Author: Main Administrator of this WP

source: ©2011 Journal of Visualized Experiments

In vivo Near Infrared Fluorescence (NIRF) Intravascular Molecular Imaging of Inflammatory Plaque, a Multimodal Approach to Imaging of Atherosclerosis

In vivo Near Infrared Fluorescence (NIRF) Intravascular Molecular Imaging of Inflammatory Plaque, a Multimodal Approach to Imaging of Atherosclerosis

The vascular response to injury is a well-orchestrated inflammatory response triggered by the accumulation of macrophages within the vessel wall leading to an accumulation of lipid-laden intra-luminal plaque, smooth muscle cell proliferation and progressive narrowing of the vessel lumen. The formation of such vulnerable plaques prone to rupture underlies the majority of cases of acute myocardial infarction. The complex molecular and cellular inflammatory cascade is orchestrated by the recruitment of T lymphocytes and macrophages and their paracrine effects on endothelial and smooth muscle cells.

Molecular imaging in atherosclerosis has evolved into an important clinical and research tool that allows in vivo visualization of inflammation and other biological processes. Several recent examples demonstrate the ability to detect high-risk plaques in patients, and assess the effects of pharmacotherapeutics in atherosclerosis.4 While a number of molecular imaging approaches (in particular MRI and PET) can image biological aspects of large vessels such as the carotid arteries, scant options exist for imaging of coronary arteries. The advent of high-resolution optical imaging strategies, in particular near-infrared fluorescence (NIRF), coupled with activatable fluorescent probes, have enhanced sensitivity and led to the development of new intravascular strategies to improve biological imaging of human coronary atherosclerosis.

Fig. 1 Schematic of 2D NIRF Catheter To extend the clinical potential of a 1D NIRF sensing approach, we constructed a novel 2-D NIRF-catheter for intravascular imaging. The custom-built catheter consists of an optical fiber (125 micron diameter housed in polyethylene tubing: 2.9F) that illuminates using a 750 nm laser excitation source. Laser light is emitted at a 90 degree angle relative to fiber axis. The system utilizes two automated motors (rotational and translational) to enable concomitant 360 degree imaging and longitudinal pullback to obtain true 2D imaging. Images used with permission from reference 11.

Near infrared fluorescence (NIRF) molecular imaging utilizes excitation light with a defined band width (650-900 nm) as a source of photons that, when delivered to an optical contrast agent or fluorescent probe, emits fluorescence in the NIR window that can be detected using an appropriate emission filter and a high sensitivity charge-coupled camera. As opposed to visible light, NIR light penetrates deeply into tissue, is markedly less attenuated by endogenous photon absorbers such as hemoglobin, lipid and water, and enables high target-to-background ratios due to reduced autofluorescence in the NIR window. Imaging within the NIR ‘window’ can substantially improve the potential for in vivo imaging.

Inflammatory cysteine proteases have been well studied using activatable NIRF probes, and play important roles in atherogenesis. Via degradation of the extracellular matrix, cysteine proteases contribute importantly to the progression and complications of atherosclerosis. In particular, the cysteine protease, cathepsin B, is highly expressed and colocalizes with macrophages in experimental murine, rabbit, and human atheromata. In addition, cathepsin B activity in plaques can be sensed in vivo utilizing a previously described 1-D intravascular near-infrared fluorescence technology, in conjunction with an injectable nanosensor agent that consists of a poly-lysine polymer backbone derivatized with multiple NIR fluorochromes (VM110/Prosense750, ex/em 750/780nm, VisEn Medical, Woburn, MA) that results in strong intramolecular quenching at baseline. Following targeted enzymatic cleavage by cysteine proteases such as cathepsin B (known to colocalize with plaque macrophages), the fluorochromes separate, resulting in substantial amplification of the NIRF signal. Intravascular detection of NIR fluorescence signal by the utilized novel 2D intravascular NIRF catheter now enables high-resolution, geometrically accurate in vivo detection of cathepsin B activity in inflamed plaque.

In vivo molecular imaging of atherosclerosis using catheter-based 2D NIRF imaging, as opposed to a prior 1-D spectroscopic approach, is a novel and promising tool that utilizes augmented protease activity in macrophage-rich plaque to detect vascular inflammation. The following research protocol describes the use of an intravascular 2-dimensional NIRF catheter to image and characterize plaque structure utilizing key aspects of plaque biology. It is a translatable platform that when integrated with existing clinical imaging technologies including angiography and intravascular ultrasound (IVUS), offers a unique and novel integrated multimodal molecular imaging technique that distinguishes inflammatory atheromata, and allows detection of intravascular NIRF signals in human-sized coronary arteries.
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M. A. Calfon, A. Rosenthal, G. Mallas, A. Mauskapf, R. N. Nudelman, V. Ntziachristos, F. A. Jaffer, „In vivo Near Infrared Fluorescence (NIRF) Intravascular Molecular Imaging of Inflammatory Plaque, a Multimodal Approach to Imaging of Atherosclerosis,” J. Vis. Exp., Issue 54 (2011).

source: ©2011 Journal of the American College of Cardiology

Objectives: This study sought to develop a 2-dimensional (2D) intravascular near-infrared fluorescence (NIRF) imaging strategy for investigation of arterial inflammation in coronary-sized vessels.

Background: Molecular imaging of arterial inflammation could provide new insights into the pathogenesis of acute myocardial infarction stemming from coronary atheromata and implanted stents. Presently, few high-resolution approaches can image inflammation in coronary-sized arteries in vivo.

Methods: A new 2.9-F rotational, automated pullback 2D imaging catheter was engineered and optimized for 360° viewing intravascular NIRF imaging. In conjunction with the cysteine protease-activatable imaging reporter Prosense VM110 (VisEn Medical, Woburn, Massachusetts), intra-arterial 2D NIRF imaging was performed in rabbit aortas with atherosclerosis (n =10) or implanted coronary bare-metal stents (n = 10, 3.5-mm diameter, day 7 post-implantation). Intravascular ultrasound provided coregistered anatomical images of arteries. After sacrifice, specimens underwent ex vivo NIRF imaging, fluorescence microscopy, and histological and immunohistochemical analyses.

Fig. 1 Schematic of the Constructed 2D NIRF Imaging System. The tip of the fiber contains a right angle coated prism that reflects the guide’s laser light into the artery wall and couples the subsequent fluorescent light back into the fiber. The fluorescent light is then directed to a dichroic beam splitter that selectively reflects it into a photomultiplier tube. The beam passes additional filters to minimize the parasitic signals of laser photons and autofluorescence. The inset shows the spectra of the 3 filters (I, II, III) used in the system. 2D = 2-dimensional; NIRF = near-infrared fluorescence.

Results: Imaging of coronary artery–scaled phantoms demonstrated 8-sector angular resolution and submillimeter axial resolution, nanomolar sensitivity to NIR fluorochromes, and modest NIRF light attenuation through blood. High-resolution NIRF images of vessel wall inflammation with signal-to-noise ratios >10 were obtained in real-time through blood, without flushing or occlusion. In atherosclerosis, 2D NIRF, intravascular ultrasound–NIRF fusion, microscopy, and immunoblotting studies provided insight into the spatial distribution of plaque protease activity. In stent-implanted vessels, real-time imaging illuminated an edge-based pattern of stent-induced arterial inflammation.

Conclusions: A new 2D intravascular NIRF imaging strategy provides high-resolution in vivo spatial mapping of arterial inflammation in coronary-sized arteries and reveals increased inflammation-regulated cysteine protease activity in atheromata and stent-induced arterial injury. 
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F. A. Jaffer, M. A. Calfon, A. Rosenthal, G. Mallas, R. N. Razansky, A. Mauskapf, R. Weissleder, P. Libby, V. Ntziachristos, “Two-Dimensional Intravascular Near-Infrared Fluorescence Molecular Imaging of Inflammation in Atherosclerosis and Stent-Induced Vascular Injury,” J. Am. Coll. Cardiol., Vol. 57, pp. 2516-2526 (2011).

source:©2010 Optical Society of America

Detection of high-risk coronary arterial plaques prior to rupture remains an unmet clinical challenge, in part due to the stringent resolution and sensitivity requirements for in vivo human coronary arterial imaging. To address this need, we have developed a near-infrared (NIR) fluorescence imaging catheter system for intra-vascular molecular imaging of atherosclerosis in coronary artery-sized vessels, capable of resolving two-dimensional fluorescence activity in hollow organs, such as blood vessels. Based on a rotational fiber design, the catheter system illuminates and detects perpendicular to the rotational axis, while an automated pullback mechanism enables visualization along blood vessels with a scan speed of up to 1.5 mm/sec. We demonstrate the previously undocumented capacity to produce intravascular NIR fluorescence images of hollow organs in vivo and showcase the performance metrics of the system developed using blood vessel mimicking phantoms. This imaging approach is geared toward in vivo molecular imaging of atherosclerotic biomarkers and is engineered to allow seamless integration into the cardiac catheterization laboratory.
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Fig. 1 NIR fluorescence imaging system catheter. Schematics of the experimental setup: laser light propagated through the optical cube into multimodal (MM) fiber, travels through rotational coupler and, finally, propagate into MM fiber that has angle-polished, mirror-coated front end. The delivered light excites fluorochromes in the ROI and collects corresponding emission signal, which propagates through the same MM fiber to the detection cube and measured by two PMTs. Signal is then digitized, stored and analyzed.

R. N. Razansky, A. Rosenthal, G. Mallas, D. Razansky, F. A. Jaffer, and V. Ntziachristos, “Near-infrared fluorescence catheter system for two-dimensional intravascular imaging in vivo,” Opt. Express, Vol. 18, pp. 11372-11381 (2010).

source:© 2014 Medical Physics

Purpose:
One of the major challenges in dynamic multispectral optoacoustic imaging is its relatively low signal‐to‐noise ratio which often requires repetitive signal acquisition and averaging, thus limiting imaging rate. The development of denoising methods which prevent the need for signal averaging in time presents an important goal for advancing the dynamic capabilities of the technology.

Methods:
In this paper, a denoising method is developed for multispectral optoacoustic imaging which exploits the implicit sparsity of multispectral optoacoustic signals both in space and in spectrum. Noise suppression is achieved by applying thresholding on a combined wavelet‐Karhunen–Loève representation in which multispectral optoacoustic signals appear particularly sparse. The method is based on inherent characteristics of multispectral optoacoustic signals of tissues, offering promise for general application in different incarnations of multispectral optoacoustic systems.

Fig. 1 In vivo multispectral optoacoustic measurements used for the creation of noise simulations. (a) and (b) Anatomical image of a mouse brain at an excitation wavelength of 690 nm (a) and 900 nm (b). (c) and (d) Anatomical image of the kidney area of a mouse at an excitation wavelength of 690 nm (c) and 900 nm (d).

Results:
The performance of the proposed method is demonstrated on mouse images acquired in vivo for two common additive noise sources: time‐varying parasitic signals and white noise. In both cases, the proposed method shows considerable improvement in image quality in comparison to previously published denoising strategies that do not consider multispectral information.

Conclusions:
The suggested denoising methodology can achieve noise suppression with minimal signal loss and considerably outperforms previously proposed denoising strategies, holding promise for advancing the dynamic capabilities of multispectral optoacoustic imaging while retaining image quality.
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S. Tzoumas, A, Rosenthal, C. Lutzweiler, D. Razansky, and V. Ntziachristos, “Spatiospectral denoising framework for multispectral optoacoustic imaging based on sparse signal representation,” Med. Phys., Vol. 41 (2014).

source:© 2013 Current Medical Imaging Reviews

Optoacoustic tomography enables volumetric imaging with optical contrast in biological tissue at depths beyond the optical mean free path by the use of optical excitation and acoustic detection. The hybrid nature of optoacoustic tomography gives rise to two distinct inverse problems: The optical inverse problem, related to the propagation of the excitation light in tissue, and the acoustic inverse problem, which deals with the propagation and detection of the generated acoustic waves. Since the two inverse problems have different physical underpinnings and are governed by different types of equations, they are often treated independently as unrelated problems. From an imaging standpoint, the acoustic inverse problem relates to forming an image from the measured acoustic data, whereas the optical inverse problem relates to quantifying the formed image. This review focuses on the acoustic aspects of optoacoustic tomography, specifically acoustic reconstruction algorithms and imaging-system practicalities. As these two aspects are intimately linked, and no silver bullet exists in the path towards high-performance imaging, we adopt a holistic approach in our review and discuss the many links between the two aspects. Four classes of reconstruction algorithms are reviewed: time-domain (so called back-projection) formulae, frequency-domain formulae, time-reversal algorithms, and model-based algorithms. These algorithms are discussed in the context of the various acoustic detectors and detection surfaces which are commonly used in experimental studies. We further discuss the effects of non-ideal imaging scenarios on the quality of reconstruction and review methods that can mitigate these effects. Namely, we consider the cases of finite detector aperture, limited-view tomography, spatial under-sampling of the acoustic signals, and acoustic heterogeneities and losses.
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Fig. 2 The three most common detection surfaces used in optoacoustic tomography: (a) spherical, (b) cylindrical, and (c) planar. These
detection surfaces may be achieved experimentally by scanning either a single detector or a detector array over the surface. The arrows show
the directions in which a single detectors needs to be scanned. The detector types appropriate for each of these detection surfaces are listed in
(Table 1).

A. Rosenthal, D. Razansky, V. Ntziachristos, “Acoustic inversion in optoacoustic tomography: a review,” Curr. Med. Imaging Rev., Vol. 9, pp. 318-336 (2013).

source:© 2013 IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control

The characterization of the spatial and frequency response of acoustic detectors is important for enabling accurate optoacoustic imaging. In this work, we developed a hybrid method for the characterization of the spatially dependent response of ultrasound detectors. The method is based on the experimental determination of the receive-mode electrical impulse response (EIR) of the sensor, which is subsequently convolved with the corresponding spatial impulse response (SIR), computed numerically. The hybrid method is shown to have superior performance over purely experimental techniques in terms of accurate determination of the spatial and temporal responses of ultrasonic detectors, in high as well as low sensitivity regions of the sensor. 
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Fig. 1 Effect of the spatial impulse response (SIR) on an optoacoustic signal. (a) Optoacoustic waves emanating from the source at r ′ reach the different points of the transducer, r d1 and r d2 , at different times t1 and t2(c represents the speed of sound). (b) Geometry for the numerical example: the sensor is 1.8 mm along the y direction, 15 mm along the z direction (here the sensor is shown from the side) and it is cylindrically focused to 40 mm. The source is located at 33 mm from the sensor along its median axis x . The relative dimensions have been exaggerated for ease of representation. (c) Simulated optoacoustic signal (solid blue curve) and the distorted signal (dashed red curve) that results after convolution with the SIR at a point out of focus. Inset: SIR used for convolution. (d) Frequency spectra of the simulated signal (solid blue curve) and the signal convolved with the SIR (dashed red curve). Inset: spectrum of the SIR.

M. Á. A. Caballero, A. Rosenthal, A. Bühler, D. Razansky and V. Ntziachristos, “Optoacoustic determination of the spatial and temporal responses of ultrasound transducers,” IEEE Trans. Ultrason. Ferroelectr. Freq. Control, Vol. 60, pp. 1234-1244 (2013).

source: © 2012 Optical Society of America

Optoacoustic (photoacoustic) mesoscopic and microscopic imaging is often implemented by linearly scanning a spherically focused ultrasound transducer. In this case, the resolution and sensitivity along the scan direction are limited by diffraction and therefore degrade rapidly for imaging depths away from the focal point. Partial restoration of the lost resolution can be achieved by using data-processing techniques, such as the virtual detector delay-and-sum method. However, these techniques are based on an approximate description of the detector properties, which limits the improvement in image quality they achieve. Herein we propose a reconstruction method based on an exact model of the optoacoustic generation and propagation that incorporates the spatial response of the sensor. The proposed method shows superior imaging performance over previously considered techniques.
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Fig. 1. (a) Geometry for the simulated scan. (b) Result of the scan for absorber 1. The color scale is linear, and the image is normalized to its maximum. (c) VD processing at the signal level: the signal of interest is on-axis (blue/solid), and is to be corrected with the aid of signals acquired at other sensor positions (one of them is shown in red/dotted). The off-axis signal is first time-shifted (black/dashed) and then (d) added to the signal of interest. (blue/solid), resulting in the VD-processed signal (black/dashed).

M. Á. A. Caballero, A. Rosenthal, J. Gâteau, D. Razansky, and V. Ntziachristos, “Model-based optoacoustic imaging using focused detector scanning,” Vol. 37, pp. 4080–4082 (2012).

source:© 2012 IEEE Transactions on Medical Imaging

The use of model-based algorithms in tomographic imaging offers many advantages over analytical inversion methods. However, the relatively high computational complexity of model-based approaches often restricts their efficient implementation. In practice, many modern imaging modalities, such as computed-tomography, positron-emission tomography, or optoacoustic tomography, normally use a very large number of pixels/voxels for image reconstruction. Consequently, the size of the forward-model matrix hinders the use of many inversion algorithms. In this paper, we present a new framework for model-based tomographic reconstructions, which is based on a wavelet-packet representation of the imaged object and the acquired projection data. The frequency localization property of the wavelet-packet base leads to an approximately separable model matrix, for which reconstruction at each spatial frequency band is independent and requires only a fraction of the projection data. Thus, the large model matrix is effectively separated into a set of smaller matrices, facilitating the use of inversion schemes whose complexity is highly nonlinear with respect to matrix size. The performance of the new methodology is demonstrated for the case of 2-D optoacoustic tomography for both numerically generated and experimental data.
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FIg 4. (a) An image of a cross section of a mouse used in the numerical examples and (b) its optoacoustic tomographic reconstruction in the case of noisy projection data. The reconstruction was performed by inverting the matrix relation in (15) using the LSQR algorithm.

A. Rosenthal, D. Razansky, and V. Ntziachristos, “Efficient framework for model-based tomographic image reconstruction using wavelet packets,” IEEE Trans. Med. Imag., Vol. 31, pp. 1346-1357 (2012)

source: © 2011 Medical Physics

Purpose:
Optoacoustic imaging is an emerging noninvasive imaging modality that can resolve optical contrast through several millimeters to centimeters of tissue with diffraction‐limited resolution of ultrasound. Yet, quantified reconstruction of tissue absorption maps requires optoacoustic signals to be collected from as many locations around the object as possible. In many tomographic imaging scenarios, however, only limited‐view or partial projection data are available, which has been shown to generate image artifacts and overall loss of quantification accuracy.

Methods:
In this article, the recently introduced interpolated‐matrix‐model optoacoustic inversion method is tested in different limited‐view scenarios and compared to the standard backprojection algorithm. Both direct (TGSVD) and iterative (PLSQR) regularization methods are proposed to improve the accuracy of image reconstructions with their performance tested on simulated and experimental data.

Fig.1 (a) Reconstructable (bright solid line) and “invisible” (bright dashed line) boundaries of a round object with a square insertion partially lying in the “visibility region” (shaded area) and the “invisibility domain” for a detector moving along the dark solid arc. Dashed boundaries blur away since they do not fulfill detection criterion, i.e., they do not have a normal passing at least through one detector position. (b) Backprojection reconstruction of the phantom shown in (a). Boundaries that do not fulfill the detection criterion blur away. (c) The condition number of the matrix urn:x-wiley:00942405:mp6916:equation:mp6916-math-0138 as a function of the detection arc. The higher the condition number, the more error‐prone the inversion of urn:x-wiley:00942405:mp6916:equation:mp6916-math-0139 becomes. (d) IMMI reconstruction of the phantom. IMMI reconstructions show stripe artifacts in the invisibility domain.

Results:
While for full‐view tomographic data the model‐based inversion has been generally shown to attain higher reconstruction accuracy compared to backprojection algorithms, the incomplete tomographic datasets lead to ill‐conditioned forward matrices and, consequently, to error‐prone inversions, with strong artifacts following a distinct ripple‐type pattern. The proposed regularization techniques are shown to stabilize the inversion and eliminate the artifacts.

Conclusions:
Overall, it has been determined that the regularized interpolated‐matrix‐model‐based optoacoustic inversions show higher accuracy than reconstructions with the standard backprojection algorithm. Finally, the combination of model‐based inversion with PLSQR or TGSVD regularization methods can lead to an accurate reconstruction of limited‐projection angle optoacoustic data and practical systems for optoacoustic imaging in many realistic cases where the full‐view dataset is unavailable. 
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A. Buehler, A. Rosenthal , T. Jetzfellner , A. Dima , D. Razansky, and V. Ntziachristos, „Model-Based Optoacoustic inversions with incomplete projection data,” Med. Phys., Vol. 38, pp.1694-1704 (2011)