Search Results

Hybrid3D that provides software applications used to process, display, and manage nuclear medicine and other medical imaging data transferred from other workstation or acquisition stations.

HERMES Hybrid3D is a reading and processing module for the advanced needs in medical imaging. It offers multi-modal (PET/CT/MR/SPECT) coregistration and interactive fusion of multiple datasets. HybridViewer 3D handles viewing and fusion of multi-sequence MRI studies with oblique orientation and allows switching between original and standard TCS view orientation as well as defining own slice directions. 3D segmentation, cropping and interpolation techniques allow complex tasks in VOI definition and can cover cases like cavities, splitting structures into subsections or logic operations (compute intersections, merge, grow). Results can be imported and exported as DICOM and are therefore available for research in 3rd party tools. Additionally, it provides tools for advanced 3D fusion rendering of studies and VOIs.

The Lung Lobe Quantification module in Hybrid 3D, introduces an efficient and automated workflow solution to accurately compute 3D lobar anatomy from CT (with or without contrast). The workflow supports the addition of functional images (SPECT V/Q, SUV SPECT, CT iodine maps, hyperpolarized xenon MRI, etc.) to accurately relate lobar anatomy to function.

Here's a breakdown of the acceptance criteria and study information for the Hybrid3D device, extracted from the provided text:

Acceptance Criteria and Device Performance

The provided document describes comparisons between the new device (Hybrid3D v2.0) and its predicate devices (HERMES Medical Imaging Suite v5.6 and Hybrid3D v1.0). The acceptance criteria are implicitly defined by the "good results" or numerical thresholds reported in the comparative testing.

Study Information

The document describes verification and validation testing, focusing on comparisons with predicate devices.

1. Sample sizes used for the test set and the data provenance:

   • Phantom studies: Used for linear measurements and Hounsfield unit estimations. The number of phantom studies is not specified, but it states "phantom studies acquired with cameras from two different vendors."
   • Patient studies: Used for quantitative parameters (SUV max, mean, peak) and automatic registration. The number of patient studies is not specified, but it mentions "patient studies acquired with cameras from two different vendors" for SUV calculations and "serial PT/CT patient studies, a PT study and external MR study, and a SPECT study and external CT study" for automatic registration testing.
   • Data Provenance: Not explicitly stated, but the company is based in Stockholm, Sweden, and the description of "cameras from two different vendors" and various types of patient studies suggests a diverse dataset. It is implied to be retrospective as it's for verification against existing data.

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

   • This information is not provided in the document. The comparisons are made against results generated by the predicate devices, not interpreted by independent human experts. The document does mention "operator variation" as a possible reason for differences in SUV peak and mean values, implying human involvement in drawing VOIs on both the new and predicate applications. However, these operators are not explicitly designated as "experts" for ground truth establishment.

3. Adjudication method for the test set:

   • This information is not provided. The testing appears to be quantitative comparison against predicate device outputs rather than an adjudication process involving human reviewers for discrepancies.

4. 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 multi-reader multi-case (MRMC) comparative effectiveness study was not done or reported in this document. The device, Hybrid3D, is a software application for processing, displaying, and managing medical imaging data, including automated workflows like lung lobe quantification, but the testing focuses on its performance relative to predicate devices, not on human reader improvement with or without AI assistance.

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

   • The testing described is essentially standalone in the sense that the device's calculations and outputs (linear measurements, Hounsfield units, SUV values, registration, image labeling, RT structure sets) are compared directly with those of the predicate device. While human operators are involved in setting up the comparisons (e.g., drawing VOIs), the evaluation is of the software's output itself. The "Lung Lobe Quantification module" mentioned in the description implies an automated (standalone) workflow component, but dedicated standalone performance of this specific module isn't detailed in the testing summary beyond its general functionality.

6. The type of ground truth used:

   • The ground truth is based on comparison with predicate devices' performance and outputs. For quantitative measures (linear, HU, SUV), the "ground truth" is implied to be the values generated by the predicate device (HERMES Medical Imaging Suite v5.6 and Hybrid3D v1.0). For automatic registration and image labeling, the "ground truth" is equivalence to the predicate's behavior.

7. The sample size for the training set:

   • This information is not provided. The document describes the device and its validation but does not mention any machine learning components that would require a distinct training set. The "Lung Lobe Quantification module" uses an "efficient and automated workflow solution to accurately compute 3D lobar anatomy from CT," which might involve machine learning, but there is no specific mention of a training set or its size.

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

   • This information is not provided, as no training set is explicitly mentioned or detailed in the document.

Ask a specific question about this device