药物活度分析仪Twilite
供应商:北京泰坤工业设备有限公司[查看公司详情]
所在地:北京市昌平区回龙观金燕龙大厦2028
价格:1300000元/件
经营模式:生产型
联系人:施之春
产品简介
血液活度动态在线分析系统
一、产品介绍:
该系统适用于药物动力学血液放射活度实时量测研究(可配合于PET、SPECT、PET/MRI系统)
Twilite 是由 Swisstrace 公司所研发设计的高灵敏度自动血液取样系统。此系统可与 PET 、SPECT、或 PET/MR 影像系统结合使用,无论是小实验动物、大人体,均能够在线高分辨率的测得解析度的实时血液活度变化信息。
Twilite 系统的核心是一个设计精巧的侦测头(探测器),由 LYSO 晶体与屏蔽外来辐射用的医疗级钨加工製成,因此与 MR 影像系统相容。闪烁讯号透过两条可自订长度的率光导管传输光子侦测单元。此设计使得侦测头端没有任何电子件,所以能够避免来自其他设备所造成的电磁干扰问题。此外,这样的设计也能够将人体研究实验的潜在风险小化。
数据采集是使用 PMOD 公司所开发的 PSAMPLE 软件,藉由 TCP/IP 介面传输,允许同时记录多套 Swisstrace 系统的讯号,例如可同时使用 Twilite 系统与 Twin beta probe 系统。此外,尚有两个类比讯号输入孔可同时记录来自其他仪器的讯号,例如Laser Doppler Flowprobes、ECG 或来自辅助设备的触发讯号。 PMOD 软件的功能模块可对取得的放射活度信号进行离线处理分析。
此系统也可不连接电脑单独使用。仪器前方的触摸式面板可直接进行操作,并即时显示测量数据。
Twilite 系统性能。除了拥有的灵敏度外,即使在高辐射值的环境下,仍然呈现处稳定的线性度与信噪比。
Swisstrace 公司的开发人员在放射定量实验方面具有相当深厚的经验。系统设计乃针对 PET 系统(包含小动物与人)化。侦测头精巧的尺寸尤其适合使用于小动物正子造影系统中,搭配动、静脉分流管(arterio-venous shunt), Twilite 系统可测得全血的动脉输入函数(arterial input function, AIF)而不必将血液抽离体外。
二、实验结构
|
图1 图2 图3
1、 连接股动脉与股静脉的分流管(自购)
2 、蠕动帮浦(Peristaltic Pump)(自购)
3、 Twilite 钨制侦测头
4 、LYSO 晶体1
5 、LYSO 晶体2
6、 光导管:传输光子讯号PMT。标准长度2 m,若需使用于MR 系统可延长10 m
7 、光子侦测单元
8、 两个模拟讯号输入孔(可与其他品牌仪器配合使用,监控呼吸、ECG 或血压等)
9 、TCP/IP 传输接口:可透过因特网传输或直接与计算机连接,使用PMOD 软件PSAMPLE 模块进行数据撷取
10、 安装PMOD 软件的计算机,进行数据撷取与分析(自购)【Twilite 系统仅包含3 ~ 9项产品】
11、动静脉分流管(小鼠用PE10,大鼠用PE50)可同时用于血压量测、药物注射及血液样本采集等其他功能,如图3所示。血液样本采集可用解剖刀在导管上划一个小口,在采集时间点将导管往缺口方向推,即可取得血液样本。
12、结构和曲线函数(如图)
左图为实验架构。血流以蠕动帮浦驱动,从股动脉流出体外,经过耦合讯号侦测头后,再由股静脉回到体内。t1与t2两个三向阀分别用来进行血液取样与药物注射。右图为Twilite 系统所测得的小鼠动脉输入曲线。
三、系统规格
序号 |
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|
|
1 | 侦测头 | 尺寸 | 80 × 62 × 56 mm (L ×W × H). 约5 kg |
材质 | 由医疗级钨加工制成 | ||
闪烁晶体 | LYSO | ||
联机 | 两条率光导管,长度2 ~ 10 m | ||
2 | 性能 | 灵敏度 | 导管内径0.28 mm: 0.2 cps/kBq/ml(小鼠) |
导管内径0.58 mm: 0.8 cps/kBq/ml(大鼠) | |||
导管内径1.00 mm: 2.4 cps/kBq/ml(人体) | |||
3 | 线性度 |
| 6000 cps 以下线性,在10000 cps 误差小于1% |
4 | 光子侦测单元 | 安装 | 可安装于机架的外壳设计,内含光子计数器与撷取电路。 |
操作 | 可单独操作,执行系统检查与校正等功能,触摸屏实时数据显[cps]。 | ||
5 | 输入 | 辅助模拟输入 | 面板前方提供两个BNC 规格模拟讯号输入孔(0 ~ 3.3 V) |
6 | 数据撷取 | 软件 | 软件PMOD 软件PSAMPLE 模块 |
操作系统 | Windows 7, XP, vista, MacOSX, Linux | ||
传输接口 | TCP/IP (可选配无线传输) |
四、用户名单
序号 | 客户 | 仪器数量 |
1 | University of Zurich | 1 |
2 | Federal Institute of Technology, Zurich | 1 |
3 | Research Institution Juelich Germany | 1 |
4 | University of Antwerp, Belgium | 1 |
5 | Research Institute, Paris | 1 |
6 | University of Hannover | 1 |
7 | University of Oslo | 1 |
8 | Genentech, San Francisco | 2 |
9 | Amgen Biotechnology | 1 |
五、合作伙伴
PMOD Technologies Ltd. Unitectra
Zurich, Switzerland Zurich, Switzerland
University of Zurich CSEM
Zurich, Switzerland Neuchatel, Switzerland
六、药物动力学实验论文(部分摘要)
Quantification of Brain Glucose
Metabolism by 18F-FDG PET
with Real-Time Arterial and Image-Derived Input
Function in Mice
Malte F. Alf1,2, Matthias T. Wyss3,4,
Alfred Buck3, Bruno Weber4, Roger Schibli1,5, and Stefanie D. Krämer11Center
for
Radiopharmaceutical Sciences of ETH, PSI, and USZ, Institute of
Pharmaceutical Sciences, Department of Chemistry and
Applied Biosciences,
ETH Zurich, Zurich, Switzerland; 2Laboratory of Functional and Metabolic
Imaging, Institute of Physics of
Biological Systems, Ecole Polytechnique
Fédérale de Lausanne, Lausanne, Switzerland; 3Department of Nuclear
Medicine,
University Hospital Zurich, Zurich, Switzerland; 4Institute of
Pharmacology and Toxicology, University of Zurich, Zurich,Switzerland;
and
5Center for Radiopharmaceutical Sciences of ETH, PSI, and USZ, Paul Scherrer
Institute PSI, Villigen, Switzerla
Kinetic modeling of
PET data derived from mouse modelsremains hampered by thetechnical
inaccessibility of an accurateinput function (IF).
In this work, we tested
the feasibility of IF measurement with an arteriovenous shunt and a
coincidencecounter in mice and compared the method
with an imagederived IF
(IDIF) obtained by ensemble-learning independent component analysis of the
heart region. Methods: 18F-FDG brain kinetics were quantified in 2 mouse
strains, CD1 and C57BL/6, using the standard 2-tissue-compartment model. Fits
obtained with the 2 IFs were compared regarding their goodness of fit as
assessed by the residuals, fit parameter SD, and Bland–Altman analysis.
Results: On average, cerebral glucose metabolic rate was 10% higher for
IDIF-based quantification.The precision of model parameter fitting was
significantly higher using the shunt-based IF, rendering the quantification of
single process rate constants feasible. Conclusion: We demonstrated that the
arterial IF can be measured in mice with a femoral arteriovenous shunt. This
technique resulted in higher precision for kinetic modeling parameters than did
use of the IDIF. However,for longitudinal or high-throughput studies, the use
of a minimally invasive IDIF based on ensemble-learning independent component
analysis represents a suitable alternative.
Key Words:
energy metabolism; PET; molecular imaging; glucose; kinetic modeling
J Nucl
Med 2013; 54:1–7 DOI: 10.2967/jnumed.112.107474
PET
with 18F-FDG is a commonly used method to determine glucose metabolism in
animal and human tissues (1). Full quantification of 18F-FDG kinetics can be
achieved by applying a 2-tissue-compartment model (2). The model requires the
time course of the 18F-FDG concentration in the target organ(tissue
time–activity curve) and in arterial plasma (input function, IF). In human
brain PET, the IF is commonly measured from a catheter placed in the radial
artery. An alternative is derivation of the IF from PET images via values
measured in a volume of interest placed over the cardiac ventricles or a large
vessel. A prerequisite of image-derived IFs (IDIFs) is the location of the
blood pool and the organ of interest in the same field of view. In general, one
or more arterial blood samples are measured to calibrate the IDIF. In a recent
review article for human PET(3), the authors concluded that arterial blood
sampling remains the preferred method to define the IF, because invasiveness is
hardly reduced by the use of an IDIF.
In small animals,
the small blood volume is the major constraint for manual blood sampling. This
constraint prompted the development of several alternative methods, such as the
sampling of very small volumes via a microfluidic chip (4) or the use of
b-probes for measuring the blood radioactivity (5,6). Despite these new
physical methods, the main research focus has been on developing the use of
IDIFs, where blood radioactivity is estimated directly from the dynamic PET
images. IDIF generation from simple analysis of blood pool volumes such as the
liver or the heart ventricles is flawed by 18F-FDG uptake by the liver or
spillover from surrounding myocardium, cardiac motion, and partial-volume
effects. Compensation can be achieved to varying degrees by image processing
methods such as factor analysis (7), modelbased techniques (8), independent
component analysis (9), so-called hybrid IDIFs (e.g., 10,11), and cardiac
gating combined with improved image reconstruction algorithms (12). Most of
these methods rely on at least 1 measure from a blood sample for scaling of the
IDIF.Hence, blood sampling is not entirely
obviated.
To our knowledge, there is currently no gold
standard to define the real-time 18F-FDG arterial IF in mice in a reliable and
easily accessible manner. In this study, we adapted a method for direct blood
radioactivity measurements and an approach for the generation of IDIFs for use
in mice. We acquired real-time blood radioactivity curves by means of a new
coincidence counter in combination with an arteriovenous shunt and compared the
findings to IDIFs generated from PET data of the cardiac region with an
ensemblelearning independent component analysis (EL-ICA) algorithm (13).We used
2 different mouse strains to explore the possible strain dependency of our
methods: C57BL/6 mice, because they are relevant for studies of genetically
modified animals, and CD1 mice, because they are valuable as disease
models,such as cerebral ischemia (14). The purpose of this work was 2-fold.
First, we evaluated whether the arteriovenous-shunt/ counter technique, which
was previously demonstrated in rats (15), is also feasible in mice. Second, we
compared 18F-FDG kinetic parameters and fit precisions obtained with the
experimental shunt IF and the IDIF.