Spectral Bone Marrow

by GE Medical Systems  · Based in United States → — AI-powered deep learning for optimized bone marrow visualization in CT.
Hematology Oncology Radiology
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Regulatory Status Disclosed

Overview

Spectral Bone Marrow by GE HealthCare is an automated, deep learning-based image processing application designed to assist in the visualization of bone marrow during the evaluation of traumatic and non-traumatic bone pathologies. The software is engineered to support radiologists, emergency physicians, and orthopedic specialists in clinical and hospital settings.

The application integrates into the clinical workflow by automatically processing spectral CT datasets. It leverages deep learning algorithms to segment skeletal structures across various anatomical regions, including the neck, thorax, abdomen, pelvis, and extremities. Once segmented, the tool automatically generates and outputs color-coded material density overlays fused onto standard monochromatic or Virtual Unenhanced (VUE) grayscale images.

Notable capabilities of the application include:

  • Automated Deep Learning Segmentation: Automatically identifies and segments skeletal structures to isolate the anatomy of interest without manual intervention.
  • Spectral Color Overlay: Generates and overlays material density information directly onto grayscale images to highlight bone marrow characteristics.
  • Seamless PACS Integration: Automatically processes and routes the generated multiplanar images directly to the Picture Archiving and Communication System (PACS) for immediate clinical review.

Reviewed by Pouyan Golshani, MD — Interventional Radiologist

Key Features

  • Deep-learning based bone segmentation
  • Automated spectral color-coded information overlay on grayscale images
  • Automatic generation of fused material density images
  • Optimized visualization of bone marrow
  • Multiplanar export of fused images
  • Fully automated post-processing workflow
  • Hosted on GE's Edison Health Link (EHL) computational platform
  • Assists in review of traumatic and non-traumatic bone pathologies

Use Cases

  • Enhanced visualization of bone marrow in CT scans
  • Review of traumatic bone pathologies
  • Review of non-traumatic bone pathologies
  • Assisting physicians in medical diagnosis
  • Improving radiology workflow efficiency

What Physicians Need to Know

Key Capabilities
Automated deep learning (DL)-based segmentation and coloring of skeletal structures for bone marrow visualization. Automatically generates fused material density images of segmented bone regions over a base monochromatic image. Outputs spectral color-coded information overlayed on standard greyscale images and provides automatic axial, coronal, and sagittal fused views. Customizable profiles allow users to specify parameters for routine generation of color overlay series, which can be integrated into scan protocols.
Clinical Utility
Assists with visualization of bone marrow in traumatic and non-traumatic bone pathologies. Intended to provide clinicians with an alternative method for visualizing material density images, enhancing efficiency and improving the reading experience with clinically relevant fused views. May improve lesion detection and visualization by increasing image contrast, potentially reducing the need for additional examinations.
Integration Options
Automatically processes and sends series to prescribed DICOM destinations (PACS). The application is installed on the Edison Health Link (EHL) platform and relies on its DICOM services and capabilities. Participated in industry-wide testing programs by Integrating the Healthcare Enterprise (IHE) to facilitate interoperability.
Compliance Status
The underlying Deep Learning Image Reconstruction for Gemstone Spectral Imaging (DLIR-GSI) has received U.S. FDA 510(k) clearance. Developed under GE HealthCare's quality system, adhering to 21CFR 820 and ISO 13485 regulations. GE HealthCare holds numerous AI-enabled device authorizations from the U.S. FDA.
Pricing Model
Specific pricing model details for Spectral Bone Marrow are not publicly available. GE HealthCare generally offers AI solutions as part of its broader imaging platforms or software portfolios, with some AI solutions available to Smart Subscription customers.
User Experience
Designed to enhance clinician visualization efficiency and improve the reading experience. Offers an effortless workflow for post-processing spectral imaging, requiring no manual interaction. Customizable profiles contribute to an efficient workflow by allowing users to specify desired parameters.
Support Quality
GE HealthCare's AI tools are verified for accuracy by a team of clinical thought leaders, educators, practicing physicians, and AI experts. Customers can contact GE HealthCare Sales or Service representatives for support.
Implementation Complexity
Installed on the Edison Health Link (EHL) platform, leveraging its existing DICOM services. Requires user validation testing to ensure proper interoperability with other DICOM-compliant equipment within the healthcare facility. Integration into existing CT scanner workflows is facilitated by the ability to add profiles to scan protocols.
Evidence Base
Utilizes deep learning (DL) for segmentation and visualization. The underlying DLIR-GSI technology was supported by a clinical reader study involving 40 retrospective cases and 5 board-certified radiologists, confirming diagnostic quality and preferred noise texture. Further clinical evaluation with challenging cases also confirmed diagnostic image quality. GE HealthCare collaborates with leading technology partners and leverages a large internal database for AI algorithm training.
Physician Tip

Spectral Bone Marrow, powered by deep learning, automates bone marrow visualization and material decomposition, which can significantly streamline your workflow and potentially improve diagnostic confidence for traumatic and non-traumatic bone pathologies. Leverage the customizable profiles to integrate this tool seamlessly into your existing CT scan protocols for consistent and efficient image generation. Always perform local validation to ensure optimal interoperability with your specific PACS and DICOM environment.

This tool is designed to integrate into existing radiology workflows by automatically processing and sending images to DICOM-compliant PACS systems. Its reliance on the Edison Health Link (EHL) platform and adherence to IHE standards indicate a commitment to broader healthcare enterprise integration. Custom profiles can be embedded directly into scan protocols for a more automated post-processing workflow.

Details

Category Oncology AI, Radiology & Imaging AI
Pricing Contact for pricing
DeploymentSoftware application hosted on GE's Edison Health Link (EHL) computational platform.
Compliance
BAA AvailableUnknown AI-estimated
HIPAA CompliantUnknown AI-estimated
FDA Status 1 AI-estimated

Spectral Bone Marrow (K223514) received FDA 510(k) clearance on March 9, 2023, as an automated image processing software application utilizing deep learning technology for bone segmentation to facilitate optimized visualization of bone marrow in spectral body and extremity CT images.
Integrations
EHR Not specified
Specialties Hematology, Oncology, Radiology

What the Web Says

GE HealthCare's Spectral Bone Marrow is an automated image processing software that uses deep learning to segment bone and optimize the visualization of bone marrow in spectral CT images. It aims to improve diagnostic value for evaluating traumatic and non-traumatic bone pathologies and enhance overall reader efficiency. The software generates color-coded material density images overlaid on monochromatic CT or Virtual Unenhanced (VUE) images.

Overall: Mixed

Strengths

  • Automated deep learning-based segmentation of skeletal structures.
  • Optimized visualization of bone marrow in spectral CT images.
  • Improved diagnostic value for bone marrow evaluation.
  • Increased overall reader efficiency.
  • Automated post-processing workflow for creating images.
  • Can generate calcium-suppressed water maps to evaluate bone marrow edema, potentially benefiting patients with contraindications to MR imaging or when MR is unavailable.

Limitations

  • Limited publicly available detailed reviews from physicians, healthcare IT, or tech reviewers.
  • No information found on Reddit, G2, or Capterra.
  • Not available for sale in the US at one point (as of September 2022).
  • Requires validation with other DICOM equipment to ensure proper exchange of intended information.

Based on reviews from: GE Healthcare, accessdata.fda.gov

Last updated: 2026-07-18

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Videos

Product demos, reviews, and walkthroughs for Spectral Bone Marrow.

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Frequently Asked Questions

AI in bone marrow analysis is primarily used for automating tasks such as cell classification and counting, detecting leukemia and its subtypes, and analyzing bone marrow smears and flow cytometry data. It can also assist in quantifying cellularity and identifying subtle abnormalities in whole-slide images, aiming to improve diagnostic precision and efficiency.
Implementing AI in bone marrow diagnostics requires adherence to regulatory frameworks such as the FDA in the US, and GDPR and the EU AI Act in Europe, as these systems often qualify as medical devices. Compliance involves ensuring data governance, detailed dataset documentation, and robust cybersecurity measures to protect sensitive patient information.
Physicians must consider ethical implications such as patient privacy, potential algorithmic bias, and the need for transparency in AI decision-making. It is crucial to ensure AI models are trained on diverse, representative datasets to prevent exacerbating health disparities and to maintain human oversight to preserve professional accountability and patient safety.
The primary alternative remains traditional manual microscopic examination by experienced hematopathologists, alongside conventional methods like histomorphology, immunohistochemistry, flow cytometry, and cytogenetics. While these methods are the current gold standard, they are often time-consuming, labor-intensive, and susceptible to inter-observer variability, which AI aims to reduce.
The cost of implementing AI in bone marrow diagnostics can vary significantly, ranging from $50,000 to over $1,000,000 for comprehensive solutions, depending on complexity and customization. These expenses encompass software development, data collection and annotation, infrastructure setup, integration with existing systems, and crucial regulatory compliance efforts.
Current limitations include challenges with domain shift, where AI models may struggle to generalize across different clinical settings, and difficulties in detecting artifacts in histological samples. Other significant hurdles are the lack of large, well-annotated datasets for training and the 'black box' nature of some AI decisions, which can hinder interpretability and necessitate continued human oversight.

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Suggest an Edit → | Last Verified: 2026-04-18 | First Added: 2026-04-18
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