← Product Code [QKB](/submissions/RA/subpart-b%E2%80%94diagnostic-devices/QKB) · K250780 # ARTAssistant (K250780) _Manteia Technologies Co., Ltd. · QKB · Dec 5, 2025 · Radiology · SESE_ **Canonical URL:** https://fda.innolitics.com/submissions/RA/subpart-b%E2%80%94diagnostic-devices/QKB/K250780 ## Device Facts - **Applicant:** Manteia Technologies Co., Ltd. - **Product Code:** [QKB](/submissions/RA/subpart-b%E2%80%94diagnostic-devices/QKB.md) - **Decision Date:** Dec 5, 2025 - **Decision:** SESE - **Submission Type:** Traditional - **Regulation:** 21 CFR 892.2050 - **Device Class:** Class 2 - **Review Panel:** Radiology - **Attributes:** AI/ML, Software as a Medical Device ## Intended Use The primary function of ARTAssistant is to facilitate image processing with image registration and synthetic CT (sCT) generation in adaptive radiation therapy. This enables users to meticulously design ART plans based on the processed images. ## Device Story ARTAssistant is a standalone software system for adaptive radiotherapy (ART) workflows. It ingests medical images (CT, MRI, CBCT) in DICOM format. The system performs automatic rigid and deformable image registration and generates synthetic CT (sCT) images from MR or CBCT inputs using a deep learning model. It also provides manual contouring tools for organs-at-risk in head, neck, thorax, abdomen, and pelvic regions. Used in clinical settings by radiation oncology staff, the software assists in processing images to support the design of daily adaptive treatment plans. By enabling sCT generation and registration, it allows clinicians to utilize non-CT modalities for treatment adaptation on standard image-guided accelerators. Output is reviewed by clinicians to inform radiation treatment planning, potentially improving the precision of dose delivery in adaptive radiotherapy. ## Clinical Evidence Bench testing only. No clinical trials performed. Registration accuracy evaluated using Normalized Mutual Information (NMI) and Hausdorff Distance (HD) against predicate (K221706) using multi-modality image sets from five major scanner vendors. Image conversion accuracy validated via AAPM TG-119 method (gamma pass rates) and Dice similarity coefficients for anatomical/geometric accuracy. Deep learning model trained on 560 sets and tested on 247 independent sets from U.S. clinics covering six cancer types (intracranial, nasopharyngeal, esophagus, lung, liver, cervical). ## Technological Characteristics Standalone software running on Windows. Features intensity-based auto-rigid and auto-deformable registration algorithms. Utilizes a deep learning model for synthetic CT generation from MR/CBCT. Compatible with DICOM 3.0 compliant images from various scanner vendors (GE, Philips, Siemens, Varian, Elekta). Supports manual contouring and contour QA. No patient contact; no electrical/mechanical components. ## Regulatory 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). ## Predicate Devices - AccuContour ([K221706](/device/K221706.md)) ## Reference Devices - MRI Planner ([K211841](/device/K211841.md)) - RayStation ([K220141](/device/K220141.md)) ## Submission Summary (Full Text) > This content was OCRed from public FDA records by [Innolitics](https://innolitics.com). If you use, quote, summarize, crawl, or train on this content, cite Innolitics at https://innolitics.com. > > Innolitics is a medical-device software consultancy. We help companies design, build, and clear FDA-regulated software and AI/ML devices, including [a 510(k)](https://innolitics.com/services/510ks/), [a De Novo](https://innolitics.com/services/regulatory/), [a SaMD](https://innolitics.com/services/end-to-end-samd/), [an AI/ML medical device](https://innolitics.com/services/medical-imaging-ai-development/), or [an FDA regulatory strategy](https://innolitics.com/services/regulatory/). {0} FDA U.S. FOOD & DRUG ADMINISTRATION December 5, 2025 Manteia Technologies Co., Ltd. Chao Fang Quality Manager Unit 3001-3005 No. 5 Huizhan North Road Xiamen, 361008 China Re: K250780 Trade/Device Name: ARTAssistant Regulation Number: 21 CFR 892.2050 Regulation Name: Medical Image Management And Processing System Regulatory Class: Class II Product Code: QKB Dated: October 31, 2025 Received: October 31, 2025 Dear Chao Fang: 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 (the 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 available 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. U.S. Food & Drug Administration 10903 New Hampshire Avenue Silver Spring, MD 20993 www.fda.gov {1} K250780 - Chao Fang Page 2 Additional information about changes that may require a new premarket notification are provided in the FDA guidance documents entitled "Deciding When to Submit a 510(k) for a Change to an Existing Device" (https://www.fda.gov/media/99812/download) and "Deciding When to Submit a 510(k) for a Software Change to an Existing Device" (https://www.fda.gov/media/99785/download). Your device is also subject to, among other requirements, the Quality System (QS) regulation (21 CFR Part 820), which includes, but is not limited to, 21 CFR 820.30, Design controls; 21 CFR 820.90, Nonconforming product; and 21 CFR 820.100, Corrective and preventive action. Please note that regardless of whether a change requires premarket review, the QS regulation requires device manufacturers to review and approve changes to device design and production (21 CFR 820.30 and 21 CFR 820.70) and document changes and approvals in the device master record (21 CFR 820.181). 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 (reporting of medical device-related adverse events) (21 CFR Part 803) for devices or postmarketing safety reporting (21 CFR Part 4, Subpart B) for combination products (see https://www.fda.gov/combination-products/guidance-regulatory-information/postmarketing-safety-reporting-combination-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 Part 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR Parts 1000-1050. All medical devices, including Class I and unclassified devices and combination product device constituent parts are required to be in compliance with the final Unique Device Identification System rule ("UDI Rule"). The UDI Rule requires, among other things, that a device bear a unique device identifier (UDI) on its label and package (21 CFR 801.20(a)) unless an exception or alternative applies (21 CFR 801.20(b)) and that the dates on the device label be formatted in accordance with 21 CFR 801.18. The UDI Rule (21 CFR 830.300(a) and 830.320(b)) also requires that certain information be submitted to the Global Unique Device Identification Database (GUDID) (21 CFR Part 830 Subpart E). For additional information on these requirements, please see the UDI System webpage at https://www.fda.gov/medical-devices/device-advice-comprehensive-regulatory-assistance/unique-device-identification-system-udi-system. Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR 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-devices/medical-device-safety/medical-device-reporting-mdr-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/medical-devices/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-devices/device-advice-comprehensive-regulatory- {2} K250780 - Chao Fang Page 3 assistance/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, ![img-0.jpeg](img-0.jpeg) Lora D. Weidner, Ph.D. Assistant Director Radiation Therapy Team DHT8C: Division of Radiological Imaging and Radiation Therapy Devices OHT8: Office of Radiological Health Office of Product Evaluation and Quality Center for Devices and Radiological Health Enclosure {3} ARTAssistant Page 8 of 44 | Indications for Use | | | | --- | --- | --- | | Please type in the marketing application/submission number, if it is known. This textbox will be left blank for original applications/submissions. | K250780 | ? | | Please provide the device trade name(s). | | ? | | ARTAssistant | | | | Please provide your Indications for Use below. | | ? | | The primary function of ARTAssistant is to facilitate image processing with image registration and synthetic CT (sCT) generation in adaptive radiation therapy. This enables users to meticulously design ART plans based on the processed images. | | | | Please select the types of uses (select one or both, as applicable). | ☑ Prescription Use (Part 21 CFR 801 Subpart D) ☐ Over-The-Counter Use (21 CFR 801 Subpart C) | ? | {4} K250780 510(k) Summary # 510(k) Summary The following information is provided as required by 21 CFR 807.92. The assign 510(k) Number: K250780 # I. SUBMITTER Manteia Technologies Co., Ltd. Unit 3001-3005, No.5 Huizhan North Road, Xiamen, Fujian, P.R. China Establishment Registration Number: 3016686005 Contact Person: Chao Fang Position: Quality Manager Email: ra@manteiatech.com Date of Prepared: 12/04/2025 # II. DEVICE Device/Trade Name: ARTAssistant Common or Usual Name: Adaptive radiotherapy assistant system Classification Name: Medical image management and processing system Regulatory Class: Class II Product Code: QKB Regulation Number: 21CFR 892.2050 Review Panel: Radiology # III. PREDICATE DEVICE Predicate Device: AccuContour, K221706 Reference Device 1: MRI Planner, K211841 Reference Device 2: RayStation, K220141 # IV. DEVICE DESCRIPTION ARTAssistant, is a standalone software which is positioned as an adaptive radiotherapy auxiliary system, aiming to provide a complete solution to assist the implementation of adaptive radiotherapy, helping hospitals to implement adaptive radiotherapy on ordinary image-guided accelerators based on the current situation. This system is mainly used to assist in the image processing of online adaptive radiotherapy, thereby helping users complete the design of the daily adaptive radiotherapy plan based on the processed images. The product has three main functions on image processing: (1) Automatic registration: rigid and deformable registration, and {5} 510(k) Summary (2) Image conversion: generation of synthetic CT from CBCT or MR, and (3) Image contouring: it can manual contour organs-at-risk, in head and neck, thorax, abdomen and pelvis (for both male and female) areas assisted contouring tools. It also has the following general functions: - Receive, add/edit/delete, transmit, input/export medical images and DICOM data; - Patient management; - Review of processed images. V. INDICATIONS FOR USE The primary function of ARTAssistant is to facilitate image processing with image registration and synthetic CT (sCT) generation in adaptive radiation therapy. This enables users to meticulously design ART plans based on the processed images. 2 / 8 {6} 510(k) Summary VI. COMPARISON OF TECHNOLOGICAL CHARACTERISTICS WITH THE PREDICATE DEVICE | ITEM | Subject Device | Predicate Device K221706 | Reference Device K211841 | Reference Device K220141 | | --- | --- | --- | --- | --- | | Regulatory Information | | | | | | Regulation No. | 21CFR 892.2050 | 21CFR 892.2050 | 21CFR 892.5050 | 21CFR 892.5050 | | Product Code | QKB | QKB | MUJ | MUJ | | Class | II | II | II | II | | Indications of Use | The primary function of ARTAssistant is to facilitate image processing with image registration and synthetic CT (sCT) generation in adaptive radiation therapy. This enables users to meticulously design ART plans based on the processed images. | It is used by radiation oncology department to register multi-modality images and segment (non-contrast) CT images, to generate needed information for treatment planning, treatment evaluation and treatment adaptation. | MRI Planner is a software-only medical device intended for use by trained radiation oncologists, dosimetrists and physicists to process images from MRI systems to 1) provide the operator with information of tissue properties for radiation attenuation estimation purposes in photon external beam radiotherapy treatment planning, and to 2) derive contours for input to radiation treatment planning by assisting in localization and definition of healthy anatomical structures. MRI Planner is not intended to automatically contour tumors or | RayStation is a software system for radiation therapy and medical oncology. Based on user input, RayStation proposes treatment plans. After a proposed treatment plan is reviewed and approved by authorized intended users, RayStation may also be used to administer treatments. The system functionality can be configured based on user needs. | {7} 510(k) Summary | ITEM | Subject Device | Predicate Device K221706 | Reference Device K211841 | Reference Device K220141 | | --- | --- | --- | --- | --- | | | | | tumor clinical target volumes. MRI Planner is indicated for radiotherapy planning of adult patients for primary and metastatic cancers in the brain and head-neck regions, as well as soft tissue cancers in the pelvic region. MRI Planner generates synthetic CT images for radiation attenuation estimation purposes for the pelvis, brain and head-neck regions only. MRI Planner generates automatically derived contours of the bladder, colon and femoral heads, for prostate cancer patients only. | | | Operating System | Windows | Windows | Windows | Windows | | Technological Characteristics | | | | | | Auto Rigid Registration Algorithm | Intensity based | Intensity based | N/A | N/A | | Auto Deformable Registration Algorithm | Intensity based | Intensity based | N/A | N/A | | Image Conversion Algorithm | Deep learning | N/A | Machine Learning | Machine Learning | {8} 510(k) Summary | ITEM | Subject Device | Predicate Device K221706 | Reference Device K211841 | Reference Device K220141 | | --- | --- | --- | --- | --- | | Registration Feature | | | | | | Image Registration | Auto rigid registration and auto deformable registration | Auto rigid registration and auto deformable registration | N/A | N/A | | Compatible Modality | Auto rigid registration: CT, MRI, PET Auto deformable registration: CT, MRI, CBCT | Auto rigid registration: CT, MRI, PET Auto deformable registration: CT, MRI, CBCT | N/A | N/A | | Compatible Scanner Models | No Limitation on scanner model, DICOM 3.0 compliance required | No Limitation on scanner model, DICOM 3.0 compliance required | N/A | N/A | | Image Conversion Feature | | | | | | Image Conversion | Generates synthetic CT from both MR and CBCT | N/A | Generates synthetic CT from MR | Generates synthetic CT from CBCT | | Image Enhance Feature | | | | | | CBCT Image Enhance | YES | N/A | YES | N/A | | Image Contouring Feature | | | | | | Manual Contouring | YES | YES | N/A | N/A | | Compatible Scanner Models | No Limitation on scanner model, DICOM 3.0 compliance required | No Limitation on scanner model, DICOM 3.0 compliance required | N/A | N/A | | Contour QA | YES | YES | N/A | N/A | {9} 510(k) Summary ## VII. PERFORMANCE DATA The following performance data were provided in support of the substantial equivalence determination. ## Biocompatibility Testing ARTAssistant is a software only device and will not come in contact with the patient, thus biocompatibility testing is not applicable. ## Electrical Safety and Electromagnetic Compatibility (EMC) ARTAssistant is a software only device, and no Electrical safety and electromagnetic compatibility testing was conducted for the Subject Device. ## Software Verification and Validation Testing Software verification and validation testings were conducted, and documentation was provided as recommended by FDA's Guideline for Industry and FDA Staff - Content of Premarket Submission for Device Software Functions. Verification and validation of the software was conducted to ensure that the product meet users needs and intended use. ARTAssistant passed all software verification and validation tests. ## Performance Test Report on Rigid Registration Function The automatic rigid registration algorithm performance test was performed against the reference device (K221706) to evaluate the rigid registration accuracy. All fixed images and moving images were generated in healthcare institutions in U.S. The scanner models covered five major vendors. And the image registration feature is only tested on multi-modality image sets from different patients. The Normalized Mutual Information (NMI) and Hausdorff Distance (HD) were used for evaluation. NMI and HD values were calculated on two sets of images for both the proposed device and predicate device (K221706), respectively. The NMI and HD values of the subject device was compared with that of the predicate device. According to the results, it could be concluded that the NMI and HD values of the proposed device was non-inferiority compares with that of the predicate device. ## Performance Test Report on Deformable Registration Function The automatic deformable registration algorithm performance test was performed against the reference device (K221706) to evaluate the deformable registration accuracy. All fixed images and moving images were generated in healthcare institutions in U.S. The scanner models covered five major vendors. And the image registration feature is only tested on multi-modality image sets from different patients. The Normalized Mutual Information (NMI) and Hausdorff Distance (HD) were used for evaluation. NMI and HD values were calculated on two sets of images for both the proposed device and predicate device (K221706), 6 / 8 {10} 510(k) Summary respectively. The NMI and HD values of the subject device was compared with that of the predicate device. According to the results, it could be concluded that the NMI and HD values of the proposed device was non-inferiority compares with that of the predicate device. ## Performance Test Report on Image Conversion Function The performance test on image conversion function was carried out to evaluate the accuracy of image conversion function for the test article, ARTAssistant, by using TG119 method. Test images included in this study were all generated in U.S., and covered 6 cancer types including intracranial tumor, nasopharyngeal carcinoma, esophagus cancer, lung cancer, liver cancer and cervical cancer. After generating SCT from MR/CBCT images, the gamma value of RTDose is compared with the gamma value of sRTDose, and Gamma Pass Rate of all test results is within the acceptable range of AAPM TG-119, which demonstrates the accuracy of the image conversion function. Additionally, another performance test of the image conversion function was conducted to evaluate the anatomic and geometric accuracy of synthetic CT (sCT) generated from CBCT and MR. This involved comparing the segmentation results of each ROI on CBCT/MR against those on sCT and calculating the Dice similarity coefficient. The results indicate that the geometric accuracy of sCT images generated from both CBCT and MR meets the requirements. For the deep learning model for image conversion there were 560 training and 247 testing image sets. The training image set source is from China, and the testing image source is from the United States. They are independent of each other. The test data set information is as follows: (1) Among the patients used for CT testing 57% were male and 43% female. Patient ages range 21-40: 13%, 41-60: 44.1%, 61-80: 36.8%, 81-100: 6.1%. Race: 78% White, 12% Black or African American, 10% Other. (2) Test datasets spanned across treatment subgroups most typically found in 4 radiation therapy treatment clinics. MR/CT test dataset covers Intracranial Tumor (13.8%), Nasopharyngeal Carcinoma (19%), Esophagus Cancer (19%), Lung Cancer (17.2%), Liver Cancer (12.9%), Cervical Cancer (18.1%); CBCT/CT test dataset covers Intracranial Tumor (16.8%), Nasopharyngeal Carcinoma (16.8%), Esophagus Cancer (16.8%), Lung Cancer (16.8%), Liver Cancer (16%), Cervical Cancer (16.8%). (3) The CT images were obtained using scanners supplied by GE/Philips/Siemens, including 28.3% by GE, 41.7% by Philips and 30% by Siemens. The MR images were obtained using scanners supplied by GE/Philips/Siemens, including 21.6% by GE, 56.9% by Philips and 21.6% by Siemens. The CBCT images were obtained using scanners supplied by Varian/Elekta, including 58.8% by Varian, 41.2% by Elekta. And the images contained different slice thicknesses, distributed as follows: 19% 1mm, 22.8% 2mm, 17.4% 2.5mm, 17% 3mm, 23.8% 5mm slice thickness. ## Mechanical and Acoustic Testing Not Applicable (Standalone Software). {11} 510(k) Summary ## Animal Study Not Applicable (Standalone Software). ## Clinical Studies Clinical trials were not performed as part of the development of this product. Clinical testing on patients is not advantageous in demonstrating substantial equivalence or safety and effectiveness of the device since testing can be performed such that no human subjects are exposed to risk. ## VIII. CONCLUSIONS ARTAssistant is believed to be substantially equivalent to the predicate device (K221706) in terms of its indications for use, technical characteristics, and overall performance. The information provided in this submission indicates substantial equivalence to the predicate device. Therefore, Manteia Technologies Co., Ltd. considers the subjective device, ARTAssistant, is substantially equivalent to the predicate device AccuContour (K221706). 8 / 8 --- **Source:** [https://fda.innolitics.com/submissions/RA/subpart-b%E2%80%94diagnostic-devices/QKB/K250780](https://fda.innolitics.com/submissions/RA/subpart-b%E2%80%94diagnostic-devices/QKB/K250780) **Published by [Innolitics](https://innolitics.com)** — a medical-device software consultancy. We help companies design, build, and clear FDA-regulated software and AI/ML devices. 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