X-ray induced photoacoustic tomography, also called X-ray acoustic computer tomography (XACT) is investigated in
this paper. Short pulsed (μs-range) X-ray beams from a medical linear accelerator were used to generate ultrasound. The ultrasound signals were collected with an ultrasound transducer (500 KHz central frequency) positioned around an
object. The transducer, driven by a computer-controlled step motor to scan around the object, detected the resulting
acoustic signals in the imaging plane at each scanning position. A pulse preamplifier, with a bandwidth of 20 KHz–2
MHz at −3 dB, and switchable gains of 40 and 60 dB, received the signals from the transducer and delivered the
amplified signals to a secondary amplifier. The secondary amplifier had bandwidth of 20 KHz–30 MHz at −3 dB, and a
gain range of 10–60 dB. Signals were recorded and averaged 128 times by an oscilloscope. A sampling rate of 100 MHz
was used to record 2500 data points at each view angle. One set of data incorporated 200 positions as the receiver moved
360°. The x-ray generated acoustic image was then reconstructed with the filtered back projection algorithm. The twodimensional
XACT images of the lead rod embedded in chicken breast tissue were found to be in good agreement with
the shape of the object. This new modality may be useful for a number of applications, such as providing the location of
a fiducial, or monitoring x-ray dose distribution during radiation therapy.
KEYWORDS: Breast, Tissues, Tissue optics, Magnetic resonance imaging, Near infrared, Tumors, In vivo imaging, Image segmentation, Near infrared spectroscopy, Natural surfaces
We demonstrate quantitative functional imaging using image-guided near-infrared spectroscopy (IG-NIRS) implemented with the boundary element method (BEM) for reconstructing 3-D optical property estimates in breast tissue in vivo. A multimodality MRI-NIR system was used to collect measurements of light reflectance from breast tissue. The BEM was used to model light propagation in 3-D based only on surface discretization in order to reconstruct quantitative values of total hemoglobin (HbT), oxygen saturation, water, and scatter. The technique was validated in experimental measurements from heterogeneous breast-shaped phantoms with known values and applied to a total of seven subjects comprising six healthy individuals and one participant with cancer imaged at two time points during neoadjuvant chemotherapy. Using experimental measurements from a heterogeneous breast phantom, BEM for IG-NIRS produced accurate values for HbT in the inclusion with a <3% error. Healthy breast tissues showed higher HbT and water in fibroglandular tissue than in adipose tissue. In a subject with cancer, the tumor showed higher HbT compared to the background. HbT in the tumor was reduced by 9 µM during treatment. We conclude that 3-D MRI-NIRS with BEM provides quantitative and functional characterization of breast tissue in vivo through measurement of hemoglobin content. The method provides potentially complementary information to DCE-MRI for tumor characterization.
This study investigates differences in the response of breast tumor tissue versus healthy fibroglandular tissue to inspired gases. Cycles of carbogen and oxygen gas are administered while measuring the changes with magnetic-resonance-guided near-infrared imaging in a pilot study of breast cancers. For two patients, analyses are performed with cross-correlation techniques, which measure the strength of hemodynamic modulation. The results show that the overall vasoresponse, indicated by total hemoglobin, of healthy tissue has approximately a 72% and 41% greater correlation to the gas stimulus than the tumor region, in two patients respectively, when background physiological changes are controlled. These data support the hypothesis that tumor vasculature has a poorly functioning vasodilatory mechanism, most likely caused by dysfunctional smooth muscle cells lining the vasculature. This study presents a methodology to quantitatively analyze inspired gas changes in human breast tumors, and demonstrates this technique in a pilot patient population.
It is well known that diffuse optical tomography (DOT) has limited spatial resolution, and sufficient contrast recovery is
limited to lesions greater than ~6 mm[1]. However, with the addition of multimodality methods that combine high
spatial resolution imaging, such as MRI, it has been shown that quantification and feature recovery improves[2].
However, it is not known how well MRg-DOS will perform with characterizing small lesions in 3D. These limits need
to be established in order to determine the practical limitations of optical imaging.
This paper investigates the contrast resolution limits of 3 dimensional MRg-DOS. Short irregular inclusions of various
diameters are added to a homogeneous background. Two case studies are presented which represent these limiting
situations.
Magnetic resonance (MR) guided diffuse optical spectroscopy (DOS) has shown promise in several case studies in
aiding the characterization of breast lesions[1, 2]. It has been proposed that the increased quantification and resolution
with a priori structural guidance yields higher diagnostic value in characterizing tumors. To date, these systems have
merged MR anatomical recovery with optical contrast recovery. However, the MR has a wealth of spectral and
functional data that may aid in further improving lesion characterization by appending both new and overlapping
physiological information to optical methods.
It has been well documented that spectral recovery of water and lipids is inaccurate with few wavelengths. Yet, recovery
of these chromophores is important both because of the possible importance of these as indicators of breast cancer,
adema, and inflammation. In addition, crosstalk between water and oxyhemoglobin may lead to erroneous tissue
properties, which may affect lesion diagnosis. The use of multiple MR sequences with DOS enables the separation of
water and lipids via MRI, and improves recovery of tissue oxygenation and hemoglobin content. However, in most cases, MRI is not a quantitative device; this paper investigates the best reconstruction methods to incorporate this data into the optical reconstruction for quantitatively accurate chromophore recovery in the presence of imperfect MR water/fat separation. Specifically, it investigates whether incorporating water/fat information directly or through a maximum likelihood algorithm yields the optimal solution both in terms of reduced crosstalk between oxyhemoglobin and water, and compares results to having no priori knowledge of water and fat.
Image-guided near infrared spectroscopy (IG-NIRS) can provide high-resolution vascular, metabolic and molecular
characterization of localized tissue volumes in-vivo. The approach for IG-NIRS uses hybrid systems where the spatial
anatomical structure of tissue obtained from standard imaging modalities (such as MRI) is combined with tissue
information from diffuse optical imaging spectroscopy. There is need to optimize these hybrid systems for large-scale
clinical trials anticipated in the near future in order to evaluate the feasibility of this technology across a larger
population. However, existing computational methods such as the finite element method mesh arbitrary image volumes,
which inhibit automation, especially with large numbers of datasets. Circumventing this issue, a boundary element
method (BEM) for IG-NIRS systems in 3-D is presented here using only surface rendering and discretization. The
process of surface creation and meshing is faster, more reliable, and is easily generated automatically as compared to full
volume meshing. The proposed method has been implemented here for multi-spectral non-invasive characterization of
tissue. In phantom experiments, 3-D spectral BEM-based spectroscopy recovered the oxygen dissociation curve with
mean error of 6.6% and tracked variation in total hemoglobin linearly.
Video rate diffuse tomography can be implemented within the magnetic resonance breast exam. The following paper outlines the basics of a spectrally encoded source set up, being designed and tested for use in breast imaging within a specialized breast surface coil. The system design maximizes input power to the breast, while confining the spectrum to a 10 nm bandwidth of near-infrared light. The center spectral band can be varied, since it is supplied by a tunable Ti:Sapphire laser. The encoding of each source is achieved by splitting the signal into individual nanometer bands through a high resolution grating, and focusing the output of this into each source fiber. This source configuration then requires spectral detection at the output, and so each detection fiber is delivered to a high resolution spectrometer to resolve the detected intensities. Breast imaging with this system has some subtle dynamic range issues, which means that light from sources farthest from the detector pickup are likely not providing useful data, but the closest 4-6 fibers near each source can provide useful data. The implementation of this is being carried out within a magnetic resonance breast array, and initial testing of the signals is shown, along with diagrams and photographs of the system configuration.
Near-Infrared (NIR) Diffuse Optical Tomography (DOT) is a non-invasive imaging technique which is used to obtain
functional and physiological images of soft tissue, such as the female breast, specifically for the detection and
characterization of breast cancer. The vast majority of the work to date has been limited to two dimensional (2D)
models which have provided valuable insight into tissue function and physiology enabling a better understanding of
tumor development and treatment. Although the 2D image reconstruction approach is fast and computationally efficient,
it has limitations as it does not correctly represent the volume under investigation and therefore do not provide the most
accurate model for image reconstruction. Three dimensional (3D) modeling and image reconstruction is becoming more
accessible through the development of sophisticated numerical models and computationally fast algorithms. A robust
and general method is presented which reconstructs 3D functional images using a more accurate and realistic spectral
model of 3D light propagation in tissue. Results from a single patient example are presented to demonstrate the clinical
importance of 3D image reconstruction in optical tomography for the detection and characterization of breast cancer.
Recent interest in the use of dual modality imaging in the field of optical Near Infrared (NIR) Tomography has
increased, specifically with use of structural information, from for example, MRI. Although MRI images provide high
resolution structural information about tissue, they lack the contrast and functional information needed to investigate
physiology, whereas NIR data has been established as a high contrast imaging modality, but one which suffers from low
resolution. To this effect, the use of dual modality data has been shown to increase the qualitative and quantitative
accuracy of clinical information that can be obtained from tissue. Results so far have indicated that providing accurate apriori
structural information is available, such dual modality imaging techniques can be used for the detection and
characterization of breast cancer in-vivo, as well as the investigation of brain function and physiology in both human
and small animal studies.
Although there has been much interest and research into the best suitable and robust use of a-priori structural
information within the reconstruction of optical properties of tissue, little work has been done into the investigation of
how much accuracy is needed from the structural MRI images in order to obtain the most clinically reliable information.
In this paper, we will present and demonstrate the two most common application of a-priori information into image
reconstruction, namely soft and hard priori. The effect of inaccuracies of the a-priori structural information within the
reconstructed NIR images are presented showing that providing that the error of the a-priori information is within 20%
in terms of size and location, adequate NIR images can be reconstructed.
Incorporating near infrared (NIR) diffuse optical tomography into magnetic resonance imaging (MRI) increases the
value of MR breast cancer imaging because it adds functional imaging of hemoglobin, oxygen saturation, water, lipid
content, and scattering parameters, properties that infer tissue health. Reconstruction algorithms that incorporate MR into
a diffusive modality accrue unavoidable errors from improper tissue segmentation of the MR image, which create
inaccuracies in the structural prior. This paper focuses on identifying the most accurate reconstruction approach based on
imperfect prior knowledge of tissue boundaries. Specifically, it focuses on how unavoidable segmentation errors of
different breast densities affect edge-constraining reconstruction methods to determine the correct approach. Results
show that these reconstruction methods all retain the improperly defined edges, but are quantitatively accurate even
when the anatomical boundaries mismatch the optical boundaries by as much as 50%. The most accurate approach is one
where the problem has been reduced to the least number of unknowns, and the edges are constrained through
regularization.
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