PET/CT in radiation oncology

ctDNA as an Adjunct to Posttreatment PET for Head and Neck Cancer Recurrence Risk Assessment

OBJECTIVE

Circulating tumor DNA (ctDNA) detection is an emerging technique that identifies minimal residual disease in patients with solid tumors. ctDNA can act as an adjunct method to help overcome the limitations of positron emission tomography (PET) and select patients who are at high risk for recurrence.

STUDY DESIGN

Retrospective Single Institutional Study.

SETTING

University Hospital Setting.

METHODS

Twenty-nine patients who underwent definitive treatment for squamous cell carcinoma of the head and neck (HNSCC) from 8/2021 to 01/2023 had ctDNA levels analyzed at 1 to 3, 6, 9, and 12 months after definitive treatment. A personalized, tumor-informed, multiplex polymerase chain reaction (PCR) next-generation sequencing (NGS) assay was used to detect the ctDNA levels. The primary outcome was recurrence-free probability (RFP), and the secondary outcomes were overall survival (OS), sensitivity, specificity, and the test's negative (NPV) and positive predictive values (PPV).

RESULTS

The median age of patients was 65 years (interquartile range: 56-69), with majority being males (n = 22, 76%). The primary sites were larynx (n = 12), oropharynx (n = 10), and oral cavity (n = 6). Posttreatment ctDNA was detected in 7 patients, all of whom had disease recurrence. ctDNA detection after definitive treatment was associated with a higher risk of disease recurrence (hazard ratio: 9.94, 95% confidence interval: 1.56-63.3, P = .015). ctDNA identified recurrence with 100% specificity and 78% sensitivity. The NPV and PPV were 91% and 100%. PET had 78% sensitivity but only 68% specificity with 86% NPV, and 54% PPV.

CONCLUSION

Based on our data, ctDNA can be an excellent adjunct test for posttreatment PET and can help guide physicians in cases where PET results are inconclusive and difficult to interpret.

Data-driven gated positron emission tomography/computed tomography for radiotherapy

Purpose

Software-based data-driven gated (DDG) positron emission tomography/computed tomography (PET/CT) has replaced hardware-based 4D PET/CT. The purpose of this article was to review DDG PET/CT, which could improve the accuracy of treatment response assessment, tumor motion evaluation, and target tumor contouring with whole-body (WB) PET/CT for radiotherapy (RT).

Material and methods

This review covered the topics of 4D PET/CT with hardware gating, advancements in PET instrumentation, DDG PET, DDG CT, and DDG PET/CT based on a systematic literature review. It included a discussion of the large axial field-of-view (AFOV) PET detector and a review of the clinical results of DDG PET and DDG PET/CT.

Results

DDG PET matched or outperformed 4D PET with hardware gating. DDG CT was more compatible with DDG PET than 4D CT, which required hardware gating. DDG CT could replace 4D CT for RT. DDG PET and DDG CT for DDG PET/CT can be incorporated in a WB PET/CT of less than 15 min scan time on a PET/CT scanner of at least 25 cm AFOV PET detector.

Conclusions

DDG PET/CT could correct the misregistration and tumor motion artifacts in a WB PET/CT and provide the quantitative PET and tumor motion information of a registered PET/CT for RT.

Advances in Radionuclides and Radiolabelled Peptides for Cancer Therapeutics

Radiopharmaceutical therapy, which can detect and treat tumours simultaneously, was introduced more than 80 years ago, and it has changed medical strategies with respect to cancer. Many radioactive radionuclides have been developed, and functional, molecularly modified radiolabelled peptides have been used to produce biomolecules and therapeutics that are vastly utilised in the field of radio medicine. Since the 1990s, they have smoothly transitioned into clinical application, and as of today, a wide variety of radiolabelled radionuclide derivatives have been examined and evaluated in various studies. Advanced technologies, such as conjugation of functional peptides or incorporation of radionuclides into chelating ligands, have been developed for advanced radiopharmaceutical cancer therapy. New radiolabelled conjugates for targeted radiotherapy have been designed to deliver radiation directly to cancer cells with improved specificity and minimal damage to the surrounding normal tissue. The development of new theragnostic radionuclides, which can be used for both imaging and therapy purposes, allows for more precise targeting and monitoring of the treatment response. The increased use of peptide receptor radionuclide therapy (PRRT) is also important in the targeting of specific receptors which are overexpressed in cancer cells. In this review, we provide insights into the development of radionuclides and functional radiolabelled peptides, give a brief background, and describe their transition into clinical application.

Biodistribution and image characteristics of 124 I-positron emission tomography in dogs with neuroendocrine neoplasia

Radioactive iodine is frequently used for staging of human thyroid carcinomas. Iodine-124 scans performed using position emission tomography (PET) allow for more precise dosimetry of therapeutic radioiodine. The distribution of I-124 has not previously been described in veterinary medicine. The purpose of this prospective, exporatory, descriptive study is to evaluate the whole-body distribution of I-124 in dogs with suspected thyroid carcinoma. Ten dogs with either a cytologic diagnosis of a neuroendocrine neoplasm or biochemical hyperthyroidism were enrolled in a prospective clinical study. Whole-body I-124 PET/CT scans were performed and were evaluated for physiologic and pathologic uptake of I-124. The maximum and mean standardized uptake values (SUVmean) were recorded for several normal and abnormal tissues. Varying degrees of uptake were found in thyroid tumors (SUVmean = 66.37), ectopic thyroid masses (21.44), presumed metastatic lesions in lymph nodes (32.14), and the pulmonary parenchyma (4.50). In most dogs, physiologic uptake above background, measured in maximum SUV, was identified in parotid and mandibular salivary glands (14.00 and 1.57) the urinary tract (1.83), the gastrointestinal tract (19.90 stomach, 6.15 colon), the liver (1.41), and the heart (1.88). Occasionally, uptake was identified in the nasolacrimal duct (3.42), salivary duct (2.73), gallbladder (2.68), and anal gland (2.22). Physiologic uptake was also identified in normal thyroid glands and ectopic thyroid tissue. This study provides a baseline of pathologic and physiologic uptake of I-124 in dogs with thyroid carcinoma, to guide interpretation of future studies.

Variational PET/CT Tumor Co-segmentation Integrated with PET Restoration

PET and CT are widely used imaging modalities in radiation oncology. PET imaging has a high contrast but blurry tumor edges due to its limited spatial resolution, while CT imaging has a high resolution but a low contrast between tumor and soft normal tissues. Tumor segmentation from either a single PET or CT image is difficult. It is known that co-segmentation methods utilizing the complementary information between PET and CT can improve segmentation accuracy. These information can be either consistent or inconsistent in the image-level. How to correctly localize tumor edges with these inconsistent information is a major challenge for co-segmentation methods. In this study, we proposed a novel variational method for tumor co-segmentation in PET/CT, with a fusion strategy specifically designed to handle the information inconsistency between PET and CT in an adaptive way - the method can automatically decide which modality should be more trustful when PET and CT disagree to each other for localizing the tumor boundary. The proposed method was constructed based on the Γ-convergence approximation of the Mumford-Shah (MS) segmentation model. A PET restoration process was integrated into the co-segmentation process, which further eliminate the uncertainty for tumor segmentation introduced by the blurring of tumor edges in PET. The performance of the proposed method was validated on a test dataset with fifty non-small cell lung cancer patients. Experimental results demonstrated that the proposed method had a high accuracy for PET/CT co-segmentation and PET restoration, and can accurately estimate the blur kernel of the PET scanner as well. For those complex images in which the tumors exhibit Fluorodeoxyglucose (FDG) uptake inhomogeneity or even invade adjacent soft normal tissues, the proposed method can still accurately segment the tumors. It achieved an average dice similarity indexes (DSI) of 0.85 ± 0.06, volume error (VE) of 0.09 ± 0.08, and classification error (CE) of 0.31 ± 0.13.

Adaptive region-growing with maximum curvature strategy for tumor segmentation in 18F-FDG PET

Accurate tumor segmentation in PET is crucial in many oncology applications. We developed an adaptive region-growing (ARG) algorithm with a maximum curvature strategy (ARG_MC) for tumor segmentation in PET. The ARG_MC repeatedly applied a confidence connected region-growing algorithm with increasing relaxing factor f. The optimal relaxing factor (ORF) was then determined at the transition point on the f-volume curve, where the volume just grew from the tumor into the surrounding normal tissues. The ARG_MC along with five widely used algorithms were tested on a phantom with 6 spheres at different signal to background ratios and on two clinic datasets including 20 patients with esophageal cancer and 11 patients with non-Hodgkin lymphoma (NHL). The ARG_MC did not require any phantom calibration or any a priori knowledge of the tumor or PET scanner. The identified ORF varied with tumor types (mean ORF  =  9.61, 3.78 and 2.55 respectively for the phantom, esophageal cancer, and NHL datasets), and varied from one tumor to another. For the phantom, the ARG_MC ranked the second in segmentation accuracy with an average Dice similarity index (DSI) of 0.86, only slightly worse than Daisne's adaptive thresholding method (DSI  =  0.87), which required phantom calibration. For both the esophageal cancer dataset and the NHL dataset, the ARG_MC had the highest accuracy with an average DSI of 0.87 and 0.84, respectively. The ARG_MC was robust to parameter settings and region of interest selection, and it did not depend on scanners, imaging protocols, or tumor types. Furthermore, the ARG_MC made no assumption about the tumor size or tumor uptake distribution, making it suitable for segmenting tumors with heterogeneous FDG uptake. In conclusion, the ARG_MC was accurate, robust and easy to use, it provides a highly potential tool for PET tumor segmentation in clinic.

A Dirichlet process mixture model for automatic (18)F-FDG PET image segmentation: Validation study on phantoms and on lung and esophageal lesions

PURPOSE

The aim of this study was to implement a Dirichlet process mixture (DPM) model for automatic tumor edge identification on (18)F-fluorodeoxyglucose positron emission tomography ((18)F-FDG PET) images by optimizing the parameters on which the algorithm depends, to validate it experimentally, and to test its robustness.

METHODS

The DPM model belongs to the class of the Bayesian nonparametric models and uses the Dirichlet process prior for flexible nonparametric mixture modeling, without any preliminary choice of the number of mixture components. The DPM algorithm implemented in the statistical software package R was used in this work. The contouring accuracy was evaluated on several image data sets: on an IEC phantom (spherical inserts with diameter in the range 10-37 mm) acquired by a Philips Gemini Big Bore PET-CT scanner, using 9 different target-to-background ratios (TBRs) from 2.5 to 70; on a digital phantom simulating spherical/uniform lesions and tumors, irregular in shape and activity; and on 20 clinical cases (10 lung and 10 esophageal cancer patients). The influence of the DPM parameters on contour generation was studied in two steps. In the first one, only the IEC spheres having diameters of 22 and 37 mm and a sphere of the digital phantom (41.6 mm diameter) were studied by varying the main parameters until the diameter of the spheres was obtained within 0.2% of the true value. In the second step, the results obtained for this training set were applied to the entire data set to determine DPM based volumes of all available lesions. These volumes were compared to those obtained by applying already known algorithms (Gaussian mixture model and gradient-based) and to true values, when available.

RESULTS

Only one parameter was found able to significantly influence segmentation accuracy (ANOVA test). This parameter was linearly connected to the uptake variance of the tested region of interest (ROI). In the first step of the study, a calibration curve was determined to automatically generate the optimal parameter from the variance of the ROI. This "calibration curve" was then applied to contour the whole data set. The accuracy (mean discrepancy between DPM model-based contours and reference contours) of volume estimation was below (1 ± 7)% on the whole data set (1 SD). The overlap between true and automatically segmented contours, measured by the Dice similarity coefficient, was 0.93 with a SD of 0.03.

CONCLUSIONS

The proposed DPM model was able to accurately reproduce known volumes of FDG concentration, with high overlap between segmented and true volumes. For all the analyzed inserts of the IEC phantom, the algorithm proved to be robust to variations in radius and in TBR. The main advantage of this algorithm was that no setting of DPM parameters was required in advance, since the proper setting of the only parameter that could significantly influence the segmentation results was automatically related to the uptake variance of the chosen ROI. Furthermore, the algorithm did not need any preliminary choice of the optimum number of classes to describe the ROIs within PET images and no assumption about the shape of the lesion and the uptake heterogeneity of the tracer was required.

Simultaneous Tumor Segmentation, Image Restoration, and Blur Kernel Estimation in PET Using Multiple Regularizations

Accurate tumor segmentation from PET images is crucial in many radiation oncology applications. Among others, partial volume effect (PVE) is recognized as one of the most important factors degrading imaging quality and segmentation accuracy in PET. Taking into account that image restoration and tumor segmentation are tightly coupled and can promote each other, we proposed a variational method to solve both problems simultaneously in this study. The proposed method integrated total variation (TV) semi-blind de-convolution and Mumford-Shah segmentation with multiple regularizations. Unlike many existing energy minimization methods using either TV or L₂ regularization, the proposed method employed TV regularization over tumor edges to preserve edge information, and L₂ regularization inside tumor regions to preserve the smooth change of the metabolic uptake in a PET image. The blur kernel was modeled as anisotropic Gaussian to address the resolution difference in transverse and axial directions commonly seen in a clinic PET scanner. The energy functional was rephrased using the Γ-convergence approximation and was iteratively optimized using the alternating minimization (AM) algorithm. The performance of the proposed method was validated on a physical phantom and two clinic datasets with non-Hodgkin's lymphoma and esophageal cancer, respectively. Experimental results demonstrated that the proposed method had high performance for simultaneous image restoration, tumor segmentation and scanner blur kernel estimation. Particularly, the recovery coefficients (RC) of the restored images of the proposed method in the phantom study were close to 1, indicating an efficient recovery of the original blurred images; for segmentation the proposed method achieved average dice similarity indexes (DSIs) of 0.79 and 0.80 for two clinic datasets, respectively; and the relative errors of the estimated blur kernel widths were less than 19% in the transversal direction and 7% in the axial direction.

A comparative study of target volumes based on 18F-FDG PET-CT and ten phases of 4DCT for primary thoracic squamous esophageal cancer

Purpose

To investigate the correlations in target volumes based on ¹⁸F-FDG PET/CT and four-dimensional CT (4DCT) to detect the feasibility of implementing PET in determining gross target volumes (GTV) for tumor motion for primary thoracic esophageal cancer (EC).

Methods

Thirty-three patients with EC sequentially underwent contrast-enhanced 3DCT, 4DCT, and ¹⁸F-FDG PET-CT thoracic simulation. The internal gross target volume (IGTV)₁₀ was obtained by combining the GTV from ten phases of 4DCT. The GTVs based on PET/CT images were defined by setting of different standardized uptake value thresholds and visual contouring. The difference in volume ratio, conformity index (CI), and degree of inclusion (DI) between IGTV₁₀ and GTV_PET was compared.

Results

The images from 20 patients were suitable for further analysis. The optimal volume ratio of 0.95±0.32, 1.06±0.50, 1.07±0.49 was at standardized uptake value (SUV)_2.5, SUV_20%, or manual contouring. The mean CIs were from 0.33 to 0.54. The best CIs were at SUV_2.0 (0.51±0.11), SUV_2.5 (0.53±0.13), SUV_20% (0.53±0.12), and manual contouring (0.54±0.14). The mean DIs of GTV_PET in IGTV₁₀ were from 0.60 to 0.90, and the mean DIs of IGTV₁₀ in GTV_PET ranged from 0.35 to 0.78. A negative correlation was found between the mean CI and different SUV (P=0.000).

Conclusion

None of the PET-based contours had both close spatial and volumetric approximation to the 4DCT IGTV₁₀. Further evaluation and optimization of PET as a tool for target identification are required.

The use of zeolites to generate PET phantoms for the validation of quantification strategies in oncology

PURPOSE

In recent years, segmentation algorithms and activity quantification methods have been proposed for oncological (18)F-fluorodeoxyglucose (FDG) PET. A full assessment of these algorithms, necessary for a clinical transfer, requires a validation on data sets provided with a reliable ground truth as to the imaged activity distribution, which must be as realistic as possible. The aim of this work is to propose a strategy to simulate lesions of uniform uptake and irregular shape in an anthropomorphic phantom, with the possibility to easily obtain a ground truth as to lesion activity and borders.

METHODS

Lesions were simulated with samples of clinoptilolite, a family of natural zeolites of irregular shape, able to absorb aqueous solutions of (18)F-FDG, available in a wide size range, and nontoxic. Zeolites were soaked in solutions of (18)F-FDG for increasing times up to 120 min and their absorptive properties were characterized as function of soaking duration, solution concentration, and zeolite dry weight. Saturated zeolites were wrapped in Parafilm, positioned inside an Alderson thorax-abdomen phantom and imaged with a PET-CT scanner. The ground truth for the activity distribution of each zeolite was obtained by segmenting high-resolution finely aligned CT images, on the basis of independently obtained volume measurements. The fine alignment between CT and PET was validated by comparing the CT-derived ground truth to a set of zeolites' PET threshold segmentations in terms of Dice index and volume error.

RESULTS

The soaking time necessary to achieve saturation increases with zeolite dry weight, with a maximum of about 90 min for the largest sample. At saturation, a linear dependence of the uptake normalized to the solution concentration on zeolite dry weight (R(2) = 0.988), as well as a uniform distribution of the activity over the entire zeolite volume from PET imaging were demonstrated. These findings indicate that the (18)F-FDG solution is able to saturate the zeolite pores and that the concentration does not influence the distribution uniformity of both solution and solute, at least at the trace concentrations used for zeolite activation. An additional proof of uniformity of zeolite saturation was obtained observing a correspondence between uptake and adsorbed volume of solution, corresponding to about 27.8% of zeolite volume. As to the ground truth for zeolites positioned inside the phantom, the segmentation of finely aligned CT images provided reliable borders, as demonstrated by a mean absolute volume error of 2.8% with respect to the PET threshold segmentation corresponding to the maximum Dice.

CONCLUSIONS

The proposed methodology allowed obtaining an experimental phantom data set that can be used as a feasible tool to test and validate quantification and segmentation algorithms for PET in oncology. The phantom is currently under consideration for being included in a benchmark designed by AAPM TG211, which will be available to the community to evaluate PET automatic segmentation methods.

Synthesis and evaluation of a peptide targeted small molecular Gd-DOTA monoamide conjugate for MR molecular imaging of prostate cancer

Tumor extracellular matrix has an abundance of cancer related proteins that can be used as biomarkers for cancer molecular imaging. Innovative design and development of safe and effective targeted contrast agents to these biomarkers would allow effective MR cancer molecular imaging with high spatial resolution. In this study, we synthesized a low molecular weight CLT1 peptide targeted Gd(III) chelate CLT1-dL-(Gd-DOTA)(4) specific to clotted plasma proteins in tumor stroma for cancer MR molecular imaging. CLT1-dL-(Gd-DOTA)(4) was synthesized by conjugating four Gd-DOTA monoamide chelates to a CLT1 peptide via generation 1 lysine dendrimer. The T(1) relaxivity of CLT1-dL-(Gd-DOTA)(4) was 40.4 mM(-1) s(-1) per molecule (10.1 mM(-1) s(-1) per Gd) at 37 °C and 1.5 T. Fluorescence imaging showed high binding specificity of CLT1 to orthotopic PC3 prostate tumor in mice. The contrast agent resulted in improved tumor contrast enhancement in male athymic nude mice bearing orthotopic PC3 prostate tumor xenograft at a dose of 0.03 mmol Gd/kg. The peptide targeted MRI contrast agent is promising for high-resolution MR molecular imaging of prostate tumor.

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.

[Metabolically active volumes automatic delineation methodologies in PET imaging: review and perspectives]

PET imaging is now considered a gold standard tool in clinical oncology, especially for diagnosis purposes. More recent applications such as therapy follow-up or tumor targeting in radiotherapy require a fast, accurate and robust metabolically active tumor volumes delineation on emission images, which cannot be obtained through manual contouring. This clinical need has sprung a large number of methodological developments regarding automatic methods to define tumor volumes on PET images. This paper reviews most of the methodologies that have been recently proposed and discusses their framework and methodological and/or clinical validation. Perspectives regarding the future work to be done are also suggested.

PET functional volume delineation: a robustness and repeatability study

PURPOSE

Current state-of-the-art algorithms for functional uptake volume segmentation in PET imaging consist of threshold-based approaches, whose parameters often require specific optimization for a given scanner and associated reconstruction algorithms. Different advanced image segmentation approaches previously proposed and extensively validated, such as among others fuzzy C-means (FCM) clustering, or fuzzy locally adaptive bayesian (FLAB) algorithm have the potential to improve the robustness of functional uptake volume measurements. The objective of this study was to investigate robustness and repeatability with respect to various scanner models, reconstruction algorithms and acquisition conditions.

METHODS AND MATERIALS

Robustness was evaluated using a series of IEC phantom acquisitions carried out on different PET/CT scanners (Philips Gemini and Gemini Time-of-Flight, Siemens Biograph and GE Discovery LS) with their associated reconstruction algorithms (RAMLA, TF MLEM, OSEM). A range of acquisition parameters (contrast, duration) and reconstruction parameters (voxel size) were considered for each scanner model, and the repeatability of each method was evaluated on simulated and clinical tumours and compared to manual delineation.

RESULTS

For all the scanner models, acquisition parameters and reconstruction algorithms considered, the FLAB algorithm demonstrated higher robustness in delineation of the spheres with low mean errors (10%) and variability (5%), with respect to threshold-based methodologies and FCM. The repeatability provided by all segmentation algorithms considered was very high with a negligible variability of <5% in comparison to that associated with manual delineation (5-35%).

CONCLUSION

The use of advanced image segmentation algorithms may not only allow high accuracy as previously demonstrated, but also provide a robust and repeatable tool to aid physicians as an initial guess in determining functional volumes in PET.

PET/CT today and tomorrow in veterinary cancer diagnosis and monitoring: fundamentals, early results and future perspectives

Functional imaging using positron emission tomography (PET) plays an important role in the diagnosis, staging, image-guided treatment planning and monitoring of malignant diseases. PET imaging complements conventional anatomical imaging such as computed tomography (CT) and magnetic resonance imaging (MRI). The strength of CT scanning lies in its high spatial resolution, allowing for anatomical characterization of disease. PET imaging, however, moves beyond anatomy and characterizes tissue based on functions such as metabolic rate. Combined PET/CT scanners were introduced commercially in 2001 and a number of technological advancements have since occurred. Radiolabelled tracers such as (18)F-fluorodeoxyglucose (FDG) and (18)F-fluorothymidine (FLT) allow visualization of various metabolic processes within cancer cells. Many studies in human oncology evaluating the utility of PET/CT have demonstrated clinical benefits. Few veterinary studies have been performed, but initial studies show promise for improved detection of malignancy, more thorough staging of canine cancer and determination of early response and disease recrudescence.

Roles of mitochondria in human disease

The chapters throughout this volume illustrate the many contributions of mitochondria to the maintenance of normal cell and tissue function, experienced as the health of the individual. Mitochondria are essential for maintaining aspects of physiology as fundamental as cellular energy balance, the modulation of calcium signalling, in defining cellular redox balance, and they house significant biosynthetic pathways. Mitochondrial numbers and volume within cells are regulated and have an impact on their functional roles, while, especially in the CNS (central nervous system), mitochondrial trafficking is critical to ensure the cellular distribution and strategic localization of mitochondria, presumably driven by local energy demand. Maintenance of a healthy mitochondrial population involves a complex system of quality control, involving degrading misfolded proteins, while damaged mitochondria are renewed by fusion or removed by autophagy. It seems evident that mechanisms that impair any of these processes will impair mitochondrial function and cell signalling pathways, leading to disordered cell function which manifests as disease. As gatekeepers of cell life and cell death, mitochondria regulate both apoptotic and necrotic cell death, and so at its most extreme, disturbances involving these pathways may trigger untimely cell death. Conversely, the lack of appropriate cell death can lead to inappropriate tissue growth and development of tumours, which are also characterized by altered mitochondrial metabolism. The centrality of mitochondrial dysfunction to a surprisingly wide range of major human diseases is slowly becoming recognized, bringing with it the prospect of novel therapeutic approaches to treat a multitude of unpleasant and pervasive diseases.

Evaluation of the spatial dependence of the point spread function in 2D PET image reconstruction using LOR-OSEM

PURPOSE

The use of positron emission tomography (PET) imaging has proved beneficial in the staging and diagnosis of several cancer disease sites. Additional applications of PET imaging in treatment planning and the evaluation of treatment response are limited by the relatively low spatial resolution of PET images. Including point spread function (PSF) information in the system matrix (SM) of iterative reconstruction techniques has been shown to produce improved spatial resolution in PET images.

METHODS

In this study, the authors sampled the spatially variant PSF at over 6000 locations in the field of view for a General Electric Discovery ST PET/CT (General Electric Healthcare, Waukesha, WI) scanner in 2D acquisition mode. The authors developed PSF blurred SMs based on different combinations of the radial, depth, and azimuthal spatial dependencies to test the overall spatial dependence of the PSF on image quality. The PSF blurred SMs were included in a LOR-OSEM reconstruction algorithm and used for image reconstruction of geometric phantoms. The authors also examined the effect of sampling density on PSF characterization to design a more efficient sampling scheme.

RESULTS

The authors found that depth dependent change in the amplitude of the detector response was the most important factor affecting image quality. A SM created from a PSF that introduced r (perpendicular to the LOR), d (parallel to the LOR), or r and d dependent blurring across the radial lines of response led to visually identifiable improvements in spatial resolution and contrast in reconstructed images compared to images reconstructed with a purely geometric SM with no PSF blurring. Images reconstructed using a SM with r and d dependent blurring across the radial lines of response showed improved spatial resolution and contrast-noise ratios compared to images reconstructed with a SM that had only r dependent blurring. Additionally, the authors determined that the PSF could be adequately characterized with roughly 85% fewer samples through the use of a better optimized sampling scheme.

CONCLUSIONS

PET image reconstruction using a SM made from an accurately characterized PSF that accounts for r and d dependencies results in improved spatial resolution and contrast-noise relations, which may aid in lesion boundary detection for treatment planning or quantitative assessment of treatment response.

PET-guided delineation of radiation therapy treatment volumes: a survey of image segmentation techniques

Historically, anatomical CT and MR images were used to delineate the gross tumour volumes (GTVs) for radiotherapy treatment planning. The capabilities offered by modern radiation therapy units and the widespread availability of combined PET/CT scanners stimulated the development of biological PET imaging-guided radiation therapy treatment planning with the aim to produce highly conformal radiation dose distribution to the tumour. One of the most difficult issues facing PET-based treatment planning is the accurate delineation of target regions from typical blurred and noisy functional images. The major problems encountered are image segmentation and imperfect system response function. Image segmentation is defined as the process of classifying the voxels of an image into a set of distinct classes. The difficulty in PET image segmentation is compounded by the low spatial resolution and high noise characteristics of PET images. Despite the difficulties and known limitations, several image segmentation approaches have been proposed and used in the clinical setting including thresholding, edge detection, region growing, clustering, stochastic models, deformable models, classifiers and several other approaches. A detailed description of the various approaches proposed in the literature is reviewed. Moreover, we also briefly discuss some important considerations and limitations of the widely used techniques to guide practitioners in the field of radiation oncology. The strategies followed for validation and comparative assessment of various PET segmentation approaches are described. Future opportunities and the current challenges facing the adoption of PET-guided delineation of target volumes and its role in basic and clinical research are also addressed.

Role of PET/CT imaging in stereotactic body radiotherapy

Stereotactic body radiotherapy (SBRT) is a relatively new technique that enables delivery of high doses of radiation to malignancies throughout the body with a higher degree of precision than conventional radiation modalities. PET and computed tomography are rapidly being adopted for the evaluation of patients with cancer, and its role in conjunction with SBRT is under active investigation. This article reviews the literature regarding the utility of PET and computed tomography in treatment planning, follow-up imaging, relationship with clinical outcomes, and other topics in patients treated with SBRT. These questions are investigated for cancers of the lung, head and neck, pancreas and liver. A brief overview of various commercially available SBRT treatment systems is also included.

Accurate automatic delineation of heterogeneous functional volumes in positron emission tomography for oncology applications

PURPOSE

Accurate contouring of positron emission tomography (PET) functional volumes is now considered crucial in image-guided radiotherapy and other oncology applications because the use of functional imaging allows for biological target definition. In addition, the definition of variable uptake regions within the tumor itself may facilitate dose painting for dosimetry optimization.

METHODS AND MATERIALS

Current state-of-the-art algorithms for functional volume segmentation use adaptive thresholding. We developed an approach called fuzzy locally adaptive Bayesian (FLAB), validated on homogeneous objects, and then improved it by allowing the use of up to three tumor classes for the delineation of inhomogeneous tumors (3-FLAB). Simulated and real tumors with histology data containing homogeneous and heterogeneous activity distributions were used to assess the algorithm's accuracy.

RESULTS

The new 3-FLAB algorithm is able to extract the overall tumor from the background tissues and delineate variable uptake regions within the tumors, with higher accuracy and robustness compared with adaptive threshold (T(bckg)) and fuzzy C-means (FCM). 3-FLAB performed with a mean classification error of less than 9% +/- 8% on the simulated tumors, whereas binary-only implementation led to errors of 15% +/- 11%. T(bckg) and FCM led to mean errors of 20% +/- 12% and 17% +/- 14%, respectively. 3-FLAB also led to more robust estimation of the maximum diameters of tumors with histology measurements, with <6% standard deviation, whereas binary FLAB, T(bckg) and FCM lead to 10%, 12%, and 13%, respectively.

CONCLUSION

These encouraging results warrant further investigation in future studies that will investigate the impact of 3-FLAB in radiotherapy treatment planning, diagnosis, and therapy response evaluation.

18F-FDG PET/CT for image-guided and intensity-modulated radiotherapy

Advances in technology have allowed extremely precise control of radiation dose delivery and localization within a patient. The ability to confidently delineate target tumor boundaries, however, has lagged behind. (18)F-FDG PET/CT, with its ability to distinguish metabolically active disease from normal tissue, may provide a partial solution to this problem. Here we review the current applications of (18)F-FDG PET/CT in a variety of disease sites, including non-small cell lung cancer, head and neck cancer, and pancreatic adenocarcinoma. This review focuses on the use of (18)F-FDG PET/CT to aid in planning radiotherapy and the associated benefits and challenges. We also briefly consider novel radiopharmaceuticals that are beginning to be used in the context of radiotherapy planning.