A method for partial volume correction of PET-imaged tumor heterogeneity using expectation maximization with a spatially varying point spread function

Use of real-time phase-contrast MRI to quantify the effect of spontaneous breathing on the cerebral arteries

Quantification of the effect of breathing on the cerebral circulation provides a better mechanistic understanding of the brain's circulatory system and is important in the early diagnosis of certain neurological diseases. However, conventional cine phase-contrast (CINE-PC) MRI cannot be used in this field of study because it only provides an average cardiac cycle flow curve reconstructed from multiple cardiac cycles. Unlike CINE-PC, phase-contrast echo-planar imaging (EPI-PC) can be used to quantify the blood flow rate in "real-time" and thus assess the effect of breathing on blood flow. Here, we first used post-processing software (developed in-house) to determine the feasibility of quantifying cerebral arterial blood flow with EPI-PC (relative to CINE-PC) in 16 participants. In a second step, we developed a new time-domain method for quantifying the intensity and the phase shift of the effects of breathing on the mean flow rate, stroke volume, cardiac period and amplitude of cerebral blood flow (in 10 participants). Our results showed that EPI-PC can quantify cerebral arterial blood flow rate with much the same degree of accuracy as CINE-PC but is more strongly influenced by differences in magnetic susceptibility. We found that breathing affected the mean flow rate, stroke volume and cardiac period of cerebral arterial blood flow.

Dynamic distortion in resonant galvanometric optical scanners

High-speed optical systems are revolutionizing biomedical imaging in microscopy, DNA sequencing, and flow cytometry, as well as numerous other applications, including data storage, display technologies, printing, and autonomous vehicles. These systems often achieve the necessary imaging or sensing speed through the use of resonant galvanometric optical scanners. Here, we show that the optical performance of these devices suffers due to the dynamic mirror distortion that arises from the variation in torque with angular displacement. In one of two scanners tested, these distortions result in a variation of signal-to-noise (Strehl) ratio by an order of magnitude across the field of view, degrading transverse resolution by more than a factor of 2. This mirror distortion could be mitigated through the use of stiffer materials, such as beryllium or silicon carbide, at the expense of surface roughness, as these cannot be polished to the same degree of smoothness as common optical glasses. The repeatability of the dynamic distortion indicates that computational and optical corrective methods are also possible.

Effect of uncertainties in quantitative 18 F-FDG PET/CT imaging feedback for intratumoral dose-response assessment and dose painting by number

PURPOSE

Intratumoral dose response can be detected using serial fluoro-2-deoxyglucose-(FDG) positron emission tomography (PET)/computed tomography (CT) imaging feedback during treatment and used to guide adaptive dose painting by number (DPbN). However, to reliably implement this technique, the effect of uncertainties in quantitative PET/CT imaging feedback on tumor voxel dose-response assessment and DPbN needs to be determined and reduced.

METHODS

Three major uncertainties, induced by (a) PET imaging partial volume effect (PVE) and (b) tumor deformable image registration (DIR), and (c) variation of the time interval between FDG injection and PET image acquisition (TI), were determined using serial FDG-PET/CT images acquired during chemoradiotherapy of 18 head and neck cancer patients. PET imaging PVE was simulated using the discrepancy between with and without iterative deconvolution-based PVE corrections. Effect of tumor DIR uncertainty was simulated using the discrepancy between two DIR algorithms, including one with and one without soft-tissue mechanical correction for the voxel displacement. The effect of TI variation was simulated using linear interpolation on the dual-point PET/CT images. Tumor voxel pretreatment metabolic activity (SUV₀ ) and dose-response matrix (DRM) discrepancies induced by each of the three uncertainties were quantified, respectively. Adverse effects of tumor voxel SUV₀ and DRM discrepancies on tumor control probability (TCP) in DPbN were assessed.

RESULTS

Partial volume effect and TI variations of 10 mins induced a mean ± standard deviation (SD) of tumor voxel SUV₀ discrepancies to be -0.7% ± 9.2% and 0% ± 4.8%, respectively. Tumor voxel DRM discrepancies induced by PVE, tumor DIR discrepancy, and TI variations were 0.6% ± 8.9%, 1.7% ± 9.1%, and 0% ± 7%, respectively. Partial volume effect induced SUV₀ and DRM discrepancies correlated significantly with the tumor shape and FDG uptake heterogeneity. Tumor DIR uncertainty-induced DRM discrepancy correlated significantly with the tumor volume and shrinkage during treatment. Among the three uncertainties, PVE dominated the adverse effects on the TCP, with a mean ± SD of TCP reduction to be 12.7% ± 9.8% for all tumors if no compensation was applied for.

CONCLUSIONS

Effect of uncertainties in quantitative FDG-PET/CT imaging feedback on intratumoral dose-response quantification was not negligible. These uncertainties primarily caused by PVE and tumor DIR were highly dependent on individual tumor shape, volume, shrinkage during treatment, and pretreatment SUV heterogeneity, which can be managed individually. The adverse effects of these uncertainties could be minimized by using proper PVE corrections and DIR methods and compensated for in the clinical implementation of DPbN.

Design and evaluation of an accurate CNR-guided small region iterative restoration-based tumor segmentation scheme for PET using both simulated and real heterogeneous tumors

Tumor delineation accuracy directly affects the effectiveness of radiotherapy. This study presents a methodology that minimizes potential errors during the automated segmentation of tumors in PET images. Iterative blind deconvolution was implemented in a region of interest encompassing the tumor with the number of iterations determined from contrast-to-noise ratios. The active contour and random forest classification-based segmentation method was evaluated using three distinct image databases that included both synthetic and real heterogeneous tumors. Ground truths about tumor volumes were known precisely. The volumes of the tumors were in the range of 0.49-26.34 cm³, 0.64-1.52 cm³, and 40.38-203.84 cm³ respectively. Widely available software tools, namely, MATLAB, MIPAV, and ITK-SNAP were utilized. When using the active contour method, image restoration reduced mean errors in volumes estimation from 95.85 to 3.37%, from 815.63 to 17.45%, and from 32.61 to 6.80% for the three datasets. The accuracy gains were higher using datasets that include smaller tumors for which PVE is known to be more predominant. Computation time was reduced by a factor of about 10 in the smaller deconvolution region. Contrast-to-noise ratios were improved for all tumors in all data. The presented methodology has the potential to improve delineation accuracy in particular for smaller tumors at practically feasible computational times. Graphical abstract Evaluation of accurate lesion volumes using CNR-guided and ROI-based restoration method for PET images.

PET Image Deblurring and Super-Resolution with an MR-Based Joint Entropy Prior

The intrinsically limited spatial resolution of PET confounds image quantitation. This paper presents an image deblurring and super-resolution framework for PET using anatomical guidance provided by high-resolution MR images. The framework relies on image-domain post-processing of already-reconstructed PET images by means of spatially-variant deconvolution stabilized by an MR-based joint entropy penalty function. The method is validated through simulation studies based on the BrainWeb digital phantom, experimental studies based on the Hoffman phantom, and clinical neuroimaging studies pertaining to aging and Alzheimer's disease. The developed technique was compared with direct deconvolution and deconvolution stabilized by a quadratic difference penalty, a total variation penalty, and a Bowsher penalty. The BrainWeb simulation study showed improved image quality and quantitative accuracy measured by contrast-to-noise ratio, structural similarity index, root-mean-square error, and peak signal-to-noise ratio generated by this technique. The Hoffman phantom study indicated noticeable improvement in the structural similarity index (relative to the MR image) and gray-to-white contrast-to-noise ratio. Finally, clinical amyloid and tau imaging studies for Alzheimer's disease showed lowering of the coefficient of variation in several key brain regions associated with two target pathologies.

Activity painting: PET images of freely defined activity distributions applying a novel phantom technique

The aim of this work was to develop a novel phantom that supports the construction of highly reproducible phantoms with arbitrary activity distributions for PET imaging. It could offer a methodology for answering questions related to texture measurements in PET imaging. The basic idea is to move a point source on a 3-D trajectory in the field of view, while continuously acquiring data. The reconstruction results in a 3-D activity concentration map according to the pathway of the point source. A 22Na calibration point source was attached to a high precision robotic arm system, where the 3-D movement was software controlled. 3-D activity distributions of a homogeneous cube, a sphere, a spherical shell and a heart shape were simulated. These distributions were used to measure uniformity and to characterize reproducibility. Two potential applications using the lesion simulation method are presented: evaluation in changes of textural properties related to the position in the PET field of view; scanner comparison based on visual and quantitative evaluation of texture features. A lesion with volume of 50x50x50 mm3 can be simulated during approximately 1 hour. The reproducibility of the movement was found to be >99%. The coefficients of variation of the voxels within a simulated homogeneous cube was 2.34%. Based on 5 consecutive and independent measurements of a 36 mm diameter hot sphere, the coefficient of variation of the mean activity concentration was 0.68%. We obtained up to 18% differences within the values of investigated textural indexes, when measuring a lesion in different radial positions of the PET field of view. In comparison of two different human PET scanners the percentage differences between heterogeneity parameters were in the range of 5-55%. After harmonizing the voxel sizes this range reduced to 2-16%. The general activity distributions provided by the two different vendor show high similarity visually. For the demonstration of the flexibility of this method, the same pattern was also simulated on a small animal PET scanner giving similar results, both quantitatively and visually. 3-D motion of a point source in the PET field of view is capable to create an irregular shaped activity distribution with high reproducibility.

Generalized image deconvolution by exploiting the transmission matrix of an optical imaging system

Intact optical information of an object delivered through an imaging system is deteriorated by imperfect optical elements and unwanted defects. Image deconvolution has been widely exploited as a recovery technique due to its practical feasibility, and operates by assuming linear shift-invariant property of the imaging system. However, shift invariance does not rigorously hold in all imaging situations and is not a necessary condition for solving an inverse problem of light propagation. Several improved deconvolution techniques exploiting spatially variant point spread functions have been proposed in previous studies. However, the full characterization of an optical imaging system for compensating aberrations has not been considered. Here, we present a generalized method to solve the linear inverse problem of coherent light propagations without any regularization method or constraint on shift invariance by fully measuring the transmission matrix of the imaging system. Our results show that severe aberrations produced by a tilted lens or an inserted disordered layer can be corrected properly only by the proposed generalized image deconvolution. This work generalizes the theory of image deconvolution, and enables distortion-free imaging under general imaging condition.

Classification and evaluation strategies of auto-segmentation approaches for PET: Report of AAPM task group No. 211

PURPOSE

The purpose of this educational report is to provide an overview of the present state-of-the-art PET auto-segmentation (PET-AS) algorithms and their respective validation, with an emphasis on providing the user with help in understanding the challenges and pitfalls associated with selecting and implementing a PET-AS algorithm for a particular application.

APPROACH

A brief description of the different types of PET-AS algorithms is provided using a classification based on method complexity and type. The advantages and the limitations of the current PET-AS algorithms are highlighted based on current publications and existing comparison studies. A review of the available image datasets and contour evaluation metrics in terms of their applicability for establishing a standardized evaluation of PET-AS algorithms is provided. The performance requirements for the algorithms and their dependence on the application, the radiotracer used and the evaluation criteria are described and discussed. Finally, a procedure for algorithm acceptance and implementation, as well as the complementary role of manual and auto-segmentation are addressed.

FINDINGS

A large number of PET-AS algorithms have been developed within the last 20 years. Many of the proposed algorithms are based on either fixed or adaptively selected thresholds. More recently, numerous papers have proposed the use of more advanced image analysis paradigms to perform semi-automated delineation of the PET images. However, the level of algorithm validation is variable and for most published algorithms is either insufficient or inconsistent which prevents recommending a single algorithm. This is compounded by the fact that realistic image configurations with low signal-to-noise ratios (SNR) and heterogeneous tracer distributions have rarely been used. Large variations in the evaluation methods used in the literature point to the need for a standardized evaluation protocol.

CONCLUSIONS

Available comparison studies suggest that PET-AS algorithms relying on advanced image analysis paradigms provide generally more accurate segmentation than approaches based on PET activity thresholds, particularly for realistic configurations. However, this may not be the case for simple shape lesions in situations with a narrower range of parameters, where simpler methods may also perform well. Recent algorithms which employ some type of consensus or automatic selection between several PET-AS methods have potential to overcome the limitations of the individual methods when appropriately trained. In either case, accuracy evaluation is required for each different PET scanner and scanning and image reconstruction protocol. For the simpler, less robust approaches, adaptation to scanning conditions, tumor type, and tumor location by optimization of parameters is necessary. The results from the method evaluation stage can be used to estimate the contouring uncertainty. All PET-AS contours should be critically verified by a physician. A standard test, i.e., a benchmark dedicated to evaluating both existing and future PET-AS algorithms needs to be designed, to aid clinicians in evaluating and selecting PET-AS algorithms and to establish performance limits for their acceptance for clinical use. The initial steps toward designing and building such a standard are undertaken by the task group members.

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.

Surface-based partial-volume correction for high-resolution PET

Tissue radioactivity concentrations, measured with positron emission tomography (PET) are subject to partial volume effects (PVE) due to the limited spatial resolution of the scanner. Last generation high-resolution PET cameras with a full width at half maximum (FWHM) of 2-4mm are less prone to PVEs than previous generations. Corrections for PVEs are still necessary, especially when studying small brain stem nuclei or small variations in cortical neuroreceptor concentrations which may be related to cytoarchitectonic differences. Although several partial-volume correction (PVC) algorithms exist, these are frequently based on a priori assumptions about tracer distribution or only yield corrected values of regional activity concentrations without providing PVE corrected images. We developed a new iterative deconvolution algorithm (idSURF) for PVC of PET images that aims to overcome these limitations by using two innovative techniques: 1) the incorporation of anatomic information from a cortical gray matter surface representation, extracted from magnetic resonance imaging (MRI) and 2) the use of anatomically constrained filtering to attenuate noise. PVE corrected images were generated with idSURF implemented into a non-interactive processing pipeline. idSURF was validated using simulated and clinical PET data sets and compared to a frequently used standard PVC method (Geometric Transfer Matrix: GTM). The results on simulated data sets show that idSURF consistently recovers accurate radiotracer concentrations within 1-5% of true values. Both radiotracer concentrations and non-displaceable binding potential (BPnd) values derived from clinical PET data sets with idSURF were highly correlated with those obtained with the standard PVC method (R(2) = 0.99, error = 0%-3.2%). These results suggest that idSURF is a valid and potentially clinically useful PVC method for automatic processing of large numbers of PET data sets.

Asphericity of pretherapeutic tumour FDG uptake provides independent prognostic value in head-and-neck cancer

OBJECTIVE

To propose a novel measure, namely the 'asphericity' (ASP), of spatial irregularity of FDG uptake in the primary tumour as a prognostic marker in head-and-neck cancer.

METHODS

PET/CT was performed in 52 patients (first presentation, n = 36; recurrence, n = 16). The primary tumour was segmented based on thresholding at the volume-reproducible intensity threshold after subtraction of the local background. ASP was used to characterise the deviation of the tumour's shape from sphere symmetry. Tumour stage, tumour localisation, lymph node metastases, distant metastases, SUVmax, SUVmean, metabolic tumour volume (MTV) and total lesion glycolysis (TLG) were also considered. The association of overall (OAS) and progression-free survival (PFS) with these parameters was analysed.

RESULTS

Cox regression revealed high SUVmax [hazard ratio (HR) = 4.4/7.4], MTV (HR = 4.6/5.7), TLG (HR = 4.8/8.9) and ASP (HR = 7.8/7.4) as significant predictors with respect to PFS/OAS in case of first tumour manifestation. The combination of high MTV and ASP showed very high HRs of 22.7 for PFS and 13.2 for OAS. In case of recurrence, MTV (HR = 3.7) and the combination of MTV/ASP (HR = 4.2) were significant predictors of PFS.

CONCLUSIONS

ASP of pretherapeutic FDG uptake in the primary tumour improves the prediction of tumour progression in head-and-neck cancer at first tumour presentation.

KEY POINTS

Asphericity (ASP) characterises the spatial heterogeneity of FDG uptake in tumours. ASP is a promising prognostic parameter in head-and-neck cancer. ASP is useful for identification of high-risk patients with head-and-neck cancer.

Region-Based Partial Volume Correction Techniques for PET Imaging: Sinogram Implementation and Robustness

Background/Purpose. Limited spatial resolution of positron emission tomography (PET) requires partial volume correction (PVC). Region-based PVC methods are based on geometric transfer matrix implemented either in image-space (GTM) or sinogram-space (GTMo), both with similar performance. Although GTMo is slower, it more closely simulates the 3D PET image acquisition, accounts for local variations of point spread function, and can be implemented for iterative reconstructions. A recent image-based symmetric GTM (sGTM) has shown improvement in noise characteristics and robustness to misregistration over GTM. This study implements the sGTM method in sinogram space (sGTMo), validates it, and evaluates its performance. Methods. Two 3D sphere and brain digital phantoms and a physical sphere phantom were used. All four region-based PVC methods (GTM, GTMo, sGTM, and sGTMo) were implemented and their performance was evaluated. Results. All four PVC methods had similar accuracies. Both noise propagation and robustness of the sGTMo method were similar to those of sGTM method while they were better than those of GTMo method especially for smaller objects. Conclusion. The sGTMo was implemented and validated. The performance of the sGTMo in terms of noise characteristics and robustness to misregistration is similar to that of the sGTM method and improved compared to the GTMo method.

A partial volume effect correction tailored for 18F-FDG-PET oncological studies

We have developed, optimized, and validated a method for partial volume effect (PVE) correction of oncological lesions in positron emission tomography (PET) clinical studies, based on recovery coefficients (RC) and on PET measurements of lesion-to-background ratio (L/B_m) and of lesion metabolic volume. An operator-independent technique, based on an optimised threshold of the maximum lesion uptake, allows to define an isocontour around the lesion on PET images in order to measure both lesion radioactivity uptake and lesion metabolic volume. RC are experimentally derived from PET measurements of hot spheres in hot background, miming oncological lesions. RC were obtained as a function of PET measured sphere-to-background ratio and PET measured sphere metabolic volume, both resulting from the threshold-isocontour technique. PVE correction of lesions of a diameter ranging from 10 mm to 40 mm and for measured L/B_m from 2 to 30 was performed using measured RC curves tailored at answering the need to quantify a large variety of real oncological lesions by means of PET. Validation of the PVE correction method resulted to be accurate (>89%) in clinical realistic conditions for lesion diameter > 1 cm, recovering >76% of radioactivity for lesion diameter < 1 cm. Results from patient studies showed that the proposed PVE correction method is suitable and feasible and has an impact on a clinical environment.

Correlation of PET images of metabolism, proliferation and hypoxia to characterize tumor phenotype in patients with cancer of the oropharynx

Spatial organization of tumor phenotype is of great interest to radiotherapy target definition and outcome prediction. We characterized tumor phenotype in patients with cancers of the oropharynx through voxel-based correlation of PET images of metabolism, proliferation, and hypoxia.

METHODS

Patients with oropharyngeal cancer received (18)F-fluorodeoxyglucose (FDG) PET/CT, (18)F-fluorothymidine (FLT) PET/CT, and (61)Cu-diacetyl-bis(N4-methylthiosemicarbazone) (Cu-ATSM) PET/CT. Images were co-registered and standardized uptake values (SUV) were calculated for all modalities. Voxel-based correlation was evaluated with Pearson's correlation coefficient in tumor regions. Additionally, sensitivity studies were performed to quantify the effects of image segmentation, registration, noise, and segmentation on R.

RESULTS

On average, FDG PET and FLT PET images were most highly correlated (R(FDG:FLT) = 0.76, range 0.53-0.85), while Cu-ATSM PET showed greater heterogeneity in correlation to other tracers (R(FDG:Cu-ATSM) = 0.64, range 0.51-0.79; R(FLT:Cu-ATSM) = 0.61, range 0.21-0.80). Of the tested parameters, correlation was most sensitive to image registration. Misregistration of one voxel lead to ΔR(FDG) = 0.25, ΔR(FLT) = 0.39, and ΔR(Cu-ATSM) = 0.27. Image noise and reconstruction also had quantitative effects on correlation. No significant quantitative differences were found between GTV, expanded GTV, or CTV regions.

CONCLUSIONS

Voxel-based correlation represents a first step into understanding spatial organization of tumor phenotype. These results have implications for radiotherapy target definition and provide a framework to test outcome prediction based on pretherapy distribution of phenotype.

Radiotherapy for head and neck tumours in 2012 and beyond: conformal, tailored, and adaptive?

Intensity-modulated radiation therapy (IMRT) is a conformal irradiation technique that enables steep dose gradients. In head and neck tumours this approach spares parotid-gland function without compromise to treatment efficacy. Anatomical and molecular imaging modalities may be used to tailor treatment by enabling proper selection and delineation of target volumes and organs at risk, which in turn lead to dose prescriptions that take into account the underlying tumour biology (eg, human papillomavirus status). Therefore, adaptations can be made throughout the course of radiotherapy, as required. Planned dose increases to parts of the target volumes may also be used to match the radiosensitivity of tumours (so-called dose-painting), assessed by molecular imaging. For swift implementation of tailored and adaptive IMRT, tools and procedures, such as accurate image acquisition and reconstruction, automatic segmentation of target volumes and organs at risk, non-rigid image and dose registration, and dose summation methods, need to be developed and properly validated.

Phantom study of the impact of reconstruction parameters on the detection of mini- and micro-volume lesions with a low-dose PET/CT acquisition protocol

PURPOSE

Every PET scanner suffers of the partial volume effect (PVE), that is a loss of contrast in small lesions causing a worsening in standardized uptake value (SUV) accuracy, that is critical if quantitative PET/CT imaging is used for diagnosis and therapy.

METHODS

In order to quantify PVE and optimize our clinical protocols to minimize this effect in a last generation PET/CT scanner, we utilized a cylindrical phantom equipped with ten mini- and micro-volume hollow spheres. The lesion detectability and the SUV accuracy were evaluated at a fixed spheres to background intrinsic contrast (activity concentration ratio 8:1) but in different scan conditions: (a) acquisition modality (3D vs. 2D), (b) number of subset per iteration, (c) type of post-reconstruction filter and (d) activity concentration (i.e. total counts). Also the effect of different absorber thickness was evaluated.

RESULTS

Small lesion detectability resulted better in images acquired in 3D mode rather than 2D, mainly because of the lower noise produced by the fully-3D algorithm. The number of reconstruction iterations and the post-processing filter used affected both the contrast underestimation and the spatial resolution. Decreasing the (18)F activity injected according to the low-dose protocol, the small lesions could be distinguished from the background down to a diameter of 6.2mm and the SUV accuracy did not deteriorate. Adding absorber thickness around the phantom, the image noise slightly increased while SUV accuracy did not change.

CONCLUSIONS

The hybrid PET/CT scanner we evaluated showed good performances, mainly in 3D acquisition modality. The phantom measurements showed that the most appropriate reconstruction protocol derived from a compromise between the contrast accuracy and the noise variance in PET images. The low-dose protocol clinically used demonstrated no loss in SUV accuracy and an adequate lesion detectability for lesions down to 6.2mm in diameter.

A method for model-free partial volume correction in oncological PET

Background

As is well known, limited spatial resolution leads to partial volume effects (PVE) and consequently to limited signal recovery. Determination of the mean activity concentration of a target structure is thus compromised even at target sizes much larger than the reconstructed spatial resolution. This leads to serious size-dependent underestimates of true signal intensity in hot spot imaging. For quantitative PET in general and in the context of therapy assessment in particular it is, therefore, mandatory to perform an adequate partial volume correction (PVC). The goal of our work was to develop and to validate a model-free PVC algorithm for hot spot imaging.

Methods

The algorithm proceeds in two automated steps. Step 1: estimation of the actual object boundary with a threshold based method and determination of the total activity A measured within the enclosed volume V. Step 2: determination of the activity fraction B, which is measured outside the object due to the partial volume effect (spill-out). The PVE corrected mean value is then given by C_mean = (A+B)/V. For validation simulated tumours were used which were derived from real patient data (liver metastases of a colorectal carcinoma and head and neck cancer, respectively). The simulated tumours have characteristics (regarding tumour shape, contrast, noise, etc.) which are very similar to those of the underlying patient data, but the boundaries and tracer accumulation are exactly known. The PVE corrected mean values of 37 simulated tumours were determined and compared with the true mean values.

Results

For the investigated simulated data the proposed approach yields PVE corrected mean values which agree very well with the true values (mean deviation (± s.d.): (−0.8±2.5)%).

Conclusions

The described method enables accurate quantitative partial volume correction in oncological hot spot imaging.

Functional and molecular image guidance in radiotherapy treatment planning optimization

Functional and molecular imaging techniques are increasingly being developed and used to quantitatively map the spatial distribution of parameters, such as metabolism, proliferation, hypoxia, perfusion, and ventilation, onto anatomically imaged normal organs and tumor. In radiotherapy optimization, these imaging modalities offer the promise of increased dose sparing to high-functioning subregions of normal organs or dose escalation to selected subregions of the tumor as well as the potential to adapt radiotherapy to functional changes that occur during the course of treatment. The practical use of functional/molecular imaging in radiotherapy optimization must take into cautious consideration several factors whose influences are still not clearly quantified or well understood including patient positioning differences between the planning computed tomography and functional/molecular imaging sessions, image reconstruction parameters and techniques, image registration, target/normal organ functional segmentation, the relationship governing the dose escalation/sparing warranted by the functional/molecular image intensity map, and radiotherapy-induced changes in the image intensity map over the course of treatment. The clinical benefit of functional/molecular image guidance in the form of improved local control or decreased normal organ toxicity has yet to be shown and awaits prospective clinical trials addressing this issue.

The physical basis and future of radiation therapy

The remarkable progress in radiation therapy over the last century has been largely due to our ability to more effectively focus and deliver radiation to the tumour target volume. Physics discoveries and technology inventions have been an important driving force behind this progress. However, there is still plenty of room left for future improvements through physics, for example image guidance and four-dimensional motion management and particle therapy, as well as increased efficiency of more compact and cheaper technologies. Bigger challenges lie ahead of physicists in radiation therapy beyond the dose localisation problem, for example in the areas of biological target definition, improved modelling for normal tissues and tumours, advanced multicriteria and robust optimisation, and continuous incorporation of advanced technologies such as molecular imaging. The success of physics in radiation therapy has been based on the continued "fuelling" of the field with new discoveries and inventions from physics research. A key to the success has been the application of the rigorous scientific method. In spite of the importance of physics research for radiation therapy, too few physicists are currently involved in cutting-edge research. The increased emphasis on more "professionalism" in medical physics will tip the situation even more off balance. To prevent this from happening, we argue that medical physics needs more research positions, and more and better academic programmes. Only with more emphasis on medical physics research will the future of radiation therapy and other physics-related medical specialties look as bright as the past, and medical physics will maintain a status as one of the most exciting fields of applied physics.

Non-Gaussian space-variant resolution modelling for list-mode reconstruction

Partial volume effect is an important source of bias in PET images that can be lowered by accounting for the point spread function (PSF) of the scanner. We measured such a PSF in various points of a clinical PET scanner and modelled it as a product of matrices acting in image space, taking the asymmetrical, shift-varying and non-Gaussian character of the PSF into account (AMP modelling), and we integrated this accurate image space modelling into a conventional list-mode OSEM algorithm (EM-AMP reconstruction). We showed on the one hand that when a sufficiently high number of iterations are considered, the AMP modelling lead to better recovery coefficients at reduced background noise compared to reconstruction where no or only partial resolution modelling is performed, and on the other hand that for a small number of iterations, a Gaussian modelling gave the best recovery coefficients. Moreover, we have demonstrated that a deconvolution based on the AMP system response model leads to the same recovery coefficients as the corresponding EM-AMP reconstruction, but at the expense of an increased background noise.