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AV Vascular is indicated to assist users in the visualization, assessment and quantification of vascular anatomy on CTA and/or MRA datasets, in order to assess patients with suspected or diagnosed vascular pathology and to assist with pre-procedural planning of endovascular interventions.

AV Vascular is a post-processing software application intended for visualization, assessment, and quantification of vessels in computed tomography angiography (CTA) and magnetic resonance angiography (MRA) data with a unified workflow for both modalities.

AV Vascular includes the following functions:

• Advanced visualization: the application provides all relevant views and interactions for CTA and MRA image review: 2D slides, MIP, MPR, curved MPR (cMPR), stretched MPR (sMPR), path-aligned views (cross-sectional and longitudinal MPRs), 3D volume rendering (VR).

• Vessel segmentation: automatic bone removal and vessel segmentation for head/neck and body CTA data, automatic vessel centerline, lumen and outer wall extraction and labeling for the main branches of the vascular anatomy in head/neck and body CTA data, semi-automatic and manual creation of vessel centerline and lumen for CTA and MRA data, interactive two-point vessel centerline extraction and single-point centerline extension.

• Vessel inspection: enable inspection of an entire vessel using the cMPR or sMPR views as well as inspection of a vessel locally using vessel-aligned views (cross-sectional and longitudinal MPRs) by selecting a position along a vessel of interest.

• Measurements: ability to create and save measurements of vessel and lumen inner and outer diameters and area, as well as vessel length and angle measurements.

• Measurements and tools that specifically support pre-procedural planning: manual and automatic ring marker placement for specific anatomical locations, length measurements of the longest and shortest curve along the aortic lumen contour, angle measurements of aortic branches in clock position style, saving viewing angles in C-arm notation, and configurable templated

• Saving and export: saving and export of batch series and customizable reports.

This summarization is based on the provided 510(k) clearance letter for Philips Medical Systems' AV Vascular device.

Acceptance Criteria and Device Performance for Aorto-iliac Outer Wall Segmentation

The reported device performance for all primary and secondary metrics meets the predefined acceptance criteria.

Study Details for Aorto-iliac Outer Wall Segmentation Validation

1. Sample Size used for the Test Set and Data Provenance:

   • Sample Size: 80 patients
   • Data Provenance: Retrospectively collected from 7 clinical sites in the US, 3 European hospitals, and one hospital in Asia.
   • Independence from Training Data: All performance testing datasets were acquired from clinical sites distinct from those which provided the algorithm training data. The algorithm developers had no access to the testing data, ensuring complete independence.
   • Patient Characteristics: At least 80% of patients had thoracic and/or abdominal aortic diseases and/or iliac artery diseases (e.g., thoracic/abdominal aortic aneurysm, ectasia, dissection, and stenosis). At least 20% had been treated with stents.
   • Demographics:
     • Geographics: North America: 58 (72.5%), Europe: 3 (3.75%), Asia: 19 (23.75%)
     • Sex: Male: 59 (73.75%), Female: 21 (26.25%)
     • Age (years): 21-50: 2 (2.50%), 51-70: 31 (38.75%), >71: 45 (56.25%), Not available: 2 (2.5%)

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

   • Number of Experts: Three
   • Qualifications: US-board certified radiologists.

3. Adjudication Method for the Test Set:

   • The three US-board certified radiologists independently performed manual contouring of the outer wall along the aorta and iliac arteries on cross-sectional planes for each CT angiographic image.
   • After quality control, these three aortic and iliac arterial outer wall contours were averaged to serve as the reference standard contour. This can be considered a form of consensus/averaging after independent readings.

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

   • The provided document does not indicate that a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was done to measure human reader improvement with AI assistance. The study focused on the standalone performance of the AI algorithm compared to an expert-derived ground truth.

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

   • Yes, the performance data provided specifically describes the standalone performance of the AI-based algorithm for aorto-iliac outer wall segmentation. The algorithm's output was compared directly against the reference standard without human intervention in the segmentation process.

6. Type of Ground Truth Used:

   • Expert Consensus/Averaging: The ground truth was established by averaging the independent manual contouring performed by three US-board certified radiologists.

7. Sample Size for the Training Set:

   • The document states that the testing data were independent of the training data and that developers had no access to the testing data. However, the exact sample size for the training set is not specified in the provided text.

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

   • The document implies that training data were used, but it does not describe how the ground truth for the training set was established. It only ensures that the testing data did not come from the same clinical sites as the training data and that algorithm developers had no access to the testing data.

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uCT 780 is a computed tomography x-ray system intended to produce cross-sectional images of the body by computer reconstruction of x-ray transmission data taken at different angles and planes and indicated for the whole body (including head, neck, cardiac and vascular).

uCT 780 is intended to be used for low dose CT lung cancer screening for the early detection of lung nodules that may represent cancer. 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.

• 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.

uWS-CT-Dual Energy Analysis is a post-processing software package that accepts UIH CT images acquired using different tube voltages and/or tube currents of the same anatomical location. The various materials of an anatomical region of interest have different attenuation coefficients, which depend on the used energy. These differences provide information on the chemical composition of the scanned body materials and enable images to be generated at multiple energies within the available spectrum. uWS-CT-Dual Energy Analysis software combines images acquired with low and high energy spectra to visualize this information.

The Computed Tomography X-ray system, uCT 780, is intended to produce cross-sectional images of the patient by computer reconstruction of X-ray transmission data taken at different angles and planes. These images may be obtained either with or without contrast.

This 510(k) is to request modifications for the cleared Computed Tomography X-ray system uCT 780. uCT 780 has been previously cleared by FDA via K241079.The modification performed on the uCT 780 (K241079) in this submission is due to the addition of a new high voltage generator. At the same time, we introduce a mobile configuration which supports installation in vehicles. A summary of the modified hardware is provided below:

• A new model of high voltage generator uXG 100 has been introduced in the mobile configuration, and the predicate model CT140N80X4889 is still used in other non-mobile configurations.
• A tilt lock has been introduced to the gantry and a horizontal movement lock has been introduced to the standard config patient table, and the system software has been updated to include the relevant controls and prompts.
• PSC has been modified, including adding a shock-absorbing base, strengthening the sheet metal structure, and optimizing the fixing method of PSC.

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Magnetic Resonance Imaging (MRI) is a noninvasive technique used for diagnostic imaging. MRI with its soft tissue contrast capability enables the healthcare professional to differentiate between various soft tissues, for example, fat, water, and muscle, but can also visualize bone structures.

Depending on the region of interest, contrast agents may be used.

The MR system may also be used for imaging during interventional procedures and radiation therapy planning.

The PET images and measures the distribution of PET radiopharmaceuticals in humans to aid the physician in determining various metabolic (molecular) and physiologic functions within the human body for evaluation of diseases and disorders such as, but not limited to, cardiovascular disease, neurological disorders, and cancer.

The integrated system utilizes the MRI for radiation-free attenuation correction maps for PET studies. The integrated system provides inherent anatomical reference for the fused MR and PET images due to precisely aligned MR and PET image coordinate systems.

BIOGRAPH One with software Syngo MR XB10 includes new and modified hardware and software compared to the predicate device, Biograph mMR with software syngo MR E11P-AP01. A high level summary of the new and modified hardware and software is provided below:

Hardware

New Hardware

• Gantry offset phantom
• SDB (Smart Distribution Box)

New Coils

• BM Contour XL Coil
• BM Head/Neck Pro PET-MR Coil
• BM Spine Pro PET-MR Coil
• Transfer of up-to-date RF coils from the reference device MAGNETOM Vida.

Modified Hardware

• Main components such as:
  • Detector cassettes / DEA
  • Phantom holder
  • Gantry tube
  • Backplane
  • Magnet and cabling
  • Gradient coil
  • MaRS (measurement and reconstruction system)
  • MI MARS
  • PET electronics
  • RF transmitter TBX3 3T (TX Box 3)
• Other components such as:
  • Cover
  • Filter plate
  • Patient table
  • RFCEL_TEMP

Modified Coils

• Body coil
• Transfer of up-to-date RF coils from the reference device MAGNETOM Vida with some improvements.

Software

New Features and Applications

• Fast Whole-Body workflows
• Fast Head workflow
• myExam PET-MR Assist
• CS-Vibe
• myExam Implant Suite
• DANTE blood suppression
• SMS Averaging for TSE
• SMS Averaging for TSE_DIXON
• SMS without diffusion function
• BioMatrix Motion Sensor
• RF pulse optimization with VERSE
• Deep Resolve Boost for FL3D_VIBE and SPACE
• Deep Resolve Sharp for FL3D_VIBE and SPACE
• Preview functionality for Deep Resolve Boost
• EP2D_FID_PHS
• EP_SEG_FID_PHS
• ASNR recommended protocols for imaging of ARIA
• Open Workflow
• Ultra HD-PET
• "MTC Mode"
• OpenRecon 2.0
• Deep Resolve Boost for TSE
• GRE_PC
• The following functions have been migrated for the subject device without modifications from MAGNETOM Skyra Fit and MAGNETOM Sola Fit:
  • 3D Whole Heart
• Ghost reduction (Dual polarity Grappa (DPG))
• Fleet Reference Scan
• AutoMate Cardiac (Cardiac AI Scan Companion)
• Complex Averaging
• SPACE Improvement: high bandwidth IR pulse
• SPACE Improvement: increase gradient spoiling
• The following function has been migrated for the subject device without modifications from MAGNETOM Free.Max:
  • myExam Autopilot Spine
• The following functions have been migrated for the subject device without modifications from MAGNETOM Sola:
  • myExam Autopilot Brain
  • myExam Autopilot Knee
• Transfer of further up-to-date SW functions from the reference devices.

New Software / Platform

• PET-Compatible Coil Setup
• Select&GO
• PET-MR components communication

Modified Features and Applications

• HASTE_CT
• FL3D_VIBE_AC
• PET Reconstruction
• Transfer of further up-to-date SW functions from the reference devices with some improvements.

Modified Software / Platform

• Several software functions have been improved. Which are:
  • PET Group
  • PET Viewing
  • PET RetroRecon
  • PET Status and Tune-up/QA

Other Modifications and / or Minor Changes

• Indications for use
• Contraindications
• SAR parameter
• Off-Center Planning Support
• Flip Angle Optimization (Lock TR and FA)
• Inline Image Filter
• Marketing bundle "myExam Companion"
• ID Gain
• Automatic System Shutdown (ASS) sensor (Smoke Detector)
• Patient data display (PDD)

The FDA 510(k) Clearance Letter for BIOGRAPH One refers to several AI/Deep Learning features. However, the provided document does not contain explicit acceptance criteria for these AI features in a table format, nor does it detail a comparative effectiveness study (MRMC study) for human readers. It primarily focuses on demonstrating non-inferiority to the predicate device through various non-clinical tests.

Below is an attempt to extract and synthesize the information based on the provided text, while acknowledging gaps in the information regarding specific acceptance criteria metrics and clinical studies.

Acceptance Criteria and Study Details for BIOGRAPH One AI Features

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly state numerical acceptance criteria in a dedicated table format. Instead, it describes performance in terms of achieving "convergence of the training" and "improvements compared to conventional parallel imaging," or confirming "very similar metrics" to the predicate. The "acceptance criteria" are implied by these statements and the successful completion of the described tests.

2. Sample Sizes Used for Test Set and Data Provenance

The document primarily describes a validation dataset which serves as the "test set" for the AI models during development, and an additional "test dataset" for specific evaluations.

• Deep Resolve Boost for FL3D_VIBE and SPACE:

  • Test Set Description: The "collaboration partners (testing)" data is mentioned as the source for testing, implying an external, independent test set. No specific number for this test set is provided beyond the 1265 measurements for training/validation.
  • Sample Size (Validation/Training): 27,679 3D patches from 1265 measurements.
  • Data Provenance: "in-house measurements (training and validation) and collaboration partners (testing)." The country of origin is not specified but is likely Germany (Siemens Healthineers AG) and/or China (Siemens Shenzhen Magnetic Resonance LTD.) where the manufacturing is listed.
  • Retrospective/Prospective: "Input data was retrospectively created from the ground truth by data manipulation and augmentation." This indicates retrospective data use.

• Deep Resolve Sharp for FL3D_VIBE and SPACE:

  • Test Set Description: The document states, "The high-resolution datasets were split to 70% training and 30% validation datasets before training to ensure independence of them." This implies the 30% validation dataset is used as the test set.
  • Sample Size (Validation/Training): 27,679 3D patches from 1265 measurements (split into 70% training and 30% validation).
  • Data Provenance: "in-house measurements (training and validation) and collaboration partners (testing)."
  • Retrospective/Prospective: "Input data was retrospectively created from the ground truth by data manipulation." This indicates retrospective data use.

• Deep Resolve Boost for TSE (First Mention - General Performance):

  • Test Set Description: The "evaluation on the test dataset" is mentioned. The validation set is 30% of the 500 measurements.
  • Sample Size (Validation/Training): Approximately 13,000 high resolution 3D patches from 500 measurements (split into 70% training and 30% validation).
  • Data Provenance: "in-house measurements."
  • Retrospective/Prospective: "Input data was retrospectively created from the ground truth by data manipulation." This indicates retrospective data use.

• Deep Resolve Boost for TSE (Second Mention - Banding Artifacts):

  • Test Set Description: "Additional test dataset for banding artifact reduction: more than 2000 slices." This dataset was acquired after the release of the predicate network.
  • Sample Size: More than 2000 slices.
  • Data Provenance: "in-house measurements and collaboration partners."
  • Retrospective/Prospective: Not explicitly stated for this specific additional dataset, but the training/validation data for the predicate was retrospective.

3. Number of Experts and Qualifications for Ground Truth

• Radiologist Evaluation for Deep Resolve Boost for TSE (Second Mention): The document mentions "the radiologist evaluation revealed no difference in suitability for clinical diagnostics."

  • Number of Experts: Not specified (singular "radiologist" used, but typically multiple are implied for such evaluations).
  • Qualifications: "Radiologist." No specific years of experience or subspecialty are mentioned.

• Other features: For Deep Resolve Boost/Sharp for FL3D_VIBE and SPACE, and Deep Resolve Boost for TSE (first mention), the ground truth is derived directly from acquired image data (see section 7). No independent human expert ground truth establishment for these.

4. Adjudication Method (for Test Set)

• Radiologist Evaluation for Deep Resolve Boost for TSE (Second Mention): The adjudication method is not specified in the document (e.g., 2+1, 3+1). It only states "the radiologist evaluation."

• Other features: Adjudication methods are not applicable as human experts were not establishing ground truth for objective metrics.

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

• Was an MRMC study done? No, the document does not describe an MRMC comparative effectiveness study where human readers' performance with and without AI assistance is compared. The evaluation for Deep Resolve Boost for TSE mentions "radiologist evaluation" but not in a comparative MRMC study context.
• Effect Size: Not applicable, as no MRMC study was performed.

6. Standalone (Algorithm Only) Performance

• Was standalone performance done? Yes, the performance testing for all Deep Resolve features (Boost and Sharp for FL3D_VIBE, SPACE, and TSE) was conducted "algorithm only" by evaluating metrics like PSNR, SSIM, MSE, and LPIPS, and then visual inspection/radiologist evaluation. These refer to the algorithm's direct output performance.

7. Type of Ground Truth Used

• Deep Resolve Boost for FL3D_VIBE and SPACE: "The acquired datasets (as described above) represent the ground truth for the training and validation."
• Deep Resolve Sharp for FL3D_VIBE and SPACE: "The acquired datasets represent the ground truth for the training and validation." Input data was manipulated (cropped k-space) to create low-resolution input and high-resolution output/ground truth from the same dataset.
• Deep Resolve Boost for TSE (First Mention): "The acquired datasets represent the ground truth for the training and validation." Input data was manipulated (cropped k-space) to create low-resolution input and high-resolution output/ground truth from the same dataset.
• Deep Resolve Boost for TSE (Second Mention): "The acquired training/validation datasets... represent the ground truth for the training and validation." Input data was manipulated by undersampling k-space, adding noise, and mirroring k-space.
• Summary: The ground truth for all AI features was derived from acquired, high-resolution original image data (retrospectively manipulated to simulate inputs). For Deep Resolve Boost for TSE (second mention), there was also an implicit "expert consensus" or "expert reading" component for the "radiologist evaluation" regarding clinical suitability.

8. Sample Size for the Training Set

• Deep Resolve Boost for FL3D_VIBE and SPACE: 81% of 1265 measurements (for 27,679 3D patches).
• Deep Resolve Sharp for FL3D_VIBE and SPACE: 70% of 1265 measurements (for 27,679 3D patches).
• Deep Resolve Boost for TSE (First Mention): 70% of 500 measurements (for approx. 13,000 high resolution 3D patches).
• Deep Resolve Boost for TSE (Second Mention): More than 23,250 slices (93% of the combined training/validation dataset from K213693).

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

• Deep Resolve Boost for FL3D_VIBE and SPACE: The "acquired datasets" represent the ground truth. "Input data was retrospectively created from the ground truth by data manipulation and augmentation. This process includes further undersampling of the data by discarding k-space lines as well as creating sub-volumes of the acquired data."
• Deep Resolve Sharp for FL3D_VIBE and SPACE: The "acquired datasets represent the ground truth." "Input data was retrospectively created from the ground truth by data manipulation. k-space data has been cropped such that only the center part of the data was used as input. With this method corresponding low-resolution data as input and high-resolution data as output / ground truth were created for training and validation."
• Deep Resolve Boost for TSE (First Mention): Similar to Deep Resolve Sharp for FL3D_VIBE and SPACE: "The acquired datasets represent the ground truth for the training and validation. Input data was retrospectively created from the ground truth by data manipulation. k-space data has been cropped such that only the center part of the data was used as input. With this method corresponding low-resolution data as input and high-resolution data as output / ground truth were created for training and validation."
• Deep Resolve Boost for TSE (Second Mention): "The acquired training/validation datasets... represent the ground truth for the training and validation. Input data was retrospectively created from the ground truth by data manipulation and augmentation. This process includes further undersampling of the data by discarding k-space lines, lowering of the SNR level by addition of noise and mirroring of k-space data."

In summary, for all AI features, the ground truth for training was established by using high-quality, originally acquired MRI data that was then retrospectively manipulated (e.g., undersampled, cropped, noise added) to create synthetic "lower quality" input data for the AI model to learn from, with the original high-quality data serving as the target output or ground truth.

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The AnyScan 3.0 NM Scanner Family is intended for use 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 or additional uses.

SPECT: The SPECT subsystem is intended to provide projection and cross-sectional images through computer reconstruction of the data, representing radioisotope distribution in the patient body or in a specific organ using planar and tomographic scanning modes for isotopes with energies up to 588 keV.

CT: CT component is intended to provide cross sectional images of the body by computer reconstruction of x-ray transmission data providing anatomical information.

PET: The PET component is intended to provide cross- sectional images representing the distribution of tomographic scanning modes.

SPECT+CT: The SPECT and CT components used together acquire SPECT/CT images. The SPECT images can be corrected for attenuation with the CT images, and can be combined (image registration) to merge the patient's physiological (SPECT) and anatomical (CT) images.

PET+CT: The PET and CT components used together acquire PET/CT images. The PET images can be corrected for attenuation with the CT images, and can be combined (image registration) to merge the patient's physiological (PET) and anatomical (CT) images.

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

Software: The Nucline software is an acquisition, display and analysis package intended to aid the clinician to extract diagnostic information supported by image assessment tools, image enhancement features and image quantification of pathologies in images produced from SPECT, CT, PET and other imaging modalities.

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 AnyScan 3.0 NM Scanner Family will enable clinicians to utilize the device to perform separate studies in SPECT-CT, PET-CT, SPECT, PET and multi-slice CT modalities.

The AnyScan 3.0 NM Scanner Family includes the following products:
AnyScan 3.0 NM Scanner Family

The partial product names 'TRIO' and 'DUO' only differentiate the number of built-in SPECT detectors.

The partial product names 'IQMAX' and 'TheraMAX' only differentiate the type of built-in SPECT detector. The SPECT gamma camera generates nuclear medicine images based on the uptake of radioisotope tracers in a patient's body, and supports integration with CT's anatomical detail for precise reference of the location of the metabolic activity.

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 component 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 combination of SPECT, CT, and PET in a single device has several benefits. The SPECT subsystem images biochemical function while the CT subsystem images anatomy. The combination enables scans that not only indicate function, e.g., how active a tumor is, but precise localization, e.g., the precise location of that tumor in the body.

Combined SPECT and CT subsystems are intended for SPECT imaging enhanced with spatially registered CT image-based corrections, anatomical localization of tracer uptake and anatomical mapping. CT can be used to correct for the attenuation in SPECT acquisitions. Attenuation in SPECT is an unwanted side effect of the gamma rays scattering and being absorbed by tissue. This can lead to errors in the final image. The CT directly measures attenuation and can be used to create a 3D attenuation map of the patient which can be used to correct the SPECT images. The SPECT-CT scanner can be used to image and track how much dose was delivered to both the target and the surrounding tissue. The system maintains independent functionality of the CT and SPECT subsystems.

Combined PET and CT subsystems are intended for PET imaging enhanced with spatially registered CT image-based corrections, anatomical localization of tracer uptake and anatomical mapping. system maintains independent functionality of the CT and PET subsystems, allowing for single modality CT and/or PET diagnostic imaging.

A patient positioning light marker is generated by a low-power (Class II per IEC 60825-1) red laser.

Nucline software is installed on acquisition workstation to perform patient management, data management, scan control, image reconstruction and image archival and evaluation. All images conform to DICOM imaging format requirements.

The systems also include display equipment, data storage devices, patient and equipment supports, software, and accessories.

InterView XP; InterView FUSION (K221984) and software is integrated for DICOM image visualization and post-processing.

ClariCT (K212074) software is integrated for DICOM CT de-noising.

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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, localization, diagnosis, staging, restaging, follow-up, therapeutic planning and therapeutic outcome assessment of (but not limited to) oncological, cardiovascular, neurological diseases and disorders. Additionally, this device can be operated independently as a whole body multi-slice CT scanner.

AiCE-i for PET is intended to improve image quality and reduce image noise for FDG whole body data by employing deep learning artificial neural network methods which can explore the statistical properties of the signal and noise of PET data. The AiCE algorithm can be applied to improve image quality and denoising of PET images.

Deviceless PET Respiratory gating system, for use with Cartesion Prime PET-CT system, is intended to automatically generate a gating signal from the list-mode PET data. The generated signal can be used to reconstruct motion corrected PET images affected by respiratory motion. In addition, a single motion corrected volume can automatically be generated. Resulting motion corrected PET images can be used to aid clinicians in detection, localization, evaluation, diagnosis, staging, restaging, follow-up of diseases and disorders, radiotherapy planning, as well as their therapeutic planning, and therapeutic outcome assessment. Images of lesions in the thorax, abdomen and pelvis are mostly affected by respiratory motion. Deviceless PET Respiratory gating system may be used with PET radiopharmaceuticals, in patients of all ages, with a wide range of sizes, body habitus and extent/type of disease.

The Cartesion Prime (PCD-1000A/3) V10.21 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 scan field of view (FOV) of 700 mm. The high-throughput PET system has a digital PET detector utilizing SiPM sensors with temporal resolution of < 250 ps (238 ps typical). Cartesion Prime (PCD-1000A/3) V10.21 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 body with 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, detection, localization, evaluation, diagnosis, staging, restaging, follow-up of diseases and disorders, as well as their therapeutic planning, and therapeutic outcome assessment. This device can also function independently as a whole body multi-slice CT scanner.

The subject device incorporates the latest reconstruction technology, AiCE-i for PET (Advanced Intelligent Clear-IQ Engine- integrated), intended to improve image quality and reduce image noise for FDG whole body data by employing deep learning artificial neural network methods which can more fully explore the statistical properties of the signal and noise of PET data. The AiCE algorithm will be able to better differentiate signal from noise and can be applied to improve image quality and denoising of PET images compared to conventional PET imaging reconstruction.

A Deviceless PET Respiratory gating system has been implemented for use with the subject device. With this subject device, respiration is extracted using a pre-trained neural network. Respiratory-gated reconstruction is performed at a speed equal to or faster than that with "Normal".

Here's a breakdown of the acceptance criteria and study details for the Cartesion Prime PET-CT System, based on the provided FDA 510(k) clearance letter:

Acceptance Criteria and Device Performance for Cartesion Prime PET-CT System (K251370)

The submission describes two primary feature enhancements: AiCE-i for PET (AiCE2) and Deviceless PET Respiratory gating system (DRG2).

1. Table of Acceptance Criteria and Reported Device Performance

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

For AiCE-i for PET (AiCE2) - Clinical Images:

• Sample Size: 10 PET DICOM clinical 18F-FDG whole body cases.
• Data Provenance: Not explicitly stated, but the submission notes "selected to cover characteristics common to the intended U.S. patient population." The training data for AiCE2 is mentioned to have over half acquired from the U.S.

For Deviceless PET Respiratory Gating (DRG2) - Clinical Images:

• Sample Size: 10 patients.
• Data Provenance: Not explicitly stated, but the submission notes "selected to cover characteristics common to the intended U.S. patient population." The training data for DRG2 was acquired entirely from the U.S.

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

For AiCE-i for PET (AiCE2) - Clinical Images:

• Number of Experts: Three (3) physicians.
• Qualifications: At least 20 years of experience in nuclear medicine.

For Deviceless PET Respiratory Gating (DRG2) - Clinical Images:

• Number of Experts: Three (3) physicians.
• Qualifications: At least 20 years of experience in nuclear medicine.

4. Adjudication Method for the Test Set

The adjudication method is not explicitly stated as 2+1, 3+1, or none. However, for both clinical image evaluations, it states that "All three physicians reported/determined that..." This implies a consensus-based adjudication among the three experts was used to reach the conclusions. It does not indicate individual disagreements were arbitrated by a fourth reader.

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

A formal MRMC comparative effectiveness study, designed to quantify the effect size of human readers improving with AI assistance, was not explicitly described in the provided text. The clinical image evaluations involved expert review and comparison, but the focus was on the algorithm's performance and image quality, not a direct measurement of human reader improvement with vs. without AI assistance.

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

Yes, standalone performance was extensively evaluated for both features:

• AiCE-i for PET:
  • Bench tests for pediatric use (CRC, BGV, CNR, artifacts, SUVmean equivalence).
  • Bench tests for image intensity (SUV max/mean accuracy, tumor volume improvement).
  • Phantom testing (NEMA NU-2, Adult and Pediatric NEMA phantoms, Small Pool phantom) comparing AiCE2 to AiCE1 and conventional methods across quantitative metrics (SUVmean, BGV, CRC, SNR) and for artifact absence.
• Deviceless PET Respiratory Gating:
  • Bench tests for AI operational mode (equivalence to external device gating, improvements in SUV max/mean, tumor volume).
  • Evaluation against predicate DRG1 using reconstructed clinical raw data and quantified outputs.

7. The Type of Ground Truth Used

• For AiCE-i for PET (AiCE2):
  • Phantom Studies: Objective, physics-based ground truth (e.g., known sphere sizes, activity concentrations) for quantitative metrics like SUV, CRC, BGV, SNR.
  • Clinical Image Evaluation: Expert consensus/opinion of three nuclear medicine physicians for subjective assessments like diagnostic quality, image sharpness, and noise levels.
• For Deviceless PET Respiratory Gating (DRG2):
  • Bench Tests/Comparison to DRG1: Quantitative measurements of SUV (max and mean) and tumor volume from reconstructed data, likely compared against a known or established ground truth from reference reconstructions.
  • Clinical Image Evaluation: Expert consensus/opinion of three nuclear medicine physicians for subjective assessments related to diagnostic quality and motion correction effectiveness.

8. The Sample Size for the Training Set

• For AiCE-i for PET (AiCE2): Subset assembled from FDG studies of sixteen (16) cancer patients.
• For Deviceless PET Respiratory Gating (DRG2): FDG studies of 27 cancer patients.

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

The text indicates that both AI algorithms (AiCE2 and DRG2) use deep learning artificial neural network methods. The ground truth for training these networks is implicitly derived from the input PET data itself, with the algorithms learning statistical properties of signal and noise or motion patterns.

• For AiCE-i for PET: The algorithm was "trained to automatically adapt to different noise levels to produce consistently high-quality images." This suggests the training data contained examples of both "noisy" input and perhaps "ideal" or "denoised" outputs (or features that guided the network to achieve denoised outputs with improved image quality), where the "ground truth" was likely the desired image characteristics or underlying signal.
• For Deviceless PET Respiratory Gating: The neural network was "trained on FDG studies... to extract motion information from acquired PET data and to generate a corresponding gating signal." This implies the "ground truth" for training involved identifying and characterizing respiratory motion within the raw PET data, possibly using external motion tracking data if available during training, or highly curated datasets where experts delineated motion patterns. The text does not explicitly state how this ground truth was established, only that it was trained on these patient studies.

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CT VScore+ is a software application intended for non-invasive evaluation of calcified lesions of the coronary arteries based on ECG-gated, non-contrast cardiac CT images for patients aged 30 years or older. The device automatically generates calcium scores for the coronary arteries (combined LM+LAD, RCA, LCX) and highlights the segmented calcium on the original CT image. The device also offers the option for the user to display the calcium scores in the context of reference data from the MESA and Hoff-Kondos databases.

The segmented arteries include combined LM+LAD, RCA, and LCX. To obtain separate LM and LAD results, the user must perform manual segmentation. The segmentation map of calcifications is intended for informational use only and is not intended for detection or diagnostic purposes. The 3D Calcium View output is provided strictly as an informational and supplementary output and should never be used alone as the method of reviewing the calcium segmentation.

CT VScore+ is a software application intended for non-invasive evaluation of calcified lesions of the coronary arteries based on ECG-gated, non-contrast cardiac CT images for patients aged 30 years or older. The application runs on the Vitrea platform.

The device automatically generates Agatston and volume calcium scores for each of the coronary arteries (combined LM+LAD, RCA, LCX) based on the volume and density of the calcium deposits and highlights the Segmented calcium on the original CT image. The device also offers the option for the user to display the calcium scores in the context of reference data from the MESA and Hoff-Kondos databases.

The software uses deep learning-based segmentation methods. Users can edit the automated segmentation, including manually assigning calcifications to anatomical structures.

The device automatically outputs a combined LM+LAD score as the final automated output. To obtain separate LM and LAD results, the user must perform manual segmentation using the provided editing tools.

The device is Software as a Medical Device (SaMD) that operates on ECG-gated, non-contrast cardiac CT DICOM images.

The device does not interact directly with the patient. The device is a software application that runs on the Vitrea platform and processes ECG-gated non-contrast cardiac CT DICOM images. The device automatically generates Agatston and volume calcium scores for each of the coronary arteries (LAD+LM, RCA, LCX) based on the volume and density of the calcium deposits and highlights the segmented calcium on the original CT image. Results can be exported to image management, archival, or reporting systems that support DICOM standards for further review and interpretation.

Results can also be saved in DICOM Structured Reports (DICOM SR) format.

The CT VScore+ device is a software application for non-invasive evaluation of calcified lesions of the coronary arteries from ECG-gated, non-contrast cardiac CT images. The study presented demonstrates the analytical validity and performance of the device against predefined acceptance criteria.

1. Table of Acceptance Criteria and Reported Device Performance

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

• Sample Size (Test Set): 236 independent cases.
• Data Provenance: The pivotal validation dataset was sourced from diverse U.S. sites and scanner vendors. The development dataset, from which the test set was independent, included data from four institutions (two US sites and two Japanese sites). The 236 cases for validation were "independent" at both the patient level and the site level from the development dataset. It is retrospective data.

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

• Number of Experts: Three.
• Qualifications of Experts: U.S. board-certified radiologists/cardiologists. (Specific years of experience are not mentioned).

4. Adjudication Method for the Test Set

• Adjudication Method: A "2+1 consensus process" was used. This typically means that if two experts agree, their consensus defines the ground truth. If there's a disagreement between two, the third expert acts as a tie-breaker or adjudicator.

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

• The provided document does not mention a multi-reader multi-case (MRMC) comparative effectiveness study to assess the effect size of human readers improving with AI vs. without AI assistance. The study focuses on the standalone performance of the AI algorithm against a consensus ground truth.

6. Standalone Performance Study (Algorithm Only)

• Yes, a standalone performance study was conducted. The metrics listed in the table (ICC, Kappa, Dice Score) directly assess the performance of the CT VScore+ algorithm in isolation against the established ground truth.

7. Type of Ground Truth Used

• Type of Ground Truth: Expert consensus. Specifically, the reference standard ground truth was established by consensus manual scoring on an FDA-cleared device (Vitrea CT VScore, K243240) and a 2+1 consensus process by three U.S. board-certified radiologists/cardiologists.

8. Sample Size for the Training Set

• Sample Size (Training Set): 94 cases (part of the 210 cases used for development).

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

• The document implies that the ground truth for the training set (part of the development dataset) was established similarly to the validation set's ground truth, i.e., "by consensus manual scoring on an FDA-cleared device (Vitrea CT VScore, K243240)" by experts, given that the development process involved ensuring "robust and unbiased performance." However, the exact details of ground truth establishment specifically for the training set are not explicitly broken out as they are for the pivotal validation dataset. It's reasonable to infer a similar rigorous process if the data was used for deep learning model development.

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RCT700 is CBCT and panoramic x-ray imaging system with cephalometric. Which is intended to radiographic examination of the dento-maxillofacial, sinus, TMJ, Airway and ENT structure for diagnostic support for adult and pediatric patients. And a model scan is included as an option. Cephalometric image also includes wrist to obtain carpus images for growth and maturity assessment for orthodontic treatment.

The device is to be operated and used by dentists or other legally qualified heath care professionals

RCT700 provides 3D computed tomography for scanning hard tissues such as bone and teeth. By rotating the C-arm, which houses a high-voltage generator, an X-ray tube and a detector on each end, CBCT images of dental maxillofacial structures are obtained by recombining data scanned from the same level at different angles. Functionalities include panoramic image scanning for obtaining images of whole teeth, and a cephalometric option for obtaining cephalometric images.
The software of RCT700 saves the patient and image data and offers an inquiry function, in addition, supports the image generate function intended to obtain images using the RCT700 equipment and various sensors for diagnosis.

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The PennPET Explorer PET system is a diagnostic imaging device that, together with the co-located IQon CT scanner, combines Positron Emission Tomography (PET) and X-ray Computed Tomography (CT) systems. The IQon CT system images anatomical cross-sections by computer reconstruction of X-ray transmission data. The PET system images the distribution of PET anatomy-specific radiopharmaceuticals in the patient. Together, these systems are used for the purposes of detecting, localizing, diagnosing, staging, re-staging, and follow-up for monitoring therapy response of various diseases in oncology, cardiology, and neurology.

The system is intended to image the whole body, heart, brain, lung, gastrointestinal, bone, lymphatic, and other major organs for a wide range of patient types, sizes, and extent of diseases. The CT scanner can also be operated as fully functional, independent diagnostic tool, including for use in radiation therapy planning.

The PennPET Explorer is based on the PET technology of its predicate device, the Philips Vereos PET/CT scanner, but follows the model of its reference device, the previous Philips Gemini TF PET/CT by having co-located—yet separated—PET and CT scanners served by a common patient table. The PennPET Explorer uses a newly designed 142 cm axial field-of-view (AFOV) PET gantry and is intended to be used with a co-located Philips IQon multi-energy CT and patient table.

The PennPET Explorer PET gantry is a modular system comprising six PET detector rings stacked axially, yielding a 142 cm axial FOV. This allows imaging of the human head, torso, and upper legs in a single frame without moving the patient. The entire imaging chain of components from the detectors to the data acquisition computers is supplied by Philips and consists of components that are used in the Vereos PET scanner. The mechanical structure and data processing software have been modified and developed to handle the additional data from all six PET rings simultaneously.

Each of the six detector rings is substantially equivalent to a Philips Vereos PET scanner.

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The product is intended to produce X-ray Cone-Beam Computed Tomography, Panoramic tomography and Cephalometric (optional) images. The medical institutions can use the images for diagnostic purposes in oral and maxillofacial regions. The product is intended for use in hospitals and clinics, operated and used by trained professionals under the guidance of a physician.

The device is used for X-ray image diagnosis of oral and maxillofacial region in medical institutions through X-ray cone-beam computed tomography, panoramic and cephalometric photography. The device is intended for use in hospitals and clinics, operated and used by trained professionals under the guidance of a physician.

This device is divided into two models: Matrix 7000(Rubik X1), Matrix 7800(Rubik X3).

The device is composed of X-ray tube head, plate detector, control device, positioning aid, frame, cephalometric positioning shooting aid, workstation software, etc.

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syngo.CT Dual Energy is designed to operate with CT images based on two different X-ray spectra.

The various materials of an anatomical region of interest have different attenuation coefficients, which depend on the used energy. These differences provide information on the chemical composition of the scanned body materials. syngo.CT Dual Energy combines images acquired with low and high energy spectra to visualize this information. Depending on the region of interest, contrast agents may be used.

The general functionality of the syngo.CT Dual Energy application is as follows:

• Bone Marrow ²⁾
• Bone Removal ¹⁾
• Brain Hemorrhage ¹⁾
• Gout Evaluation ¹⁾
• Hard Plaques ¹⁾
• Heart PBV
• Kidney Stones ¹⁾ ²⁾ ³⁾
• Liver VNC ¹⁾
• Lung Mono ¹⁾
• Lung Perfusion ¹⁾
• Lung Vessels ¹⁾
• Monoenergetic ¹⁾ ²⁾
• Monoenergetic Plus ¹⁾ ²⁾
• Optimum Contrast ¹⁾ ²⁾
• Rho/Z ¹⁾ ²⁾
• SPP (Spectral Post-Processing Format) ¹⁾ ²⁾
• SPR (Stopping Power Ratio) ¹⁾ ²⁾
• Virtual Non-Calcium (VNCa) ¹⁾ ²⁾
• Virtual Unenhanced ¹⁾

The availability of each feature depends on the Dual Energy scan mode.

¹⁾ This functionality supports data from Siemens Healthineers Photon-Counting CT scanners acquired in QuantumPlus modes.

²⁾ This functionality supports data from Siemens Healthineers Photon-Counting CT scanners acquired in QuantumPeak modes.

³⁾ Kidney Stones is designed to support the visualization of the chemical composition of kidney stones and especially the differentiation between uric acid and non-uric acid stones. For full identification of the kidney stone, additional clinical information should be considered such as patient history and urine testing. Only a well-trained radiologist can make the final diagnosis upon consideration of all available information. The accuracy of identification is decreased in obese patients.

Dual energy offers functions for qualitative and quantitative post-processing evaluations. syngo.CT Dual Energy is a post-processing application consisting of several post-processing application classes that can be used to improve the visualization of the chemical composition of various energy dependent materials in the human body when compared to single energy CT. Depending on the organ of interest, the user can select and modify different application classes or parameters and algorithms.

Different body regions require specific tools that allow the correct evaluation of data sets. syngo.CT Dual Energy provides a range of application classes that meet the requirements of each evaluation type. The different application classes for the subject device can be combined into one workflow.

The product is intended to be used for at least 21-year-old humans.

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