Content deleted Content added
Scope creep (talk | contribs) comment update |
m Open access bot: url-access updated in citation with #oabot. |
||
| (8 intermediate revisions by 5 users not shown) | |||
Line 1:
{{Short description|Medical imaging and optical technique}}
'''Diffuse
<!--A picture of the workflow associated with the modality is shown in [https://www.photon-force.com/pfweb/wp-content/uploads/2022/11/Diffuse-Correlation-Spectroscopy-DCS.jpg Figure 1]-->
▲Diffuse Correlation Spectroscopy (DCS) is a novel type of medical imaging and optical technique that utilizes near-infrared light to directly and non-invasively measure tissue blood flow.<ref name=":0">{{Cite journal |last1=Durduran |first1=Turgut |last2=Yodh |first2=Arjun G. |date=January 2014 |title=Diffuse correlation spectroscopy for non-invasive, micro-vascular cerebral blood flow measurement |journal=NeuroImage |language=en |volume=85 |issue=1 |pages=51–63 |doi=10.1016/j.neuroimage.2013.06.017 |pmc=3991554 |pmid=23770408}}</ref> The imaging modality was created by Dr. David Boas and Dr. Arjun Yodh in 1995.<ref name=":1">{{Cite journal |last=Yu |first=Guoqiang |title=Diffuse Correlation Spectroscopy (DCS): A Diagnostic Tool for Assessing Tissue Blood Flow in Vascular-Related Diseases and Therapies |url=https://www.eurekaselect.com/article/46892 |journal=Current Medical Imaging |year=2012 |language=en |volume=8 |issue=3 |pages=194–210 |doi=10.2174/157340512803759875}}</ref> A picture of the workflow associated with the modality is shown in [https://www.photon-force.com/pfweb/wp-content/uploads/2022/11/Diffuse-Correlation-Spectroscopy-DCS.jpg Figure 1].
Blood flow is one the most important factors affecting the delivery of oxygen and other nutrients to tissues. Abnormal blood flow is associated with many diseases such as stroke and cancer. Tumors from cancer can generate abnormal tumor blood flow compared to the surrounding tissue. Current treatments attempt to decrease blood flow to cancer cells. Therefore, there is an urgent need for a way to measure blood flow. However, blood flow is difficult to measure because of sensitivity and stability of the measurement as it depends on magnitude of flow, ___location, and the diameter of individual vessels.<ref name=":1" />
Current imaging modalities used to measure blood flow include [[Doppler ultrasonography|Doppler ultrasound]], [[Positron emission tomography|PET]], and [[Magnetic resonance imaging|MRI]]. Doppler ultrasound is limited to large vessels. PET requires arterial blood sampling and exposure to ionizing radiation. MRI cannot be used for patients with pacemakers and those with metal implants. All together, these imaging modalities have large and costly instrumentation and are not conducive to continuous measurements.<ref name=":1" />
With these considerations in mind, the first methodology used to measure blood flow is [[
This led to the ‘dynamic’ NIRS technique or Diffuse correlation spectroscopy. It measures the motions of the scatters while also maintaining the advantages of NIRS. The primary moving scatterers are red blood cells. The main advantages of this method is no ionizing radiation, no contrast agents, high temporal resolution, and large penetration depth. The utility of DCS technology has been demonstrated in tumors, brains, and skeletal muscles. The general approach with DCS is that the temporal statistics of the fluctuations of the scattered light within a speckle area or pixel is monitored. Then, the electric field temporal autocorrelation function is measured. A model for photon propagation through tissues, the measured autocorrelation signal is used to determine the motion of blood flow.<ref name=":1" />
== Mathematical
Diffuse
The physical effect that makes the blood flow measurement possible is the temporal electric field autocorrelation function, shown in equation 1, diffuses through tissue in a manner that is similar to the light fluence rate.
<math>\langle E^*(r,t)\cdot E(t,\tau) \rangle (1)</math>
In a highly scattering media, the photon fluence rate obeys the time-dependent [[diffusion equation]], shown in equation 2. Optical imaging variables used in these equation are [https://imgur.com/a/5TDR72u here].
<math>\nabla \cdot (D\nabla\phi(r,t)) - \nu\mu_a\phi(r,t) + \nu S(r,t) = {\partial \phi(r,t) \over\partial t} (2)</math>
Line 35 ⟶ 27:
Using the same set of approximations, the [[Optical autocorrelation|temporal field autocorrelation function]] obeys a formally similar diffusion equation, shown in equation 3.
<math>[\nabla \cdot D(r) \nabla - \nu \mu_a(r) - \frac{\alpha}{3} \nu \mu_s^' k_o^2 \langle \Delta r^2(\tau)\rangle]G_1(r,\tau) = -\nu S(r)~(3)</math>
The mean-square particle displacement has been found to be reasonably well approximated as an “effective” [[Brownian motion]], i.e., ''D<sub>B</sub>'' represents the effective diffusion coefficient of the moving scatterers. In order to estimate relative blood flow from DCS data, we fit the measured intensity autocorrelation functions to solutions of the equation in equation 3.<ref name=":0" /> Currently, there is no evidence explaining why Brownian-motion correlation curves work effectively. This is the current empirical approach. The unit of α''D<sub>B</sub>'' (cm<sup>2</sup>/s) has been found to correlate well with other blood flow measurement modalities and is used to measure blood flow. Therefore, is the blood flow index (BFI). To calculate the relative blood flow (rBF), the equation is shown in equation 4 where BFI<sub>0</sub> is the DCS blood flow measurement at a baseline.<ref name=":1" />
Line 42 ⟶ 33:
<math>rBF = \frac{BFI}{BFI_0} ~ (4)</math>
== Instrumentation and
The instrumentation needed in order to conduct the data acquisition
The first step of data acquisition is probing the tissue with multimode optical fibers that deliver a long coherence length laser light to the tissue. The second step of data acquisition is collecting photons emitted from the tissue surface with single-mode or few-mode fibers. The third step of data acquisition is the APDs detect the photons from the single-mode or few-mode fibers. The APDs act like detectors. The APDs will have a transistor-transistor logic output or binary outputs with the use of transistors. These outputs will be
== Application Example ==
A clinical application of DCS is for use in diagnosis of cancers. An example of this is measuring red blood cell flow in breast tumors. In this experiment, both healthy patients and patients with breast tumors were recruited. Researchers scanned the tumor with a hand-held optical probe with 4 sources and detectors 2.5
==
There are many advantages to this method. The first advantage is that DCS can be used for patients of all ages. This is significant as some modalities such as MRI are difficult to use for certain populations. The second advantage is that DCS instrumentation is easy to assemble and requires only one wavelength that can be chosen. The third advantage is that the theoretical concepts of DCS can be adapted to other blood flow imaging techniques.<ref name=":2">{{Cite journal |last1=Buckley |first1=Erin M. |last2=Parthasarathy |first2=Ashwin B. |last3=Grant |first3=P. Ellen |last4=Yodh |first4=Arjun G. |last5=Franceschini |first5=Maria Angela |date=June 2014 |title=Diffuse correlation spectroscopy for measurement of cerebral blood flow: future prospects |journal=Neurophotonics |volume=1 |issue=1 |pages=011009 |doi=10.1117/1.NPh.1.1.011009 |issn=2329-423X |pmc=4292799 |pmid=25593978}}</ref>
Line 60 ⟶ 51:
== References ==
{{reflist}}
[[Category:Medical imaging]]
[[Category:Spectroscopy]]
| |||