GIQuant is a post-processing software integrated into existing medical imaging workflows that is intended to derive motion related parameters from abdominal data obtained during magnetic resonance imaging (MRI). GIQuant is designed to aid trained physicians in advanced image assessment, treatment consideration, and monitoring of therapeutic response. The information provided by GIQuant should not be used in isolation when making patient management decisions.
Device Story
GIQuant is a standalone software application for medical imaging post-processing; runs on standard computer hardware. Inputs: DICOM-formatted abdominal MRI data (dynamic 'cine' T1/T2 weighted images). Processing: Performs non-rigid registration to align image features in time-series data; computes motility parameter maps derived from image registration deformation fields (SD Jacobian). Outputs: Parametric maps in DICOM format for export to PACS or OS file storage. Used in clinical settings by trained physicians (e.g., radiologists) to assist in image assessment and therapeutic monitoring. Output provides quantitative movement metrics within regions of interest, aiding clinical decision-making. Benefits: Provides objective motion-related parameters to support patient management.
Clinical Evidence
No clinical data. Evidence consists of software verification and validation testing per FDA guidance (May 2005) and technical performance assessment. Technical testing utilized synthetically manipulated 'ground truth' datasets to validate algorithm performance, measured target registration error on fixed anatomical points, and established reproducibility using test-retest image datasets. Software developed per IEC 62304:2006, ISO 14971:2019, and NEMA PS 3.1-3.20 (2016).
Technological Characteristics
Standalone software; runs on off-the-shelf computer hardware. Processes DICOM-compliant MR images. Core technology: Non-rigid registration of time-series data; parameterization via SD Jacobian of deformation fields. Connectivity: DICOM (PS 3.10) compliant; exports to PACS. Software development standards: IEC 62304, ISO 14971, NEMA PS 3.1-3.20.
Indications for Use
Indicated for use by trained physicians to derive motion-related parameters from abdominal MRI data to aid in advanced image assessment, treatment consideration, and monitoring of therapeutic response.
Regulatory Classification
Identification
A medical image management and processing system is a device that provides one or more capabilities relating to the review and digital processing of medical images for the purposes of interpretation by a trained practitioner of disease detection, diagnosis, or patient management. The software components may provide advanced or complex image processing functions for image manipulation, enhancement, or quantification that are intended for use in the interpretation and analysis of medical images. Advanced image manipulation functions may include image segmentation, multimodality image registration, or 3D visualization. Complex quantitative functions may include semi-automated measurements or time-series measurements.
Special Controls
*Classification.* Class II (special controls; voluntary standards—Digital Imaging and Communications in Medicine (DICOM) Std., Joint Photographic Experts Group (JPEG) Std., Society of Motion Picture and Television Engineers (SMPTE) Test Pattern).
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Motilent Ltd. % Ian Knott Head of Regulatory and Quality IDEALondon, 69 Wilson Street London, EC2A 2BB UNITED KINGDOM
November 8, 2021
Re: K211356
Trade/Device Name: GIQuant Regulation Number: 21 CFR 892.2050 Regulation Name: Medical image management and processing system Regulatory Class: Class II Product Code: LLZ Dated: September 7, 2021 Received: October 7, 2021
Dear Ian Knott:
We have reviewed your Section 510(k) premarket notification of intent to market the device referenced above and have determined the device is substantially equivalent (for the indications for use stated in the enclosure) to legally marketed predicate devices marketed in interstate commerce prior to May 28, 1976, the enactment date of the Medical Device Amendments, or to devices that have been reclassified in accordance with the provisions of the Federal Food, Drug, and Cosmetic Act (Act) that do not require approval of a premarket approval application (PMA). You may, therefore, market the device, subject to the general controls provisions of the Act. Although this letter refers to your product as a device, please be aware that some cleared products may instead be combination products. The 510(k) Premarket Notification Database located at https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpmn/pmn.cfm identifies combination product submissions. The general controls provisions of the Act include requirements for annual registration, listing of devices, good manufacturing practice, labeling, and prohibitions against misbranding and adulteration. Please note: CDRH does not evaluate information related to contract liability warranties. We remind you, however, that device labeling must be truthful and not misleading.
If your device is classified (see above) into either class II (Special Controls) or class III (PMA), it may be subject to additional controls. Existing major regulations affecting your device can be found in the Code of Federal Regulations, Title 21, Parts 800 to 898. In addition, FDA may publish further announcements concerning your device in the Federal Register.
Please be advised that FDA's issuance of a substantial equivalence determination does not mean that FDA has made a determination that your device complies with other requirements of the Act or any Federal statutes and regulations administered by other Federal agencies. You must comply with all the Act's requirements, including, but not limited to: registration and listing (21 CFR Part 807); labeling (21 CFR Part 801); medical device reporting of medical device-related adverse events) (21 CFR 803) for
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devices or postmarketing safety reporting (21 CFR 4, Subpart B) for combination products (see https://www.fda.gov/combination-products/guidance-regulatory-information/postmarketing-safety-reportingcombination-products); good manufacturing practice requirements as set forth in the quality systems (QS) regulation (21 CFR Part 820) for devices or current good manufacturing practices (21 CFR 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR 1000-1050.
Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR Part 807.97). For questions regarding the reporting of adverse events under the MDR regulation (21 CFR Part 803), please go to https://www.fda.gov/medical-device-safety/medical-device-reportingmdr-how-report-medical-device-problems.
For comprehensive regulatory information about medical devices and radiation-emitting products, including information about labeling regulations, please see Device Advice (https://www.fda.gov/medicaldevices/device-advice-comprehensive-regulatory-assistance) and CDRH Learn (https://www.fda.gov/training-and-continuing-education/cdrh-learn). Additionally, you may contact the Division of Industry and Consumer Education (DICE) to ask a question about a specific regulatory topic. See the DICE website (https://www.fda.gov/medical-device-advice-comprehensive-regulatoryassistance/contact-us-division-industry-and-consumer-education-dice) for more information or contact DICE by email (DICE@fda.hhs.gov) or phone (1-800-638-2041 or 301-796-7100).
Sincerely,
For
Thalia T. Mills, Ph.D. Director Division of Radiological Health OHT7: Office of In Vitro Diagnostics and Radiological Health Office of Product Evaluation and Quality Center for Devices and Radiological Health
Enclosure
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DEPARTMENT OF HEALTH AND HUMAN SERVICES Food and Drug Administration
# Indications for Use
510(k) Number (if known)
K211356
Device Name GIQuant
Indications for Use (Describe)
GIQuant is a post-processing software into existing medical imaging workflows that is intended to derive motion related parameters from abdominal data obtained during magnetic resonance imaging (MRJ).
GIQuant is designed to aid trained physicians in advanced image assessment, treatment consideration, and monitoring of therapeutic response. The information provided by GIQuant should not be used in isolation when making patient management decisions.
| Type of Use (Select one or both, as applicable) | |
|----------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------|
| <div> <span> <span style="font-size: 20px;">☑</span> Prescription Use (Part 21 CFR 801 Subpart D) </span> </div> | <div> <span>☐ Over-The-Counter Use (21 CFR 801 Subpart C)</span> </div> |
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Image /page/3/Picture/1 description: The image shows the logo for Motilent. The logo features the word "Motilent" in a sans-serif font, with the "M" stylized as a blue, wavy line. There is a blue dot on the left side of the "M" and another blue dot on the right side of the "M".
Document Title:
# 5.0 GlQuant 510k - 510k summary
This 510(k) summary of safety and effectiveness information is being submitted in accordance with the requirements of SMDA 1990.
### 1. Submitter:
- Motilent Ltd. IDEALondon, 69 Wilson Street, London, EC2A 2BB, United Kingdom
Contact Person: lan Knott Submitter, Holder and Owner Phone: +44 (0) 7817476978 E-mail: lan.Knott@motilent.co.uk
Date Summary Prepared: April 12, 2021
- 2. Device:
| Device Name: | GIQuant |
|------------------------|------------------------------------------------|
| Device Common<br>Name: | Radiological Image Processing Software |
| Regulation Number: | 21 CFR 892.2050 |
| Regulation name: | Medical image management and processing system |
| Device Classification: | Class II |
| Product code: | LLZ |
### 3. Predicate Device Information:
GIQuant is substantially equivalent to the following legally marketed devices that are currently cleared by the FDA:
| 510(k) reference | Device Name | Manufacturer | Clearance date |
|------------------|-------------------------|-------------------------|-----------------|
| K123302 | IB Clinic v1.0 (clinic) | Imaging Biometrics, LLC | 11 January 2013 |
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Image /page/4/Picture/0 description: The image shows the logo for Motilent. The logo features a stylized, light blue line that resembles a winding path or a digestive tract, with a small dot at the beginning and end. Below the graphic is the word "Motilent" in a dark blue, sans-serif font.
### 4. Device Description:
GIQuant is a standalone software medical imaging post processing application that runs on standard computer hardware. GIQuant performs numerical analysis and generates image parameter maps, based on DICOM images captured via Magnetic Resonance Imaging.
These actions include:
- . Receipt of MR DICOM image studies from DICOM storage and communication devices.
- .. Registration of images generated at different time points.
- . Comparison of registered images.
- 트 Computation of motility parameter maps based on dynamic abdominal MR imaging data.
- . Output of the above maps in DICOM format for export to PACS.
GIQuant can be deployed within its own image storage and communication infrastructure or alternatively it can be "plugged in" and launched from within other FDA cleared applications.
## 5. Indications for Use:
GIQuant is a post-processing software integrated into existing medical imaging workflows that is intended to derive motion related parameters from abdominal data obtained during magnetic resonance imaging (MRI).
GIQuant is designed to aid trained physicians in advanced image assessment, treatment consideration, and monitoring of therapeutic response. The information provided by GlQuant should not be used in isolation when making patient management decisions.
# 6. Comparison of Technological Characteristics with the predicate device
The intended use and performance characteristics for the subject device are substantially equivalent to the predicate device listed in section 3 above as both devices are intended for image analysis, image processing and generation of parametric maps to provide additional information beyond standard imaging.
| | Subject Device | Predicate device |
|--------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Device Name | GIQuant | IB Clinic v1.0 (clinic) |
| 510(k) Clearance number | N/A | K123302 |
| Common Name | Radiological Image Processing Software | Radiological Image Processing Software |
| Regulation Number: | 892.2050 | 892.2050 |
| Regulation Name | Medical image management and processing system | Medical image management and processing system |
| Product code | LLZ | LLZ |
| Indications for use: | GIQuant is a post-processing software integrated into existing medical imaging workflows that is intended to derive motion related parameters from abdominal data | IB Clinic v1.0 (Clinic) is a post-processing software toolkit designed to be integrated into existing medical image visualization applications running on standard computer hardware. Clinic accepts relevant DICOM |
| | obtained during magnetic resonance imaging (MRI).<br>GIQuant is designed to aid trained physicians in advanced image assessment, treatment consideration, and monitoring of therapeutic response. The information provided by GIQuant should not be used in isolation when making patient management decisions. | image sets, such as dynamic perfusion and diffusion image sets. Clinic generates various perfusion- and diffusion-related parameters, standardized image sets, and image intensity differences. The results are saved to a DICOM image file and may be further visualized on an imaging workstation.<br>Clinic is designed to aid trained physicians in advanced image assessment, treatment consideration, and monitoring of therapeutic response. The information provided by Clinic should not be used in isolation when making patient management decisions. |
| Standalone Software | Yes | Yes |
| Energy Sources | N/A | N/A |
| Operates on off-the-shelf hardware | Yes | Yes |
| Standard windowing user interface | yes | yes |
| Post processing | yes | yes |
| Conforms to DICOM standards (PS 3.10) | yes | yes |
| Input data - Processes MR data | Yes | Yes |
| Input data - Abdominal Imaging | Yes | Yes |
| Input data - Imaging Modality | MR | MR and CT |
| Input data - Compatible MRI systems | All approved MRI systems generating DICOM compliant images | All approved MRI systems generating DICOM compliant images |
| Input data - Imaging Acquisition Protocol* | Dynamic 'cine' MR imaging, combination of T1 and T2 weighted images | Dynamic T1 and T2 weighted images |
| Input data - Data format | DICOM | DICOM |
| Transformation Segmentation | Whole image registration | Whole image registration |
| Transformation Registration | Non-rigid registration to align image features in time series data<br>Registration of 2d image data<br>Registration applied on raw image data | Non-rigid registration to align image features in time series data<br>Registration of 2d image data<br>Registration applied on raw image data |
| Transformation Parameterization* | Derived from image registration deformation fields represented as the SD Jacobian | Derived from variations in intensity over time |
| Output data -Results generation | Parametric map | Parametric map |
| Output data -Spatial localisation | 1:1 anatomical mapping | 1:1 anatomical mapping |
| Output data -File format | DICOM | DICOM |
| Output data -Summary metrics | Summary metrics can be obtained to quantify movement within a region of interest | Summary metrics can be obtained to quantify changes in intensity over time within a region of interest |
| Output data- Networking | Exported to PACS and/or OS file storage | Exported to PACS and/or OS file storage |
| Reporting - Intended user | Result interpreted by trained user (e.g Radiologist) | Result interpreted by trained user (e.g Radiologist) |
| Reporting - Viewing | Results can be viewed on DICOM compatible PACS viewer | Results can be viewed on DICOM compatible PACS viewer |
| Reporting - Availability of data | Original data available for viewing with output | Original data available for viewing with output |
A table comparing the key features of the subject and predicate device is provided below.
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Image /page/5/Picture/0 description: The image shows the logo for Motilent. The logo features a stylized, light blue, curvy line that resembles a simplified representation of the digestive system. The word "Motilent" is written in a dark blue sans-serif font below the graphic. The logo is clean and modern.
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Image /page/6/Picture/0 description: The image features the logo for Motilent. The logo consists of a stylized, light blue graphic resembling a connected series of curves, with a small dot at the beginning and end of the curves. Below the graphic, the word "Motilent" is written in a dark blue, sans-serif font.
* As evidenced from the table above, the subject and predicate device share the same core technological functionality however minor differences exist between the two devices under review. These differences are not considered to raise different questions with relation to safety and effectiveness and have been evaluated through performance testing.
### 7. Performance Data:
Software verification and validation testing has been conducted and documentation provided as per the recommendations of FDA guidance, "Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices" (May, 2005).
The software was developed in compliance with the requirements of IEC 62304:2006, ISO 14971:2019, and NEMA PS 3.1-3.20 (2016).
### Software verification testing
Software verification testing was conducted at a unit, integration and systems level to verify the implementation of functional requirements.
### Technical Performance testing
Technical performance assessment established the performance and suitability of GlQuant's algorithm in the context of shared questions with relation to safety and effectiveness. Methods utilized in performance testing included utilizing summary metrics obtained through region of interest placement on synthetically manipulated 'ground truth' datasets as well as measuring target registration error on a fixed point of anatomy when a region of interest propagated through a dataset is corrected by an expert user. Reproducibility was established through test-retest image data sets.
### Validation Testing
Software Validation testing was conducted to confirm the implementation of functional requirements within the context of a simulated real world use environment.
The tests results demonstrate that GlQuant functioned as intended, is acceptable for clinical use, and is safe and effective as its predicate device, without introducing new questions of safety and efficacy.
### 8. Conclusion:
GIQuant shares the same intended use as the cited predicate device. Verification and validation testing performed demonstrates that GlQuant performs as intended in the specified use condition. Technical performance data shows that, where technological differences are apparent, GlQuant performs to a suitable level to address shared questions with relation to safety and effectiveness. It can therefore be concluded that GIQuant is substantially equivalent to the predicate device.
Declarations:
- This summary includes only information that is also covered in the body of the 510(k).
- This summary does not contain any puffery or unsubstantiated labelling claims.
- This summary does not contain any raw data, i.e., contains only summary data.
- This summary does not contain any trade secret or confidential commercial information.
- This summary does not contain any patient identification information.
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Part 1 — Search, results, and everyday workflows 16 min
Part 2 — Embeddings: the galaxy map 3 min
1. Search: exact and fuzzy
Type a phrase like "coronary artery calcification" into the search box. You get two kinds of results. Exact results match the literal phrase — prefix searches work ("coronary artery calcificati") but suffix searches do not. Fuzzy results match on the meaning and intent of your phrase rather than the exact words, and are sorted by relevance score. Hover over the Exact or Fuzzy badge on any row to see exactly why it matched.
Use the checkboxes above the results to narrow: SaMD keeps only software-only devices, AI / ML keeps only devices with AI.
Exact vs. fuzzy search: what's the difference?
Exact matches on the literal phrase (prefix search works, suffix does not). Fuzzy matches on the meaning and intent of the phrase rather than the exact words. Hover over the badge on any row to see why it matched.
You search "coronary artery calcification" and want only software devices with AI. What two filters do you apply?
Narrow by SaMD (software-only devices), then narrow by AI/ML (devices with AI).
2. The results table
Scroll right in the results table. The intended use is extracted for you — no need to open the PDF. The device story gives a high-level snapshot of what the device does and how it's used. The AI Performance sub-table shows each output name, acceptance criteria, observed values, and development/test dataset descriptions — the same format Innolitics uses for regulatory strategy outputs, and the fastest high-level fingerprint of an AI device. It is AI-generated but has been very reliable in practice.
Where do you find a device's intended use without opening the PDF?
Scroll right in the search results table. The intended use column is extracted for you; no need to dig into the 510(k) summary PDF.
What does the AI Performance sub-table show, and why is it useful?
Output name, acceptance criteria, observed values, development dataset description, and test dataset description. It's the same format we use for regulatory strategy output and Fast 510(k) input, and the fastest high-level fingerprint of an AI device. AI-generated but reliable in practice.
3. Judging fuzzy relevance
Fuzzy results trail off in relevance as you scroll. Use three signals to decide how far down to go: the fuzzy badge explanations, the intended use column, and whether your target output (e.g., Cobb angle) still appears in the AI Performance sub-table. Once it stops appearing, you're past the relevant zone. A top hit with a low score (~0.4) and a stretched explanation is a hint the closest predicates are far away — the project may be headed for De Novo. Note the fuzzy search is a pattern match: it doesn't handle negation ("not") well, and hardware devices can appear — filter by SaMD/AI ML to cut them.
How do you judge how far down fuzzy search results to go?
Use the relevancy signals: the fuzzy badge explanations, the intended use column, and whether the target output (e.g., Cobb angle) still appears in the AI Performance sub-table. Once it stops appearing, results are trailing off in relevancy.
4. Device detail page: chat and citations
Click a device name to open its detail page: device facts on the left, a chat window on the right. Ask something like "Describe the training data". The answer carries little citation bubbles — click one to jump to the highlighted passage in the source PDF, so you can verify every AI answer against the document. There's also a Download PDF button for sharing.
How do you verify an AI chat answer on the device detail page?
Click the citation bubbles to jump to the relevant highlight in the source document.
Reading rule for every project: how many summaries do you read in full?
At least the three most relevant 510(k) or De Novo summaries, in full. After that, use targeted chat questions to confirm your memory quickly. The tool supports this professional habit — it doesn't replace it.
5. Side-by-side comparison
Select multiple rows in the results table (aim for under ~10), then open the PDF Viewer tab. Ask one question — it goes to all selected devices in parallel, each with citations. This is the fastest way to compare and contrast devices: training data, PCCP scope, how they handled adding new scanners, and so on.
What does the side-by-side PDF viewer mode do?
Select multiple devices, open the PDF viewer tab, and ask one question (e.g., "Describe the training data"). It queries all selected devices simultaneously with citations, so you can compare and contrast quickly.
6. Collections
With rows selected, go to the Collections tab and create a labeled collection (e.g., "Cobb Angle Project"). Reload that selection any time — before a client call, pull up the collection and ask questions across all of its devices at once.
How do you save a set of selected devices for later use?
Select the rows, go to the Collections tab, and create a labeled collection (e.g., "Cobb Angle Project"). You can reload the selection anytime and carry it into the PDF viewer and other tabs that support selections.
7. Product codes and the regulations tree
Click a product code in the results to jump to it in the regulations tree — identification text, sibling product codes, and devices you can open in a PDF viewer on the right. Click a regulation number to see its identification, special controls, and related product codes. You can also search by product code or regulation number at the top of the tree. Always read the special controls if any exist for your device — it broadens your search and sharpens pre-kickoff research.
What can you do from the regulations tree view?
Browse product codes and regulation numbers, read the identification text and special controls, browse sibling product codes, open device PDFs on the right, and search by product code or regulation number at the top of the tree.
8. Chart view
Click Show Chart and segment by regulation number (or product code) to see which regulations dominate your result set. Clicking a regulation takes you into the regulations tree. Great for spotting that most matches are, say, hardware laparoscopic devices — a cue to go back and filter.
How do you see which regulations dominate a search result set?
Click "Show Chart" and segment by Regulation Number. Clicking a regulation takes you to the regulations tree.
9. The predicate graph
Open the Predicates tab for a family-tree view of predicate relationships. Click a node to trace its parents and children; selections from search carry over pre-selected. Commonly predicated devices are worth reading — a lot of people predicated them for a reason. The visual lineage is also handy on client calls, e.g. to show how a predicate family evolved and justify why your predicate still holds.
In the predicate graph, why are commonly predicated devices worth reading?
A lot of people predicated them for a reason. Clicking a node traces parents and children, and selections from search carry over pre-selected.
10. Embeddings: the galaxy map
The Embeddings tab plots every matching document in a 2-D "galaxy map" where semantically similar devices cluster together. Hover or click clusters to explore, and let AI label the clusters for you. Embeddings beat product codes for grouping: two devices can carry different product codes (LLZ vs. QIH) yet do the same thing — the embedding captures the meaning of the intended use and device story. This is also exactly how retrieval-augmented generation (RAG) works under the hood, and it makes a great visual on client calls.
Try it yourself
Head to the search page and work through a few of these AI/ML fuzzy searches to build intuition: perivascular fat on CT · aortic valve calcification opportunistic screening on noncontrast CT · breast cancer prediction on digital pathology slides · autism detection · gestational age prediction · a hearing aid that can also detect a pulse · foundation model based analysis of ECG · large language models · penetration test. Watch how the relevance scores, intended use, and AI Performance tables tell you when results stop being meaningful.