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  • NewsProfessor Lee Jae-sung Honored with the 2026 Edward J. Hoffman Memorial Award

    Brightonix Imaging congratulates Professor Lee Jae-sung, Co-founder of Brightonix Imaging and Professor at Seoul National University, on receiving the 2026 Edward J. Hoffman Memorial Award from Society of Nuclear Medicine and Molecular Imaging. Professor Lee Jae-sung, Professor at Seoul National University College of Medicine and the College of Transdisciplinary Studies, has been honored with the 2026 Edward J. Hoffman Memorial Award by the Society of Nuclear Medicine and Molecular Imaging (SNMMI), the world's leading organization in nuclear medicine and molecular imaging. He is the first Asian researcher to receive this distinguished honor. The Edward J. Hoffman Memorial Award was established in memory of Dr. Edward J. Hoffman, co-inventor of the Positron Emission Tomography (PET) system and a pioneer in modern nuclear medicine imaging. The award recognizes individuals who have made outstanding contributions to the advancement of nuclear medicine imaging instrumentation and image analysis technologies. Professor Lee was recognized for his pioneering contributions to PET instrumentation, quantitative medical imaging, AI-driven image analysis, and neuroimaging research. In particular, his work in the development and clinical implementation of high-resolution digital PET systems and AI-driven image analysis software was highly acclaimed. Over the past two decades, Professor Lee and his research team have led research in advanced imaging technologies, including digital radiation detectors, next-generation PET systems, and PET/MRI fusion imaging platforms. More recently, they have focused on developing innovative AI-driven methodologies to overcome the limitations of conventional nuclear medicine image analysis. Professor Lee has also successfully translated research innovations into commercial applications. As a co-founder of Brightonix Imaging, he has contributed to the development and commercialization of PET imaging systems in South Korea, supporting the global adoption of advanced medical imaging technologies developed domestically. Commenting on the award, Professor Lee stated: “This award is a testament to the dedication and hard work of my students, co-researchers, and colleagues who have accompanied me throughout this long journey of research and development. I remain committed to advancing medical imaging technologies that enable more accurate diagnoses and personalized treatments.” Brightonix Imaging extends its sincere congratulations to Professor Lee on this remarkable achievement and wishes him continued success in advancing the field of molecular imaging and nuclear medicine. Source: Financial News, June 11, 2026.

  • NewsBrightonix Imaging CEO Jae sung Lee Receives Minister of Science and ICT Commendation – Recognized for Technological Innovation at 2025 Pan-minis...

    Kim Beop-min, head of the Pan-Ministerial Full-Cycle Medical Device research and development Project Group, delivers opening remarks at the 2025 Pan-Ministerial Medical Device research and development (R&D) Awards at Lotte Hotel Seoul in Jung-gu, Seoul, on the morning of the 22nd. /Courtesy of Pan-Ministerial Full-Cycle Medical Device research and development Project GroupThe Korea Medical Device Development Fund (KMDF) shared the outcomes of the first-phase program it supported over six years.On the 22nd in the morning at Lotte Hotel Seoul in Jung-gu, Seoul, the fund held the 2025 pan-ministerial medical device research and development (R&D) awards and presented commendations for 20 outstanding results among 467 research projects supported over the past six years.This program is supported jointly by four government ministries—the Ministry of Science and ICT, the Ministry of Trade, Industry and Energy, the Ministry of Health and Welfare, and the Ministery of Food and Drug Safety—covering the entire research cycle from advanced medical device design to application in clinical settings.Launched in 2020, the first-phase program ran for six years through this year, investing a total of 947.9 billion won from state funds and private capital and conducting 467 research projects.Deputy Minister Kim Tae-hyung of the pan-ministerial medical device fund said, "Over six years, there were around 2,500 papers and patents, and corporations carrying out the projects attracted about 550 billion won in investment."Kim also explained, "Of the roughly 300 corporations newly listed on KOSDAQ from 2021 to Nov. 2025, 25 are in the bio-health sector, and 10 of those are corporations that carried out the fund's projects," adding, "They were listed on KOSDAQ during the project period." Kim said, "Government R&D support is becoming an opportunity to enhance corporations' credibility and future value beyond simple budget support."Deputy Minister Kim Tae-hyung of the Pan-Ministerial Full-Cycle Medical Device research and development Project Group presents key project outcomes at the 2025 Pan-Ministerial Medical Device research and development (R&D) Awards held at the Sapphire Hall of Lotte Hotel Seoul in Sogong-dong, Jung-gu, Seoul, on the 22nd. /Courtesy of Heo Ji-yoon, reporterSince 2023, the fund has selected 10 representative projects each year. For these commendations, about 59 institutions applied through a public call, and based on research and development performance, contribution to research and development, ripple effects, and public contribution, internal and external experts selected 20 awards, including government commendations, professional institution awards, and the fund director-general's awards.Brightonix Imaging, the only domestic corporation developing positron emission tomography (PET) equipment systems, and Curiosis, which developed a system that automates experimentation and analysis processes in laboratories, research labs, and factories using technologies such as computers and robots, received commendations from the Minister of the Ministry of Science and ICT.Seoul National University Hospital and medical artificial intelligence (AI) corporation AIRS Medical received commendations from the Minister of the Ministry of Trade, Industry and Energy.The Minister of the Ministry of Health and Welfare award went to Seers Technology, which developed a patch-type wearable electrocardiogram testing system that diagnoses and manages cardiac diseases such as arrhythmia, and to Curaco, which developed a device linked to the hospital electronic medical record (EMR) system that automatically manages patients' bowel movements in hospitals.Angel Robotics, which developed a domestically produced wearable gait rehabilitation robot, and Todac, which localized cochlear implant devices that had been entirely imported, received the National Research Foundation of Korea (NRF) award, while i-SENS, which developed a domestic continuous glucose monitor (CGM), and Samsung Medical Center received the Korea Health Industry Development Institute (KHIDI) institutional award.Emocog, which developed the digital therapeutic device "Cogthera" for patients with mild cognitive impairment (MCI), received the Korea Planning&Evaluation Institute of Industrial Technology (KEIT) institutional award, while DRTECH, Boditech Med, VIMWORKS, HUINNO, Samduck Commerce, and others received the Director General's commendations.The ChosunBiz medical-bio team has shed light on the R&D status and footprint of corporations selected for the first-phase fund. Accordingly, Oh Gwang-jin, editor-in-chief of ChosunBiz's Economy Chosun, received a meritorious service award.Fund Director Kim Beop-min said, "Their achievements will serve as a strategic inflection point that elevates the capabilities of domestic medical devices and lays the groundwork for new innovation," adding, "We hope that six years of national investment and researchers' dedicated efforts will lead to improved public health and tangible contributions."The second-phase program will launch next year under the name "pan-ministerial advanced medical device research and development project." Through 2032 over the next seven years, a total of 940.8 billion won will be invested, including 838.3 billion won in state funds and 102.5 billion won in private capital, to develop six world-first or world-best medical devices and localize 13 essential medical devices.Source: Pan‑ministerial fund drives Korea medical device firms to secure 467 projects and ₩550bn investment - chosunBiz

  • NewsBrightonix Imaging Demonstrates Excellence in AI-Based Dopamine PET Quantification

    Brightonix Imaging Demonstrates Excellence of AI-Based Dopamine PET Quantification Technology Enables Dopamine Transporter PET Quantification Without MRIBrightonix Imaging has demonstrated the superior quantitative performance of its AI-based PET image quantification software, BTXBrain, through the research paper titled “Accurate Automated Quantification of Dopamine Transporter PET Without MRI Using Deep Learning-based Spatial Normalization,” published in Nuclear Medicine & Molecular Imaging (NMMI).The company announced that this study earned the NMMI Outstanding Research Award at the 2025 Autumn Conference of the Korean Society of Nuclear Medicine (KSNM), held from November 14–15 at KINTEX in Ilsan.Conducted in collaboration with Professor Hongyoon Choi (Department of Nuclear Medicine, Seoul National University Hospital) and Professor Yu Kyeong Kim (Department of Nuclear Medicine, Seoul Metropolitan Government – Seoul National University Boramae Medical Center), the study was published in the July 2024 issue of NMMI. It received significant attention for introducing a deep learning–based normalization method that enables dopamine transporter PET quantification without MRI, offering a substantial improvement in both the accuracy and convenience of automated quantification.Traditionally, MRI data has been essential for accurate dopamine transporter PET quantification. In this study, however, the research team successfully developed an image analysis solution capable of performing high-precision spatial normalization using only PET images, providing meaningful clinical benefits—particularly in cases where MRI scans are difficult or impractical.Seung Kwan Kang, Head of AI/Algorithm Development at Brightonix Imaging, stated, “This award acknowledges that our technology addresses clinically meaningful challenges and contributes to real-world medical practice. We will continue advancing AI-based nuclear medicine image quantification to establish new standards in the diagnosis and treatment assessment of Parkinson’s disease.”Brightonix Imaging is a leading innovator in Positron Emission Tomography (PET) and AI-driven medical image analysis technologies, providing cutting-edge solutions for clinical and research applications. Source: 브라이토닉스이미징, AI 기반 도파민 PET 정량화 기술의 우수성 입증 - 한국의약통신(http://www.kmpnews.co.kr)

  • NewsBrightonix Imaging’s High-Performance ‘PHAROS’ PET Receives FDA Clearance

    Brightonix Imaging’s High-Performance ‘PHAROS’ PET Receives FDA ClearanceBrightonix, South Korea –  August 15, 2025 – Brightonix Imaging, a global leader in cutting-edge medical imaging technology, is proud to announce that its flagship product, the PHAROS PET Scanner, has received FDA clearance for commercial distribution in the United States. This milestone marks a new era of precision and efficiency in nuclear imaging and positions Brightonix Imaging at the forefront of medical innovation.The PHAROS PET is a state-of-the art clinical positron emission tomography (PET) system designed to deliver exceptional image quality, offering healthcare providers a powerful tool for early disease detection, precise diagnosis, and optimized treatment planning. With its innovative design and enhanced performance capabilities, the PHAROS scanner is poised to set new standards in neurology.  Furthermore, the PHAROS is multi-functional with the ability to physically orient the patient seat and detector configurations for extremity and breast imaging, as well as converting to both lying and seated modes for brain imaging.  

  • PaperImproving 18F-FDG PET Quantification Through a Spatial Normalization Method

    Background: Quantification of 18F-FDG PET images is useful for accurate diagnosis and evaluation of various brain diseases, including brain tumors, epilepsy, dementia, and Parkinson disease. However, accurate quantification of 18F-FDG PET images requires matched 3-dimensional T1 MRI scans of the same individuals to provide detailed information on brain anatomy. In this paper, we propose a transfer learning approach to adapt a pretrained deep neural network model from amyloid PET to spatially normalize 18F-FDG PET images without the need for 3-dimensional MRI. Methods: The proposed method is based on a deep learning model for automatic spatial normalization of 18F-FDG brain PET images, which was developed by fine-tuning a pretrained model for amyloid PET using only 103 18F-FDG PET and MR images. After training, the algorithm was tested on 65 internal and 78 external test sets. All T1 MR images with a 1-mm isotropic voxel size were processed with FreeSurfer software to provide cortical segmentation maps used to extract a ground-truth regional SUV ratio using cerebellar gray matter as a reference region. These values were compared with those from spatial normalization-based quantification methods using the proposed method and statistical parametric mapping software. Results: The proposed method showed superior spatial normalization compared with statistical parametric mapping, as evidenced by increased normalized mutual information and better size and shape matching in PET images. Quantitative evaluation revealed a consistently higher SUV ratio correlation and intraclass correlation coefficients for the proposed method across various brain regions in both internal and external datasets. The remarkably good correlation and intraclass correlation coefficient values of the proposed method for the external dataset are noteworthy, considering the dataset’s different ethnic distribution and the use of different PET scanners and image reconstruction algorithms. Conclusion: This study successfully applied transfer learning to a deep neural network for 18F-FDG PET spatial normalization, demonstrating its resource efficiency and improved performance. This highlights the efficacy of transfer learning, which requires a smaller number of datasets than does the original network training, thus increasing the potential for broader use of deep learning–based brain PET spatial normalization techniques for various clinical and research radiotracers.Keywords: brain PET, quantification, spatial normalization, glucose metabolismJournal of Nuclear Medicine August 2024, jnumed.123.267360; DOI: https://doi.org/10.2967/jnumed.123.267360Link: https://jnm.snmjournals.org/content/early/2024/08/29/jnumed.123.267360

  • PaperImpact of shortening time on diagnosis of 18F-florbetaben PET

    Background: 18F-Florbetaben amyloid positron emission tomography (PET) scan is crucial for diagnosing Alzheimer’s disease, typically involving a 20 min acquisition. However, maintaining such prolonged scans can be challenging in some cases. This study explores the diagnostic impact and feasibility of reducing scan durations by comparing quantitative measures between shortened and standard scans. Additionally, we identified the optimal Centiloid threshold to distinguish between positive and negative amyloid results.Results: We analyzed 307 PET scans from our memory clinic, each followed up for a minimum of two years. The scans, conducted 90 to 110 min after approximately 300 MBq of 18F-Florbetaben injection, were categorized into four sets of 5 min durations: 5, 10, 15, and 20 min. Nuclear medicine physicians validated and rated each scan as either amyloid-positive or negative. For quantitative assessments, we employed the standardized uptake value ratio (SUVR) and Centiloid scales, comparing total SUVR and Centiloid values across five subregions (global, frontal, posterior cingulate-precuneus, lateral temporal, and parietal) using Bland–Altman analysis. Receiver operator characteristic (ROC) curves were utilized to develop optimal Centiloid thresholds. Comparing the images at 5, 10, 15, and 20 min images, SUVR and Centiloid values gradually increased with prolonged scan times. The mean SUVR difference between 5 and 20 min was 0.03 for the amyloid-positive and 0.01 for the amyloid-negative groups; Centiloid differences were 4.60 and 2.38, respectively. Additionally, no significant variation was observed in total SUVR and Centiloid values among the durations across all subregions in positive and negative groups (all p > 0.1). ROC analysis indicated that a Centiloid threshold of 21.86 at 5 min provided optimal agreement with visual assessments (AUC = 0.985, sensitivity = 0.950, specificity = 0.972), especially using the global area.Conclusions: This study demonstrated that 5 min image scans with an optimal threshold of CL = 21.86 exhibited minimal bias in SUVR and Centiloid values compared to longer scans (10, 15, and 20 min). Our findings suggest that shorter scan times are a viable and effective option for brain amyloid PET imaging in clinical settings.Keywords: Alzheimer's disease, PET, Florbetaben, Shortening time, Centiloid threshold, AmyloidEJNMMI Res 14, 114 (2024). DOI: 10.1186/s13550-024-01181-8Link: Impact of shortening time on diagnosis of 18F-florbetaben PET

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