Effects of rigid and non-rigid image registration on test-retest variability of quantitative [18F]FDG PET/CT studies

ABSTRACT: BACKGROUND: [18F]fluoro-2-deoxy-D-glucose ([18F]FDG) positron emission tomography (PET) is a valuable tool for monitoring response to therapy in oncology. In longitudinal studies, however, patients are not scanned in exactly the same position. Rigid and non-rigid image registration can be applied in order to reuse baseline volumes of interest (VOI) on consecutive studies of the same patient. The purpose of this study was to investigate the impact of various image registration strategies on standardized uptake value (SUV) and metabolic volume test-retest variability (TRT). METHODS: Test-retest whole-body [18F]FDG PET/CT scans were collected retrospectively for 11 subjects with advanced gastrointestinal malignancies (colorectal carcinoma). Rigid and non-rigid image registration techniques with various degrees of locality were applied to PET, CT, and non-attenuation corrected PET (NAC) data. VOI were drawn independently on both test and retest scans. VOI drawn on test scans were projected onto retest scans and the overlap between projected VOI and manually drawn retest VOI was quantified using the Dice similarity coefficient (DSC). In addition, absolute (unsigned) differences in TRT of SUVmax, SUVmean, metabolic volume and total lesion glycolysis (TLG) were calculated in on one hand the test VOI and on the other hand the retest VOI and projected VOI. Reference values were obtained by delineating VOIs on both scans separately. RESULTS: Non-rigid PET registration showed the best performance (median DSC: 0.82, other methods: 0.71-0.81). Compared with the reference, none of the registration types showed significant absolute differences in TRT of SUVmax, SUVmean and TLG (p > 0.05). Only for absolute TRT of metabolic volume, significant lower values (p < 0.05) were observed for all registration strategies when compared to delineating VOIs separately, except for non-rigid PET registrations (p = 0.1). Non-rigid PET registration provided good volume TRT (7.7%) that was smaller than the reference (16%). CONCLUSION: In particular, non-rigid PET image registration showed good performance similar to delineating VOI on both scans separately, and with smaller TRT in metabolic volume estimates.van Velden F.H.P., van Beers P., Nuyts J., Velasquez L.M., Hayes W., Lammertsma A.A., Boellaard R., Loeckx D., ''Effects of rigid and non-rigid image registration on test-retest variability of quantitative [18F]FDG PET/CT studies'', EJNMMI research, vol. 2, no. 10, 2012.status: publishe

Outcome prediction of head and neck squamous cell carcinoma by MRI radiomic signatures.

OBJECTIVES: Head and neck squamous cell carcinoma (HNSCC) shows a remarkable heterogeneity between tumors, which may be captured by a variety of quantitative features extracted from diagnostic images, termed radiomics. The aim of this study was to develop and validate MRI-based radiomic prognostic models in oral and oropharyngeal cancer. MATERIALS AND METHODS: Native T1-weighted images of four independent, retrospective (2005-2013), patient cohorts (n = 102, n = 76, n = 89, and n = 56) were used to delineate primary tumors, and to extract 545 quantitative features from. Subsequently, redundancy filtering and factor analysis were performed to handle collinearity in the data. Next, radiomic prognostic models were trained and validated to predict overall survival (OS) and relapse-free survival (RFS). Radiomic features were compared to and combined with prognostic models based on standard clinical parameters. Performance was assessed by integrated area under the curve (iAUC). RESULTS: In oral cancer, the radiomic model showed an iAUC of 0.69 (OS) and 0.70 (RFS) in the validation cohort, whereas the iAUC in the oropharyngeal cancer validation cohort was 0.71 (OS) and 0.74 (RFS). By integration of radiomic and clinical variables, the most accurate models were defined (iAUC oral cavity, 0.72 (OS) and 0.74 (RFS); iAUC oropharynx, 0.81 (OS) and 0.78 (RFS)), and these combined models outperformed prognostic models based on standard clinical variables only (p < 0.001). CONCLUSIONS: MRI radiomics is feasible in HNSCC despite the known variability in MRI vendors and acquisition protocols, and radiomic features added information to prognostic models based on clinical parameters. KEY POINTS: • MRI radiomics can predict overall survival and relapse-free survival in oral and HPV-negative oropharyngeal cancer. • MRI radiomics provides additional prognostic information to known clinical variables, with the best performance of the combined models. • Variation in MRI vendors and acquisition protocols did not influence performance of radiomic prognostic models

Bias Reduction for Low-Statistics PET: Maximum Likelihood Reconstruction With a Modified Poisson Distribution

Positron emission tomography data are typically reconstructed with maximum likelihood expectation maximization (MLEM). However, MLEM suffers from positive bias due to the non-negativity constraint. This is particularly problematic for tracer kinetic modeling. Two reconstruction methods with bias reduction properties that do not use strict Poisson optimization are presented and compared to each other, to filtered backprojection (FBP), and to MLEM. The first method is an extension of NEGML, where the Poisson distribution is replaced by a Gaussian distribution for low count data points. The transition point between the Gaussian and the Poisson regime is a parameter of the model. The second method is a simplification of ABML. ABML has a lower and upper bound for the reconstructed image whereas AML has the upper bound set to infinity. AML uses a negative lower bound to obtain bias reduction properties. Different choices of the lower bound are studied. The parameter of both algorithms determines the effectiveness of the bias reduction and should be chosen large enough to ensure bias-free images. This means that both algorithms become more similar to least squares algorithms, which turned out to be necessary to obtain bias-free reconstructions. This comes at the cost of increased variance. Nevertheless, NEGML and AML have lower variance than FBP. Furthermore, randoms handling has a large influence on the bias. Reconstruction with smoothed randoms results in lower bias compared to reconstruction with unsmoothed randoms or randoms precorrected data. However, NEGML and AML yield both bias-free images for large values of their parameter.status: publishe

Effect of rigid and non-rigid image registration on test-retest repeatability of quantitative measures derived from FDG PET/CT oncology studies

van Velden F.H.P., Loeckx D., Velasquez L., Hayes W., Hoetjes N., Boellaard R., Nuyts J., ''Effect of rigid and non-rigid image registration on test-retest repeatability of quantitative measures derived from FDG PET/CT oncology studies'', Annual congress of the European Association of Nuclear Medicine - EANM 2010, October 9-13, 2010, Vienna, Austria.status: publishe

In vivo validation of reconstruction-based resolution recovery for human brain studies

The aim of this study was to validate in vivo the accuracy of a reconstruction-based partial volume correction (PVC), which takes into account the point spread function of the imaging system. The NEMA NU2 Image Quality phantom and five healthy volunteers (using [11C]flumazenil) were scanned on both HR+ and high-resolution research tomograph (HRRT) scanners. HR+ data were reconstructed using normalization and attenuation-weighted ordered subsets expectation maximization (NAW-OSEM) and a PVC algorithm (PVC-NAW-OSEM). HRRT data were reconstructed using 3D ordinary Poisson OSEM (OP-OSEM) and a PVC algorithm (PVC-OP-OSEM). For clinical studies, parametric volume of distribution (VT) images were generated. For phantom data, good recovery was found for both OP-OSEM (0.84 to 0.97) and PVC-OP-OSEM (0.91 to 0.98) HRRT reconstructions. In addition, for the HR+, good recovery was found for PVC-NAW-OSEM (0.84 to 0.94), corresponding well with OP-OSEM. Finally, for clinical data, good correspondence was found between PVC-NAW-OSEM and OP-OSEM-derived VT values (slope: 1.02±0.08). This study showed that HR+ image resolution using PVC-NAW-OSEM was comparable to that of the HRRT scanner. As the HRRT has a higher intrinsic resolution, this agreement validates reconstruction-based PVC as a means of improving the spatial resolution of the HR+ scanner and thereby improving the quantitative accuracy of positron emission tomography

Radiomics in vulvar cancer: first clinical experience using 18F-FDG PET/CT images

This study investigates whether radiomic features derived from preoperative positron emission tomography (PET) images could predict both tumor biology and prognosis in women with invasive squamous cell carcinoma of the vulva. Methods Patients were retrospectively included when they had a unifocal primary cancer of 65 2.6 cm in diameter, had received a preoperative 18F-Fluorodeoxyglucose (18F-FDG) PET/computed tomography (CT) scan followed by surgery and had at least six months of follow-up data. 18F-FDG-PET images were analyzed by semi-automatically drawing on the primary tumor in each PET image, followed by the extraction of 83 radiomic features. Unique radiomic features were identified by principal component analysis (PCA), after which they were compared with histopathology using non-pairwise group comparison and linear regression. Univariate and multivariate Cox regression analyses were used to correlate the identified features with progression-free survival (PFS) and overall survival (OS). Survival curves were estimated using the Kaplan-Meier method.Results Forty women were included. PCA revealed four unique radiomic features, which were not associated with histopathologic characteristics such as grading, depth of invasion, lymph-vascular space invasion and metastatic lymph nodes. No statistically significant correlation was found between the identified features and PFS. However, Moran\u2019s I, a feature that identifies global spatial autocorrelation, was correlated with OS (P=0.03). Multivariate Cox regression analysis showed that extracapsular invasion of the metastatic lymph nodes and Moran\u2019s I were independent prognostic factors for PFS and OS. Conclusion Our data show that PCA is usable to identify specific radiomic features. Although the identified features did not correlate strongly with tumor biology, Moran\u2019s I was found to predict patient prognosis. Larger studies are required to establish the clinical relevance of the observed findings