Search Results

The GE Omni Legend is a PET/CT system for producing attenuation corrected PET images. It is intended to be used by qualified health care professionals for imaging the distribution and localization of any positron-emitting radiopharmaceutical in a patient, for the assessment of metabolic (molecular) and physiologic function in patients, with a wide range of sizes and extent of disease, of all ages.

Omni Legend is intended to image the whole body, head, heart, brain, lung, breast, bone, the gastrointestinal and lymphatic systems, and other organs. The images produced by the system may be used by physicians to aid in radiotherapy treatment planning. therapy quidance and monitoring. and in interventional radiology procedures. The images may also be used for precise functional and anatomical mapping (localization, registration, and fusion).

When used with radiopharmaceuticals approved by the regulatory authority in the country of use, the raw and image data is an aid in; detection, localization, diagnosis, staging, restaging, monitoring, and/or follow up, of abnormalities, lesions, tumors, inflammation, organ function, disorders, and/or disease, such as, but not limited to, those in oncology, cardiology, and neurology.

Examples of which are: Cardiology:

• · Cardiovascular disease
• · Myocardial perfusion
• · Myocardial viability
• · Cardiac inflammation
• · Coronary artery disease

Neurology:

• · Epilepsy
  · Dementia, such as Alzheimer's disease, Lewy body dementia, Parkinson's disease with dementia, and frontotemporal dementia

· Movement disorders, such as Parkinson's and Huntington's disease

• · Tumors
• · Inflammation
• · Cerebrovascular disease such as acute stroke, chronic and acute ischemia
• · Traumatic Brain Injury (TBI)
• Oncology/Cancer:
• · Non-Small Cell Lung Cancer
• · Small Cell Lung Cancer
• · Breast Cancer
• · Prostate Cancer
• · Hodgkin disease
• · Non-Hodgkin lymphoma
• · Colorectal Cancer
• · Melanoma

Omni Legend is also intended for stand-alone, diagnostic CT imaging in accordance with the standalone CT system's cleared indications for use.

GE HealthCare's subject Omni Legend device, same as the unmodified predicate device, is a hybrid digital PET/CT diagnostic imaging system combining a GEHC Positron Emission Tomography (PET) System and a current production diagnostic GEHC Computed Tomography (CT) System. The proposed Omni Legend is a conventional, general-purpose PET/CT system using existing technological characteristics with identical Intended Use and Indications for Use as its predicate.

Omni Legend is made available with a "6 Ring", "4 Ring" configuration of its PET detector that correspondent provide an AFOV of 32 cm, 21cm, 16 cm. Omni Legend's major components include PET gantry / detector, GEHC commercially available Revolution Maxima CT system (K192686), patient table, operator console / workspace, computing hardware, power distribution unit, system software, and image reconstruction software. The operator console and system software control image acquisition and reconstruction, image display and post processing analysis, protocol and patient management, CT dose display, networking, filming, etc.

Here's a summary of the acceptance criteria and study information for the GE Omni Legend device, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The provided text does not explicitly state acceptance criteria in a quantifiable, pass/fail format for the clinical reader study. However, it does specify types of evaluations and the general outcome.

2. Sample Size for Test Set and Data Provenance

• Sample Size: The document states that the clinical reader study used "a clinically representative sample for evaluating the performance of Omni Legend's Enhanced AC option." However, the specific number of cases or patient exams in this test set is not provided.
• Data Provenance: Not explicitly stated. The document refers to "PET/CT exams acquired on Omni Legend," implying prospective acquisition on the device, but does not specify the country of origin. It also doesn't explicitly state whether the data was retrospective or prospective, though "acquired on Omni Legend" suggests prospective collection for the study.

3. Number of Experts and Qualifications for Ground Truth

• Number of Experts: The clinical reader study involved "NM physicians." The specific number of physicians is not provided.
• Qualifications of Experts: The experts were identified as "NM physicians" (Nuclear Medicine physicians). No further details on their years of experience or board certifications are given.

4. Adjudication Method for the Test Set

The document does not describe an adjudication method for the clinical reader study. It states that "Each image was read by NM physicians who provided an assessment of overall diagnostic image quality... as well as the ability of Enhanced AC to correct artifacts." This suggests individual assessments rather than a consensus or adjudicated ground truth process involving the readers themselves.

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

• The document describes a clinical reader study but does not explicitly state that it was an MRMC comparative effectiveness study comparing human readers with AI vs. without AI assistance. The study primarily focused on evaluating the "Enhanced AC" software option, with readers assessing diagnostic image quality and artifact correction.
• Effect Size: As it wasn't explicitly an MRMC study designed to measure human improvement with AI assistance, no effect size of human readers improving with AI vs. without AI assistance is reported. The study's conclusion is about the acceptability of the device's enhanced features.

6. Standalone Performance Study (Algorithm Only)

The document does not explicitly describe a standalone (algorithm only, without human-in-the-loop) performance study for the "Enhanced AC" option. The clinical study involved human readers assessing the images produced by the system (which includes the Enhanced AC), but it wasn't an isolated evaluation of the algorithm's output without human interpretation. The non-clinical testing included "qualitative and quantitative evaluation of PET images corrected with Enhanced AC, including demonstration of improvement in lesion quantitation," which could be considered a form of standalone evaluation, but it's not a clinical performance study.

7. Type of Ground Truth Used

For the clinical reader study, the ground truth was based on the expert assessment/consensus of NM physicians regarding the "overall diagnostic image quality" and the "ability of Enhanced AC to correct artifacts" and whether these assessments demonstrated "acceptable diagnostic results." It is not explicitly stated if pathology, outcomes data, or another objective measure was used to establish ground truth for this specific reader study. For the non-clinical testing, the "ground truth" would be established by physical measurements and phantom studies comparing results against known values as per NEMA standards.

8. Sample Size for Training Set

The document does not provide any information regarding the sample size used for training the algorithms (e.g., "Enhanced AC" or "Precision DL").

9. How Ground Truth for Training Set Was Established

The document does not provide any information on how the ground truth for any training set was established.

Ask a specific question about this device

Precision DL is a deep learning-based image processing method intended to enhance image quality of non-ToF PET images for clinical oncology purpose, using F-18 FDG. Precision DL may be used for patients of all ages.

Precision DL is a deep learning-based image processing method intended for PET oncology 18F-FDG images obtained using the predicate device Omni Legend PET/CT system. Precision DL enhances the non-ToF Q.Clear images to have image quality performance similar to PET images obtained using ToF capable PET systems, including enhancement in image Contrast Recovery (CR), Contrast to Noise Ratio (CNR), and quantitation accuracy. Precision DL's training used clinical data from diverse clinical sites, accounting for relevant variations in patients and sites' protocols.

Precision DL brings three deep learning models to provide users the choice between different strengths of contrast enhancement and noise reduction. The three models, Low, Medium, and High Precision DL, are trained such that the High Precision DL brings the highest contrast enhancement and lowest noise reduction, while the Low Precision DL brings the lowest contrast enhancement and highest noise reduction. Medium Precision DL brings contrast-noise tradeoff in between High and Low Precision DL.

Precision DL is deployed within the acquisition and processing software of Omni Legend, for processing images produced using non-ToF Q.Clear image reconstruction.

Here's an analysis of the acceptance criteria and study for Precision DL, based on the provided FDA 510(k) summary:

Device: Precision DL (Deep Learning-based image processing for non-ToF PET images)

Intended Use: Enhance image quality of non-ToF PET images for clinical oncology using F-18 FDG.

1. Table of Acceptance Criteria and Reported Device Performance

The 510(k) summary describes performance improvements over non-ToF Q.Clear reconstruction. It does not explicitly state discrete acceptance criteria values but rather demonstrates general improvements in imaging metrics and equivalence to ToF images.

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

• Sample Size (Clinical Data for Bench Testing): 80 PET-CT exams.
  • 40 exams from an Omni Legend system.
  • 40 exams from Discovery MI systems (with hardware-based ToF).
• Sample Size (Clinical Reader Study): Not explicitly stated precisely for the number of cases and images. It mentions "clinical cases of the same patients obtained on Discovery MI and Omni Legend with Precision DL."
• Data Provenance: Multiple clinical sites in North America, Europe, and Israel. The data was "segregated, completely independent, and not used in any stage of the algorithm development, including training." This indicates prospective or retrospectively collected data used for testing only.

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

• Number of Experts (for Ground Truth related to phantom data): Not applicable for phantom data, as ground truth is known from inserted lesions.
• Number of Experts (for Clinical Reader Study): "Board certified radiologists." The exact number is not explicitly stated in the summary, nor are their specific years of experience. However, the study involved reviews and preference questions by these experts.

4. Adjudication Method

• The summary mentions a "clinical reader study" where "board certified radiologists... answered blinded preference questions comparing clinical cases." This suggests individual reader assessments were aggregated, but it does not explicitly state an adjudication method like 2+1 or 3+1 for resolving discrepancies in diagnostic findings. The focus appears to be on overall image quality and preference rather than a specific diagnostic consensus for each case.

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

• Yes, a clinical reader study was performed, which included "board certified radiologists" reviewing "clinical cases." They answered "blinded preference questions comparing clinical cases of the same patients obtained on Discovery MI and Omni Legend with Precision DL."
• Effect Size of Human Readers with AI vs. Without AI Assistance: The summary states, "The results of the reader study and preference questions support the determination of substantial equivalence. All readers attested that their assessments of Precision DL demonstrated acceptable diagnostic results." While it indicates positive results and physician acceptance, it does not quantify an effect size of how much human readers improved their performance (e.g., in diagnostic accuracy, confidence, or reduced read time) with AI assistance compared to reading without AI assistance (i.e., using only non-ToF Q.Clear images). The study primarily focused on image quality acceptability and preference.

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

• Yes, a standalone performance assessment was conducted as part of the "additional engineering bench testing." This included quantitative assessments using both clinical and phantom data for metrics such as Quantitation Accuracy, Contrast Recovery, Contrast-to-Noise Ratio, Lesion Detectability, and Dose/Time Reduction. This part of the testing directly evaluated the algorithm's output (processed images) against established ground truths/references.

7. Type of Ground Truth Used

• For Bench Testing (Quantitative Metrics):
  • Phantom Data: Known quantitation from inserted lesions of known size, location, and contrast.
  • Clinical Data: Discovery MI's ToF PET images served as a reference for comparison, implying they are considered the gold standard for high-quality images that Precision DL aims to emulate.
• For Clinical Reader Study: The "ground truth" here is implied to be the expert consensus on acceptable diagnostic quality and preference rather than a definitive diagnosis based on pathology or long-term outcomes for each case. The goal was to confirm that the enhanced images retained or improved diagnostic acceptability.

8. Sample Size for the Training Set

• The summary states, "Precision DL's training used clinical data from diverse clinical sites, accounting for relevant variations in patients and sites' protocols."
• However, the specific sample size for the training set is not provided in the given text.

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

• The summary indicates that Precision DL "is trained to enhance non-ToF images to have IQ performance similar to ToF images." This implies that the ground truth for training would likely be high-quality ToF PET images (potentially from a system like Discovery MI) that the algorithm was designed to mimic or achieve certain quality metrics aligned with ToF performance.
• The text doesn't detail the process of establishing ground truth for individual images within the training set, but it's reasonable to infer a reference standard from ToF images was used.

Ask a specific question about this device

The GE Omni Legend is a PET/CT system for producing attenuation corrected PET images. It is intended to be used by qualified health care professionals for imaging the distribution and localization of any positron-emitting radiopharmaceutical in a patient, for the assessment of metabolic (molecular) and physiologic function in patients, with a wide range of sizes and extent of disease, of all ages.

Omni Legend is intended to image the whole body, head, heart, brain, bone, the gastrointestinal and lymphatic systems, and other organs. The images produced by the system may be used by physicians to aid in radiotherapy treatment planning, therapy guidance and monitoring, and in interventional radiology procedures. The images may also be used for precise functional and anatomical mapping (localization, registration, and fusion).

When used with radiopharmaceuticals approved by the regulatory in the country of use, the raw and image data is an aid in; detection, localization, evaluation, diagnosis, staging, monitoring, and/or follow up, of abnormalities, lesions, tumors, inflammation, infection, organ function, disorders, and/or disease, such as, but not limited to, those in oncology, cardiology, and neurology.

Examples of which are:

Cardiology:

• Cardiovascular disease
• · Myocardial perfusion
• · Myocardial viability
• Cardiac inflammation
• · Coronary artery disease

Neurology:

· Epilepsy

· Dementia, such as Alzheimer's disease, Lewy body dementia, Parkinson's disease with dementia, and frontotemporal dementia

• · Movement disorders, such as Parkinson's and Huntington's disease
• Tumors
• · Inflammation
• · Cerebrovascular disease such as acute stroke, chronic and acute ischemia
• · Traumatic Brain Injury (TBI)
• Oncology/Cancer:
• Non-Small Cell Lung Cancer
• · Small Cell Lung Cancer
• Breast Cancer
• · Prostate Cancer
• · Hodgkin disease
• Non-Hodgkin lymphoma
• · Colorectal Cancer
• · Melanoma

Omni Legend is also intended for stand-alone, diagnostic CT imaging in accordance with the stand-alone CT system's cleared indications for use.

GE's Omni Legend is a hybrid digital PET/CT diagnostic imaging system combining a GE Positron Emission Tomography System and the commercially available GE Revolution Maxima CT System, for excellent, best-in-class imaging performance. Omni Legend is intended for CT attenuation corrected, anatomically localized PET imaging of the distribution of positron-emitting radiopharmaceuticals. It is intended to image the whole body, head, heart, brain, lung, breast, bone, the gastrointestinal and lymphatic systems, and other organs. It is also intended for stand-alone, diagnostic CT imaging.

Omni Legend's major components include the PET system, Revolution Maxima CT system, patient table, operator console, computing hardware, power distribution unit (PDU), system software, and reconstruction software.

The PET System uses the same design elements used in the predicate Discovery MI Gen2, including use of digital detection (SiPMs). The most significant difference is that the digital detection on Omni Legend uses BGO as a scintillator instead of the Lutetium-based scintillator (LYSO/LGSO) used on Discovery MI Gen2. Omni Legend's digital BGO-based detection achieves the very high sensitivity desired. The Discovery IQ reference device also uses BGO as the scintillator material for its analogic detection. Omni Legend's PET system offers scalable ring configurations (3-ring and 6-ring) to have scalable Axial Field of Views (AFOV) of 16 and 32 cm respectively, with corresponding imaging performances.

The CT System is GE's commercially available 64 detector row Revolution Maxima, which may also be used for standalone, diagnostic CT imaging.

The Operator Console and System Software control image acquisition and reconstruction, image display and post processing analysis, protocol and patient management, CT dose display, networking, filming, etc. The software carries over functionalities available on the Discovery MI Gen2 product line and is updated to support the changes introduced with Omni Legend and to bring enhancement, including ACQC, RadRx 2.0, and Express Mode.

The Patient Table and PDU are identical to those of the predicate Discovery MI Gen2.

The provided text describes the GE Omni Legend PET/CT system and its submission for FDA 510(k) clearance, asserting its substantial equivalence to a predicate device. However, the document does not contain a detailed table of specific acceptance criteria or quantitative performance metrics for the device, nor does it provide a comprehensive report of a study that directly proves the device meets specific, pre-defined acceptance criteria with numerical outcomes.

Instead, it describes the types of tests performed and the general conclusions, focusing on demonstrating equivalence to a predicate device rather than meeting absolute performance thresholds.

Therefore, for the specific request:

1. Table of acceptance criteria and the reported device performance:

The document does not provide a specific table with numerical acceptance criteria and corresponding reported device performance values. It generally states that performance testing compliant with NEMA NU 2-2018 was conducted for various parameters.

General Performance Evaluation (as inferred from the text):

Note: The document only mentions that these tests were performed and that the results "support substantial equivalence" or "demonstrated acceptable diagnostic results," but it does not provide the specific numerical acceptance thresholds or the actual measured performance values for these metrics.

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

• Test Set Sample Size: Not explicitly stated. The document mentions "a clinically representative sample for evaluation of Omni Legend's performance."
• Data Provenance: Not explicitly stated regarding country of origin. The study was a "clinical reader study using PET/CT exams acquired on Omni Legend," implying prospective acquisition for the purpose of the study, though the specific recruitment method (e.g., patient volunteers vs. retrospective scans) is not detailed. It is highly likely, given GE's global presence and the setting of the submission (FDA), that the data was collected under ethical guidelines in a clinical setting relevant to the product development, but the specific origins are not mentioned.

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

• Number of Experts: Not precisely stated. The document says "Each image was read by NM physicians." This implies multiple physicians, but the exact number is not provided.
• Qualifications of Experts: "NM physicians" (Nuclear Medicine physicians). No specific experience level (e.g., "10 years of experience") is mentioned.

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

• Adjudication Method: Not explicitly mentioned. The statement "Each image was read by NM physicians who provided an assessment of overall diagnostic image quality using a Likert Scale. All of the physicians attested that their assessments demonstrated acceptable diagnostic results" suggests independent reads followed by a collective attestation of acceptability, rather than a formal adjudication process (like 2+1 or 3+1 for discordance resolution).

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:

• A clinical reader study was conducted. However, the description states it was to evaluate "Omni Legend's performance" and for "assessment of overall diagnostic image quality."
• This study does not appear to be an MRMC comparative effectiveness study comparing human readers with AI assistance vs. without AI assistance. The document indicates the study was to evaluate the image quality of the system itself, not the impact of an AI algorithm on human reading performance.
• Therefore, no effect size for human readers improving with AI vs. without AI assistance is reported because that was not the stated purpose or design of the clinical reader study described.

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

• The document describes the Omni Legend as a "PET/CT system" and mentions its components and functionalities. It does not describe a specific standalone "algorithm" being assessed independently of the system's image acquisition and reconstruction. The performance testing (NEMA NU 2-2018 tests) are for the entire imaging system (e.g., sensitivity, resolution), not a separate diagnostic algorithm.
• Thus, a standalone algorithm-only performance study, as typically understood in AI/CADx submissions, is not detailed or implied for a distinct diagnostic algorithm component. The testing described is for the device as an image-generating system.

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

• The ground truth for the clinical reader study appears to be expert assessment/consensus based on the NM physicians' evaluations of "overall diagnostic image quality." There is no mention of pathology, long-term outcomes, or other objective data used as ground truth for the diagnostic image quality assessment. The physicians attested to acceptable diagnostic results.

8. The sample size for the training set:

• The document makes no mention of a training set sample size. This is because the submission is for a PET/CT imaging system, not an AI/ML algorithm that typically requires a large training set. While the system may incorporate advanced processing, the submission focuses on the hardware's performance (digital BGO detector, CT system) and its ability to produce diagnostic images, rather than a trainable AI model used for diagnosis.

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

• Since no training set is mentioned or implied for a trainable AI/ML algorithm in this submission, the establishment of ground truth for a training set is not applicable to the information provided. The "ground truth" discussed is related to the evaluation of the final image quality and diagnostic acceptability by physicians, not for training a model.

Ask a specific question about this device

The device is a diagnostic imaging system that combines Positron Emission Tomography (PET) and X-ray Computed Tomography (CT) systems. The CT component produces cross-sectional images of the body by computer reconstruction of X-ray transmission data. The PET component images the distribution of PET radiopharmaceuticals in the patient body. The PET component utilizes CT images for attenuation correction and anatomical reference in the fused PET and CT images.

This device is to be used by a trained health care professional to gather metabolic and functional information from the distribution of the radiopharmaceutical in the body for the assessment of metabolic and physiologic functions. This information can assist in the evaluation, detection, diagnosis, therapeutic planning and therapeutic outcome assessment of (but not limited to) cancer, cardiovascular disease and brain dysfunction. Additionally, this device can be operated independently as a whole body multi-slice CT scanner.

Cartesion Prime, PCD-1000A, system combines a high-end CT and a high-throughput PET designed to acquire CT, PET and fusion images. The high-end CT system is a multi-slice helical CT scanner with a gantry aperture of 780 mm and a maximum scanning field of 700 mm. The high-throughput PET system has a digital PET detector utilizing SiPM sensors with temporal resolution of 280 ps. Cartesion Prime, PCD-1000A is intended to acquire PET images of any desired region of the whole body and CT images of the same region (to be used for attenuation correction or image fusion), to detect the location of positron emitting radiopharmaceuticals in the obtained images. This device is used to gather the metabolic and functional information from the distribution of radiopharmaceuticals in the body for the assessment of metabolic and physiologic functions. This information can assist research, diagnosis, therapeutic planning, and therapeutic outcome assessment. This device can also function independently as a whole body multi-slice CT scanner.

The provided text does not contain acceptance criteria or a study proving that an AI/algorithm-based device meets specific acceptance criteria.

The document is a 510(k) summary for a PET/CT imaging system (Cartesion Prime, PCD-1000A). It describes the device, its intended use, and argues for its substantial equivalence to previously cleared predicate devices. While it mentions "testing" and "risk analysis and verification/validation testing," this largely refers to standard engineering and performance testing of the imaging hardware and software, rather than a clinical study evaluating an AI/algorithm's diagnostic performance against established criteria and ground truth.

Specifically, the document focuses on:

• Device Description: The physical and functional characteristics of the PET/CT scanner.
• Indications for Use: What the device is intended for (diagnostic imaging, assessment of metabolic/physiologic functions, aiding in evaluation, diagnosis, therapeutic planning, outcome assessment for various diseases).
• Substantial Equivalence: A comparison of the subject device's technical specifications (PET sensitivity, timing resolution, CT detector, etc.) with predicate devices to demonstrate it performs similarly.
• Safety and Standards Compliance: Adherence to quality systems regulations (ISO 13485, 21 CFR 820) and various IEC/NEMA standards, as well as general statements about software documentation and cybersecurity.
• Testing (General): "Risk analysis and verification/validation testing conducted through bench testing" and that "PET image quality metrics were performed which validated that the subject device met established specifications for spatial resolution, sensitivity, NECR, energy/timing resolution and PET/CT alignment." This is about the scanner's image quality and technical performance, not an AI's diagnostic accuracy.

There is no mention of:

• An AI/algorithm component requiring specific performance evaluation.
• Diagnostic performance metrics (e.g., sensitivity, specificity, AUC) for an AI.
• Human readers, MRMC studies, or human-in-the-loop performance.
• Ground truth establishment methods for a diagnostic algorithm.
• Training or test sets for an AI.

Therefore, I cannot fulfill the request to describe the acceptance criteria and the study that proves an AI/algorithm device meets those criteria based on the provided text. The document is about a medical imaging device (a PET/CT scanner), not an AI/algorithm that performs diagnostic tasks.

Ask a specific question about this device

The Siemens Biograph Vision PET/CT systems are combined X-Ray Computed Tomography (CT) and Positron Emission Tomography (PET) scanners that provide registration and fusion of high resolution physiologic and anatomic information.

The CT component produces cross-sectional images of the body by computer reconstruction of X-Ray transmission data from either the same axial plane taken at different angles or spiral planes taken at different angles. The PET subsystem images and measures the distribution of PET radiopharmaceuticals in humans for the purpose of determining various metabolic (molecular) and physiologic functions within the human body and utilizes the CT for fast attenuation correction maps for PET studies and precise anatomical reference for the fused PET and CT images.

The system maintains independent functionality of the CT and PET devices, allowing for single modality CT and / or PET diagnostic imaging.

These systems are intended to be utilized by appropriately trained health care professionals to aid in detecting, localizing, diagnosing, staging and restaging of lesions, tumors, disease and organ function for the evaluation of diseases and disorders such as, but not limited to, cardiovascular disease, neurological disorders and cancer. The images produced by the system can also be used by the physician to aid in radiotherapy treatment planning and interventional radiology procedures.

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

• As defined by professional medical societies. Please refer to clinical literature, including the results of the National Lung Screening Trial (N Engl J Med 2011; 365:395-409) and subsequent literature, for further information.

The Biograph Vision systems are combined multi-slice X-Ray Computed Tomography (CT) and Positron Emission Tomography (PET) scanners. These systems are designed for whole body oncology, neurology and cardiology examinations.

The Biograph Vision systems provide registration and fusion of high-resolution metabolic and anatomic information from the two major components of each system (PET and CT). Additional components of the system include a patient handling system and acquisition and processing workstations with associated software.

Biograph Vision software is a command based program used for patient management, data management, scan control, image reconstruction and image archival and evaluation. All images conform to DICOM imaging format requirements.

The Biograph Vision PET/CT, which is the subject of this application, is substantially equivalent to the commercially available Biograph mCT (K173578). The key difference between the Biograph mCT (predicate device) and the Biograph Vision PET/CT is the replacement of PhotoMultiplier Tubes (PMT) with Silicon PhotoMultipliers (SiPM). SiPMs are a photon sensitive technology built by combining a solid state photodiode array and a silicon substrate. The SiPMs allow close coupling to the scintillators (crystals) and a higher active area of detectors to scintillators. This combination results in superior performance compared to the photomultiplier tubes design used in previous generation PET block detectors.

The provided text is a 510(k) Summary for the Siemens Biograph Vision PET/CT system. It details the device, its intended use, and performance testing results to demonstrate substantial equivalence to a predicate device.

Acceptance Criteria and Device Performance:

The document primarily focuses on the physical performance characteristics of the PET component of the Biograph Vision system, tested according to NEMA NU2:2012 standards.

1. Table of Acceptance Criteria and Reported Device Performance:

Study Proving Device Meets Acceptance Criteria:

The study described is Performance Testing in accordance with NEMA NU2:2012, conducted on the Biograph Vision 600.

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

• Sample Size: The document does not specify a "sample size" in terms of patient images or clinical cases. The testing appears to involve physical phantoms and measurements as per NEMA NU2:2012 standards, not a clinical test set of patient scans. Therefore, sample size in the context of clinical images is not applicable here.
• Data Provenance: Not applicable as this is performance testing of the device itself using phantom measurements, not clinical data collection. The testing was conducted by Siemens Medical Solutions USA, Inc.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of those Experts:
Not applicable. The ground truth for this type of performance testing is defined by the NEMA NU2:2012 standard itself, which specifies the phantoms, measurement procedures, and calculation methods. It does not involve human expert interpretation for establishing ground truth.

4. Adjudication Method for the Test Set:
Not applicable. As the testing involves objective physical measurements and calculations based on a recognized standard (NEMA NU2:2012), there is no need for human adjudication of results.

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, an MRMC comparative effectiveness study was not done. The document describes a PET/CT scanner and its physical performance, not an AI-powered diagnostic tool requiring human reader studies to demonstrate improved performance.

6. If a Standalone (i.e. algorithm only without human-in-the-loop performance) was done:
Yes, in a sense, the performance testing described is "standalone" for the device's physical capabilities. The NEMA NU2:2012 tests evaluate the intrinsic imaging performance of the PET component (resolution, sensitivity, count rate, image quality based on phantom measurements) without human interpretation in the loop.

7. The Type of Ground Truth Used:
The ground truth used for this performance testing is the physical properties and known measurements of standardized phantoms as defined by the NEMA NU2:2012 standard. This is a technical ground truth, not a medical ground truth (like pathology, expert consensus, or outcomes data).

8. The Sample Size for the Training Set:
Not applicable. This is a hardware device (PET/CT scanner), not a machine learning algorithm that requires a "training set" of data for development.

9. How the Ground Truth for the Training Set was Established:
Not applicable for the same reason as above.

Ask a specific question about this device