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Mesoscopic Fluorescence Tomography of a Photosensitizer (HPPH) 3D Biodistribution in Skin Cancer

Posted Jan 31, 2014 Updated Apr 10, 2023
By Mehmet S. Ozturk RPI , Daniel Rohrbach RPCI , Ulas Sunar PhD , Xavier Intes PhD
11 min read

Rationale and Objectives

Photodynamic therapy (PDT) is a promising strategy for treating cancer. PDT involves three components: a photosensitizer (PS) drug, a specific wavelength of drug-activating light, and oxygen. A challenge in PDT is the unknown biodistribution of the PS in the target tissue. In this preliminary study, we report the development of a new approach to image in three dimensions the PS biodistribution in a noninvasive and fast manner.

Materials and Methods

A mesoscopic fluorescence tomography imaging platform was used to image noninvasively the biodistribution of 2-[1-hexyloxyethyl]-2 devinyl pyropheophorbide-a (HPPH) in preclinical skin cancer models. Seven tumors were imaged and optical reconstructions were compared to nonconcurrent ultrasound data.

Results

Successful imaging of the HPPH biodistribution was achieved on seven skin cancer tumors in preclinical models with a typical acquisition time of 1 minute. Two-dimensional fluorescence signals and estimated three-dimensional PS distributions were located within the lesions. However, HPPH distribution was highly heterogeneous with the tumors. Moreover, HPPH distribution volume and tumor volume as estimated by ultrasound did not match.

Conclusions

The results of this proof-of-concept study demonstrate the potential of MFMT to image rapidly the HPPH three-dimensional biodistribution in skin cancers. In addition, these preliminary data indicate that the PS biodistribution in skin cancer tumors is heterogeneous and does not match anatomical data. Mesoscopic fluorescence molecular tomography, by imaging fluorescence signals over large areas with high spatial sampling and at fast acquisition speeds, may be a new imaging modality of choice for planning and optimizing of PDT treatment.

The most common cancer treatments include surgery, radiation therapy, chemotherapy, immunotherapy, and monoclonal antibody therapy. These therapies may be used either alone or in combination with other therapies. Although improvement of these treatment modalities to enhance their tumor selectivity is under way, development of novel alternative treatment approaches that may be safer, reduce functional tissue damage, and improve patients’ quality of life is a major focus area of research. Among all new modalities investigated, photodynamic therapy (PDT) emerges as a promising alternative treatment strategy . PDT is a noninvasive treatment using a combination of a photosensitizer (PS) drug, a specific wavelength of drug-activating light and oxygen. Light activation of a PS results in energy transfer cascades that ultimately yield cytotoxic reactive oxygen species.

PDT has undergone extensive investigations in cancer treatment and various PS drugs have been approved for clinical use in the United States and other countries . PDT is being used mainly to treat skin diseases and easily accessible malignant and premalignant lesions . PDT is a local therapy with the major advantage that the PS itself is minimally toxic in the absence of light; therefore, PS accumulation in nonspecific tissues confers minimal systemic toxicity. Furthermore, light activation is performed below the maximum permissible exposure limit and at wavelengths that do not harm the tissues. Hence, PDT can be applied repeatedly should a single treatment fail. The efficacy of PDT is largely dependent on the functional state of the tissue, the PS uptake, and light fluence delivered locally. If these parameters can be controlled/assessed relatively easily in superficial lesions, it is a significant challenge for pathologies that are thick or located a few millimeters deep. In such scenarios, the PS biodistribution profile in tissue is not homogeneous, even in the case of local topical application. Second, light propagation in tissues is highly affected by the optical properties distribution in the sample. Hence, PDT efficiency can be hampered by intralesion PS heterogeneous biodistribution and light flux profiles.

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Materials and methods

Preclinical Model Protocol

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Tumor Imaging

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Figure 1, (a) The tail of the animal was immersed in a matching liquid. Raster scanning was applied on the tumor area ( inset ). (b) Laser diode excites the fluorophore by raster scanning over a 6 × 3 mm 2 area. Emission signal descanned and directed onto detector array with a beam splitter (BS) followed by an emission filter.

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Mesoscopic Fluorescence Imaging System

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MFMT Reconstructions

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x’=(ATA+λD)−1ATb x

’

=

(

A

T

A

+

λ

D

)

−

1

A

T

b

where b is obtained from the fluorescence measurement, x′ is the reconstructed fluorescence distribution, λD is a regularization depth-dependent parameter, and A is the whole Jacobian that is computed based by the Monte Carlo–forward model. Thanks to the symmetry of the imaging space, A is populated for all the positions of the raster-scanned beam, based on a single set of sensitivity matrices, which are computed for one optode set (one source and seven detectors). Optical properties, μ s ′ and μ a for Jacobian ( A ) matrix are chosen as 3 mm −1 and 0.002 mm −1 , respectively. Both 3D reconstruction of absorption and/or fluorescence contrast can be performed using this model . A depth-dependent regularization technique is used to mitigate the ill-posedness of the reflectance geometry. D is a diagonal matrix whose elements are the square root of the corresponding diagonal elements of A__T__A . The scaling factor (λ) for this diagonal matrix is selected based on the L-curve analysis . The conjugate gradient method is applied to solve this linear system (function cgs , MATLAB R2009b, MathWorks, Natick, MA). The iterative algorithm was stopped if 100 iterations or if a tolerance of 10 −2 was reached. Overall, the Monte Carlo–forward model and optical reconstructions were computed in less than 4 minutes for the typical 106,400 data measurements (15,200 sources positions × seven detectors) on a personal computer (Intel Core i7-3820 central processor unit, 3.6 GHz, 64 GB).

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Ultrasound Imaging

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Co-registration of US and MFMT Data Sets

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Figure 2, A clear inclusion (PDMS) was depicted in (a) ; cylinder inclusion allowed a hollow opening to host a capillary. Depending on the depth of the hole, we arranged the fluorescence inclusion position in the PDMS. (b) A phantom was cut with a scalpel over the scanning area ( red rectangle ). Inclusion was positioned ( dashed rectangle ) before solidification of agar solution. (c) Raw data demonstrate three different localized fluorescence signals in the phantom through the inclusion as acquired by Detector 1. For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.

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Image Analysis

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Figure 3, (a) Ultrasound A-scan image shows the membrane that separates the transducer media from the outside (membrane), tumor area, and the skin of the tail (scale bar, 1 mm). (b) Segmentation was performed based on thresholding at 10% of maximum intensity thanks to high contrast between tumor and the surrounding area. (c) The same slice of ultrasound (US) and mesoscopic fluorescence molecular tomography (MFMT) reconstruction was overlaid to show the correspondence with each other. (d) The cross-section of the reconstructed images was shown together; tail ( gray ), US ( blue ), and MFMT ( red ); white border shows the segmentation boundary for a US slice (a) and a segmented slice (b) . For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.

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Results and discussion

Phantom Study Results

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Figure 4, Mesoscopic fluorescence molecular tomography reconstruction ( red ) overlaid with the segmented ultrasound images. (a) Case 1: Fluorescence-filled capillary placed through the hollow opening ( yellow ) inside the inclusion ( green ). Inclusion placed just beneath the matrix ( blue ) surface. (b) Case 2: Inclusion ( green ) is adjacent to the fluorescence dye–filled capillary. (c) Case 3: Fluorescent capillary placed at the top part of the inclusion and placed at the edge of the phantom. Yellow dashed area represents the interrogation area. Scale bar, 1 mm. For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.

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Tumor Imaging Results

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Figure 5, Background subtracted raw data for the no tumor tail and seven different tumors. (a) First-detector raw data; (b) second-detector raw data. Both detectors deliver high-contrast and highly localized signal. Images show a 3 × 6 mm 2 scanning area.

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MFMT Reconstructions and US Co-registration

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Figure 6, Both ultrasound (US; blue ) and mesoscopic fluorescence molecular tomography (MFMT; red ) was used to generate this three-dimensional image of the tumor. US data also shows the tail structure ( pink ). MFMT data were acquired by raster scanning a 6 × 3 mm 2 area over the tail; a 20% isosurface value (ISO) value used to visualize the MFMT image (a) . The top view (b) shows the overlay of two reconstructions and the scanning area ( orange box ). (c) Interrogation depth and depth co-registration is shown. For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.

Figure 7, Ultrasound reconstruction overlaid with the (a) first and (b) second detectors. Black lines are the tail boundary. Scale bar, 1 mm.

Figure 8, All tumors are shown in increasing volume order according to the ultrasound (US) volume estimation. Tail ( gray ), US tumor ( blue ), and mesoscopic fluorescence molecular tomography tumor ( red ) are all in shown in same scale. Scale bar, 1 mm. For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.

Table 1

Ultrasound and MFMT Reconstructed Tumor Volumes

Volume (mm 3 ) US 10% ISO 20% ISO 30% ISO 40% ISO Tumor 1 0.22 2.35 1.58 0.66 0.31 Tumor 2 2.8 4 1.98 1.26 0.85 Tumor 3 4.18 5.8 3.4 2 1.3 Tumor 4 4.8 3.11 1.44 0.77 0.38 Tumor 5 8.11 12 7.2 6.15 3.4 Tumor 6 9.6 11.3 5 2.16 0.6 Tumor 7 10 6.7 4.4 2.6 1.5

ISO, isosurface value for MFMT reconstruction.

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Discussion

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Conclusions

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Acknowledgments

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Academic Radiology, Volume 21, Issue 2
Journals General Radiology
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