K212550 · Hura Imaging, Inc. · LLZ · Nov 8, 2021 · Radiology
Device Facts
Record ID
K212550
Device Name
Hura CTP v1.0
Applicant
Hura Imaging, Inc.
Product Code
LLZ · Radiology
Decision Date
Nov 8, 2021
Decision
SESE
Submission Type
Traditional
Regulation
21 CFR 892.2050
Device Class
Class 2
Attributes
Software as a Medical Device, Real-World Evidence
Real-World Evidence
Submission
Device
Sponsor
RWD Sources
RWE Use Summary
Key Tags
K212550 · Nov 8, 2021
Hura CTP v1.0
Hura Imaging, Inc.
Retrospective clinical DICOM image datasets
A retrospective clinical study was conducted to validate the device's performance by comparing noise reduction, SNR, CNR, and perfusion parameter consistency (ICC) between vendor-generated images and images processed with Hura CTP v1.0.
Retrospective clinical study; Retrospective analysis of clinical DICOM datasets
Clinical head CT Perfusion (CTP) cases; Sample Size: 40 datasets; Number of Sites: 4 sites
Vendor-generated DICOM images (without Hura CTP processing)
Noise standard deviation (SD), SNR, CNR, NRMSE of time density curves (TDCs), and ICC of perfusion parameters (CBF, CBV, TTP)
Indications for Use
Hura CTP™ v1.0 is intended as an image processing software to reduce noise of head CT Perfusion (CTP) DICOM images through multiple algorithm steps. The software reduces image noise and enhances image contrast (e.g. contrast-to-noise ratio (CNR) and signal-to-noise ratio (SNR)) of the CTP DICOM images. Hura CTP™ v1.0 is non-iterative; hence the low computational overhead enables fast processing and allows no interruption to clinical workflow. Hura CTP™ v1.0 outputs head CTP DICOM images with enhanced image quality to a designated directory defined by the user. The processed DICOM images can be imported to a third-party post-processing software for quantification of hemodynamic parameters. The use of this algorithm may enhance the image contrast of head CTP DICOM images depending on the clinical task, patient size, and clinical practice. A consultation with a radiologist and a physicist should be made to determine the appropriate imaging protocol to obtain diagnostic image quality for the clinical task. Hura CTP™ v1.0 is intended for use only by trained and qualified clinical personnel (e.g. radiologists). Hura CTP™ v1.0 is also intended to be used by trained and qualified personnel for installation and maintenance of the software.
Device Story
Hura CTP v1.0 is a software-only image processing tool for head CT Perfusion (CTP) DICOM images. It operates as a local application on standard workstations (PC, Mac, UNIX). The device utilizes a k-space weighted image average (KWIA) algorithm, adapted from 4D dynamic MRI, to perform motion compensation, Fourier transform, spatial/temporal filtering in the frequency domain, and inverse Fourier transform. It outputs noise-reduced DICOM images to a user-defined directory for import into third-party post-processing software. Used by radiologists and technicians in clinical settings, the device aims to improve image quality (SNR/CNR) without interrupting clinical workflows. It does not replace clinical judgment; it assists by providing enhanced images for hemodynamic quantification, potentially aiding diagnostic accuracy in perfusion imaging.
Clinical Evidence
Evidence includes bench testing, phantom validation, and a retrospective clinical study. Phantom studies (Philips/Toshiba scanners) showed significant SNR/CNR increases (43-62%, p<0.01) and NRMSE <5%. Retrospective clinical study (40 datasets, 4 OEMs) showed SNR increases of 48-62% and CNR increase of 50% (p<0.01) for grey/white matter. Perfusion map ICC values were excellent (≥0.88). No clinical diagnostic accuracy endpoints were primary; focus was on image quality metrics and preservation of perfusion parameter fidelity.
Technological Characteristics
Software-only; runs on Windows, Mac OS X, Unix. Uses k-space weighted image average (KWIA) algorithm for spatial/temporal frequency domain filtering. Processes DICOM-compliant images. Non-iterative processing. Leverages Insight Toolkit (ITK) library. No patient contact. Standalone local application.
Indications for Use
Indicated for trained clinical personnel (e.g., radiologists) to reduce noise and enhance contrast in head CT Perfusion (CTP) DICOM images to facilitate subsequent hemodynamic parameter quantification.
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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November 8, 2021
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Hura Imaging, Inc. % Andrew Wu Branch Manager and Software Consultant Rook Quality Systems, Inc. 1155 Mount Vernon Highway. Suite 800 DUNWOODY GA 30338
# Re: K212550
Trade/Device Name: Hura CTP v1.0 Regulation Number: 21 CFR 892.2050 Regulation Name: Medical image management and processing system Regulatory Class: Class II Product Code: LLZ Dated: July 30, 2021 Received: August 13, 2021
# Dear Andrew Wu:
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
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801); medical device reporting of medical device-related adverse events) (21 CFR 803) for 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 mediation-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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# Indications for Use
510(k) Number (if known) K212550
Device Name Hura CTP™ v1.0
#### Indications for Use (Describe)
Hura CTP™ v1.0 is intended as an image processing software to reduce noise of head CT Perfusion (CTP) DICOM images through multiple algorithm steps.
The software reduces image noise and enhances image contrast (e.g. contrast-to-noise ratio (CNR) and signal-to-noise ratio (SNR)) of the CTP DICOM images. Hura CTP™ v1.0 is non-iterative; hence the low computational overhead enables fast processing and allows no interruption to clinical workflow.
Hura CTP™ v1.0 outputs head CTP DICOM images with enhanced image quality to a designated directory defined by the user. The processed DICOM images can be imported to a third-party post-processing software for quantification of hemodynamic parameters.
The use of this algorithm may enhance the image contrast of head CTP DICOM images depending on the clinical task, patient size, and clinical practice. A consultation with a radiologist and a physicist should be made to determine the appropriate imaging protocol to obtain diagnostic image quality for the clinical task.
Hura CTP™ v1.0 is intended for use only by trained and qualified clinical personnel (e.g. radiologists). Hura CTP™ v1.0 is also intended to be used by trained and qualified personnel for installation and maintenance of the software.
| Type of Use ( <i>Select one or both, as applicable</i> ) | <div> <span> <b> Prescription Use (Part 21 CFR 801 Subpart D) </b> </span> <span> Over-The-Counter Use (21 CFR 801 Subpart C) </span> </div> |
|----------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
|----------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
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K212550 V. 510(k) Summary
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#### Date Prepared
July 30th, 2021
#### Manufacturer and 510(k) Owner
Hura Imaging, Inc.
23120 Park Sorrento
Calabasas CA 91302
| Official Contact: | Danny Wang |
|-------------------|---------------------|
| Telephone | 310-948-3390 |
| Email | djw@huraimaging.com |
#### Representative/Consultant
Andrew Wu
Rook Quality Systems
1155 Mount Vernon Hwy, Suite 800
Dunwoody, GA 30338
Telephone: +886-912-258-980
Email: andrew.wu@rookqs.com
#### Device Information
| Trade/Proprietary Name | Hura CTP TM v1.0 |
|------------------------|------------------------------------------------|
| Common Device Name | Image Processing System |
| Classification Name | Medical Image Management and Processing System |
| Regulation Number | 21 CFR 892.2050 |
| Product Code(s) | LLZ |
| Classification | Class II |
| Review Panel | Radiology |
| Use | Prescription |
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#### Indications for Use
Hura CTP™ v1.0 is intended as an image processing software to reduce noise of head CT Perfusion (CTP) DICOM images through multiple algorithm steps.
The software reduces image noise and enhances image contrast-to-noise ratio (CNR) and signal-to-noise ratio (SNR)) of the CTP DICOM images. Hura CTP™ v1.0 is non-iterative; hence the low computational overhead enables fast processing and allows no interruption to clinical workflow.
Hura CTP™ v1.0 outputs head CTP DICOM images with enhanced image quality to a designated directory defined by the user. The processed DICOM images can be imported to third-party post-processing software for quantification of hemodynamic parameters.
The use of this algorithm may enhance the image contrast of head CTP DICOM images depending on the clinical task, patient size, and clinical practice. A consultation with a radiologist and a physicist should be made to determine the appropriate imaging protocol to obtain diagnostic image quality for the clinical task.
Hura CTP™ v1.0 is intended for use only by trained and qualified clinical personnel (e.g. radiologists). Hura CTP™ v1.0 is also intended to be used by trained and qualified personnel for installation and maintenance of the software.
# Device Description
Hura CTP™ v1.0 is an image processing software which reduces noise of CTP DICOM images and and signal-to-noise ratio. Hura CTP™ v1.0 is based on a new algorithm enhances image contrast termed k-space weighted image average (KWIA) that was adapted from accelerated 4D dynamic MRI with projection view-sharing. There are two major advantages of KWIA compared to existing denoising method for CTP:
- 1) KWIA is computationally simple and fast (non-iterative); hence the low computation overhead enables fast processing and allows no interruption to clinical workflow.
- 2) KWIA does not make assumptions of noise characteristics and preserves the texture and resolution of CT images.
The software consists of three modules, namely the image input module, the processing module, and the output module. The image input module is responsible for interfacing with DICOM compliant CT scanners and receiving DICOM images. The image processing module is responsible for motion compensation, performing Fourier transform on DICOM images, applying KWIA, and performing inverse Fourier transform to output noise-reduced images. Both original and the noise-reduced DICOM images are then saved to the specified file directory. Hura CTP™ v1.0 is written in C/C++ language and runs as a local application on a standard PC, Mac, or UNIX workstation.
Insight Toolkit (ITK) serves as an important off-the-shelf library that KWIA algorithm leverages for a number of computational operations. The output module is responsible for transmitting noise-reduced CTP DICOM images to a designated directory defined by the user. The DICOM images can be imported to
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a third-party post-processing software (e.g. iNtuition, RAPID, Vitrea, etc.) for quantification of hemodynamic parameters. The software should be used only by trained professionals including, but not limited to, physicians, medical physicists, and technicians.
#### Technological Characteristics
The Hura CTP™ v1.0 is a software only device that runs on standard workstations running Windows, Mac OS X, and Unix.
The Hura CTP™ v1.0 receives head CT Perfusion (CTP) DICOM images from local workstation then applies the KWIA algorithm to reduce noise and enhance contrast of the input images. Processed image files are stored on the local workstation along with a file containing motion metrics, if requested.
The device does not contact the patient, nor does it control any life-sustaining devices. Information provided by the Hura CTP™ v1.0 is not intended in any way to eliminate, replace, or substitute for, in whole or in part, the healthcare provider's judgment and analysis of the patient's condition.
#### Equivalence to Predicate Device
Hura Imaging submits the following information to demonstrate that the Hura CTP™ v1.0 is substantially equivalent to the following legally marketed predicate device:
| 510(k) Number | K063391 |
|--------------------------------------|-------------------------------------------------------|
| Predicate Device Name / Manufacturer | Sapheneia Clarity™ / Sapheneia Commercial Products AB |
| Regulation Number | CFR 892.2050 |
| Regulation Name | Picture Archiving and Communications System |
| Regulatory Class | Class II |
| Primary Product Code | LLZ |
The subject device has the same intended use and similar technological characteristics in comparison with the primary predicate device (K063391). Sapheneia Clarity™ (K063391) is an image processing software which reduces noise and enhances contrast of relative structures. Thus, the intended use is the same.
Hura Imaging believes the following technological similarities are shared by the subject and predicate device:
- . Both the subject and predicate device are post-processing software devices which run on offthe-shelf operating system and intend to employ noise reduction techniques to achieve contrast enhancement on DICOM images.
- . Both the subject and predicate device are intended for use only by trained and qualified clinical personnel (e.g. radiologists).
- Both the subject and predicate device are adapted to existing radiology departmental workflow.
- Both the subject and predicate device process DICOM-compliant image data.
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Hura Imaging believes the following technological differences between the subject and the predicate device do not raise further questions or concerns on the safety and effectiveness of the subject device.
- Location of image processing enhancement engine: Subject device is installed as local application on the desktop computer onsite (e.g. hospital facility) whereas the predicate device is installed at the server onsite (e.g. hospital facility). This technological difference does not raise new issues of safety and effectiveness as compared to the predicate device.
- Modalities: Subject device receives head CT Perfusion images from commercial CT scanners which meet the criteria for scanner characteristics defined in the instructions for use (e.g. gantry aperture >= 70cm, No. of rows >= 16, power output >= 40kW, tube voltage >= 80kVp, tube current >= 30mA, reconstruction matrices >= 512x512, scan FOV >= 18cm), whereas the predicate system receives images from a variety of diagnostic systems. In addition, the compatible CT scanners shall acquire images which meet the criteria for image characteristics defined in the instructions for use (e.g. file format, pixel size, number of slice, number of time frames, and presence of artifact). Verification tests were carried out to demonstrate that the subject device demonstrate compatibility to scanners and the software can achieve its intended use. Hence, this technological difference does not raise new issues of safety and effectiveness as compared to the predicate device.
- User Interface: Subject device does not have graphic user interface and the processed images are reviewed on existing PACS workstation whereas the predicate device likely has user interface to allow viewing of multi-modality images. Both the subject device and predicate device are intended to be used by trained/qualified personnel for installation and maintenance of the software. Subject device runs as a local application that receives a copy of DICOM digital medical image data from the modality or another DICOM source, processes the data and then outputs the noise-reduced image as a local file. The original images are not replaced or removed. The noise-reduced, output image will be labeled distinctively to avoid confusion to the user. This technological difference does not raise new issues of safety and effectiveness as compared to the predicate device.
- Operating System: Subject device can be operated on Windows, Mac OS X, and Unix whereas the predicate device can be operated on Windows. Verification tests were carried out to demonstrate that the subject device achieves its intended use on all the operation systems. Hence, this technological difference does not raise new issues of safety and effectiveness as compared to the predicate device.
- CT Acquisition Protocol: Subject device is a post-processing software to reduce noise and enhance contrast of relevant structures of images acquired via standard CTP acquisition protocol whereas the predicate device is a post-processing software to reduce noise and enhance contrast of relevant structures of images acquired via predefined or specific acquisition protocol. This technological difference does not raise new issues of safety and effectiveness as compared to the predicate device.
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- Image Enhancement Algorithm: Subject device employs analysis of spatial frequencies of CTP ● images and enhances the CTP signal through spatial and temporal filtering in the frequency domain that preserves the fidelity of dynamic perfusion signal while simultaneously reducing the noise. Predicate device employs analysis of the image structure in the neighborhood of each pixel. The dominant structure can be distinguished by estimate methods from embedding noise, and further strengthened to improve SNR. This technological difference does not raise new issues of safety and effectiveness as compared to the predicate device.
Hura Imaging plans to include the following reference device to support the substantially equivalent decision:
| 510(k) Number | K131447 |
|--------------------------------------|---------------------------------------------|
| Reference Device Name / Manufacturer | iNtuition/ TeraRecon, Inc. |
| Regulation Number | CFR 892.2050 |
| Regulation Name | Picture Archiving and Communications System |
| Regulatory Class | Class II |
| Primary Product Code | LLZ |
The reference device serves as a tool to generate multi-parametric perfusion maps for performance comparison. Inclusion of the reference device in performance comparison study ensures that the diagnostic quality of the multi-parametric perfusion map is not negatively impacted by the denoising technique proposed by Hura Imaging believes that the iNuition's performance is generalizable across scanners and protocols for which the subject device is claimed compatible with. Hura Imaging chose iNtuition for our performance validation studies because it can be applied for the analysis of both CTP phantom and clinical CTP data acquired from 9 CT scanners by 4 main manufacturers (GE, Philips, Siemens, Toshiba) to generate multi-parametric perfusion maps.
Hura Imaging believes that the Hura CTP™ v1.0 described in this notification and for use under the conditions of the proposed labeling is substantially equivalent to the legally marketed predicate device (K063391) based on the information summarized in the following Table 1 – Substantial Equivalence Summary.
| Topic | Predicate Device (Sapheneia<br>Clarity™, K063391) | Subject Device (Hura CTP™ v1.0,<br>K212550) |
|---------------------------------------------|-------------------------------------------------------------|--------------------------------------------------------|
| Physical<br>Characteristics | Software package that operates on<br>off-the-shelf hardware | Same |
| Computer | PC Compatible | Same |
| Image Processing<br>Enhancement<br>Location | Onsite on the desktop computer<br>server | Onsite on the desktop computer as<br>local application |
| DICOM Standard<br>Compliance | The software processes DICOM<br>compliant image data | Same |
| Operating System | Windows | Windows, Mac OS X, and Unix |
| Table 1 – Substantial Equivalence Summary | | |
|---------------------------------------------|--|--|
| | | |
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| Modalities | Multi-modality | Head CT Perfusion images |
|-----------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| User Interface | The software is designed for use on a<br>radiology workstation. It is unknown<br>whether there is a user interface. | None – enhanced images are viewed<br>on existing PACS workstations |
| Protocols | Predefined or specific acquisition<br>protocol settings | Standard clinical CTP protocols |
| Image Enhancement<br>Algorithm<br>Description | Sapheneia Clarity™ employs a<br>sophisticated statistical analysis of the<br>image structure in the neighborhood<br>of each pixel. Using robust estimation<br>methods, the dominant structures are<br>separated from the embedding noise.<br>Once the structure has been<br>determined, it is possible to<br>strengthen the interesting parts while<br>simultaneously reducing the noise. | Hura CTP™ v1.0 employs analysis of<br>spatial frequencies of CT perfusion<br>(CTP) images and enhances the CTP<br>signal through spatial and temporal<br>filtering in the frequency domain that<br>preserves the fidelity of dynamic<br>perfusion signal while simultaneously<br>reducing the noise. |
| Image Acquisition | The acquisition remains the same, i.e.<br>the image processing can be<br>generated from multiple modalities<br>and with predefined or specific<br>acquisition protocol settings. | Same |
# Performance Data
The subject device is designed in conformance with:
- NEMA PS 3.1 3.20 (2016) Digital Imaging and Communications in Medicine (DICOM) Set ●
- ISO 14971:2019 – Medical Devices – Application of Risk Management to Medical Devices
- IEC 62304:2006/AMD 1:2015 – Medical Device Software – Software Life-Cycle Processes
- . Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices
- Guidance for General Principle of Software Validation; Final Guidance for Industry and FDA Staff
- Guidance for Off-the-Shelf Software Use in Medical Devices
- . Guidance for the Content of Premarket Submission for Management of Cybersecurity in Medical Devices
- Guidance for Software as Medical Device (SAMD): Clinical Evaluation
All specifications of the Hura CTP™ v1.0 are verified by a number of tests before release. Bench testing including verification tests on:
- DICOM image import and export
- Command line functions
- Image denoising and contrast enhancement
- . Motion correction and export metrics
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A phantom validation study and a validation study with simulated small feature inserted were conducted for validating Hura CTP™ v1.0.
For CTP phantom DICOM images acquired on both Philips Brilliance and Toshiba Aquilion scanners, noise standard deviation (SD) values were significantly reduced using Hura CTP™ v1.0 compared to those by the vendor. The average SNR increased from 4.89±2.13 to 7.91±3.38 (62% increase, P<0.01) for Philips and from 6.88±2.88 to 9.83±3.76 (43% increase, P<0.01) for Toshiba, and the average CNR increased from 1.13±0.62 to 1.81±0.95 (60% increase, P<0.01) for Philips and from 1.76±0.94 to 2.51±1.25 (43% increase, P<0.01) for Toshiba using Hura CTP™ v1.0 compared to the vendor. The normalized root mean square error (NRMSE) of time density curves (TDCs) of two tissue regions as well as the artery and vein were all within 5% for 30 CTP phantom scans acquired on both Philips Brilliance and Toshiba Aquilion scanners at 5 perfusion rates and 6 radiation dose (RD) levels. For parametric perfusion maps of CTP phantom DICOM images acquired on the Philips Brilliance and Toshiba Aquilion scanners, the mean tissue SNR values of CBV, Tmax and TTP maps all significantly increased from 5% to 16% (P<0.01) using Hura CTP™ v1.0 compared to vendor generated DICOM images. Furthermore, all intra-class correlation coefficient (ICC) values including 95% Cl were excellent (all estimated ICC values ≥ 0.86 with lower bounds of 95% Cl ≥ 0.78) for perfusion parameters generated using Hura CTP™ v1.0 processed and vendor DICOM images.
For the 40 CTP datasets of two clinical cases with inserted simulated small objects acquired on Siemens Sensation and Toshiba Aquilion scanners, the average SNR of the inserted small object increased from 12.31±6.76 to 13.35±7.32 (8.4% increase, P<0.01) using DICOM imaging processed with Hura CTP™ v1.0 compared to those without Hura CTP™ v1.0 processing. The NRMSE of TDCs of the small object were all within 5% for the 40 CTP datasets of the two clinical cases. For parametric perfusion maps of the 40 CTP datasets, all ICC values including 95% Cl were excellent (all estimated ICC values ≥ 0.94 with lower bounds of 95% Cl ≥ 0.89) for perfusion parameters generated using DICOM images with and without post-processing using Hura CTP™ v1.0.
#### Clinical Data
A retrospective clinical study was conducted for validating HuraCTP™ V1.0.
For the 40 datasets of clinical DICOM images acquired at the 4 sites on CT scanners manufactured by the 4 OEMs, the SD values of both grey and white matter were significantly reduced using Hura CTP™ v1.0 compared to those by the vendor. The average SNR increased from 4.8±1.16 to 7.12±1.73 (48% increase, P<0.01) for grey matter and from 3.43±0.71 to 5.57±1.25 (62% increase, P<0.01) for white matter, and the average CNR between grey and white matter increased from 1.03±0.51 to 1.55±0.72 (50% increase, P<0.01) using Hura CTP™ v1.0 compared to the vendor. The NRMSE of TDCs of grey and white matter as well as the artery and vein were all within 5% for the 40 CTP scans acquired at the 4 sites. For quantitative parametric perfusion maps of DICOM images acquired at the 4 sites, the mean SNR values of both grey and white matter CBF, white matter CBV, and grey and white matter TTP all significantly increased from 1.4% to 5% (P<0.01) using Hura CTP™ v1.0 compared to vendor generated DICOM images. For parametric perfusion maps of DICOM images acquired at the 4 sites, all ICC values including
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95% Cl were excellent (all estimated ICC values ≥ 0.88 with lower bounds of 95% Cl ≥ 0.8) for perfusion parameters generated using Hura CTP™ v1.0 processed and vendor DICOM images.
Hura Imaging believes that the aforementioned non-clinical validation demonstrate that the subject device is designed in such a way that, when used under the conditions and for the purposes intended, the safety and effectiveness, as well as the performance characteristic of the subject device is substantially equivalent to the predicate device.
# Substantial Equivalence Conclusion
Hura Imaging has the same intended use and performance characteristics as the predicate device. Based on the software verification performed, it can be concluded that the differences in technological characteristics between the Hura CTP™ v1.0 and the predicate device do not raise different questions of safety and effectiveness under specified use conditions. The indications for use, technological characteristics, and performance characteristics for the Hura CTP™ v1.0 are assessed to be substantially equivalent to the predicate device. There is no identified hazard that requires additional benefit versus risk analysis in support of substantial equivalence.
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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.