established by expert consensus/interpretation from the board-certified radiologists. The text doesn't mention pathology or outcomes data as the primary ground truth for the reader study, but rather the radiologists' assessment of diagnostic quality and visualization.

8. Sample Size for the Training Set:

• The document states that the neural network was "trained specifically to reconstruct CT GSI Images" using "single energy acquired images on the CT Scanner" (for the predicate) and "dual energy acquired images on the CT Scanner" (for the proposed device). It also mentions "information obtained from extensive phantom and clinical data" was used for noise characteristics.
• However, the specific sample size (number of images or cases) for the training set is NOT provided in the text.

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

• The text implies that the neural network was trained to produce an "image appearance similar to traditional FBP images while maintaining or improving the performance of ASiR-V." This suggests that the "ground truth" for training was implicitly the characteristics of high-quality CT images, likely leveraging existing FBP and ASiR-V reconstructed images from "extensive phantom and clinical data."
• For noise modeling, ground truth was based on "characterization of the photon statistics as it propagates through the preprocessing and calibration imaging chain" and using a trained neural network that "models the scanned object using information obtained from extensive phantom and clinical data."
• Specific details on how the ground truth was rigorously established for the training data (e.g., expert annotations, pathology correlation, quantitative metrics derived from known phantoms) are NOT explicitly described. The training appears to be focused on matching or improving upon established reconstruction methods using a large dataset.

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This device is indicated to acquire and display cross-sectional volumes of the whole the head, with the capability to image whole organs in a single rotation. Whole organs include, but are not limited to brain, heart, pancreas, etc.

The Aquilion ONE has the capability to provide volume sets of the entire organ. These volume sets can be used to perform specialized studies, using indicated software, of the whole organ by a trained and qualified physician.

FIRST is an iterative reconstruction algorithm intended to reduce exposure dose and improve high contrast spatial resolution for abdomen, pelvis, chest, cardiac, extremities and head applications.

AiCE is a noise reduction algorithm that improves image quality and reduces image noise by employing Deep Convolutional Neural Network methods for abdomen, pelvis, inner ear and extremities applications.

The Spectral Imaging System allows the system to acquire two nearly simultaneous CT images of an anatomical location using distinct tube voltages and/or tube currents by rapid KV switching. The X-ray dose will be the sum of the dose at each respective tube voltage and current in a rotation.

Information regarding the material composition of various organs, tissues, and contrast materials may be gained from the differences in X-ray attenuation between these distinct energies.

When used by a qualified physician, a potential application is to determine the course of treatment.

Aquilion ONE (TSX-306A/3) V10.0 with Spectral Imaging System is a whole body multi-slice helical CT scanner, consisting of a gantry, couch and a console used for data processing and display. This device captures cross sectional volume data sets used to perform specialized studies, using indicated software/hardware, by a trained and qualified physician. This system is based upon the technology and materials of previously marketed Canon CT systems.

Here's a breakdown of the acceptance criteria and the studies that prove the device meets them, based on the provided FDA 510(k) summary:

1. Table of Acceptance Criteria and Reported Device Performance

The FDA 510(k) summary does not explicitly list numerical "acceptance criteria" in the format of a table with pass/fail thresholds. Instead, it describes various tests and claims that demonstrate substantial equivalence to previously cleared devices. The performance is reported in terms of qualitative assessments (e.g., "diagnostic quality," "substantially equivalent") and quantitative improvements (e.g., "dose reduction," "improvement in low contrast detectability," "noise reduction").

Here's an interpretation of the implied acceptance criteria and reported performance:

• More similar to high dose FBP (compared to FIRST);
• More similar to FBP (compared to FIRST);
• Improved (compared to FIRST);
• More natural (compared to FIRST). | AiCE noise appearance/texture:
• More similar to high dose filtered backprojection (compared to FIRST);
• More similar to filtered backprojection (compared to FIRST);
• Improved (compared to FIRST);
• More natural (compared to FIRST). |
  | Clinical Image Quality | AiCE images for abdomen/pelvis, brain, inner ear, and extremity applications are of diagnostic quality. | Representative abdomen/pelvis, brain, inner ear and extremity AiCE images were confirmed to be of diagnostic quality by an American Board Certified Radiologist. |

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

• Spectral Imaging Performance Testing - Bench: The sample size for phantoms used in bench testing is not specified beyond "various phantoms" and "Catphan and Body Phantom." Data provenance is laboratory bench testing.
• Spectral Imaging Performance Testing - Clinical Images: The sample size is not specified beyond "Representative abdomen/pelvis, lung, and extremity Spectral Images." The data provenance of these clinical images (country of origin, retrospective/prospective) is not specified in the provided text.
• Non-Spectral Imaging and AiCE Performance Testing - Bench: The sample size for phantoms used in bench testing is not specified beyond "various phantoms." Data provenance is laboratory bench testing.
• AiCE Imaging Performance Testing - Clinical Images: The sample size is not specified beyond "Representative abdomen/pelvis, brain, inner ear and extremity AiCE images." The data provenance of these clinical images (country of origin, retrospective/prospective) is not specified in the provided text.

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

• For both Spectral Imaging Clinical Images and AiCE Clinical Images: "an American Board Certified Radiologist" was used. This indicates one expert. The specific years of experience are not mentioned, but "American Board Certified" signifies a high level of qualification.

4. Adjudication Method for the Test Set

• For both Spectral Imaging Clinical Images and AiCE Clinical Images, only one expert (an American Board Certified Radiologist) reviewed the images. Therefore, there was no adjudication method between multiple experts employed for these clinical image quality assessments. The phrase "it was confirmed" implies a singular decision.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done

• No, a MRMC comparative effectiveness study was not explicitly mentioned in the provided text for evaluating human reader improvement with AI assistance. The clinical image reviews were done by a single radiologist to confirm "diagnostic quality" of the AI-processed images, not to compare human reader performance with and without AI. The quantitative performance (dose reduction, LCD improvement, noise reduction) was assessed via model observer evaluation or phantom studies.

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

• Yes, extensive standalone performance testing was done for both Spectral Imaging and AiCE.
  • Spectral Imaging: Bench testing utilizing phantoms assessed various image quality metrics (CNR, CT Number Accuracy, Uniformity, SSP, MTF-Wire, SD, NPS, LCD) to demonstrate substantial equivalence to the predicate. Other phantom studies supported claims of beam hardening artifact reduction and iodine concentration correlation.
  • AiCE: Bench testing utilizing phantoms assessed similar image quality metrics and pediatric phantom/protocol performance, demonstrating substantial equivalence. A model observer evaluation was specifically mentioned for quantitative assessments of dose reduction, LCD improvement, and noise reduction compared to FBP and AIDR 3D, which is a standalone algorithm-only performance assessment. Other phantom studies supported claims regarding artifact appearance, spatial resolution, and noise appearance/texture.

7. The Type of Ground Truth Used

• For Bench Testing (Spectral and AiCE): The ground truth was based on physical phantom measurements and known properties/compositions of the phantoms. For example, known concentrations of materials for iodine correlation, or predefined structures for MTF and LCD assessments.
• For Clinical Image Reviews (Spectral and AiCE): The ground truth was expert consensus (single expert), specifically the judgment of an American Board Certified Radiologist that the images were of "diagnostic quality." This is a form of expert opinion or interpretation.

8. The Sample Size for the Training Set

• The document does not specify the sample size for the training set for either AiCE (Deep Convolutional Neural Network) or FIRST (Iterative Reconstruction Algorithm). It only mentions that AiCE employs "Deep Convolutional Neural Network methods."

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

• The document does not provide details on how the ground truth for the training set was established. It mentions the use of "Deep Convolutional Neural Network methods" for AiCE, which implies a supervised learning approach requiring labeled training data, but the specifics of that labeling process or who performed it are omitted.

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