Deformable and rigid registration of MRI and microPET images for photodynamic therapy of cancer in mice

Subcutaneous Xenograft Models for Studying PDT In Vivo

The most facile, reproducible, and robust in vivo models for evaluating the anticancer efficacy of photodynamic therapy (PDT) are subcutaneous xenograft models of human tumors. The accessibility and practicality of light irradiation protocols for treating subcutaneous xenograft models also increase their value as relatively rapid tools to expedite the testing of novel photosensitizers, respective formulations, and treatment regimens for PDT. This chapter summarizes the methods used in the literature to prepare various types of subcutaneous xenograft models of human cancers and syngeneic models to explore the role of PDT in immuno-oncology. This chapter also summarizes the PDT treatment protocols tested on the subcutaneous models, and the procedures used to evaluate the efficacy at the molecular, macromolecular, and host organism levels.

Data processing of 3D and 4D in-vivo electron paramagnetic resonance imaging co-registered with ultrasound. 3D printing as a registration tool

We present the concept of image registration using ultrasound (US) and electron paramagnetic resonance (EPR) imaging and discuss the benefits of this solution, as well as its limitations. Both phantoms and murine tumors were used to test US and EPR image co-registration. Comparison of dental molding cast immobilization and predesigned cradle revealed that the latter approach is more effective in stabilizing the fiducial position. In vivo imaging of mouse tumors, image registration and comparison of fiducials system for 3D spatial as well as 4D spatial-spectral EPR imaging supported by 3D US were demonstrated. Ultrasound may provide a convenient alternative to other anatomical imaging methods for image registration in preclinical research. Of particular interest is a fusion of US tissue structure, doppler vascular function and EPR oxygen or redox imaging.

Improved Treatment of Pancreatic Cancer With Drug Delivery Nanoparticles Loaded With a Novel AKT/PDK1 Inhibitor

OBJECTIVES

This research study sought to improve the treatment of pancreatic cancer by improving the drug delivery of a promising AKT/PDK1 inhibitor, PHT-427, in poly(lactic-co-glycolic) acid (PLGA) nanoparticles.

METHODS

PHT-427 was encapsulated in single-emulsion and double-emulsion PLGA nanoparticles (SE-PLGA-427 and DE-PLGA-427). The drug release rate was evaluated to assess the effect of the second PLGA layer of DE-PLGA-427. Ex vivo cryo-imaging and drug extraction from ex vivo organs was used to assess the whole-body biodistribution in an orthotopic model of MIA PaCa-2 pancreatic cancer. Anatomical magnetic resonance imaging (MRI) was used to noninvasively assess the effects of 4 weeks of nanoparticle drug treatment on tumor size, and diffusion-weighted MRI longitudinally assessed changes in tumor cellularity.

RESULTS

DE-PLGA-427 showed delayed drug release and longer drug retention in the pancreas relative to SE-PLGA-427. Diffusion-weighted MRI indicated a consistent decrease in cellularity during drug treatment with both types of drug-loaded nanoparticles. Both SE- and DE-PLGA-427 showed a 6-fold and 4-fold reduction in tumor volume relative to untreated tumors and an elimination of primary pancreatic tumor in 68% of the mice.

CONCLUSIONS

These results indicated that the PLGA nanoparticles improved drug delivery of PHT-427 to pancreatic tumors, which improved the treatment of MIA PaCa-2 pancreatic cancer.

Rationale for the Combination of Nuclear Medicine with Magnetic Resonance for Pre-clinical Imaging

Multi-modality combinations of SPECT/CT and PET/CT have proven to be highly successful in the clinic and small animal SPECT/CT and PET/CT are becoming the norm in the research and drug development setting. However, the use of ionizing radiation from a high-resolution CT scanner is undesirable in any setting and particularly in small animal imaging (SAI), in laboratory experiments where it can result in radiation doses of sufficient magnitude that the experimental results can be influenced by the organism's response to radiation. The alternative use of magnetic resonance (MR) would offer a high-resolution, non-ionizing method for anatomical imaging of laboratory animals. MR brings considerably more than its 3D anatomical capability, especially regarding the imaging of laboratory animals. Dynamic MR imaging techniques can facilitate studies of perfusion, oxygenation, and diffusion amongst others. Further, MR spectroscopy can provide images that can be related to the concentration of endogenous molecules in vivo. MR imaging of injected contrast agents extends MR into the domain of molecular imaging. In combination with nuclear medicine (NM) SPECT and PET modalities in small animal imaging, MR would facilitate studies of dynamic processes such as biodistribution, pharmacokinetics, and pharmacodynamics. However, the detectors for nearly all PET and SPECT systems are still based on vacuum tube technology, namely: photomultiplier tubes (PMT's) in which the signal is generated by transporting electrons over a substantial distance within an evacuated glass tube, making them inoperable in even small magnetic fields. Thus the combination of SPECT or PET with MR has not been practical until the recent availability of semiconductor detectors such as silicon avalanche photodiodes (APD's) for PET and CdZnTe (CZT) detectors for SPECT coupled with the availability of high-density low noise ASIC electronics to read out the semiconductor detectors. The strong advantage of these technologies over PMT's is their insensitivity to magnetic fields which makes their use in co-axial multi-modality nuclear medicine/magnetic resonance instrumentation possible.

A fully automatic multi-atlas based segmentation method for prostate MR images

Most of multi-atlas segmentation methods focus on the registration between the full-size volumes of the data set. Although the transformations obtained from these registrations may be accurate for the global field of view of the images, they may not be accurate for the local prostate region. This is because different magnetic resonance (MR) images have different fields of view and may have large anatomical variability around the prostate. To overcome this limitation, we proposed a two-stage prostate segmentation method based on a fully automatic multi-atlas framework, which includes the detection stage i.e. locating the prostate, and the segmentation stage i.e. extracting the prostate. The purpose of the first stage is to find a cuboid that contains the whole prostate as small cubage as possible. In this paper, the cuboid including the prostate is detected by registering atlas edge volumes to the target volume while an edge detection algorithm is applied to every slice in the volumes. At the second stage, the proposed method focuses on the registration in the region of the prostate vicinity, which can improve the accuracy of the prostate segmentation. We evaluated the proposed method on 12 patient MR volumes by performing a leave-one-out study. Dice similarity coefficient (DSC) and Hausdorff distance (HD) are used to quantify the difference between our method and the manual ground truth. The proposed method yielded a DSC of 83.4%±4.3%, and a HD of 9.3 mm±2.6 mm. The fully automated segmentation method can provide a useful tool in many prostate imaging applications.

Register cardiac fiber orientations from 3D DTI volume to 2D ultrasound image of rat hearts

Two-dimensional (2D) ultrasound or echocardiography is one of the most widely used examinations for the diagnosis of cardiac diseases. However, it only supplies the geometric and structural information of the myocardium. In order to supply more detailed microstructure information of the myocardium, this paper proposes a registration method to map cardiac fiber orientations from three-dimensional (3D) magnetic resonance diffusion tensor imaging (MR-DTI) volume to the 2D ultrasound image. It utilizes a 2D/3D intensity based registration procedure including rigid, log-demons, and affine transformations to search the best similar slice from the template volume. After registration, the cardiac fiber orientations are mapped to the 2D ultrasound image via fiber relocations and reorientations. This method was validated by six images of rat hearts ex vivo. The evaluation results indicated that the final Dice similarity coefficient (DSC) achieved more than 90% after geometric registrations; and the inclination angle errors (IAE) between the mapped fiber orientations and the gold standards were less than 15 degree. This method may provide a practical tool for cardiologists to examine cardiac fiber orientations on ultrasound images and have the potential to supply additional information for diagnosis of cardiac diseases.

3D in vivo imaging of rat hearts by high frequency ultrasound and its application in myofiber orientation wrapping

Cardiac ultrasound plays an important role in the imaging of hearts in basic cardiovascular research and clinical examinations. 3D ultrasound imaging can provide the geometry or motion information of the heart. Especially, the wrapping of cardiac fiber orientations to the ultrasound volume could supply useful information on the stress distributions and electric action spreading. However, how to acquire 3D ultrasound volumes of the heart of small animals in vivo for cardiac fiber wrapping is still a challenging problem. In this study, we provide an approach to acquire 3D ultrasound volumes of the rat hearts in vivo. The comparison between both in vivo and ex vivo geometries indicated 90.1% Dice similarity. In this preliminary study, the evaluations of the cardiac fiber orientation wrapping errors were 24.7° for the acute angle error and were 22.4° for the inclination angle error. This 3D ultrasound imaging and fiber orientation estimation technique have potential applications in cardiac imaging.

Targeted iron-oxide nanoparticle for photodynamic therapy and imaging of head and neck cancer

Photodynamic therapy (PDT) is a highly specific anticancer treatment modality for various cancers, particularly for recurrent cancers that no longer respond to conventional anticancer therapies. PDT has been under development for decades, but light-associated toxicity limits its clinical applications. To reduce the toxicity of PDT, we recently developed a targeted nanoparticle (NP) platform that combines a second-generation PDT drug, Pc 4, with a cancer targeting ligand, and iron oxide (IO) NPs. Carboxyl functionalized IO NPs were first conjugated with a fibronectin-mimetic peptide (Fmp), which binds integrin β1. Then the PDT drug Pc 4 was successfully encapsulated into the ligand-conjugated IO NPs to generate Fmp-IO-Pc 4. Our study indicated that both nontargeted IO-Pc 4 and targeted Fmp-IO-Pc 4 NPs accumulated in xenograft tumors with higher concentrations than nonformulated Pc 4. As expected, both IO-Pc 4 and Fmp-IO-Pc 4 reduced the size of HNSCC xenograft tumors more effectively than free Pc 4. Using a 10-fold lower dose of Pc 4 than that reported in the literature, the targeted Fmp-IO-Pc 4 NPs demonstrated significantly greater inhibition of tumor growth than nontargeted IO-Pc 4 NPs. These results suggest that the delivery of a PDT agent Pc 4 by IO NPs can enhance treatment efficacy and reduce PDT drug dose. The targeted IO-Pc 4 NPs have great potential to serve as both a magnetic resonance imaging (MRI) agent and PDT drug in the clinic.

Breast Tissue Classification in Digital Tomosynthesis Images Based on Global Gradient Minimization and Texture Features

Digital breast tomosynthesis (DBT) is a pseudo-three-dimensional x-ray imaging modality proposed to decrease the effect of tissue superposition present in mammography, potentially resulting in an increase in clinical performance for the detection and diagnosis of breast cancer. Tissue classification in DBT images can be useful in risk assessment, computer-aided detection and radiation dosimetry, among other aspects. However, classifying breast tissue in DBT is a challenging problem because DBT images include complicated structures, image noise, and out-of-plane artifacts due to limited angular tomographic sampling. In this project, we propose an automatic method to classify fatty and glandular tissue in DBT images. First, the DBT images are pre-processed to enhance the tissue structures and to decrease image noise and artifacts. Second, a global smooth filter based on L0 gradient minimization is applied to eliminate detailed structures and enhance large-scale ones. Third, the similar structure regions are extracted and labeled by fuzzy C-means (FCM) classification. At the same time, the texture features are also calculated. Finally, each region is classified into different tissue types based on both intensity and texture features. The proposed method is validated using five patient DBT images using manual segmentation as the gold standard. The Dice scores and the confusion matrix are utilized to evaluate the classified results. The evaluation results demonstrated the feasibility of the proposed method for classifying breast glandular and fat tissue on DBT images.

Hyperspectral Imaging for Cancer Surgical Margin Delineation: Registration of Hyperspectral and Histological Images

The determination of tumor margins during surgical resection remains a challenging task. A complete removal of malignant tissue and conservation of healthy tissue is important for the preservation of organ function, patient satisfaction, and quality of life. Visual inspection and palpation is not sufficient for discriminating between malignant and normal tissue types. Hyperspectral imaging (HSI) technology has the potential to noninvasively delineate surgical tumor margin and can be used as an intra-operative visual aid tool. Since histological images provide the ground truth of cancer margins, it is necessary to warp the cancer regions in ex vivo histological images back to in vivo hyperspectral images in order to validate the tumor margins detected by HSI and to optimize the imaging parameters. In this paper, principal component analysis (PCA) is utilized to extract the principle component bands of the HSI images, which is then used to register HSI images with the corresponding histological image. Affine registration is chosen to model the global transformation. A B-spline free form deformation (FFD) method is used to model the local non-rigid deformation. Registration experiment was performed on animal hyperspectral and histological images. Experimental results from animals demonstrated the feasibility of the hyperspectral imaging method for cancer margin detection.

Mapping Cardiac Fiber Orientations from High-Resolution DTI to High-Frequency 3D Ultrasound

The orientation of cardiac fibers affects the anatomical, mechanical, and electrophysiological properties of the heart. Although echocardiography is the most common imaging modality in clinical cardiac examination, it can only provide the cardiac geometry or motion information without cardiac fiber orientations. If the patient's cardiac fiber orientations can be mapped to his/her echocardiography images in clinical examinations, it may provide quantitative measures for diagnosis, personalized modeling, and image-guided cardiac therapies. Therefore, this project addresses the feasibility of mapping personalized cardiac fiber orientations to three-dimensional (3D) ultrasound image volumes. First, the geometry of the heart extracted from the MRI is translated to 3D ultrasound by rigid and deformable registration. Deformation fields between both geometries from MRI and ultrasound are obtained after registration. Three different deformable registration methods were utilized for the MRI-ultrasound registration. Finally, the cardiac fiber orientations imaged by DTI are mapped to ultrasound volumes based on the extracted deformation fields. Moreover, this study also demonstrated the ability to simulate electricity activations during the cardiac resynchronization therapy (CRT) process. The proposed method has been validated in two rat hearts and three canine hearts. After MRI/ultrasound image registration, the Dice similarity scores were more than 90% and the corresponding target errors were less than 0.25 mm. This proposed approach can provide cardiac fiber orientations to ultrasound images and can have a variety of potential applications in cardiac imaging.

Multiscale segmentation of the skull in MR images for MRI-based attenuation correction of combined MR/PET

BACKGROUND AND OBJECTIVE

Combined magnetic resonance/positron emission tomography (MR/PET) is a relatively new, hybrid imaging modality. MR-based attenuation correction often requires segmentation of the bone on MR images. In this study, we present an automatic segmentation method for the skull on MR images for attenuation correction in brain MR/PET applications.

MATERIALS AND METHODS

Our method transforms T1-weighted MR images to the Radon domain and then detects the features of the skull image. In the Radon domain we use a bilateral filter to construct a multiscale image series. For the repeated convolution we increase the spatial smoothing in each scale and make the width of the spatial and range Gaussian function doubled in each scale. Two filters with different kernels along the vertical direction are applied along the scales from the coarse to fine levels. The results from a coarse scale give a mask for the next fine scale and supervise the segmentation in the next fine scale. The use of the multiscale bilateral filtering scheme is to improve the robustness of the method for noise MR images. After combining the two filtered sinograms, the reciprocal binary sinogram of the skull is obtained for the reconstruction of the skull image.

RESULTS

This method has been tested with brain phantom data, simulated brain data, and real MRI data. For real MRI data the Dice overlap ratios are 92.2%±1.9% between our segmentation and manual segmentation.

CONCLUSIONS

The multiscale segmentation method is robust and accurate and can be used for MRI-based attenuation correction in combined MR/PET.

Nonrigid point registration for 2D curves and 3D surfaces and its various applications

A nonrigid B-spline-based point-matching (BPM) method is proposed to match dense surface points. The method solves both the point correspondence and nonrigid transformation without features extraction. The registration method integrates a motion model, which combines a global transformation and a B-spline-based local deformation, into a robust point-matching framework. The point correspondence and deformable transformation are estimated simultaneously by fuzzy correspondence and by a deterministic annealing technique. Prior information about global translation, rotation and scaling is incorporated into the optimization. A local B-spline motion model decreases the degrees of freedom for optimization and thus enables the registration of a larger number of feature points. The performance of the BPM method has been demonstrated and validated using synthesized 2D and 3D data, mouse MRI and micro-CT images. The proposed BPM method can be used to register feature point sets, 2D curves, 3D surfaces and various image data.

Biodistribution Study of Nanoparticle Encapsulated Photodynamic Therapy Drugs Using Multispectral Imaging

Photodynamictherapy (PDT) uses a drug called a photosensitizer that is excited by irradiation with a laser light of a particular wavelength, which generates reactive singlet oxygen that damages the tumor cells. The photosensitizer and light are inert; therefore, systemic toxicities are minimized in PDT. The synthesis of novel PDT drugs and the use of nanosized carriers for photosensitizers may improve the efficiency of the therapy and the delivery of the drug. In this study, we formulated two nanoparticles with and without a targeting ligand to encapsulate phthalocyanines 4 (Pc 4) molecule and compared their biodistributions. Metastatic human head and neck cancer cells (M4e) were transplanted into nude mice. After 2-3 weeks, the mice were injected with Pc 4, Pc 4 encapsulated into surface coated iron oxide (IO-Pc 4), and IO-Pc 4 conjugated with a fibronectin-mimetic peptide (FMP-IO-Pc 4) which binds specifically to integrin β1. The mice were imaged using a multispectral camera. Using multispectral images, a library of spectral signatures was created and the signal per pixel of each tumor was calculated, in a grayscale representation of the unmixed signal of each drug. An enhanced biodistribution of nanoparticle encapsulated PDT drugs compared to non-formulated Pc 4 was observed. Furthermore, specific targeted nanoparticles encapsulated Pc 4 has a quicker delivery time and accumulation in tumor tissue than the non-targeted nanoparticles. The nanoparticle-encapsulated PDT drug can have a variety of potential applications in cancer imaging and treatment.

Automatic 3D Segmentation of the Kidney in MR Images Using Wavelet Feature Extraction and Probability Shape Model

Numerical estimation of the size of the kidney is useful in evaluating conditions of the kidney, especially, when serial MR imaging is performed to evaluate the kidney function. This paper presents a new method for automatic segmentation of the kidney in three-dimensional (3D) MR images, by extracting texture features and statistical matching of geometrical shape of the kidney. A set of Wavelet-based support vector machines (W-SVMs) is trained on the MR images. The W-SVMs capture texture priors of MRI for classification of the kidney and non-kidney tissues in different zones around the kidney boundary. In the segmentation procedure, these W-SVMs are trained to tentatively label each voxel around the kidney model as a kidney or non-kidney voxel by texture matching. A probability kidney model is created using 10 segmented MRI data. The model is initially localized based on the intensity profiles in three directions. The weight functions are defined for each labeled voxel for each Wavelet-based, intensity-based, and model-based label. Consequently, each voxel has three labels and three weights for the Wavelet feature, intensity, and probability model. Using a 3D edge detection method, the model is re-localized and the segmented kidney is modified based on a region growing method in the model region. The probability model is re-localized based on the results and this loop continues until the segmentation converges. Experimental results with mouse MRI data show the good performance of the proposed method in segmenting the kidney in MR images.

Mouse atlas registration with non-tomographic imaging modalities-a pilot study based on simulation

PURPOSE

This study investigates methodologies for the estimation of small animal anatomy from non-tomographic modalities, such as planar X-ray projections, optical cameras, and surface scanners. The key goal is to register a digital mouse atlas to a combination of non-tomographic modalities, in order to provide organ-level anatomical references of small animals in 3D.

PROCEDURES

A 2D/3D registration method was developed to register the 3D atlas to the combination of non-tomographic imaging modalities. Eleven combinations of three non-tomographic imaging modalities were simulated, and the registration accuracy of each combination was evaluated.

RESULTS

Comparing the 11 combinations, the top-view X-ray projection combined with the side-view optical camera yielded the best overall registration accuracy of all organs. The use of a surface scanner improved the registration accuracy of skin, spleen, and kidneys.

CONCLUSIONS

The methodologies and evaluation presented in this study should provide helpful information for designing preclinical atlas-based anatomical data acquisition systems.

Combined micro-PET/micro-CT imaging of lung tumours in SPC-raf and SPC-myc transgenic mice

Introduction

SPC-raf and SPC-myc transgenic mice develop disseminated and circumscribed lung adenocarcinoma respectively, allowing for assessment of carcinogenesis and treatment strategies. The purpose of this study was to investigate the technical feasibility, the correlation of initial findings to histology and the administered radiation dose of combined micro-PET/micro-CT in these animal models.

Material and Methods

14 C57BL/6 mice (4 nontransgenic, 4 SPC-raf transgenic, 6 SPC-myc transgenic) were examined using micro-CT and ¹⁸F-Fluoro-deoxyglucose micro-PET in-vivo. Micro-PET data was corrected for random events and scatter prior to reconstruction with a 3D-FORE/2D-OSEM iterative algorithm. Rigid micro-PET/micro-CT registration was performed. Tumour-to-non-tumour ratios were calculated for different lung regions and focal lesions. Diffuse tumour growth was quantified using a semiautomated micro-CT segmentation routine reported earlier. Regional histologic tumour load was assessed using a 4-point rating scale. Gamma radiation dose was determined using thermoluminescence dosimeters.

Results

Micro-CT allowed visualisation of diffuse and circumscribed tumours in SPC-raf and SPC-myc transgenic animals along with morphology, while micro-PET provided information on metabolism, but lacked morphologic detail. Mean tumour-to-non-tumour ratio was 2.47 for circumscribed lesions. No significant correlation could be shown between histological tumour load and tumour-to-nontumour ratio for diffuse tumours in SPC-raf transgenic animals. Calculation of the expected dose based on gamma dosimetry yielded approximately 140 mGy/micro-PET examination additional to approximately 200 mGy due to micro-CT.

Conclusions

Combined micro-PET/micro-CT imaging allows for in-vivo assessment of lung tumours in SPC-raf and SPC-myc transgenic mice. The technique has potential for the evaluation of carcinogenesis and treatment strategies in circumscribed lung tumours.

MR∕PET quantification tools: registration, segmentation, classification, and MR-based attenuation correction

PURPOSE

Combined MR∕PET is a relatively new, hybrid imaging modality. A human MR∕PET prototype system consisting of a Siemens 3T Trio MR and brain PET insert was installed and tested at our institution. Its present design does not offer measured attenuation correction (AC) using traditional transmission imaging. This study is the development of quantification tools including MR-based AC for quantification in combined MR∕PET for brain imaging.

METHODS

The developed quantification tools include image registration, segmentation, classification, and MR-based AC. These components were integrated into a single scheme for processing MR∕PET data. The segmentation method is multiscale and based on the Radon transform of brain MR images. It was developed to segment the skull on T1-weighted MR images. A modified fuzzy C-means classification scheme was developed to classify brain tissue into gray matter, white matter, and cerebrospinal fluid. Classified tissue is assigned an attenuation coefficient so that AC factors can be generated. PET emission data are then reconstructed using a three-dimensional ordered sets expectation maximization method with the MR-based AC map. Ten subjects had separate MR and PET scans. The PET with [(11)C]PIB was acquired using a high-resolution research tomography (HRRT) PET. MR-based AC was compared with transmission (TX)-based AC on the HRRT. Seventeen volumes of interest were drawn manually on each subject image to compare the PET activities between the MR-based and TX-based AC methods.

RESULTS

For skull segmentation, the overlap ratio between our segmented results and the ground truth is 85.2 ± 2.6%. Attenuation correction results from the ten subjects show that the difference between the MR and TX-based methods was <6.5%.

CONCLUSIONS

MR-based AC compared favorably with conventional transmission-based AC. Quantitative tools including registration, segmentation, classification, and MR-based AC have been developed for use in combined MR∕PET.

Automated analysis of small animal PET studies through deformable registration to an atlas

Purpose

This work aims to develop a methodology for automated atlas-guided analysis of small animal positron emission tomography (PET) data through deformable registration to an anatomical mouse model.

Methods

A non-rigid registration technique is used to put into correspondence relevant anatomical regions of rodent CT images from combined PET/CT studies to corresponding CT images of the Digimouse anatomical mouse model. The latter provides a pre-segmented atlas consisting of 21 anatomical regions suitable for automated quantitative analysis. Image registration is performed using a package based on the Insight Toolkit allowing the implementation of various image registration algorithms. The optimal parameters obtained for deformable registration were applied to simulated and experimental mouse PET/CT studies. The accuracy of the image registration procedure was assessed by segmenting mouse CT images into seven regions: brain, lungs, heart, kidneys, bladder, skeleton and the rest of the body. This was accomplished prior to image registration using a semi-automated algorithm. Each mouse segmentation was transformed using the parameters obtained during CT to CT image registration. The resulting segmentation was compared with the original Digimouse atlas to quantify image registration accuracy using established metrics such as the Dice coefficient and Hausdorff distance. PET images were then transformed using the same technique and automated quantitative analysis of tracer uptake performed.

Results

The Dice coefficient and Hausdorff distance show fair to excellent agreement and a mean registration mismatch distance of about 6 mm. The results demonstrate good quantification accuracy in most of the regions, especially the brain, but not in the bladder, as expected. Normalized mean activity estimates were preserved between the reference and automated quantification techniques with relative errors below 10 % in most of the organs considered.

Conclusion

The proposed automated quantification technique is reliable, robust and suitable for fast quantification of preclinical PET data in large serial studies.

3D Prostate Segmentation of Ultrasound Images Combining Longitudinal Image Registration and Machine Learning

We developed a three-dimensional (3D) segmentation method for transrectal ultrasound (TRUS) images, which is based on longitudinal image registration and machine learning. Using longitudinal images of each individual patient, we register previously acquired images to the new images of the same subject. Three orthogonal Gabor filter banks were used to extract texture features from each registered image. Patient-specific Gabor features from the registered images are used to train kernel support vector machines (KSVMs) and then to segment the newly acquired prostate image. The segmentation method was tested in TRUS data from five patients. The average surface distance between our and manual segmentation is 1.18 ± 0.31 mm, indicating that our automatic segmentation method based on longitudinal image registration is feasible for segmenting the prostate in TRUS images.

A Molecular Image-directed, 3D Ultrasound-guided Biopsy System for the Prostate

Systematic transrectal ultrasound (TRUS)-guided biopsy is the standard method for a definitive diagnosis of prostate cancer. However, this biopsy approach uses two-dimensional (2D) ultrasound images to guide biopsy and can miss up to 30% of prostate cancers. We are developing a molecular image-directed, three-dimensional (3D) ultrasound image-guided biopsy system for improved detection of prostate cancer. The system consists of a 3D mechanical localization system and software workstation for image segmentation, registration, and biopsy planning. In order to plan biopsy in a 3D prostate, we developed an automatic segmentation method based wavelet transform. In order to incorporate PET/CT images into ultrasound-guided biopsy, we developed image registration methods to fuse TRUS and PET/CT images. The segmentation method was tested in ten patients with a DICE overlap ratio of 92.4% ± 1.1 %. The registration method has been tested in phantoms. The biopsy system was tested in prostate phantoms and 3D ultrasound images were acquired from two human patients. We are integrating the system for PET/CT directed, 3D ultrasound-guided, targeted biopsy in human patients.

An MR image-guided, voxel-based partial volume correction method for PET images

PURPOSE

Partial volume effect in positron emission tomography (PET) can cause incorrect quantification of radiopharmaceutical uptake in functional imaging. A PET partial volume correction method is presented to attenuate partial volume blurring and to yield voxel-based corrected PET images.

METHODS

By modeling partial volume effect as a convolution of point spread function of the PET scanner, the reconstructed PET images are corrected by iterative deconvolution with an edge-preserving smoothness constraint. The constraint is constructed to restore discontinuities extracted from coregistered MR images but maintains the smoothness in radioactivity distribution. The correction is implemented in a Bayesian deconvolution framework and is solved by a conjugate gradient method. The performance of the method was compared with the geometric transfer matrix (GTM) method on a simulated dataset. The method was evaluated on synthesized brain FDG-PET data and phantom MRI-PET experiments.

RESULTS

The true PET activity of objects with a size of greater than the full-width at half maximum of the point spread function has been effectively restored in the simulated data. The partial volume correction method is quantitatively comparable to the GTM method. For synthesized FDG-PET with true activity 0 μci/cc for cerebrospinal fluid (CSF), 228 μci/cc for white matter (WM), and 621 μci/cc for gray matter (GM), the method has improved the radioactivity quantification from 186 ± 16 μci/cc to 30 ± 7 μci/cc in CSF, 317 ± 15 μci/cc to 236 ± 10 μci/cc for WM, 438 ± 4 μci/cc to 592 ± 5 μci/cc for GM. Both visual and quantitative assessments show improvement of partial volume correction in the synthesized and phantom experiments.

CONCLUSIONS

The partial volume correction method improves the quantification of PET images. The method is comparable to the GTM method but does not need MR image segmentation or prior tracer distribution information. The voxel-based method can be particularly useful for combined PET/MRI studies.

A multiscale and multiblock fuzzy C-means classification method for brain MR images

PURPOSE

Classification of magnetic resonance (MR) images has many clinical and research applications. Because of multiple factors such as noise, intensity inhomogeneity, and partial volume effects, MR image classification can be challenging. Noise in MRI can cause the classified regions to become disconnected. Partial volume effects make the assignment of a single class to one region difficult. Because of intensity inhomogeneity, the intensity of the same tissue can vary with respect to the location of the tissue within the same image. The conventional "hard" classification method restricts each pixel exclusively to one class and often results in crisp results. Fuzzy C-mean (FCM) classification or "soft" segmentation has been extensively applied to MR images, in which pixels are partially classified into multiple classes using varying memberships to the classes. Standard FCM, however, is sensitive to noise and cannot effectively compensate for intensity inhomogeneities. This paper presents a method to obtain accurate MR brain classification using a modified multiscale and multiblock FCM.

METHODS

An automatic, multiscale and multiblock fuzzy C-means (MsbFCM) classification method with MR intensity correction is presented in this paper. We use a bilateral filter to process MR images and to build a multiscale image series by increasing the standard deviation of spatial function and by reducing the standard deviation of range function. At each scale, we separate the image into multiple blocks and for every block a multiscale fuzzy C-means classification method is applied along the scales from the coarse to fine levels in order to overcome the effect of intensity inhomogeneity. The result from a coarse scale supervises the classification in the next fine scale. The classification method is tested with noisy MR images with intensity inhomogeneity.

RESULTS

Our method was compared with the conventional FCM, a modified FCM (MFCM) and multiscale FCM (MsFCM) method. Validation studies were performed on synthesized images with various contrasts, on the simulated brain MR database, and on real MR images. Our MsbFCM method consistently performed better than the conventional FCM, MFCM, and MsFCM methods. The MsbFCM method achieved an overlap ratio of 91% or higher. Experimental results using real MR images demonstrate the effectiveness of the proposed method. Our MsbFCM classification method is accurate and robust for various MR images.

CONCLUSIONS

As our classification method did not assume a Gaussian distribution of tissue intensity, it could be used on other image data for tissue classification and quantification. The automatic classification method can provide a useful quantification tool in neuroimaging and other applications.

3D Non-rigid Registration Using Surface and Local Salient Features for Transrectal Ultrasound Image-guided Prostate Biopsy

We present a 3D non-rigid registration algorithm for the potential use in combining PET/CT and transrectal ultrasound (TRUS) images for targeted prostate biopsy. Our registration is a hybrid approach that simultaneously optimizes the similarities from point-based registration and volume matching methods. The 3D registration is obtained by minimizing the distances of corresponding points at the surface and within the prostate and by maximizing the overlap ratio of the bladder neck on both images. The hybrid approach not only capture deformation at the prostate surface and internal landmarks but also the deformation at the bladder neck regions. The registration uses a soft assignment and deterministic annealing process. The correspondences are iteratively established in a fuzzy-to-deterministic approach. B-splines are used to generate a smooth non-rigid spatial transformation. In this study, we tested our registration with pre- and post-biopsy TRUS images of the same patients. Registration accuracy is evaluated using manual defined anatomic landmarks, i.e. calcification. The root-mean-squared (RMS) of the difference image between the reference and floating images was decreased by 62.6±9.1% after registration. The mean target registration error (TRE) was 0.88±0.16 mm, i.e. less than 3 voxels with a voxel size of 0.38×0.38×0.38 mm3 for all five patients. The experimental results demonstrate the robustness and accuracy of the 3D non-rigid registration algorithm.

Diffusion-weighted MRI for monitoring tumor response to photodynamic therapy

PURPOSE

To examine diffusion-weighted MRI (DW-MRI) for assessing the early tumor response to photodynamic therapy (PDT).

MATERIALS AND METHODS

Subcutaneous tumor xenografts of human prostate cancer cells (CWR22) were initiated in athymic nude mice. A second-generation photosensitizer, Pc 4, was delivered to each animal by a tail vein injection 48 h before laser illumination. A dedicated high-field (9.4 Tesla) small animal MR scanner was used to acquire diffusion-weighted MR images pre-PDT and 24 h after the treatment. DW-MRI and apparent diffusion coefficients (ADC) were analyzed for 24 treated and 5 control mice with photosensitizer only or laser light only. Tumor size, prostate specific antigen (PSA) level, and tumor histology were obtained at different time points to examine the treatment effect.

RESULTS

Treated mice showed significant tumor size shrinkage and decrease of PSA level within 7 days after the treatment. The average ADC of the 24 treated tumors increased 24 h after PDT (P < 0.001) comparing with pre-PDT. The average ADC was 0.511 +/- 0.119 x 10(-3) mm(2)/s pre-PDT and 0.754 +/- 0.181 x 10(-3) mm(2)/s 24 h after the PDT. There is no significant difference in ADC values pre-PDT and 24 h after PDT in the control tumors (P = 0.20).

CONCLUSION

The change of tumor ADC values measured by DW-MRI may provide a noninvasive imaging marker for monitoring tumor response to Pc 4-PDT as early as 24 h.

Choline PET for monitoring early tumor response to photodynamic therapy

Photodynamic therapy (PDT) is a relatively new therapy that has shown promise for treating various cancers in both preclinical and clinical studies. The present study evaluated the potential use of PET with radiolabeled choline to monitor early tumor response to PDT in animal models.

METHODS

Two human prostate cancer models (PC-3 and CWR22) were studied in athymic nude mice. A second-generation photosensitizer, phthalocyanine 4 (Pc 4), was delivered to each animal by a tail vein injection 48 h before laser illumination. Small-animal PET images with (11)C-choline were acquired before PDT and at 1, 24, and 48 h after PDT. Time-activity curves of (11)C-choline uptake were analyzed before and after PDT. The percentage of the injected dose per gram of tissue was quantified for both treated and control tumors at each time point. In addition, Pc 4-PDT was performed in cell cultures. Cell viability and (11)C-choline uptake in PDT-treated and control cells were measured.

RESULTS

For treated tumors, normalized (11)C-choline uptake decreased significantly 24 and 48 h after PDT, compared with the same tumors before PDT (P < 0.001). For the control tumors, normalized (11)C-choline uptake increased significantly. For mice with CWR22 tumors, the prostate-specific antigen level decreased 24 and 48 h after PDT. Pc 4-PDT in cell culture showed that the treated tumor cells, compared with the control cells, had less than 50% (11)C-choline activity at 5, 30, and 45 min after PDT, whereas the cell viability test showed that the treated cells were viable longer than 7 h after PDT.

CONCLUSION

PET with (11)C-choline is sensitive for detecting early changes associated with Pc 4-PDT in mouse models of human prostate cancer. Choline PET has the potential to determine whether a PDT-treated tumor responds to treatment within 48 h after therapy.

Preclinical Multimodality Imaging in Vivo

Multimodality small-animal molecular imaging has become increasingly important as transgenic and knockout mice are produced to model human diseases. With the ever-increasing number and importance of human disease models, particularly in rodents (mice and rats), the ability of high-resolution multimodality molecular imaging instrumentation to contribute unique information is becoming more common and necessary. Multimodality imaging with high spatial resolution and good sensitivity, which combines modalities and records sequentially or simultaneously complementary information, offers many advantages in certain research experiments. This article discusses the current trends and new horizons in preclinical multimodality imaging in-vivo and its role in biomedical research.

Choline Molecular Imaging with Small-animal PET for Monitoring Tumor Cellular Response to Photodynamic Therapy of Cancer

We are developing and evaluating choline molecular imaging with positron emission tomography (PET) for monitoring tumor response to photodynamic therapy (PDT) in animal models. Human prostate cancer (PC-3) was studied in athymic nude mice. A second-generation photosensitizer Pc 4 was used for PDT in tumor-bearing mice. MicroPET images with (11)C-choline were acquired before PDT and 48 h after PDT. Time-activity curves of (11)C-choline uptake were analyzed before and after PDT. For treated tumors, normalized choline uptake decreased significantly 48 h after PDT, compared to the same tumors pre-PDT (p < 0.001). However, for the control tumors, normalized choline uptake increased significantly (p < 0.001). PET imaging with (11)C-choline is sensitive to detect early tumor response to PDT in the animal model of human prostate cancer.

Accuracy and reproducibility of tumor positioning during prolonged and multi-modality animal imaging studies

Dedicated small-animal imaging devices, e.g. positron emission tomography (PET), computed tomography (CT) and magnetic resonance imaging (MRI) scanners, are being increasingly used for translational molecular imaging studies. The objective of this work was to determine the positional accuracy and precision with which tumors in situ can be reliably and reproducibly imaged on dedicated small-animal imaging equipment. We designed, fabricated and tested a custom rodent cradle with a stereotactic template to facilitate registration among image sets. To quantify tumor motion during our small-animal imaging protocols, 'gold standard' multi-modality point markers were inserted into tumor masses on the hind limbs of rats. Three types of imaging examination were then performed with the animals continuously anesthetized and immobilized: (i) consecutive microPET and MR images of tumor xenografts in which the animals remained in the same scanner for 2 h duration, (ii) multi-modality imaging studies in which the animals were transported between distant imaging devices and (iii) serial microPET scans in which the animals were repositioned in the same scanner for subsequent images. Our results showed that the animal tumor moved by less than 0.2-0.3 mm over a continuous 2 h microPET or MR imaging session. The process of transporting the animal between instruments introduced additional errors of approximately 0.2 mm. In serial animal imaging studies, the positioning reproducibility within approximately 0.8 mm could be obtained.

Taxotere chemosensitivity evaluation in mice prostate tumor: validation and diagnostic accuracy of quantitative measurement of tumor characteristics by MRI, PET, and histology of mice tumor

Increased PET and MRI image intensities of mouse prostate tumors were correlated with histostaining tumor characteristics. The hypothesis was that increased intracellular sodium microMRI signal intensities and flouro-2-deoxy-glucose utilization by microPET in apoptosis rich regions in tumors were positively correlated as chemosensitivity assay of Taxotere. The PC-3 cancer cell line induced prostate tumor MRI and PET images and histology slices were digitally captured and compared in pre- and post-Taxotere treated tumors. The optimization of inversion recovery MRI parameters was done to generate sodium images of phantom. The (18)FDG biotransformation was optimized to measure PET image intensities. A criterion was developed to evaluate malignancy by histology. For correlation, regression analysis was done using imaging, histology, and immunostaining data from PC3 tumor after 24 and 48 hours post-Taxotere treatment. Apoptosis indices were calculated by histostaining and ss-DNA antibody assay. Sodium MRI and PET signal intensity distributions were comparable at specific locations relatively and measured in tumor tissue regions. In tumors, Taxotere induced an increase in intracellular sodium MRI signal 30% (p<0.001) with decreased tumor size (20%; p<0.001) and micro-PET showed FDG uptake increase 15% (p<0.001) with decreased tumor size (10%; p<0.001) than that of control tumors after 24 hours. Histological features indicated tumor risk (high 'intracellular/extracellular ratio', high mitotic index, and apoptotic index), decreased tumor viability (reduced mitotic figures, reduced diploidy or aneuploidy, and proliferation index) after Taxotere treatment. These features in co-registered intracellular sodium, microPET hypermetabolic, and monoclonal antibody (ss-DNA) sensitive regions showed (% difference > 6%). Apoptosis rich regions showed characteristic nuclei with S phase DNA histogram, appearing brighter on IC-Na images and mild active on PET images (sensitivity=65%; specificity=70%). In conclusion, MRI and PET multimodal imaging may be rapid non-invasive chemosensitivity assay to monitor the drug anticancer effect.

Automated method for small-animal PET image registration with intrinsic validation

PURPOSE

We propose and compare different registration approaches to align small-animal PET studies and a procedure to validate the results by means of objective registration consistency measurements.

PROCEDURES

We have applied a registration algorithm based on information theory, using different approaches to mask the reference image. The registration consistency allows for the detection of incorrect registrations. This methodology has been evaluated on a test dataset (FDG-PET rat brain images).

RESULTS

The results show that a multiresolution two-step registration approach based on the use of the whole image at the low resolution step, while masking the brain at the high resolution step, provides the best robustness (87.5% registration success) and highest accuracy (0.67-mm average).

CONCLUSIONS

The major advantages of our approach are minimal user interaction and automatic assessment of the registration error, avoiding visual inspection of the results, thus facilitating the accurate, objective, and rapid analysis of large groups of rodent PET images.

High-field magnetic resonance imaging of the response of human prostate cancer to Pc 4-based photodynamic therapy in an animal model

INTRODUCTION

High-field magnetic resonance imaging (MRI) is an emerging technique that provides a powerful, non-invasive tool for in vivo studies of cancer therapy in animal models. Photodynamic therapy (PDT) is a relatively new treatment modality for prostate cancer, the second leading cause of cancer mortality in American males. The goal of this study was to evaluate the response of human prostate tumor cells growing as xenografts in athymic nude mice to Pc 4-sensitized PDT.

MATERIALS AND METHODS

PC-3, a cell line derived from a human prostate malignant tumor, was injected intradermally on the back flanks of athymic nude mice. Two tumors were initiated on each mouse. One was treated and the other served as the control. A second-generation photosensitizing drug Pc 4 (0.6 mg/kg body weight) was delivered to each animal by tail vein injection 48 hours before laser illumination (672 nm, 100 mW/cm(2), 150 J/cm(2)). A dedicated high-field (9.4 T) small-animal MR scanner was used for image acquisitions. A multi-slice multi-echo (MSME) technique, permitting noninvasive in vivo assessment of potential therapeutic effects, was used to measure the T2 values and tumor volumes. Animals were scanned immediately before and after PDT and 24 hours after PDT. T2 values were computed and analyzed for the tumor regions.

RESULTS

For the treated tumors, the T2 values significantly increased (P<0.002) 24 hours after PDT (68.2+/- 8.5 milliseconds), compared to the pre-PDT values (55.8+/-6.6 milliseconds). For the control tumors, there was no significant difference (P = 0.53) between the pre-PDT (52.5+/-6.1 milliseconds) and 24-hour post-PDT (54.3+/-6.4 milliseconds) values. Histologic analysis showed that PDT-treated tumors demonstrated necrosis and inflammation that was not seen in the control.

DISCUSSION

Changes in tumor T2 values measured by multi-slice multi-echo MR imaging provide an assay that could be useful for clinical monitoring of photodynamic therapy of prostate tumors.

CT image registration in sinogram space

Object displacement in a CT scan is generally reflected in CT projection data or sinogram. In this work, the direct relationship between object motion and the change of CT projection data (sinogram) is investigated and this knowledge is applied to create a novel algorithm for sinogram registration. Calculated and experimental results demonstrate that the registration technique works well for registering rigid 2D or 3D motion in parallel and fan beam samplings. Problem and solution for 3D sinogram-based registration of metallic fiducials are also addressed. Since the motion is registered before image reconstruction, the presented algorithm is particularly useful when registering images with metal or truncation artifacts. In addition, this algorithm is valuable for dealing with situations where only limited projection data are available, making it appealing for various applications in image guided radiation therapy.

Multiscale Fuzzy C-Means Image Classification for Multiple Weighted MR Images for the Assessment of Photodynamic Therapy in Mice

We are investigating in vivo small animal imaging and analysis methods for the assessment of photodynamic therapy (PDT), an emerging therapeutic modality for cancer treatment. Multiple weighted MR images were acquired from tumor-bearing mice pre- and post-PDT and 24-hour after PDT. We developed an automatic image classification method to differentiate live, necrotic and intermediate tissues within the treated tumor on the MR images. We used a multiscale diffusion filter to process the MR images before classification. A multiscale fuzzy C-means (FCM) classification method was applied along the scales. The object function of the standard FCM was modified to allow multiscale classification processing where the result from a coarse scale is used to supervise the classification in the next scale. The multiscale fuzzy C-means (MFCM) method takes noise levels and partial volume effects into the classification processing. The method was validated by simulated MR images with various noise levels. For simulated data, the classification method achieved 96.0 ± 1.1% overlap ratio. For real mouse MR images, the classification results of the treated tumors were validated by histologic images. The overlap ratios were 85.6 ± 5.1%, 82.4 ± 7.8% and 80.5 ± 10.2% for the live, necrotic, and intermediate tissues, respectively. The MR imaging and the MFCM classification methods may provide a useful tool for the assessment of the tumor response to photodynamic therapy in vivo.

In Vivo Small Animal Imaging for Early Assessment of Therapeutic Efficacy of Photodynamic Therapy for Prostate Cancer

We are developing in vivo small animal imaging techniques that can measure early effects of photodynamic therapy (PDT) for prostate cancer. PDT is an emerging therapeutic modality that continues to show promise in the treatment of cancer. At our institution, a new second-generation photosensitizing drug, the silicon phthalocyanine Pc 4, has been developed and evaluated at the Case Comprehensive Cancer Center. In this study, we are developing magnetic resonance imaging (MRI) techniques that provide therapy monitoring and early assessment of tumor response to PDT. We generated human prostate cancer xenografts in athymic nude mice. For the imaging experiments, we used a high-field 9.4-T small animal MR scanner (Bruker Biospec). High-resolution MR images were acquired from the treated and control tumors pre- and post-PDT and 24 hr after PDT. We utilized multi-slice multi-echo (MSME) MR sequences. During imaging acquisitions, the animals were anesthetized with a continuous supply of 2% isoflurane in oxygen and were continuously monitored for respiration and temperature. After imaging experiments, we manually segmented the tumors on each image slice for quantitative image analyses. We computed three-dimensional T2 maps for the tumor regions from the MSME images. We plotted the histograms of the T2 maps for each tumor pre- and post-PDT and 24 hr after PDT. After the imaging and PDT experiments, we dissected the tumor tissues and used the histologic slides to validate the MR images. In this study, six mice with human prostate cancer tumors were imaged and treated at the Case Center for Imaging Research. The T2 values of treated tumors increased by 24 ± 14% 24 hr after the therapy. The control tumors did not demonstrate significant changes of the T2 values. Inflammation and necrosis were observed within the treated tumors 24 hour after the treatment. Preliminary results show that Pc 4-PDT is effective for the treatment of human prostate cancer in mice. The small animal MR imaging provides a useful tool to evaluate early tumor response to photodynamic therapy in mice.