(257 days)
Yes
The device description explicitly states that it uses a "pre-trained neural network" and a "convolutional neural network based method," which are forms of machine learning.
No.
HYPER DLR is an image processing function that reduces noise in PET images to aid in diagnosis, not to provide treatment or therapy.
No
HYPER DLR is an image processing function intended to reduce noise in FDG PET images. It improves image quality for radiologists and nuclear medicine physicians, but it does not directly diagnose or provide a diagnostic output. The device aids in the diagnostic process by enhancing images, but it is not a diagnostic device itself.
Yes
The device is explicitly described as a "software-only device" and its function is image post-processing on existing, cleared PET/CT devices.
Based on the provided information, this device is not an IVD (In Vitro Diagnostic).
Here's why:
- IVD Definition: In Vitro Diagnostics are tests performed on samples taken from the human body, such as blood, urine, or tissue, to detect diseases, conditions, or infections.
- Device Function: HYPER DLR is a software-only device that performs image processing on existing PET images. It does not analyze biological samples.
- Intended Use: The intended use is to reduce noise in PET images for interpretation by radiologists and nuclear medicine physicians. This is a post-processing step on medical images, not a diagnostic test performed on a biological sample.
Therefore, HYPER DLR falls under the category of medical imaging software, not an In Vitro Diagnostic device.
No
The provided text does not contain any explicit statement that the FDA has reviewed and approved or cleared a Predetermined Change Control Plan (PCCP) for this specific device.
Intended Use / Indications for Use
HYPER DLR is an image processing function intended to be used by radiologists and nuclear medicine physicians to reduce noise of the fluorodeoxyglucose (FDG) PET images.
Product codes
KPS
Device Description
HYPER DLR is a software-only device. HYPER DLR is intended to be implemented on previously cleared PET/CT devices uMI 550 (K182237) and uMI 780 (K172143). HYPER DLR serves as an alternative to the existing image smoothing options that are available on the predicate devices. HYPER DLR is an image post-processing technique which uses a pre-trained neural network to predict low noise PET image from high noise PET image. After training, the network could extract the noise component from the image, thus reducing the image noise.
Mentions image processing
Yes
Mentions AI, DNN, or ML
Yes
Input Imaging Modality
PET images
Anatomical Site
Not Found
Indicated Patient Age Range
Not Found
Intended User / Care Setting
radiologists and nuclear medicine physicians
Description of the training set, sample size, data source, and annotation protocol
Not Found
Description of the test set, sample size, data source, and annotation protocol
Not Found
Summary of Performance Studies
Non-Clinical Testing:
- Image performance tests and clinical image evaluation were conducted.
- Bench testing was performed using identical raw datasets obtained on UIH's uMI 780 and uMI 550, applying both HYPER DLR and Gaussian filtering for image de-noising.
- Clinical image evaluation was performed by comparing HYPER DLR with Gaussian filtering. Each image was read by board-certified nuclear medicine physicians who provided an assessment of both image noise and overall image quality.
- Additional clinical image evaluations were performed for typical clinical scan times of uMI 550 and uMI 780 systems.
Clinical Testing:
- No Clinical Study is included in this submission.
Key Metrics
- Peak signal to noise ratio
- Structural similarity index
- Pearson correlation coefficient
- Signal to noise ratio (SNR)
- Contrast to noise ratio (CNR)
- Normalized root mean square error
- Bland-Altman plot of body & brain VOI SUVmean values
Predicate Device(s)
Reference Device(s)
Not Found
Predetermined Change Control Plan (PCCP) - All Relevant Information
Not Found
§ 892.1200 Emission computed tomography system.
(a)
Identification. An emission computed tomography system is a device intended to detect the location and distribution of gamma ray- and positron-emitting radionuclides in the body and produce cross-sectional images through computer reconstruction of the data. This generic type of device may include signal analysis and display equipment, patient and equipment supports, radionuclide anatomical markers, component parts, and accessories.(b)
Classification. Class II.
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Shanghai United Imaging Healthcare Co., Ltd. % Shumei Wang QM & RA VP No. 2258 Chengbei Road, Jiading Industrial District Shanghai, Shanghai 201807 CHINA
August 4, 2020
Re: K193210
Trade/Device Name: HYPER DLR Regulation Number: 21 CFR 892.1200 Regulation Name: Emission computed tomography system Regulatory Class: Class II Product Code: KPS Dated: June 24, 2020 Received: June 29, 2020
Dear Shumei Wang:
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
1
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) K193210
Device Name HYPER DLR
Indications for Use (Describe)
HYPER DLR is an image processing function intended to be used by radiologists and nuclear medicine physicians to reduce noise of the fluorodeoxyglucose (FDG) PET images.
X Prescription Use (Part 21 CFR 801 Subpart D)
| Over-The-Counter Use (21 CFR 801 Subpart C)
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510 (k) SUMMARY
-
- Date of Preparation June 24, 2020
2. Sponsor Identification
Shanghai United Imaging Healthcare Co.,Ltd.
No.2258 Chengbei Rd. Jiading District, 201807, Shanghai, China
Contact Person: Shumei Wang Position: QM&RA VP Tel: +86-021-67076888-6776 Fax: +86-021-67076889 Email: shumei.wang(@united-imaging.com
3. Identification of Proposed Device
Trade Name: HYPER DLR Common Name: Emission Computed Tomography System Model(s): HYPER DLR
Regulatory Information Regulation Number: 21 CFR 892.1200 Regulation Name: Emission Computed Tomography System Regulatory Class: II Product Code: KPS Review Panel: Radiology
4. Identification of Predicate Device(s)
Predicate Device 1:
510(k) Number: K172143 Device Name: Emission Computed Tomography System Model(s): uMI 780
Regulatory Information Regulation Number: 21 CFR 892.1200 Regulation Name: Emission Computed Tomography System Regulatory Class: II Product Code: KPS, JAK Review Panel: Radiology
Predicate Device 2:
510(k) Number: K182237 Device Name: Emission Computed Tomography System Model(s): uMI 550
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Image /page/4/Picture/1 description: The image shows the logo for United Imaging. The words "UNITED IMAGING" are stacked on top of each other in a bold, sans-serif font. To the right of the words is a stylized "U" shape, which is also in a bold font. The logo is simple and modern, and the colors are muted.
Regulatory Information Regulation Number: 21 CFR 892.1200 Regulation Name: Emission Computed Tomography System Regulatory Class: II Product Code: KPS, JAK Review Panel: Radiology
ડ. Device Description:
HYPER DLR is a software-only device. HYPER DLR is intended to be implemented on previously cleared PET/CT devices uMI 550 (K182237) and uMI 780 (K172143). HYPER DLR serves as an alternative to the existing image smoothing options that are available on the predicate devices. HYPER DLR is an image post-processing technique which uses a pre-trained neural network to predict low noise PET image from high noise PET image. After training, the network could extract the noise component from the image, thus reducing the image noise.
6. Indications for Use
HYPER DLR is an image processing function intended to be used by radiologists and nuclear medicine physicians to reduce noise of the fluorodeoxyglucose (FDG) PET images.
Comparison of Technological Characteristics with the Predicate Devices 7.
A comparison between the technological characteristics of proposed and predicate devices is provided as below.
| ITEM | Predicate Device 1
uMI 780 (K172143)
including a post-
smoothing function
for PET image
reconstruction | Predicate Device 2
uMI 550 (K182237)
including a post-
smoothing function
for PET image
reconstruction | Proposed Device
HYPER DLR | NOTE |
|---------------------------|-----------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------|--------------------------------------------------|
| Image Processing Location | Onsite on the facility PET/CT reconstruction computer. | Onsite on the facility PET/CT reconstruction computer. | Onsite on the facility PET/CT reconstruction computer. | Same |
| Operating system | Windows | Windows | Windows | Same |
| Workflow | Support online & offline | Support online & offline | Support online & offline | Same |
| Protocols | Standard scanner protocols | Standard scanner protocols | Standard scanner protocols | Same |
| Algorithm description | The post-smoothing function uses Gaussian filtering to reduce the noise in | The post-smoothing function uses Gaussian filtering to reduce the noise in | The software employs a convolutional neural network | Gaussian filtering suppresses the high frequency |
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Image /page/5/Picture/1 description: The image shows the logo for United Imaging. The words "UNITED IMAGING" are in bold, sans-serif font, stacked on top of each other. To the right of the words is a stylized "U" symbol, which is also in bold. The color of the text and symbol is a dark teal.
| the PET images. The | the PET images. The | based method to | component of |
|---|---|---|---|
| Gaussian filtering | |||
| works by using the | |||
| 3D Gaussian | |||
| distribution as a | |||
| point-spread | |||
| function. And the | |||
| filtering process is | |||
| achieved by | |||
| convolving the | |||
| Gaussian filter with | |||
| the reconstructed | |||
| PET image. | Gaussian filtering | ||
| works by using the | |||
| 3D Gaussian | |||
| distribution as a | |||
| point-spread | |||
| function. And the | |||
| filtering process is | |||
| achieved by | |||
| convolving the | |||
| Gaussian filter with | |||
| the reconstructed | |||
| PET image. | re-generate the | ||
| value for each | |||
| pixel. The | |||
| network extracts | |||
| the noise | |||
| component from | |||
| the image, while | |||
| retains the other | |||
| useful | |||
| components such | |||
| as image details. | the image, | ||
| which includes | |||
| noise and image | |||
| details. On the | |||
| contrary, | |||
| convolutional | |||
| neural network | |||
| is able to | |||
| distinguish the | |||
| noise | |||
| component and | |||
| the image | |||
| details, and only | |||
| removes the | |||
| noise | |||
| component from | |||
| the image. |
HYPER DLR utilizes the same hardware with the predicate devices and does not introduce any new restrictions on use. The differences do not affect the safety and the effectiveness.
Performance Data 8.
Non-Clinical Testing
Non-clinical testing including image performance tests and clinical image evaluation were conducted for the HYPER DLR during the product development. UNITED IMAGING HEALTHCARE claims conformance to the following standards and guidance:
Software
- NEMA PS 3.1-3.20(2011): Digital Imaging and Communications in Medicine A (DICOM)
-
IEC 62304: Medical Device Software - software life cycle process
- A Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices
-
Content of Premarket Submissions for Management of Cybersecurity in Medical Devices
Other Standards and Guidance
- A ISO 14971: Medical Devices - Application of risk management to medical devices
-
Code of Federal Regulations, Title 21, Part 820 - Quality System Regulation
- Code of Federal Regulations, Title 21, Subchapter J Radiological A Health
Software Verification and Validation
Software documentation for a Moderate Level of Concern software per FDA'
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Image /page/6/Picture/1 description: The image contains the logo for United Imaging. The text "UNITED" is stacked on top of the text "IMAGING". To the right of the text is a stylized letter "U" that is dark blue. The logo is simple and modern.
Guidance Document "Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices" is included as a part of this submission. The risk analysis was completed and risk control was implemented to mitigate identified hazards. The testing results show that all the software specifications have met the acceptance criteria. Verification and validation testing of the proposed
device was found acceptable to support the claim of substantial equivalence. UNITED IMAGING HEALTHCARE conforms to the Cybersecurity requirements
by implementing a process of preventing unauthorized access, modification, misuse or denial of use, or unauthorized use of information that is stored, accessed, or transferred from a medical device to an external recipient. Cybersecurity information in accordance with guidance document "Content of Premarket Submissions for Management of Cybersecurity in Medical Devices" is included in this submission.
Performance Verification
Engineering bench testing was performed to support substantial equivalence and the product performance claims. The evaluation and analysis used the identical raw datasets obtained on UIH's uMI 780 and uMI 550, and then applies both HYPER DLR and Gaussian filtering to do image de-noising. The resultant images were then compared for:
- A Peak signal to noise ratio
- A Structural similarity index
-
Pearson correlation coefficient
- A Signal to noise ratio (SNR)
- A Contrast to noise ratio (CNR)
- A Normalized root mean square error
- A Bland-Altman plot of body & brain VOI SUVmean values
Bench test showed overall image quality improvement based on the commonly used quantitative metrics. HYPER DLR can significantly improve SNR and CNR while preserving image consistency.
Clinical Image Evaluation
The clinical image evaluation was performed by comparing HYPER DLR with Gaussian filtering. Each image was read by board-certified nuclear medicine physicians who provided an assessment of both image noise and overall image quality. The results of the evaluation indicated that HYPER DLR performed lower image noise than Gaussian filtering while the image quality was sufficient for clinical diagnosis.
Additional clinical image evaluation were performed for typical clinical scan times of uMI 550 and uMI 780 systems. Under all the evaluated scan time, clinical evaluation shows that the HYPER DLR produces lower or equivalent image noise and better or equivalent image quality compared with Gaussian filtering. And all the HYPER DLR images are of diagnostic quality.
Clinical Testing
No Clinical Study is included in this submission.
Conclusions 9.
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Image /page/7/Picture/1 description: The image contains the logo for United Imaging. The logo consists of the words "UNITED IMAGING" in bold, sans-serif font, stacked on top of each other. To the right of the text is a stylized "U" shape, which is dark blue. The logo is simple and modern in design.
The changes associated with HYPER DLR do not change the indications for use from the predicate devices, and represent equivalent technological characteristic, with no impact on control mechanism, operating principle, and energy type. HYPER DLR is substantially equivalent as safety as the legally marketed predicate devices.
HYPER DLR was developed under UIH's quality management system. Design verification, along with bench testing and the clinical image evaluation demonstrate that HYPER DLR is substantially equivalent as effective as the legally marketed predicate devices.
Based on the comparison and analysis above, the proposed device has similar performance, equivalent safety and effectiveness as the predicate devices. The differences above between the proposed device and predicate devices do not affect the intended use, safety and effectiveness. And no issues are raised regarding to safety and effectiveness. The proposed device is determined to be Substantially Equivalent (SE) to the predicate devices.