A fuzzy locally adaptive Bayesian segmentation approach for volume determination in PET

A new multistage medical segmentation method based on superpixel and fuzzy clustering

The medical image segmentation is the key approach of image processing for brain MRI images. However, due to the visual complex appearance of image structures and the imaging characteristic, it is still challenging to automatically segment brain MRI image. A new multi-stage segmentation method based on superpixel and fuzzy clustering (MSFCM) is proposed to achieve the good brain MRI segmentation results. The MSFCM utilizes the superpixels as the clustering objects instead of pixels, and it can increase the clustering granularity and overcome the influence of noise and bias effectively. In the first stage, the MRI image is parsed into several atomic areas, namely, superpixels, and a further parsing step is adopted for the areas with bigger gray variance over setting threshold. Subsequently, designed fuzzy clustering is carried out to the fuzzy membership of each superpixel, and an iterative broadcast method based on the Butterworth function is used to redefine their classifications. Finally, the segmented image is achieved by merging the superpixels which have the same classification label. The simulated brain database from BrainWeb site is used in the experiments, and the experimental results demonstrate that MSFCM method outperforms the traditional FCM algorithm in terms of segmentation accuracy and stability for MRI image.

An automatic method for accurate volume delineation of heterogeneous tumors in PET

PURPOSE

Accurate volumetric tumor delineation is of increasing importance in radiation treatment planning. Many tumors exhibit only moderate tracer uptake heterogeneity and delineation methods using an adaptive threshold lead to robust results. These methods use a tumor reference value R (e.g., ROI maximum) and the tumor background Bg to compute the volume reproducing threshold. This threshold corresponds to an isocontour which defines the tumor boundary. However, the boundaries of strongly heterogeneous tumors can not be described by an isocontour anymore and therefore conventional threshold methods are not suitable for accurate delineation. The aim of this work is the development and validation of a delineation method for heterogeneous tumors.

METHODS

The new method (voxel-specific threshold method, VTM) can be considered as an extension of an adaptive threshold method (lesion-specific threshold method, LTM), where instead of a lesion-specific threshold for the whole ROI, a voxel-specific threshold is computed by determining for each voxel Bg and R in the close vicinity of the voxel. The absolute threshold for the considered voxel is then given by Tabs=T×(R-Bg)+Bg, where T=0.39 was determined with phantom measurements.

VALIDATION

30 clinical datasets from patients with non-small-cell lung cancer were used to generate 30 realistic anthropomorphic software phantoms of tumors with different heterogeneities and well-known volumes and boundaries. Volume delineation was performed with VTM and LTM and compared with the known lesion volumes and boundaries.

RESULTS

In contrast to LTM, VTM was able to reproduce the true tumor boundaries accurately, independent of the heterogeneity. The deviation of the determined volume from the true volume was (0.8±4.2)% for VTM and (11.0±16.4)% for LTM.

CONCLUSIONS

In anthropomorphic software phantoms, the new method leads to promising results and to a clear improvement of volume delineation in comparison to conventional background-corrected thresholding. In the next step, the suitability for clinical routine will be further investigated.

Tumor texture analysis in 18F-FDG PET: relationships between texture parameters, histogram indices, standardized uptake values, metabolic volumes, and total lesion glycolysis

Texture indices are of growing interest for tumor characterization in (18)F-FDG PET. Yet, on the basis of results published in the literature so far, it is unclear which indices should be used, what they represent, and how they relate to conventional indices such as standardized uptake values (SUVs), metabolic volume (MV), and total lesion glycolysis (TLG). We investigated in detail 31 texture indices, 5 first-order statistics (histogram indices) derived from the gray-level histogram of the tumor region, and their relationship with SUV, MV, and TLG in 3 different tumor types.

METHODS

Three patient groups corresponding to 3 cancer types at baseline were studied independently: patients with metastatic colorectal cancer (72 lesions), non-small cell lung cancer (24 lesions), and breast cancer (54 lesions). Thirty-one texture indices were studied in addition to SUVs, MV, and TLG, and 5 indices extracted from histogram analysis were also investigated. The relationships between indices were studied as well as the robustness of the various texture indices with respect to the parameters involved in the calculation method (sampling schemes and tumor volume of interest).

RESULTS

Regardless of the patient group, many indices were highly correlated (Pearson correlation coefficient |r| ≥ 0.80), making it desirable to focus on only a few uncorrelated indices. Three histogram indices were highly correlated with SUVs (|r| ≥ 0.84). Four texture indices were highly correlated with MV, and none was highly correlated with SUVs (|r| ≤ 0.55). The resampling formula used to calculate texture indices had a substantial impact, and resampling using at least 32 discrete values should be used for texture indices calculation. The texture indices change as a function of the segmentation method was higher than that of peak and maximum SUVs but less than mean SUV for 5 texture indices and was larger than that of MV for 14 texture indices and for the 5 histogram indices. All these results were extremely consistent across the 3 tumor types and explained many of the observations reported in the literature so far.

CONCLUSION

None of the histogram indices and only 17 of 31 texture indices were robust with respect to the tumor-segmentation method. An appropriate resampling formula with at least 32 gray levels should be used to avoid introducing a misleading relationship between texture indices and SUV. Some texture indices are highly correlated or strongly correlate with MV whatever the tumor type. Such correlation should be accounted for when interpreting the usefulness of texture indices for tumor characterization, which might call for systematic multivariate analyses.

Semiautomatic methods for segmentation of the proliferative tumour volume on sequential FLT PET/CT images in head and neck carcinomas and their relation to clinical outcome

PURPOSE

Radiotherapy of head and neck cancer induces changes in tumour cell proliferation during treatment, which can be depicted by the PET tracer (18)F-fluorothymidine (FLT). In this study, three advanced semiautomatic PET segmentation methods for delineation of the proliferative tumour volume (PV) before and during (chemo)radiotherapy were compared and related to clinical outcome.

METHODS

The study group comprised 46 patients with 48 squamous cell carcinomas of the head and neck, treated with accelerated (chemo)radiotherapy, who underwent FLT PET/CT prior to treatment and in the 2nd and 4th week of therapy. Primary gross tumour volumes were visually delineated on CT images (GTV CT). PVs were visually determined on all PET scans (PV VIS). The following semiautomatic segmentation methods were applied to sequential PET scans: background-subtracted relative-threshold level (PV RTL), a gradient-based method using the watershed transform algorithm and hierarchical clustering analysis (PV W&C), and a fuzzy locally adaptive Bayesian algorithm (PV FLAB).

RESULTS

Pretreatment PV VIS correlated best with PV FLAB and GTV CT. Correlations with PV RTL and PV W&C were weaker although statistically significant. During treatment, the PV VIS, PV W&C and PV FLAB significant decreased over time with the steepest decline over time for PV FLAB. Among these advanced segmentation methods, PV FLAB was the most robust in segmenting volumes in the third scan (67 % of tumours as compared to 40 % for PV W&C and 27 % for PV RTL). A decrease in PV FLAB above the median between the pretreatment scan and the scan obtained in the 4th week was associated with better disease-free survival (4 years 90 % versus 53 %).

CONCLUSION

In patients with head and neck cancer, FLAB proved to be the best performing method for segmentation of the PV on repeat FLT PET/CT scans during (chemo)radiotherapy. This may potentially facilitate radiation dose adaptation to changing PV.

Joint segmentation of anatomical and functional images: applications in quantification of lesions from PET, PET-CT, MRI-PET, and MRI-PET-CT images

We present a novel method for the joint segmentation of anatomical and functional images. Our proposed methodology unifies the domains of anatomical and functional images, represents them in a product lattice, and performs simultaneous delineation of regions based on random walk image segmentation. Furthermore, we also propose a simple yet effective object/background seed localization method to make the proposed segmentation process fully automatic. Our study uses PET, PET-CT, MRI-PET, and fused MRI-PET-CT scans (77 studies in all) from 56 patients who had various lesions in different body regions. We validated the effectiveness of the proposed method on different PET phantoms as well as on clinical images with respect to the ground truth segmentation provided by clinicians. Experimental results indicate that the presented method is superior to threshold and Bayesian methods commonly used in PET image segmentation, is more accurate and robust compared to the other PET-CT segmentation methods recently published in the literature, and also it is general in the sense of simultaneously segmenting multiple scans in real-time with high accuracy needed in routine clinical use.

Functional imaging in radiation therapy planning for head and neck cancer

Functional imaging and its application to radiotherapy (RT) is a rapidly expanding field with new modalities and techniques constantly developing and evolving. As technologies improve, it will be important to pay attention to their implementation. This review describes the main achievements in the field of head and neck cancer (HNC) with particular remarks on the unsolved problems.

Segmentation of PET images for computer-aided functional quantification of tuberculosis in small animal models

Pulmonary infections often cause spatially diffuse and multi-focal radiotracer uptake in positron emission tomography (PET) images, which makes accurate quantification of the disease extent challenging. Image segmentation plays a vital role in quantifying uptake due to the distributed nature of immuno-pathology and associated metabolic activities in pulmonary infection, specifically tuberculosis (TB). For this task, thresholding-based segmentation methods may be better suited over other methods; however, performance of the thresholding-based methods depend on the selection of thresholding parameters, which are often suboptimal. Several optimal thresholding techniques have been proposed in the literature, but there is currently no consensus on how to determine the optimal threshold for precise identification of spatially diffuse and multi-focal radiotracer uptake. In this study, we propose a method to select optimal thresholding levels by utilizing a novel intensity affinity metric within the affinity propagation clustering framework. We tested the proposed method against 70 longitudinal PET images of rabbits infected with TB. The overall dice similarity coefficient between the segmentation from the proposed method and two expert segmentations was found to be 91.25 ±8.01% with a sensitivity of 88.80 ±12.59% and a specificity of 96.01 ±9.20%. High accuracy and heightened efficiency of our proposed method, as compared to other PET image segmentation methods, were reported with various quantification metrics.

Potential of [18F]-fluoromisonidazole positron-emission tomography for radiotherapy planning in head and neck squamous cell carcinomas

BACKGROUND AND PURPOSE

Positron-emission tomography (PET) with [(18)F]-fluoromisonidazole (FMISO) permits consideration of radiotherapy dose escalation to hypoxic volumes in head and neck cancers (HNC). However, the definition of FMISO volumes remains problematic. The aims of this study are to confirm that delayed acquisition at 4 h is most appropriate for FMISO-PET imaging and to assess different methods of volume segmentation.

PATIENTS AND METHODS

A total of 15 HNC patients underwent several FMISO-PET/computed tomography (CT) acquisitions 2, 3 and 4 h after FMISO injection. Three automatic methods of PET image segmentation were tested: fixed threshold, adaptive threshold based on the ratio between tumour-derived and background activities (R(T/B)) and the fuzzy locally adaptive Bayesian (FLAB) method. The hypoxic fraction (HF), which is defined as the ratio between the FMISO and CT volumes, was also calculated.

RESULTS

The R(T/B) for images acquired at 2, 3 and 4 h differed significantly, with mean values of 2.5 (1.7-2.9), 3 (2-4.5) and 3.4 (2.3-6.1), respectively. The mean tumour volume, as defined manually using CT images, was 39.1 ml (1.2-116 ml). After 4 h, the mean FMISO volumes were 18.9 (0.1-81), 9.5 (0.9-33.1) and 12.5 ml (0.9-38.4 ml) with fixed threshold, adaptive threshold and the FLAB method, respectively; median HF values were 0.47 (0.1-1.93), 0.25 (0.11-0.75) and 0.35 (0.14-1.05), respectively. FMISO volumes were significantly different.

CONCLUSION

The best contrast is obtained at the 4-hour acquisition time. Large discrepancies were found between the three tested methods of volume segmentation.

Cellular automata and anisotropic diffusion filter based interactive tumor segmentation for positron emission tomography

Tumor segmentation in positron emission tomography (PET) aids clinical diagnosis and in assessing treatment response. However, the low resolution and signal-to-noise inherent in PET images, makes accurate tumor segmentation challenging. Manual delineation is time-consuming and subjective, whereas fully automated algorithms are often limited to particular tumor types, and have difficulties in segmenting small and low-contrast tumors. Interactive segmentation may reduce the inter-observer variability and minimize the user input. In this study, we present a new interactive PET tumor segmentation method based on cellular automata (CA) and a nonlinear anisotropic diffusion filter (ADF). CA is tolerant of noise and image pattern complexity while ADF reduces noise while preserving edges. By coupling CA with ADF, our proposed approach was robust and accurate in detecting and segmenting noisy tumors. We evaluated our method with computer simulation and clinical data and it outperformed other common interactive PET segmentation algorithms.

Tumor quantification in clinical positron emission tomography

Positron emission tomography (PET) is used extensively in clinical oncology for tumor detection, staging and therapy response assessment. Quantitative measurements of tumor uptake, usually in the form of standardized uptake values (SUVs), have enhanced or replaced qualitative interpretation. In this paper we review the current status of tumor quantification methods and their applications to clinical oncology. Factors that impede quantitative assessment and limit its accuracy and reproducibility are summarized, with special emphasis on SUV analysis. We describe current efforts to improve the accuracy of tumor uptake measurements, characterize overall metabolic tumor burden and heterogeneity of tumor uptake, and account for the effects of image noise. We also summarize recent developments in PET instrumentation and image reconstruction and their impact on tumor quantification. Finally, we offer our assessment of the current development needs in PET tumor quantification, including practical techniques for fully quantitative, pharmacokinetic measurements.

Variation in background intensity affects PET-based gross tumor volume delineation in non-small-cell lung cancer: the need for individualized information

PURPOSE

Efficient tumor volume delineation by the combined use of PET/CT scanning is necessary for the proper treatment of non-small cell lung cancer (NSCLC). To understand the effect of variation in background intensity on PET-based gross tumor volume (GTV) delineation, we determined the background standard uptake values (SUVs) in normal lung, aorta (blood pool), and liver tissues and determined GTVs using different methods.

METHODS

Thirty-seven previously untreated patients with pathologically confirmed NSCLC underwent PET/CT scanning with (18)F-fluorodeoxyglucose ((18)F-FDG). To obtain (18)F-FDG uptake values in normal tissues, regions of interest in the lung lobes (left upper, left lower, right upper, right middle, and right lower), aorta, and liver zones (left, intermediate, and right) were measured. The coefficient of variation (CV) of the SUV was measured for each normal structure. The CT-based GTV (GTV(CT)) was considered as the standard to which all PET-based GTVs were compared, and the correlation coefficient was analyzed to compare GTV obtained by the various delineation methods. Linear and logarithmic regression analyses were used to determine the relationship between GTV(CT) and GTV(PET).

RESULTS

Normal lung tissue showed a significantly lower SUV and less stability than tissue of the aorta or liver. For the lung, aorta, and liver, the maximum SUV (SUV(max)) was 0.82 ± 0.32, 2.35 ± 0.37, and 3.24 ± 0.50 (CV: 38.79%, 15.82%, and 15.30%) and average SUV (SUV(ave)) was 0.49 ± 0.18, 1.68 ± 0.32, and 2.34 ± 0.36 (CV: 36.38%, 18.92%, and 15.44%), respectively. The SUVs of the lung varied from lobe to lobe. The GTV delineation method using the SUV(ave) of the lung lobe in which the tumor was found as background in the source-to-background ratio (SBR) method showed the best correlation with the volume of CT-based GTV (r=0.81).

CONCLUSIONS

Our results show vast variation in the SUV among normal tissues, as well as in the different lung lobes. The tumor volume delineated using the SBR method correlated well with the CT-based tumor volume. We conclude that it is reasonable and precise to contour GTV in patients with NSCLC after taking into account the background intensity of the lung lobe in which the tumor is found.

Combining multiple FDG-PET radiotherapy target segmentation methods to reduce the effect of variable performance of individual segmentation methods

PURPOSE

Many approaches have been proposed to segment high uptake objects in 18F-fluoro-deoxy-glucose positron emission tomography images but none provides consistent performance across the large variety of imaging situations. This study investigates the use of two methods of combining individual segmentation methods to reduce the impact of inconsistent performance of the individual methods: simple majority voting and probabilistic estimation.

METHODS

The National Electrical Manufacturers Association image quality phantom containing five glass spheres with diameters 13-37 mm and two irregularly shaped volumes (16 and 32 cc) formed by deforming high-density polyethylene bottles in a hot water bath were filled with 18-fluoro-deoxyglucose and iodine contrast agent. Repeated 5-min positron emission tomography (PET) images were acquired at 4:1 and 8:1 object-to-background contrasts for spherical objects and 4.5:1 and 9:1 for irregular objects. Five individual methods were used to segment each object: 40% thresholding, adaptive thresholding, k-means clustering, seeded region-growing, and a gradient based method. Volumes were combined using a majority vote (MJV) or Simultaneous Truth And Performance Level Estimate (STAPLE) method. Accuracy of segmentations relative to CT ground truth volumes were assessed using the Dice similarity coefficient (DSC) and the symmetric mean absolute surface distances (SMASDs).

RESULTS

MJV had median DSC values of 0.886 and 0.875; and SMASD of 0.52 and 0.71 mm for spheres and irregular shapes, respectively. STAPLE provided similar results with median DSC of 0.886 and 0.871; and median SMASD of 0.50 and 0.72 mm for spheres and irregular shapes, respectively. STAPLE had significantly higher DSC and lower SMASD values than MJV for spheres (DSC, p < 0.0001; SMASD, p = 0.0101) but MJV had significantly higher DSC and lower SMASD values compared to STAPLE for irregular shapes (DSC, p < 0.0001; SMASD, p = 0.0027). DSC was not significantly different between 128 × 128 and 256 × 256 grid sizes for either method (MJV, p = 0.0519; STAPLE, p = 0.5672) but was for SMASD values (MJV, p < 0.0001; STAPLE, p = 0.0164). The best individual method varied depending on object characteristics. However, both MJV and STAPLE provided essentially equivalent accuracy to using the best independent method in every situation, with mean differences in DSC of 0.01-0.03, and 0.05-0.12 mm for SMASD.

CONCLUSIONS

Combining segmentations offers a robust approach to object segmentation in PET. Both MJV and STAPLE improved accuracy and were robust against the widely varying performance of individual segmentation methods. Differences between MJV and STAPLE are such that either offers good performance when combining volumes. Neither method requires a training dataset but MJV is simpler to interpret, easy to implement and fast.

Optimal co-segmentation of tumor in PET-CT images with context information

Positron emission tomography (PET)-computed tomography (CT) images have been widely used in clinical practice for radiotherapy treatment planning of the radiotherapy. Many existing segmentation approaches only work for a single imaging modality, which suffer from the low spatial resolution in PET or low contrast in CT. In this work, we propose a novel method for the co-segmentation of the tumor in both PET and CT images, which makes use of advantages from each modality: the functionality information from PET and the anatomical structure information from CT. The approach formulates the segmentation problem as a minimization problem of a Markov random field model, which encodes the information from both modalities. The optimization is solved using a graph-cut based method. Two sub-graphs are constructed for the segmentation of the PET and the CT images, respectively. To achieve consistent results in two modalities, an adaptive context cost is enforced by adding context arcs between the two sub-graphs. An optimal solution can be obtained by solving a single maximum flow problem, which leads to simultaneous segmentation of the tumor volumes in both modalities. The proposed algorithm was validated in robust delineation of lung tumors on 23 PET-CT datasets and two head-and-neck cancer subjects. Both qualitative and quantitative results show significant improvement compared to the graph cut methods solely using PET or CT.

[Validation of segmentation techniques for positron emission tomography using ex-vivo images of oncological surgical specimens]

OBJECTIVE

To design a novel ex-vivo acquisition technique to establish a common framework to validate different segmentation techniques for oncological PET images. To evaluate several automatic segmentation algorithms on this set of images.

MATERIAL AND METHODS

In 15 patients with cancer, ex-vivo PET studies of surgical specimens removed during surgery were performed after injection of (18)F-FDG. Images were acquired in two scanners: a clinical PET/CT and a high-resolution PET scanner. Real tumor volume was determined in each patient, and a reference image was generated for segmentation of each tumor. Images were segmented with 12 automatic algorithms and with a standard method for PET (relative threshold at 42%) and results were evaluated by quantitative parameters.

RESULTS

It has been possible to demonstrate by segmentation of PET images of surgical specimens that on high resolution PET images, 8 out of 12 evaluated segmentation techniques outperformed the standard method, whose value is 42%. However, none of the algorithms outperformed the standard method when applied on images from the clinical PET/CT. Due to the great interest of this set of PET images, all studies have been published on the Internet in order to provide a common framework for validation and comparison of different segmentation techniques.

CONCLUSIONS

We have proposed a novel technique to validate segmentation techniques for oncological PET images, acquiring ex-vivo PET studies of surgical specimens. We have demonstrated the usefulness of this set of PET images by evaluating several automatic segmentation algorithms.

Interobserver agreement of qualitative analysis and tumor delineation of 18F-fluoromisonidazole and 3'-deoxy-3'-18F-fluorothymidine PET images in lung cancer

As the preparation phase of a multicenter clinical trial using (18)F-fluoro-2-deoxy-d-glucose ((18)F-FDG), (18)F-fluoromisonidazole ((18)F-FMISO), and 3'-deoxy-3'-(18)F-fluorothymidine ((18)F-FLT) in non-small cell lung cancer (NSCLC) patients, we investigated whether 18 nuclear medicine centers would score tracer uptake intensity similarly and define hypoxic and proliferative volumes for 1 patient and we compared different segmentation methods.

METHODS

Ten (18)F-FDG, ten (18)F-FMISO, and ten (18)F-FLT PET/CT examinations were performed before and during curative-intent radiotherapy in 5 patients with NSCLC. The gold standards for uptake intensity and volume delineation were defined by experts. The between-center agreement (18 nuclear medicine departments connected with a dedicated network, SFMN-net [French Society of Nuclear Medicine]) in the scoring of uptake intensity (5-level scale, then divided into 2 levels: 0, normal; 1, abnormal) was quantified by κ-coefficients (κ). The volumes defined by different physicians were compared by overlap and κ. The uptake areas were delineated with 22 different methods of segmentation, based on fixed or adaptive thresholds of standardized uptake value (SUV).

RESULTS

For uptake intensity, the κ values between centers were, respectively, 0.59 for (18)F-FDG, 0.43 for (18)F-FMISO, and 0.44 for (18)F-FLT using the 5-level scale; the values were 0.81 for (18)F-FDG and 0.77 for both (18)F-FMISO and (18)F-FLT using the 2-level scale. The mean overlap and mean κ between observers were 0.13 and 0.19, respectively, for (18)F-FMISO and 0.2 and 0.3, respectively, for (18)F-FLT. The segmentation methods yielded significantly different volumes for (18)F-FMISO and (18)F-FLT (P < 0.001). In comparison with physicians, the best method found was 1.5 × maximum SUV (SUVmax) of the aorta for (18)F-FMISO and 1.3 × SUVmax of the muscle for (18)F-FLT. The methods using the SUV of 1.4 and the method using 1.5 × the SUVmax of the aorta could be used for (18)F-FMISO and (18)F-FLT. Moreover, for (18)F-FLT, 2 other methods (adaptive threshold based on 1.5 or 1.6 × muscle SUVmax) could be used.

CONCLUSION

The reproducibility of the visual analyses of (18)F-FMISO and (18)F-FLT PET/CT images was demonstrated using a 2-level scale across 18 centers, but the interobserver agreement was low for the (18)F-FMISO and (18)F-FLT volume measurements. Our data support the use of a fixed threshold (1.4) or an adaptive threshold using the aorta background to delineate the volume of increased (18)F-FMISO or (18)F-FLT uptake. With respect to the low tumor-on-background ratio of these tracers, we suggest the use of a fixed threshold (1.4).

Object information based interactive segmentation for fatty tissue extraction

Lymph nodes are very important factors for diagnosing gastric cancer in clinical use, and are usually distributed within the fatty tissue around the stomach. When extracting fatty tissues whose structures and textures are complicated, automatic extraction is still a challenging task, while manual extraction is time-consuming. Consequently, semi-automatic extraction, which allows introducing interactive operations, appears to be more realistic. Currently, most interactive methods need to indicate the position and main features in both the object and background. However, it is easier for radiologists to only mark object information. Due to this issue, a new Object Information based Interactive Segmentation (OIIS) method is proposed in this paper. Different from the most existing methods, OIIS just needs to input the object information, while the background information is not required. Experimental results and comparative studies show that OIIS is effective for fatty tissue extraction.

A computational pipeline for quantification of pulmonary infections in small animal models using serial PET-CT imaging

Background

Infectious diseases are the second leading cause of death worldwide. In order to better understand and treat them, an accurate evaluation using multi-modal imaging techniques for anatomical and functional characterizations is needed. For non-invasive imaging techniques such as computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET), there have been many engineering improvements that have significantly enhanced the resolution and contrast of the images, but there are still insufficient computational algorithms available for researchers to use when accurately quantifying imaging data from anatomical structures and functional biological processes. Since the development of such tools may potentially translate basic research into the clinic, this study focuses on the development of a quantitative and qualitative image analysis platform that provides a computational radiology perspective for pulmonary infections in small animal models. Specifically, we designed (a) a fast and robust automated and semi-automated image analysis platform and a quantification tool that can facilitate accurate diagnostic measurements of pulmonary lesions as well as volumetric measurements of anatomical structures, and incorporated (b) an image registration pipeline to our proposed framework for volumetric comparison of serial scans. This is an important investigational tool for small animal infectious disease models that can help advance researchers’ understanding of infectious diseases.

Methods

We tested the utility of our proposed methodology by using sequentially acquired CT and PET images of rabbit, ferret, and mouse models with respiratory infections of Mycobacterium tuberculosis (TB), H1N1 flu virus, and an aerosolized respiratory pathogen (necrotic TB) for a total of 92, 44, and 24 scans for the respective studies with half of the scans from CT and the other half from PET. Institutional Administrative Panel on Laboratory Animal Care approvals were obtained prior to conducting this research. First, the proposed computational framework registered PET and CT images to provide spatial correspondences between images. Second, the lungs from the CT scans were segmented using an interactive region growing (IRG) segmentation algorithm with mathematical morphology operations to avoid false positive (FP) uptake in PET images. Finally, we segmented significant radiotracer uptake from the PET images in lung regions determined from CT and computed metabolic volumes of the significant uptake. All segmentation processes were compared with expert radiologists’ delineations (ground truths). Metabolic and gross volume of lesions were automatically computed with the segmentation processes using PET and CT images, and percentage changes in those volumes over time were calculated. (Continued on next page)(Continued from previous page) Standardized uptake value (SUV) analysis from PET images was conducted as a complementary quantitative metric for disease severity assessment. Thus, severity and extent of pulmonary lesions were examined through both PET and CT images using the aforementioned quantification metrics outputted from the proposed framework.

Results

Each animal study was evaluated within the same subject class, and all steps of the proposed methodology were evaluated separately. We quantified the accuracy of the proposed algorithm with respect to the state-of-the-art segmentation algorithms. For evaluation of the segmentation results, dice similarity coefficient (DSC) as an overlap measure and Haussdorf distance as a shape dissimilarity measure were used. Significant correlations regarding the estimated lesion volumes were obtained both in CT and PET images with respect to the ground truths (R²=0.8922,p<0.01 and R²=0.8664,p<0.01, respectively). The segmentation accuracy (DSC (%)) was 93.4±4.5% for normal lung CT scans and 86.0±7.1% for pathological lung CT scans. Experiments showed excellent agreements (all above 85%) with expert evaluations for both structural and functional imaging modalities. Apart from quantitative analysis of each animal, we also qualitatively showed how metabolic volumes were changing over time by examining serial PET/CT scans. Evaluation of the registration processes was based on precisely defined anatomical landmark points by expert clinicians. An average of 2.66, 3.93, and 2.52 mm errors was found in rabbit, ferret, and mouse data (all within the resolution limits), respectively. Quantitative results obtained from the proposed methodology were visually related to the progress and severity of the pulmonary infections as verified by the participating radiologists. Moreover, we demonstrated that lesions due to the infections were metabolically active and appeared multi-focal in nature, and we observed similar patterns in the CT images as well. Consolidation and ground glass opacity were the main abnormal imaging patterns and consistently appeared in all CT images. We also found that the gross and metabolic lesion volume percentage follow the same trend as the SUV-based evaluation in the longitudinal analysis.

Conclusions

We explored the feasibility of using PET and CT imaging modalities in three distinct small animal models for two diverse pulmonary infections. We concluded from the clinical findings, derived from the proposed computational pipeline, that PET-CT imaging is an invaluable hybrid modality for tracking pulmonary infections longitudinally in small animals and has great potential to become routinely used in clinics. Our proposed methodology showed that automated computed-aided lesion detection and quantification of pulmonary infections in small animal models are efficient and accurate as compared to the clinical standard of manual and semi-automated approaches. Automated analysis of images in pre-clinical applications can increase the efficiency and quality of pre-clinical findings that ultimately inform downstream experimental design in human clinical studies; this innovation will allow researchers and clinicians to more effectively allocate study resources with respect to research demands without compromising accuracy.

PET-based delineation of tumour volumes in lung cancer: comparison with pathological findings

PURPOSE

The objective of the study was to validate an adaptive, contrast-oriented thresholding algorithm (COA) for tumour delineation in (18)F-fluorodeoxyglucose (FDG) positron emission tomography (PET) for non-small cell lung cancer (NSCLC) in comparison with pathological findings. The impact of tumour localization, tumour size and uptake heterogeneity on PET delineation results was also investigated.

METHODS

PET tumour delineation by COA was compared with both CT delineation and pathological findings in 15 patients to investigate its validity. Correlations between anatomical volume, metabolic volume and the pathology reference as well as between the corresponding maximal diameters were determined. Differences between PET delineations and pathological results were investigated with respect to tumour localization and uptake heterogeneity.

RESULTS

The delineated volumes and maximal diameters measured on PET and CT images significantly correlated with the pathology reference (both r > 0.95, p < 0.0001). Both PET and CT contours resulted in overestimation of the pathological volume (PET 32.5 ± 26.5%, CT 46.6 ± 27.4%). CT volumes were larger than those delineated on PET images (CT 60.6 ± 86.3 ml, PET 48.3 ± 61.7 ml). Maximal tumour diameters were similar for PET and CT (51.4 ± 19.8 mm for CT versus 53.4 ± 19.1 mm for PET), slightly overestimating the pathological reference (mean difference CT 4.3 ± 3.2 mm, PET 6.2 ± 5.1 mm). PET volumes of lung tumours located in the lower lobe were significantly different from those determined from pathology (p = 0.037), whereas no significant differences were observed for tumours located in the upper lobe (p = 0.066). Only minor correlation was found between pathological tumour size and PET heterogeneity (r = -0.24).

CONCLUSION

PET tumour delineation by COA showed a good correlation with pathological findings. Tumour localization had an influence on PET delineation results. The impact of tracer uptake heterogeneity on PET delineation should be considered carefully and individually in each patient. Altogether, PET tumour delineation by COA for NSCLC patients is feasible and reliable with the potential for routine clinical application.

Lung tumor segmentation in PET images using graph cuts

The aim of segmentation of tumor regions in positron emission tomography (PET) is to provide more accurate measurements of tumor size and extension into adjacent structures, than is possible with visual assessment alone and hence improve patient management decisions. We propose a segmentation energy function for the graph cuts technique to improve lung tumor segmentation with PET. Our segmentation energy is based on an analysis of the tumor voxels in PET images combined with a standardized uptake value (SUV) cost function and a monotonic downhill SUV feature. The monotonic downhill feature avoids segmentation leakage into surrounding tissues with similar or higher PET tracer uptake than the tumor and the SUV cost function improves the boundary definition and also addresses situations where the lung tumor is heterogeneous. We evaluated the method in 42 clinical PET volumes from patients with non-small cell lung cancer (NSCLC). Our method improves segmentation and performs better than region growing approaches, the watershed technique, fuzzy-c-means, region-based active contour and tumor customized downhill.

Introduction to the analysis of PET data in oncology

Several reviews on specific topics related to positron emission tomography (PET) ranging in complexity from introductory to highly technical have already been published. This introduction to the analysis of PET data was written as a simple guide of the different phases of analysis of a given PET dataset, from acquisition to preprocessing, to the final data analysis. Although sometimes issues specific to PET in neuroimaging will be mentioned for comparison, most of the examples and applications provided will refer to oncology. Due to the limitations of space we couldn't address each issue comprehensively but, rather, we provided a general overview of each topic together with the references that the interested reader should consult. We will assume a familiarity with the basic principles of PET imaging.

Automatic Segmentation of Lung Carcinoma Using 3D Texture Features in 18-FDG PET/CT

Target definition is the largest source of geometric uncertainty in radiation therapy. This is partly due to a lack of contrast between tumor and healthy soft tissue for computed tomography (CT) and due to blurriness, lower spatial resolution, and lack of a truly quantitative unit for positron emission tomography (PET). First-, second-, and higher-order statistics, Tamura, and structural features were characterized for PET and CT images of lung carcinoma and organs of the thorax. A combined decision tree (DT) with K-nearest neighbours (KNN) classifiers as nodes containing combinations of 3 features were trained and used for segmentation of the gross tumor volume. This approach was validated for 31 patients from two separate institutions and scanners. The results were compared with thresholding approaches, the fuzzy clustering method, the 3-level fuzzy locally adaptive Bayesian algorithm, the multivalued level set algorithm, and a single KNN using Hounsfield units and standard uptake value. The results showed the DTKNN classifier had the highest sensitivity of 73.9%, second highest average Dice coefficient of 0.607, and a specificity of 99.2% for classifying voxels when using a probabilistic ground truth provided by simultaneous truth and performance level estimation using contours drawn by 3 trained physicians.

Predicting future morphological changes of lesions from radiotracer uptake in 18F-FDG-PET images

We introduce a novel computational framework to enable automated identification of texture and shape features of lesions on ¹⁸F-FDG-PET images through a graph-based image segmentation method. The proposed framework predicts future morphological changes of lesions with high accuracy. The presented methodology has several benefits over conventional qualitative and semi-quantitative methods, due to its fully quantitative nature and high accuracy in each step of (i) detection, (ii) segmentation, and (iii) feature extraction. To evaluate our proposed computational framework, thirty patients received 2 ¹⁸F-FDG-PET scans (60 scans total), at two different time points. Metastatic papillary renal cell carcinoma, cerebellar hemongioblastoma, non-small cell lung cancer, neurofibroma, lymphomatoid granulomatosis, lung neoplasm, neuroendocrine tumor, soft tissue thoracic mass, nonnecrotizing granulomatous inflammation, renal cell carcinoma with papillary and cystic features, diffuse large B-cell lymphoma, metastatic alveolar soft part sarcoma, and small cell lung cancer were included in this analysis. The radiotracer accumulation in patients' scans was automatically detected and segmented by the proposed segmentation algorithm. Delineated regions were used to extract shape and textural features, with the proposed adaptive feature extraction framework, as well as standardized uptake values (SUV) of uptake regions, to conduct a broad quantitative analysis. Evaluation of segmentation results indicates that our proposed segmentation algorithm has a mean dice similarity coefficient of 85.75±1.75%. We found that 28 of 68 extracted imaging features were correlated well with SUV_max (p<0.05), and some of the textural features (such as entropy and maximum probability) were superior in predicting morphological changes of radiotracer uptake regions longitudinally, compared to single intensity feature such as SUV_max. We also found that integrating textural features with SUV measurements significantly improves the prediction accuracy of morphological changes (Spearman correlation coefficient = 0.8715, p<2e-16).

Computer-aided diagnosis systems for lung cancer: challenges and methodologies

This paper overviews one of the most important, interesting, and challenging problems in oncology, the problem of lung cancer diagnosis. Developing an effective computer-aided diagnosis (CAD) system for lung cancer is of great clinical importance and can increase the patient's chance of survival. For this reason, CAD systems for lung cancer have been investigated in a huge number of research studies. A typical CAD system for lung cancer diagnosis is composed of four main processing steps: segmentation of the lung fields, detection of nodules inside the lung fields, segmentation of the detected nodules, and diagnosis of the nodules as benign or malignant. This paper overviews the current state-of-the-art techniques that have been developed to implement each of these CAD processing steps. For each technique, various aspects of technical issues, implemented methodologies, training and testing databases, and validation methods, as well as achieved performances, are described. In addition, the paper addresses several challenges that researchers face in each implementation step and outlines the strengths and drawbacks of the existing approaches for lung cancer CAD systems.

Segmentation of biological target volumes on multi-tracer PET images based on information fusion for achieving dose painting in radiotherapy

Medical imaging plays an important role in radiotherapy. Dose painting consists in the application of a nonuniform dose prescription on a tumoral region, and is based on an efficient segmentation of biological target volumes (BTV). It is derived from PET images, that highlight tumoral regions of enhanced glucose metabolism (FDG), cell proliferation (FLT) and hypoxia (FMiso). In this paper, a framework based on Belief Function Theory is proposed for BTV segmentation and for creating 3D parametric images for dose painting. We propose to take advantage of neighboring voxels for BTV segmentation, and also multi-tracer PET images using information fusion to create parametric images. The performances of BTV segmentation was evaluated on an anthropomorphic phantom and compared with two other methods. Quantitative results show the good performances of our method. It has been applied to data of five patients suffering from lung cancer. Parametric images show promising results by highlighting areas where a high frequency or dose escalation could be planned.

Automated method for extraction of lung tumors using a machine learning classifier with knowledge of radiation oncologists on data sets of planning CT and FDG-PET/CT images

We have developed an automated method for extraction of lung tumors using a machine learning classifier with knowledge of radiation oncologists on data sets of treatment planning computed tomography (CT) and 18F-fluorodeoxyglucose (FDG)-positron emission tomography (PET)/CT images. First, the PET images were registered with the treatment planning CT images through the diagnostic CT images of PET/CT. Second, six voxel-based features including voxel values and magnitudes of image gradient vectors were derived from each voxel in the planning CT and PET /CT image data sets. Finally, lung tumors were extracted by using a support vector machine (SVM), which learned 6 voxel-based features inside and outside each true tumor region determined by radiation oncologists. The results showed that the average DSCs for 3 and 6 features for three cases were 0.744 and 0.899, and thus the SVM may need 6 features to learn the distinguishable characteristics. The proposed method may be useful for assisting treatment planners in delineation of the tumor region.

Comparative study with new accuracy metrics for target volume contouring in PET image guided radiation therapy

The impact of PET on radiation therapy is held back by poor methods of defining functional volumes of interest. Many new software tools are being proposed for contouring target volumes but the different approaches are not adequately compared and their accuracy is poorly evaluated due to the illdefinition of ground truth. This paper compares the largest cohort to date of established, emerging and proposed PET contouring methods, in terms of accuracy and variability. We emphasise spatial accuracy and present a new metric that addresses the lack of unique ground truth. 30 methods are used at 13 different institutions to contour functional VOIs in clinical PET/CT and a custom-built PET phantom representing typical problems in image guided radiotherapy. Contouring methods are grouped according to algorithmic type, level of interactivity and how they exploit structural information in hybrid images. Experiments reveal benefits of high levels of user interaction, as well as simultaneous visualisation of CT images and PET gradients to guide interactive procedures. Method-wise evaluation identifies the danger of over-automation and the value of prior knowledge built into an algorithm.

Comparison of different methods of incorporating respiratory motion for lung cancer tumor volume delineation on PET images: a simulation study

The interest of PET complementary information for the delineation of the target volume in radiotherapy of lung cancer is increasing. However, respiratory motion requires the determination of a functional internal target volume (ITV) on PET images for which several strategies have been proposed. The purpose of this study was the comparison of these strategies for taking into account respiratory motion and deriving the ITV: (1) adding fixed margins to the volume defined on a single binned image, (2) segmenting a motion averaged image and (3) considering the union of volumes delineated on binned frames. For this third strategy, binned frames were either non-corrected for motion, or corrected using two different methods: elastic registration or super resolution. The strategies' performances were assessed on realistic simulated datasets combining the NCAT phantom with a PET Philips GEMINI scanner model in GATE, and containing various configurations of tumor to background contrast, with both regular and irregular respiratory motion (with a range of motion amplitudes). The obtained ITVs' sensitivity (SE) and positive predictive value (PVE) with respect to the known true ITV were significantly higher (from 0.8 to 0.95) than all other techniques when using binned frames corrected for motion, independently of motion regularity, amplitude, or tumor to background contrast. Although the absolute difference was small and not always significant, images corrected using super resolution led to systematically better results than using elastic registration. The worst results were obtained when using the motion averaged image for SE (around 0.5-0.6) and using the margins added to a single frame for PPV (0.6-0.7), respectively. The best strategy to account for breathing motion for tumor ITV delineation in radiotherapy planning is to rely on the use of the union of volumes delineated on super resolution-corrected binned images.

An approach to identify, from DCE MRI, significant subvolumes of tumors related to outcomes in advanced head-and-neck cancer

PURPOSE

To develop and investigate a method to identify, from dynamic contrast enhanced (DCE) MRI, significant subvolumes of tumors related to treatment outcomes.

METHODS

A method, called global-initiated regularized local fuzzy clustering, was proposed to identify subvolumes of head-and-neck cancers (HNC) from heterogeneous distributions of tumor blood volume (BV) and blood flow (BF) for assessment of therapy response. BV and BF images, derived from DCE MRI, of 14 patients with advanced HNC were obtained before treatment and 2 weeks after the start of 7-week chemoradiation therapy (chemo-RT). The delineated subvolumes of tumors with low BV or BF before and during treatment were evaluated for their associations with local failure (LF). Receiver operating characteristic (ROC) analysis was used to assess performance of the method for prediction of local failure of HNC.

RESULTS

The sizes of the subvolumes of primary tumors with low BV, delineated by our method before and week 2 during treatment, were significantly greater in the patients with LF than with local control (LC) (p = 0.02 for pre-RT and 0.01 for week 2). While the total primary tumor volumes were reduced from baseline to week 2 during therapy to a similar extent for both the patients with LF and LC, the percentage decreases in the subvolumes of the primary tumors with low BV in the same time interval were significantly smaller for the patients with LF than those with LC (p < 0.05). ROC analysis shows that for any given sensitivity, the subvolume of the tumor with low BV week 2 during treatment has greater specificity for prediction of local failure than the pretreatment total tumor volume, the percentage change in the tumor volume week 2 during treatment, or the change in the averaged BV values of the entire tumor week 2 during therapy.

CONCLUSIONS

We developed a method to identify the significant subvolumes of primary tumors related to local failure. Large poorly perfused subvolumes of primary or nodal HNC before treatment and persisting during the early course of chemo-RT have the potential for prediction of local or regional failure, and could be candidates for local dose intensification.

Importance of quantification for the analysis of PET data in oncology: review of current methods and trends for the future

In oncology, positron emission tomography (PET) is an important tool for tumour diagnosis and staging, assessment of response to treatment and evaluation of the pharmacokinetic properties and efficacy of new drugs. Despite its quantitative potential, however, in daily clinical practice PET is used almost exclusively with 2-deoxy-2-[(18)F]fluoro-D-glucose ([(18)F]FDG) and, in addition, [(18)F]FDG data are normally assessed visually or using simple indices as the standardised uptake value (SUV). After explaining why more sophisticated quantification methods can be useful in oncology, the paper reviews the approaches that are commonly used and those available but not routinely employed. Particular emphasis is addressed to the SUV, for its importance in clinical practice. Issues specific to PET quantification in oncology and related examples are then discussed. Finally, some ideas for the development of new quantitative methods for analysing PET data in oncology and for the application of approaches already existing but not commonly employed are presented.

An algorithm for PET tumor volume and activity quantification: without specifying camera's point spread function (PSF)

PURPOSE

The authors have developed an algorithm for segmentation and removal of the partial volume effect (PVE) of tumors in positron emission tomography (PET) images. The algorithm accurately measures functional volume (FV) and activity concentration (AC) of tumors independent of the camera's full width half maximum (FWHM).

METHODS

A novel iterative histogram thresholding (HT) algorithm is developed to segment the tumors in PET images, which have low resolution and suffer from inherent noise in the image. The algorithm is initiated by manually drawing a region of interest (ROI). The segmented tumors are subjected to the iterative deconvolution thresholding segmentation (IDTS) algorithm, where the Van-Cittert's method of deconvolution is used for correcting PVE. The IDTS algorithm is fully automated and accurately measures the FV and AC, and stops once it reaches convergence. The convergence criteria or stopping conditions are developed in such a way that the algorithm does not rely on estimating the FWHM of the point spread function (PSF) to perform the deconvolution process. The algorithm described here was tested in phantom studies, where hollow spheres (0.5-16 ml) were used to represent tumors with a homogeneous activity distribution, and an irregular shaped volume was used to represent a tumor with a heterogeneous activity distribution. The phantom studies were performed with different signal to background ratios (SBR) and with different acquisition times (1 min, 3 min, and 5 min). The parameters in the algorithm were also changed (FWHM and matrix size of the Gaussian function) to check the accuracy of the algorithm. Simulated data were also used to test the algorithm with tumors having heterogeneous activity distribution.

RESULTS

The results show that changing the size and shape of the ROI during initiation of the algorithm had no significant impact on the FV. An average FV overestimation of 30% and an average AC underestimation of 35% were observed for the smallest tumor (0.5 ml) over the entire range of noise and SBR level. The difference in average FV and AC estimations from the actual volumes were less than 5% as the tumor size increased to 16 ml. For tumors with heterogeneous activity profile, the overall volume error was less than 10%. The average overestimation of FV was less than 10% and classification error was around 11%.

CONCLUSIONS

The algorithm developed herein was extensively tested and is not dependent on accurately quantifying the camera's PSF. This feature demonstrates the robustness of the algorithm and enables it to be applied on a wide range of noise and SBR within an image. The ultimate goal of the algorithm is to be able to be operated independent of the camera type used and the reconstruction algorithm deployed.

Reproducibility of functional volume and activity concentration in 18F-FDG PET/CT of liver metastases in colorectal cancer

PURPOSE

Several studies showed potential for monitoring response to systemic therapy in metastatic colorectal cancer patients with (18)F-fluorodeoxyglucose (FDG) positron emission tomography (PET). Before (18)F-FDG PET can be implemented for response evaluation the repeatability should be known. This study was performed to assess the magnitude of the changes in standardized uptake value (SUV), volume and total lesion glycolysis (TLG) in colorectal liver metastases and validate the biological basis of (18)F-FDG PET in colorectal liver metastases.

METHODS

Twenty patients scheduled for liver metastasectomy underwent two (18)F-FDG PET scans within 1 week. Bland-Altman analysis was performed to assess repeatability of SUV(max), SUV(mean), volume and TLG. Tumours were delineated using an adaptive threshold method (PET(SBR)) and a semiautomatic fuzzy locally adaptive Bayesian (FLAB) delineation method.

RESULTS

Coefficient of repeatability of SUV(max) and SUV(mean) were ∼39 and ∼31 %, respectively, independent of the delineation method used and image reconstruction parameters. However, repeatability was worse in recently treated patients. The FLAB delineation method improved the repeatability of the volume and TLG measurements compared to PET(SBR), from coefficients of repeatability of over 85 % to 45 % and 57 % for volume and TLG, respectively. Glucose transporter 1 (GLUT1) expression correlated to the SUV(mean). Vascularity (CD34 expression) and tumour hypoxia (carbonic anhydrase IX expression) did not correlate with (18)F-FDG PET parameters.

CONCLUSION

In conclusion, repeatability of SUV(mean) and SUV(max) was mainly affected by preceding systemic therapy. The repeatability of tumour volume and TLG could be improved using more advanced and robust delineation approaches such as FLAB, which is recommended when (18)F-FDG PET is utilized for volume or TLG measurements. Improvement of repeatability of PET measurements, for instance by dynamic PET scanning protocols, is probably necessary to effectively use PET for early response monitoring.

Image change detection using paradoxical theory for patient follow-up quantitation and therapy assessment

In clinical oncology, positron emission tomography (PET) imaging can be used to assess therapeutic response by quantifying the evolution of semi-quantitative values such as standardized uptake value, early during treatment or after treatment. Current guidelines do not include metabolically active tumor volume (MATV) measurements and derived parameters such as total lesion glycolysis (TLG) to characterize the response to the treatment. To achieve automatic MATV variation estimation during treatment, we propose an approach based on the change detection principle using the recent paradoxical theory, which models imprecision, uncertainty, and conflict between sources. It was applied here simultaneously to pre- and post-treatment PET scans. The proposed method was applied to both simulated and clinical datasets, and its performance was compared to adaptive thresholding applied separately on pre- and post-treatment PET scans. On simulated datasets, the adaptive threshold was associated with significantly higher classification errors than the developed approach. On clinical datasets, the proposed method led to results more consistent with the known partial responder status of these patients. The method requires accurate rigid registration of both scans which can be obtained only in specific body regions and does not explicitly model uptake heterogeneity. In further investigations, the change detection of intra-MATV tracer uptake heterogeneity will be developed by incorporating textural features into the proposed approach.

Challenges and opportunities in patient-specific, motion-managed and PET/CT-guided radiation therapy of lung cancer: review and perspective

The increasing interest in combined positron emission tomography (PET) and computed tomography (CT) to guide lung cancer radiation therapy planning has been well documented. Motion management strategies during treatment simulation PET/CT imaging and treatment delivery have been proposed to improve the precision and accuracy of radiotherapy. In light of these research advances, why has translation of motion-managed PET/CT to clinical radiotherapy been slow and infrequent? Solutions to this problem are as complex as they are numerous, driven by large inter-patient variability in tumor motion trajectories across a highly heterogeneous population. Such variation dictates a comprehensive and patient-specific incorporation of motion management strategies into PET/CT-guided radiotherapy rather than a one-size-fits-all tactic. This review summarizes challenges and opportunities for clinical translation of advances in PET/CT-guided radiotherapy, as well as in respiratory motion-managed radiotherapy of lung cancer. These two concepts are then integrated into proposed patient-specific workflows that span classification schemes, PET/CT image formation, treatment planning, and adaptive image-guided radiotherapy delivery techniques.

Comparative methods for PET image segmentation in pharyngolaryngeal squamous cell carcinoma

Purpose

Several methods have been proposed for the segmentation of ¹⁸F-FDG uptake in PET. In this study, we assessed the performance of four categories of ¹⁸F-FDG PET image segmentation techniques in pharyngolaryngeal squamous cell carcinoma using clinical studies where the surgical specimen served as the benchmark.

Methods

Nine PET image segmentation techniques were compared including: five thresholding methods; the level set technique (active contour); the stochastic expectation-maximization approach; fuzzy clustering-based segmentation (FCM); and a variant of FCM, the spatial wavelet-based algorithm (FCM-SW) which incorporates spatial information during the segmentation process, thus allowing the handling of uptake in heterogeneous lesions. These algorithms were evaluated using clinical studies in which the segmentation results were compared to the 3-D biological tumour volume (BTV) defined by histology in PET images of seven patients with T3–T4 laryngeal squamous cell carcinoma who underwent a total laryngectomy. The macroscopic tumour specimens were collected “en bloc”, frozen and cut into 1.7- to 2-mm thick slices, then digitized for use as reference.

Results

The clinical results suggested that four of the thresholding methods and expectation-maximization overestimated the average tumour volume, while a contrast-oriented thresholding method, the level set technique and the FCM-SW algorithm underestimated it, with the FCM-SW algorithm providing relatively the highest accuracy in terms of volume determination (−5.9 ± 11.9%) and overlap index. The mean overlap index varied between 0.27 and 0.54 for the different image segmentation techniques. The FCM-SW segmentation technique showed the best compromise in terms of 3-D overlap index and statistical analysis results with values of 0.54 (0.26–0.72) for the overlap index.

Conclusion

The BTVs delineated using the FCM-SW segmentation technique were seemingly the most accurate and approximated closely the 3-D BTVs defined using the surgical specimens. Adaptive thresholding techniques need to be calibrated for each PET scanner and acquisition/processing protocol, and should not be used without optimization.

Response assessment using 18F-FDG PET early in the course of radiotherapy correlates with survival in advanced-stage non-small cell lung cancer

This study investigated the possibility of early response assessment based on (18)F-FDG uptake during radiotherapy with respect to overall survival in patients with non-small cell lung cancer.

METHODS

(18)F-FDG PET/CT was performed before radiotherapy and was repeated in the second week of radiotherapy for 34 consecutive lung cancer patients. The CT volume and standardized uptake value (SUV) parameters of the primary tumor were quantified at both time points. Changes in volume and SUV parameters correlated with 2-y overall survival.

RESULTS

The average change in mean SUV in the primary tumor of patients with a 2-y survival was a decrease by 20% ± 21%-significantly different (P < 0.007) from nonsurvivors, who had an increase by 2% ± 22%. A sensitivity and specificity of 63% and 93%, respectively, to separate the 2 groups was reached for a decrease in mean SUV of 15%. Survival curves were significantly different using this cutoff (P = 0.001). The hazard ratio for a 1% decrease in mean SUV was 1.032 (95% confidence interval, 1.010-1.055). Changes in tumor volume defined on CT did not correlate with overall survival.

CONCLUSION

The use of repeated (18)F-FDG PET to assess treatment response early during radiotherapy is possible in patients undergoing radiotherapy or sequential or concurrent chemoradiotherapy. A decrease in (18)F-FDG uptake by the primary tumor correlates with higher long-term overall survival.

Gaussian process models of dynamic PET for functional volume definition in radiation oncology

In routine oncologic positron emission tomography (PET), dynamic information is discarded by time-averaging the signal to produce static images of the "standardised uptake value" (SUV). Defining functional volumes of interest (VOIs) in terms of SUV is flawed, as values are affected by confounding factors and the chosen time window, and SUV images are not sensitive to functional heterogeneity of pathological tissues. Also, SUV iso-contours are highly affected by the choice of threshold and no threshold, or other SUV-based segmentation method, is universally accepted for a given VOI type. Gaussian Process (GP) time series models describe macro-scale dynamic behavior arising from countless interacting micro-scale processes, as is the case for PET signals from heterogeneous tissue. We use GPs to model time-activity curves (TACs) from dynamic PET and to define functional volumes for PET oncology. Probabilistic methods of tissue discrimination are presented along with novel contouring methods for functional VOI segmentation. We demonstrate the value of GP models for voxel classification and VOI contouring of diseased and metastatic tissues with functional heterogeneity in prostate PET. Classification experiments reveal superior sensitivity and specificity over SUV calculation and a TAC-based method proposed in recent literature. Contouring experiments reveal differences in shape between gold-standard and GP VOIs and correlation with kinetic models shows that the novel VOIs contain extra clinically relevant information compared to SUVs alone. We conclude that the proposed models offer a principled data analysis technique that improves on SUVs for oncologic VOI definition. Continuing research will generalize GP models for different oncology tracers and imaging protocols with the ultimate goal of clinical use including treatment planning.

Integration of FDG-PET/CT into external beam radiation therapy planning: technical aspects and recommendations on methodological approaches

This work addresses the clinical adoption of FDG-PET/CT for image-guided radiation therapy planning (RTP). As such, important technical and methodological aspects of PET/CT-based RTP are reviewed and practical recommendations are given for routine patient management and clinical studies. First, recent developments in PET/CT hardware that are relevant to RTP are reviewed in the context of quality control and system calibration procedures that are mandatory for a reproducible adoption of PET/CT in RTP. Second, recommendations are provided on image acquisition and reconstruction to support the standardization of imaging protocols. A major prerequisite for routine RTP is a complete and secure data transfer to the actual planning system. Third, state-of-the-art tools for image fusion and co-registration are discussed briefly in the context of PET/CT imaging pre- and post-RTP. This includes a brief review of state-of-the-art image contouring algorithms relevant to PET/CT-guided RTP. Finally, practical aspects of clinical workflow and patient management, such as patient setup and requirements for staff training are emphasized. PET/CT-guided RTP mandates attention to logistical aspects, patient set-up and acquisition parameters as well as an in-depth appreciation of quality control and protocol standardization.

CONCLUSION

Upon fulfilling the requirements to perform PET/CT for RTP, a new dimension of molecular imaging can be added to traditional morphological imaging. As a consequence, PET/CT imaging will support improved RTP and better patient care. This document serves as a guidance on practical and clinically validated instructions that are deemed useful to the staff involved in PET/CT-guided RTP.

Reproducibility of tumor uptake heterogeneity characterization through textural feature analysis in 18F-FDG PET

(18)F-FDG PET measurement of standardized uptake value (SUV) is increasingly used for monitoring therapy response and predicting outcome. Alternative parameters computed through textural analysis were recently proposed to quantify the heterogeneity of tracer uptake by tumors as a significant predictor of response. The primary objective of this study was to evaluate the reproducibility of these heterogeneity measurements.

METHODS

Double baseline (18)F-FDG PET scans were acquired within 4 d of each other for 16 patients before any treatment was considered. A Bland-Altman analysis was performed on 8 parameters based on histogram measurements and 17 parameters based on textural heterogeneity features after discretization with values between 8 and 128.

RESULTS

The reproducibility of maximum and mean SUV was similar to that in previously reported studies, with a mean percentage difference of 4.7% ± 19.5% and 5.5% ± 21.2%, respectively. By comparison, better reproducibility was measured for some textural features describing local heterogeneity of tracer uptake, such as entropy and homogeneity, with a mean percentage difference of -2% ± 5.4% and 1.8% ± 11.5%, respectively. Several regional heterogeneity parameters such as variability in the intensity and size of regions of homogeneous activity distribution had reproducibility similar to that of SUV measurements, with 95% confidence intervals of -22.5% to 3.1% and -1.1% to 23.5%, respectively. These parameters were largely insensitive to the discretization range.

CONCLUSION

Several parameters derived from textural analysis describing heterogeneity of tracer uptake by tumors on local and regional scales had reproducibility similar to or better than that of simple SUV measurements. These reproducibility results suggest that these (18)F-FDG PET-derived parameters, which have already been shown to have predictive and prognostic value in certain cancer models, may be used to monitor therapy response and predict patient outcome.

Multi-centre calibration of an adaptive thresholding method for PET-based delineation of tumour volumes in radiotherapy planning of lung cancer

PURPOSE

To evaluate the calibration of an adaptive thresholding algorithm (contrast-oriented algorithm) for FDG PET-based delineation of tumour volumes in eleven centres with respect to scanner types and image data processing by phantom measurements.

METHODS

A cylindrical phantom with spheres of different diameters was filled with FDG realizing different signal-to-background ratios and scanned using 5 Siemens Biograph PET/CT scanners, 5 Philips Gemini PET/CT scanners, and one Siemens ECAT-ART PET scanner. All scans were analysed by the contrast-oriented algorithm implemented in two different software packages. For each site, the threshold SUVs of all spheres best matching the known sphere volumes were determined. Calibration parameters a and b were calculated for each combination of scanner and image-analysis software package. In addition, "scanner-type-specific" calibration curves were determined from all values obtained for each combination of scanner type and software package. Both kinds of calibration curves were used for volume delineation of the spheres.

RESULTS

Only minor differences in calibration parameters were observed for scanners of the same type (Δa ≤4%, Δb ≤14%) provided that identical imaging protocols were used whereas significant differences were found comparing calibration parameters of the ART scanner with those of scanners of different type (Δa ≤60%, Δb ≤54%). After calibration, for all scanners investigated the calculated SUV thresholds for auto-contouring did not differ significantly (all p>0.58). The resulting sphere volumes deviated by less than -7% to +8% from the true values.

CONCLUSION

After multi-centre calibration the use of the contrast-oriented algorithm for FDG PET-based delineation of tumour volumes in the different centres using different scanner types and specific imaging protocols is feasible.