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논문Fast and Accurate Amyloid Brain PET Quantification Without MRI Using Deep Neural Networks

Fast and Accurate Amyloid Brain PET Quantification Without MRI Using Deep Neural NetworksAbstractThis paper proposes a novel method for automatic quantification of amyloid PET using deep learning-based spatial normalization (SN) of PET images, which does not require MRI or CT images of the same patient. The accuracy of the method was evaluated for 3 different amyloid PET radiotracers compared with MRI-parcellation-based PET quantification using FreeSurfer. Methods: A deep neural network model used for the SN of amyloid PET images was trained using 994 multicenter amyloid PET images (367 18F-flutemetamol and 627 18F-florbetaben) and the corresponding 3-dimensional MR images of subjects who had Alzheimer disease or mild cognitive impairment or were cognitively normal. For comparison, PET SN was also conducted using version 12 of the Statistical Parametric Mapping program (SPM-based SN). The accuracy of deep learning-based and SPM-based SN and SUV ratio quantification relative to the FreeSurfer-based estimation in individual brain spaces was evaluated using 148 other amyloid PET images (64 18F-flutemetamol and 84 18F-florbetaben). Additional external validation was performed using an unseen independent external dataset (30 18F-flutemetamol, 67 18F-florbetaben, and 39 18F-florbetapir). Results: Quantification results using the proposed deep learning-based method showed stronger correlations with the FreeSurfer estimates than SPM-based SN using MRI did. For example, the slope, y-intercept, and R 2 values between SPM and FreeSurfer for the global cortex were 0.869, 0.113, and 0.946, respectively. In contrast, the slope, y-intercept, and R 2 values between the proposed deep learning-based method and FreeSurfer were 1.019, -0.016, and 0.986, respectively. The external validation study also demonstrated better performance for the proposed method without MR images than for SPM with MRI. In most brain regions, the proposed method outperformed SPM SN in terms of linear regression parameters and intraclass correlation coefficients. Conclusion: We evaluated a novel deep learning-based SN method that allows quantitative analysis of amyloid brain PET images without structural MRI. The quantification results using the proposed method showed a strong correlation with MRI-parcellation-based quantification using FreeSurfer for all clinical amyloid radiotracers. Therefore, the proposed method will be useful for investigating Alzheimer disease and related brain disorders using amyloid PET scans.Keywords: amyloid PET; deep learning; quantification; spatial normalizationJ Nucl Med. 2023 Apr;64(4):659-666. doi: 10.2967/jnumed.122.264414.Link: Fast and Accurate Amyloid Brain PET Quantification Without MRI Using Deep Neural Networks - PubMed (nih.gov)

논문Accurate Automated Quantification of Dopamine Transporter PET Without MRI Using Deep Learning-based Spatial Normalization

Accurate Automated Quantification of Dopamine Transporter PET Without MRI Using Deep Learning-based Spatial NormalizationPurposeDopamine transporter imaging is crucial for assessing presynaptic dopaminergic neurons in Parkinson’s disease (PD) and related parkinsonian disorders. While 18F-FP-CIT PET offers advantages in spatial resolution and sensitivity over 123I-β-CIT or 123I-FP-CIT SPECT imaging, accurate quantification remains essential. This study presents a novel automatic quantification method for 18F-FP-CIT PET images, utilizing an artificial intelligence (AI)-based robust PET spatial normalization (SN) technology that eliminates the need for anatomical images.MethodsThe proposed SN engine consists of convolutional neural networks, trained using 213 paired datasets of 18F-FP-CIT PET and 3D structural MRI. Remarkably, only PET images are required as input during inference. A cyclic training strategy enables backward deformation from template to individual space. An additional 89 paired 18F-FP-CIT PET and 3D MRI datasets were used to evaluate the accuracy of striatal activity quantification. MRI-based PET quantification using FIRST software was also conducted for comparison. The proposed method was also validated using 135 external datasets.ResultsThe proposed AI-based method successfully generated spatially normalized 18F-FP-CIT PET images, obviating the need for CT or MRI. The striatal PET activity determined by proposed PET-only method and MRI-based PET quantification using FIRST algorithm were highly correlated, with R2 and slope ranging 0.96–0.99 and 0.98–1.02 in both internal and external datasets.ConclusionOur AI-based SN method enables accurate automatic quantification of striatal activity in 18F-FP-CIT brain PET images without MRI support. This approach holds promise for evaluating presynaptic dopaminergic function in PD and related parkinsonian disorders.Nucl Med Mol Imaging (2024). https://doi.org/10.1007/s13139-024-00869-yLink: Accurate Automated Quantification of Dopamine Transporter PET Without MRI Using Deep Learning-based Spatial Normalization | Nuclear Medicine and Molecular Imaging (springer.com)

논문Age and gender effects on striatal dopamine transporter density and cerebral perfusion in individuals with non-degenerative parkinsonism: a dual-ph...

Age and gender effects on striatal dopamine transporter density and cerebral perfusion in individuals with non-degenerative parkinsonism: a dual-phase 18F-FP-CIT PET studyBackgroundDual-phase fluorine-18 labeled N-3-fluoropropyl-2β-carbomethoxy-3β-(4-iodophenyl) nortropane (18F-FP-CIT) positron emission tomography (PET) scans could be used to support disorders like Parkinson’s disease (PD). Dopamine transporter (DAT) binding and cerebral perfusion are associated with ageing and gender. We investigated the effects of age and gender on non-degenerative parkinsonism, using automated quantification in striatum: specific binding ratios (SBRs) for DAT binding in delayed phase PET (dCIT) and standardized-uptake-value ratios (SUVRs) for cerebral perfusion in early phase PET (eCIT). We also examined the correlations between SBR and SUVR.MethodsThis retrospective study analyzed subjects with dual-phase 18F-FP-CIT PET scans. The eCIT images were acquired immediately post-injection, and dCIT images were taken 120 min later. With Brightonix software, automated quantification of SBRs for dCIT and SUVRs for eCIT were acquired from visually normal scans. The effects of aging and gender were assessed by regressing SBRs and SUVRs on age for both genders. The correlations between SUVRs and SBRs were evaluated.ResultsWe studied 79 subjects (34 males and 45 females). An age-related reduction in SBRs was observed in the dorsal striatum, ventral striatum, caudate nucleus, and putamen for both genders. SUVRs were found to negatively correlate with age in the dorsal striatum, ventral striatum, caudate nucleus, and putamen for males and in the dorsal striatum and caudate nucleus for females. Positive correlations between SBRs and SUVRs in the dorsal striatum, ventral striatum, caudate nucleus, and putamen for male and in the dorsal striatum, caudate nucleus, and putamen for females.ConclusionsUsing quantified values from dual-phase 18F-FP-CIT PET with a single injection, we demonstrate a negative impact of age on SBRs (DAT binding) in the striatum for both genders and SUVRs (cerebral perfusion) in the dorsal striatum and caudate nucleus for both genders and in the ventral striatum and putamen for males. Additionally, we found positive associations between SBR and SUVR values in the dorsal striatum, caudate nucleus, and putamen for both genders and in the ventral striatum for males.EJNMMI Research volume 14, Article number: 65 (2024)Link: Age and gender effects on striatal dopamine transporter density and cerebral perfusion in individuals with non-degenerative parkinsonism: a dual-phase 18F-FP-CIT PET study | EJNMMI Research | Full Text (springeropen.com)

논문Optimization of micelle-encapsulated extremely small sized iron oxide nanoparticles as a T1 contrast imaging agent: biodistribution and safety profile

Optimization of micelle-encapsulated extremely small sized iron oxide nanoparticles as a T1 contrast imaging agent: biodistribution and safety profileBackgroundIron oxide nanoparticles (IONPs) have been cleared by the Food and Drug Administration (FDA) for various clinical applications, such as tumor-targeted imaging, hyperthermia therapy, drug delivery, and live-cell tracking. However, the application of IONPs as T1 contrast agents has been restricted due to their high r2 values and r2/r1 ratios, which limit their effectiveness in T1 contrast enhancement. Notably, IONPs with diameters smaller than 5 nm, referred to as extremely small-sized IONPs (ESIONs), have demonstrated potential in overcoming these limitations. To advance the clinical application of ESIONs as T1 contrast agents, we have refined a scale-up process for micelle encapsulation aimed at improving the hydrophilization of ESIONs, and have carried out comprehensive in vivo biodistribution and preclinical toxicity assessments.ResultsThe optimization of the scale-up micelle-encapsulation process, specifically employing Tween60 at a concentration of 10% v/v, resulted in ESIONs that were uniformly hydrophilized, with an average size of 9.35 nm and a high purification yield. Stability tests showed that these ESIONs maintained consistent size over extended storage periods and dispersed effectively in blood and serum-mimicking environments. Relaxivity measurements indicated an r1 value of 3.43 mM− 1s− 1 and a favorable r2/r1 ratio of 5.36, suggesting their potential as T1 contrast agents. Biodistribution studies revealed that the ESIONs had extended circulation times in the bloodstream and were primarily cleared via the hepatobiliary route, with negligible renal excretion. We monitored blood clearance and organ distribution using positron emission tomography and magnetic resonance imaging (MRI). Additionally, MRI signal variations in a dose-dependent manner highlighted different behaviors at varying ESIONs concentrations, implying that optimal dosages might be specific to the intended imaging application. Preclinical safety evaluations indicated that ESIONs were tolerable in rats at doses up to 25 mg/kg.ConclusionsThis study effectively optimized a scale-up process for the micelle encapsulation of ESIONs, leading to the production of hydrophilic ESIONs at gram-scale levels. These optimized ESIONs showcased properties conducive to T1 contrast imaging, such as elevated r1 relaxivity and a reduced r2/r1 ratio. Biodistribution study underscored their prolonged bloodstream presence and efficient clearance through the liver and bile, without significant renal involvement. The preclinical toxicity tests affirmed the safety of the ESIONs, supporting their potential use as T1 contrast agent with versatile clinical application.Journal of Nanobiotechnology volume 22, Article number: 419 (2024)Link: Optimization of micelle-encapsulated extremely small sized iron oxide nanoparticles as a T1 contrast imaging agent: biodistribution and safety profile | Journal of Nanobiotechnology | Full Text (biomedcentral.com)

논문Clinical Performance Evaluation of an Artificial Intelligence-Powered Amyloid Brain PET Quantification Method

Clinical Performance Evaluation of an Artificial Intelligence-Powered Amyloid Brain PET Quantification MethodPurposeThis study assesses the clinical performance of BTXBrain-Amyloid, an artificial intelligence-powered software for quantifying amyloid uptake in brain PET images.Methods150 amyloid brain PET images were visually assessed by experts and categorized as negative and positive. Standardized uptake value ratio (SUVR) was calculated with cerebellum grey matter as the reference region, and receiver operating characteristic (ROC) and precision-recall (PR) analysis for BTXBrain-Amyloid were conducted. For comparison, same image processing and analysis was performed using Statistical Parametric Mapping (SPM) program. In addition, to evaluate the spatial normalization (SN) performance, mutual information (MI) between MRI template and spatially normalized PET images was calculated and SPM group analysis was conducted.ResultsBoth BTXBrain and SPM methods discriminated between negative and positive groups. However, BTXBrain exhibited lower SUVR standard deviation (0.06 and 0.21 for negative and positive, respectively) than SPM method (0.11 and 0.25). In ROC analysis, BTXBrain had an AUC of 0.979, compared to 0.959 for SPM, while PR curves showed an AUC of 0.983 for BTXBrain and 0.949 for SPM. At the optimal cut-off, the sensitivity and specificity were 0.983 and 0.921 for BTXBrain and 0.917 and 0.921 for SPM12, respectively. MI evaluation also favored BTXBrain (0.848 vs. 0.823), indicating improved SN. In SPM group analysis, BTXBrain exhibited higher sensitivity in detecting basal ganglia differences between negative and positive groups.ConclusionBTXBrain-Amyloid outperformed SPM in clinical performance evaluation, also demonstrating superior SN and improved detection of deep brain differences. These results suggest the potential of BTXBrain-Amyloid as a valuable tool for clinical amyloid PET image evaluation.Keywords: Amyloid, Alzheimer dementia, Spatial normalization, Deep learning, QuantificationNucl Med Mol Imaging. 2024 Jun; 58(4): 246–254. doi: 10.1007/s13139-024-00861-6Link: Clinical Performance Evaluation of an Artificial Intelligence-Powered Amyloid Brain PET Quantification Method - PMC (nih.gov)

뉴스브라이토닉스이미징, ‘초격차 스타트업 1000+ 프로젝트’ 선정

브라이토닉스이미징, ‘초격차 스타트업 1000+ 프로젝트’ 선정의료영상 전문기업 ㈜브라이토닉스이미징 (대표이사 이재성)가 중소벤처기업부와 창업진흥원에서 주관하는 ‘초격차 스타트업 1000+ 육성사업(DIPS1000+) 프로젝트’에 선정됐다고 28일 발표했다. 초격차 스타트업 1000+ 프로젝트는 바이오헬스, 미래차, 친환경 에너지, AI빅데이터 등의 분야에서 독보적 기술력을 바탕으로 글로벌 진출이 가능하며, 향후 국가경제의 미래를 이끌어갈 10대 분야별 창업기업을 선정하고 집중적으로 지원하는 사업이다. 선정된 기업은 3년간 최대 6억원의 사업화 자금을 제공받아 기술개발과 시장진출, 기업 경영 등을 위한 창업사업화 활동을 지원받을 수 있다. (주)브라이토닉스이미징은 국내 의료기기 제조인증 허가를 받은 임상용 양전자방출단층촬영(PET) 시스템 ‘파로스(PHAROS)' 장비와 인공지능 기반 의료영상 분석 소프트웨어 ’BTXBrain'는 국내외 병원 및 유수 연구기관의 높은 관심을 받아 해외 시장 진출을 위해 박차를 가하고 있다. (주)브라이토닉스이미징 이재성 대표는 “이번 프로젝트 선정을 계기로 제품 기술력을 인정받아 매우 기쁘다”며, “해외시장 진출을 가속화하여 글로벌 기업으로 성장하고, 의료기기 분야의 국가경제 발전과 국내 의료기기 기술의 우수성을 알리는데 최선을 다할것”이라고 말했다.  <저작권자 ⓒ 약품신문 무단전재 및 재배포 금지>출처: 브라이토닉스이미징, ‘초격차 스타트업 1000+ 프로젝트’ 선정:약품신문 - https://www.yakpum.co.kr/15583