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ECG-less Cardiac streamlines patient preparation by enabling an alternative acquisition of cardiac CT images for general cardiac assessment without the need of a patient-attached ECG monitor. ECG-less Cardiac is for adults only.

ECG-less Cardiac is a software device that is an additional, optional cardiac scan mode that can be used on the Revolution Apex Elite, Revolution Apex, and Revolution CT with Apex edition systems. There is no change to the predicate device hardware to support the subject device. Currently, the available cardiac scan modes on the Revolution CT Family are Cardiac Axial and Cardiac Helical, which makes use of an ECG signal to physiologically trigger the cardiac acquisitions and/or to retrospectively gate the reconstruction. ECG-less Cardiac is a third cardiac scan mode that introduces the ability to acquire cardiac images without the need of a patient-attached ECG monitor. Hence, an ECG signal from the patient is not utilized for this scan mode. The ECG-less Cardiac workflow leverages the full-heart coverage capability of 160 mm configurations, fast gantry speeds (0.28 and 0.23 s/rot), and existing cardiac software options of SmartPhase and SnapShot Freeze 2 (K183161) to acquire images that are suitable for coronary and cardiac functional assessment. The ECG-less cardiac feature allows the user to acquire a cardiac CT scan without the need to complete the steps associated with utilizing an ECG monitor, such as attaching ECG electrodes to the patient, checking electrode impedance, and confirming an ECG trace is displayed on the operator console, thus optimizing the workflow. ECG-less Cardiac may be best utilized in examinations where excluding the ECG connection would streamline the patient examination, including and unloading of the patient. This may result in an improved workflow for certain clinical presentations. ECG-less Cardiac may also increase access to cardiac assessment for patients that are difficult to receive an ECG signal from. Circumstances where the subject device is expected to increase cardiac access includes scenarios where trauma patient has a diagnostic ECG attached and/or other instrumentation, such that there is added difficulty of attaching ECG leads for a gated scan, and situations where it is challenging to get an ECG signal from a patient such as a patient's t-wave triggering the scan or R-peak being difficult to detect.

The Deep Learning Image Reconstruction option is a deep learning based reconstruction method intended to produce cross-sectional images of the head and whole body by computer reconstruction of X-ray transmission data taken at different angles and planes, including Axial, Helical (Volumetric), and Cardiac acquisitions, for all ages. Deep Learning Image Reconstruction can be used for head, whole body, cardiac, and vascular CT applications.

Deep Learning Image Reconstruction is an image reconstruction method that uses a dedicated Deep Neural Network (DNN) that has been designed and trained specifically to generate CT Images to give an image appearance, as shown on axial NPS plots, similar to traditional FBP images while maintaining the performance of ASiR-V in the following areas: image noise (pixel standard deviation), low contrast detectability, high-contrast spatial resolution, and streak artifact suppression. The images produced are branded as "TrueFidelity™ CT Images". Reconstruction times with Deep Learning Image Reconstruction support a normal throughput for routine CT. Deep Learning Image Reconstruction was trained specifically on the Revolution CT/Apex platform (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 Deep Learning Image Recon: Low, Medium or High. The strength selection will vary with individual users' preferences and experience for the specific clinical need. The DLR algorithm is now being modified on the Revolution family CT systems (K133705, K163213, K19177) for improved reconstruction speed and image quality, thus triggering this premarket notification.

The GE Discovery MI Gen2 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. Discovery MI Gen2 is intended to image the whole body, head, heart, 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's disease - Non-Hodgkin's lymphoma - Colorectal Cancer - Melanoma Discovery MI Gen2 is also intended for stand-alone, diagnostic CT imaging in accordance with the stand-alone CT system's cleared indications for use.

GE's Discovery MI (DMI) Gen2, same as the unmodified predicate device, is a hybrid digital PET/CT diagnostic imaging system combining a GE Positron Emission Tomography (PET) System and a GE Computed Tomography (CT) System. The DMI Gen2 is intended for CT attenuation corrected, anatomically localized PET imaging of the distribution of positron-emitting radiopharmaceuticals. lt is intended to image the whole body, head, heart, brain, lung, breast, bone, the gastrointestinal and lymphatic systems, and other organs. The system is also intended for stand-alone, diagnostic CT imaging. GE has modified the cleared Discovery MI (K161574) within our design controls to include a 6ring configuration that provides 30 cm Axial Field of View (AFOV) coverage. DMI Gen2 employs the same detector design architecture and manufacturing process as in the predicate to offer scalable ring configurations (3-ring, 4-ring, 5-ring and 6-ring) to have scalable AFOV coverage (15cm, 20cm, 25cm and 30cm) and corresponding imaging performances.

The deep learning based Max Field-of-View (MaxFOV 2) is a CT image reconstruction method intended to produce images of the head and whole-body using Axial, Helical, and Cine acquisitions. MaxFOV 2 is designed to extend the nominal display field of view (DFoV) for cases where patient size and positioning requirements result in a portion of the patient's body to be outside of the nominal DFoV. These extended FoV images are intended for use in radiation therapy planning and are clinically useful for the simulation and planning of radiation therapy for the treatment of cancer for patients. They can also be used for visualization of patient anatomy for cases not involving therapy planning. MaxFOV 2 is intended for patients of all ages, especially bariatric patients.

The MaxFOV 2 is an enhanced Extended Field of View (EFOV) reconstruction option for GE's CT scanners. The MaxFOV 2 utilizes a new deep learning algorithm to extend the display field of view (DFOV) beyond the CT system's scan field of View (SFOV) of 50cm to up to 80cm depending on the bore size of the CT system. CT scanners use the EFOV reconstruction algorithms to visualize tissue truncated due to large patient habitus and/or off-center patient positioning. Same as the Wide View option on the predicate, the MaxFOV 2 is designed to enable a clinically useful visualization of the skin line and CT Number of human body parts located outside of the SFOV. EFOV images are especially useful for radiation therapy planning and they can also be used for visualization of patient anatomy outside of the SFOV for routine CT imaging. This DL enabled new MaxFOV2 EFOV reconstruction process offers improved performance over the existing WideView option on the predicate device. The DL MaxFOV2 algorithm was designed and tested for GE's multiple CT scanner platforms of various bore sizes from 70cm to 80cm. These CT systems with the integrated MaxFOV 2 option remain compliant with the same standards as base CT systems. This option is commercially marketed as MaxFOV2.

Critical Care Suite is a computer aided triage and notification device that analyzes frontal chest x-ray images for the presence of prespecified critical findings (pneumothorax). Critical Care Suite identifies images with critical findings to enable case prioritization or triage in the PACS/workstation. Critical Care Suite is intended for notification only and does not provide diagnostic information beyond the notification. Critical Care Suite should not be used in-lieu of full patient evaluation or solely relied upon to make or confirm a diagnosis. It is not intended to replace the review of the x-ray image by a qualified physician. Critical Care Suite is indicated for adult-size patients.

Critical Care Suite is a software module that employs Al-based image analysis algorithms to identify pre-specified critical findings (pneumothorax) in frontal chest X-ray images and flag the images in the PACS/workstation to enable prioritized review by the radiologist. Critical Care Suite employs a sequence of vendor and system agnostic AI algorithms to ensure that the input images are suitable for the pneumothorax detection algorithm and to detect the presence of pneumothorax in frontal chest X-rays: - The Quality Care Suite algorithms conduct an automated check to confirm that the input image is compatible with the pneumothorax detection algorithm and that the lung field coverage is adequate; - the PTX Classifier determines whether a pneumothorax is present in the image. If a pneumothorax is detected, Critical Care Suite enables case prioritization or triage through direct communication of the Critical Care Suite notification during image transfer to the PACS. It can also produce a Secondary Capture DICOM Image that presents the Al results to the radiologist. When deployed on a Digital Projection Radiographic Systems such as Optima XR240amx, Critical Care Suite is automatically run after image acquisition. Quality Care Suite algorithms produce an on-device notification if the lung field has atypical positioning to give the technologist the opportunity to make correction before sending the image to the PACS. The Critical Care Suite output is then sent directly to PACS upon exam closure where images with a suspicious finding are flagged for prioritized review by the Radiologist. In parallel, an on-device, technologist notification is generated 15 minutes after exam closure, indicating which cases were prioritized by Critical Care Suite in PACS. The technologist notification is contextual and does not provide any diagnostic information. The on-device, technologist notification is not intended to inform any clinical decision, prioritization, or action. The Digital Projection Radiographic System intended use remains unchanged in that the system is used for general purpose diagnostic radiographic imaging.

The Deep Learning Image Reconstruction option is a deep learning based reconstruction method intended to produce cross-sectional images of the head and whole body by computer reconstruction of X-ray transmission data taken at different angles and planes, including Axial, Helical (Volumetric), and Cardiac acquisitions, for all ages. Deep Learning Image Reconstruction can be used for head, whole body, cardiac, and vascular CT applications.

Deep Learning Image Reconstruction is an image reconstruction method that uses a dedicated Deep Neural Network (DNN) that has been designed and trained specifically to generate CT lmages to give an image appearance, as shown on axial NPS plots, similar to traditional FBP images while maintaining the performance of ASiR-V in the following areas: image noise (pixel standard deviation), low contrast detectability, high-contrast spatial resolution, and streak artifact suppression. The images produced are branded as "TrueFidelity™ CT Images". Reconstruction times with Deep Learning Image Reconstruction support a normal throughput for routine CT. Deep Learning Image Reconstruction was trained specifically on the Revolution CT family of systems (K163213, K133705). 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 Deep Learning Image Recon: Low, Medium or High. The strength selection will vary with individual users' preferences and experience for the specific clinical need.

SnapShot Freeze 2 is designed for use with gated cardiac acquisitions to reduce cardiac induced motion artifacts.

SnapShot Freeze 2 (SSF 2) is a post processing software, which can be delivered on general purpose computing platforms. SnapShot Freeze 2 is an automated motion correction algorithm designed for use with gated cardiac acquisitions from GE CT scanners to reduce cardiac induced motion artifacts in the whole heart. It is based on the same fundamental algorithm as the predicate product commercially marketed under the name CardIQ Xpress 2.0 with SnapShot Freeze Option (K120910, AKA SSF 1). Same as its predicate device the SSF 2 algorithm works on multi-phase, gated cardiac CT DICOM compatible image data and produces a new image series in which motion artifact is reduced.

GenIQ is an automated post-processing software option that is indicated for use on dynamic magnetic resonance imaging data sets to generate parametric image intensity variations over time. This dynamic change in signal intensity is used to calculate functional parameters related to tissue flow and leakage of the contrast agent from the intravascular to the extracellular space. GenIQ provides information that when interpreted by a trained physician, can be useful for assessing tissue vascular properties.

GenIQ is a software application used for the pharmacokinetic analysis of Dynamic Contrast Enhanced (DCE) MRI data sets. The application is used to perform a General Kinetic Model (GKM)-based pharmacokinetic modeling of DCE-MRI data. The goal of GenIQ is to extract functional parameters describing tissue vascular properties such as forward and backward transfer constants, plasma volume, and volume of extra-cellular space.