Clinical Evaluation of Deep Learning for Tumor Delineation on 18F-FDG PET/CT of Head and Neck Cancer.

Artificial intelligence (AI) may decrease 18F-FDG PET/CT-based gross tumor volume (GTV) delineation variability and automate tumor-volume-derived image biomarker extraction. Hence, we aimed to identify and evaluate promising state-of-the-art deep learning methods for head and neck cancer (HNC) PET GTV delineation. Methods: We trained and evaluated deep learning methods using retrospectively included scans of HNC patients referred for radiotherapy between January 2014 and December 2019 (ISRCTN16907234). We used 3 test datasets: an internal set to compare methods, another internal set to compare AI-to-expert variability and expert interobserver variability (IOV), and an external set to compare internal and external AI-to-expert variability. Expert PET GTVs were used as the reference standard. Our benchmark IOV was measured using the PET GTV of 6 experts. The primary outcome was the Dice similarity coefficient (DSC). ANOVA was used to compare methods, a paired t test was used to compare AI-to-expert variability and expert IOV, an unpaired t test was used to compare internal and external AI-to-expert variability, and post hoc Bland-Altman analysis was used to evaluate biomarker agreement. Results: In total, 1,220 18F-FDG PET/CT scans of 1,190 patients (mean age ± SD, 63 ± 10 y; 858 men) were included, and 5 deep learning methods were trained using 5-fold cross-validation (n = 805). The nnU-Net method achieved the highest similarity (DSC, 0.80 [95% CI, 0.77-0.86]; n = 196). We found no evidence of a difference between expert IOV and AI-to-expert variability (DSC, 0.78 for AI vs. 0.82 for experts; mean difference of 0.04 [95% CI, -0.01 to 0.09]; P = 0.12; n = 64). We found no evidence of a difference between the internal and external AI-to-expert variability (DSC, 0.80 internally vs. 0.81 externally; mean difference of 0.004 [95% CI, -0.05 to 0.04]; P = 0.87; n = 125). PET GTV-derived biomarkers of AI were in good agreement with experts. Conclusion: Deep learning can be used to automate 18F-FDG PET/CT tumor-volume-derived imaging biomarkers, and the deep-learning-based volumes have the potential to assist clinical tumor volume delineation in radiation oncology.

Brain perfusion estimation by Tikhonov model-free deconvolution in a long axial field of view PET/CT scanner exploring five different PET tracers.

PURPOSE: New total-body PET scanners with a long axial field of view (LAFOV) allow for higher temporal resolution due to higher sensitivity, which facilitates perfusion estimation by model-free deconvolution. Fundamental tracer kinetic theory predicts that perfusion can be estimated for all tracers despite their different fates given sufficiently high temporal resolution of 1 s or better, bypassing the need for compartment modelling. The aim of this study was to investigate whether brain perfusion could be estimated using model-free Tikhonov generalized deconvolution for five different PET tracers, [15O]H2O, [11C]PIB, [18F]FE-PE2I, [18F]FDG and [18F]FET. To our knowledge, this is the first example of a general model-free approach to estimate cerebral blood flow (CBF) from PET data. METHODS: Twenty-five patients underwent dynamic LAFOV PET scanning (Siemens, Quadra). PET images were reconstructed with an isotropic voxel resolution of 1.65 mm3. Time framing was 40 × 1 s during bolus passage followed by increasing framing up to 60 min. AIF was obtained from the descending aorta. Both voxel- and region-based calculations of perfusion in the thalamus were performed using the Tikhonov method. The residue impulse response function was used to estimate the extraction fraction of tracer leakage across the blood-brain barrier. RESULTS: CBF ranged from 37 to 69 mL blood min-1 100 mL of tissue-1 in the thalamus. Voxelwise calculation of CBF resulted in CBF maps in the physiologically normal range. The extraction fractions of [15O]H2O, [18F]FE-PE2I, [11C]PIB, [18F]FDG and [18F]FET in the thalamus were 0.95, 0.78, 0.62, 0.19 and 0.03, respectively. CONCLUSION: The high temporal resolution and sensitivity associated with LAFOV PET scanners allow for noninvasive perfusion estimation of multiple tracers. The method provides an estimation of the residue impulse response function, from which the fate of the tracer can be studied, including the extraction fraction, influx constant, volume of distribution and transit time distribution, providing detailed physiological insight into normal and pathologic tissue.

An Investigation of Lesion Detection Accuracy for Artificial Intelligence-Based Denoising of Low-Dose 64Cu-DOTATATE PET Imaging in Patients with Neuroendocrine Neoplasms.

Frequent somatostatin receptor PET, for example, 64Cu-DOTATATE PET, is part of the diagnostic work-up of patients with neuroendocrine neoplasms (NENs), resulting in high accumulated radiation doses. Scan-related radiation exposure should be minimized in accordance with the as-low-as-reasonably achievable principle, for example, by reducing injected radiotracer activity. Previous investigations found that reducing 64Cu-DOTATATE activity to below 50 MBq results in inadequate image quality and lesion detection. We therefore investigated whether image quality and lesion detection of less than 50 MBq of 64Cu-DOTATATE PET could be restored using artificial intelligence (AI). Methods: We implemented a parameter-transferred Wasserstein generative adversarial network for patients with NENs on simulated low-dose 64Cu-DOTATATE PET images corresponding to 25% (PET25%), or about 48 MBq, of the injected activity of the reference full dose (PET100%), or about 191 MBq, to generate denoised PET images (PETAI). We included 38 patients in the training sets for network optimization. We analyzed PET intensity correlation, peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and mean-square error (MSE) of PETAI/PET100% versus PET25%/PET100% Two readers assessed Likert scale-defined image quality (1, very poor; 2, poor; 3, moderate; 4, good; 5, excellent) and identified lesion-suspicious foci on PETAI and PET100% in a subset of the patients with no more than 20 lesions per organ (n = 33) to allow comparison of all foci on a 1:1 basis. Detected foci were scored (C1, definite lesion; C0, lesion-suspicious focus) and matched with PET100% as the reference. True-positive (TP), false-positive (FP), and false-negative (FN) lesions were assessed. Results: For PETAI/PET100% versus PET25%/PET100%, PET intensity correlation had a goodness-of-fit value of 0.94 versus 0.81, PSNR was 58.1 versus 53.0, SSIM was 0.908 versus 0.899, and MSE was 2.6 versus 4.7. Likert scale-defined image quality was rated good or excellent in 33 of 33 and 32 of 33 patients on PET100% and PETAI, respectively. Total number of detected lesions was 118 on PET100% and 115 on PETAI Only 78 PETAI lesions were TP, 40 were FN, and 37 were FP, yielding detection sensitivity (TP/(TP+FN)) and a false discovery rate (FP/(TP+FP)) of 66% (78/118) and 32% (37/115), respectively. In 62% (23/37) of cases, the FP lesion was scored C1, suggesting a definite lesion. Conclusion: PETAI improved visual similarity with PET100% compared with PET25%, and PETAI and PET100% had similar Likert scale-defined image quality. However, lesion detection analysis performed by physicians showed high proportions of FP and FN lesions on PETAI, highlighting the need for clinical validation of AI algorithms.

Deep learning for Dixon MRI-based attenuation correction in PET/MRI of head and neck cancer patients.

BACKGROUND: Quantitative whole-body PET/MRI relies on accurate patient-specific MRI-based attenuation correction (AC) of PET, which is a non-trivial challenge, especially for the anatomically complex head and neck region. We used a deep learning model developed for dose planning in radiation oncology to derive MRI-based attenuation maps of head and neck cancer patients and evaluated its performance on PET AC. METHODS: Eleven head and neck cancer patients, referred for radiotherapy, underwent CT followed by PET/MRI with acquisition of Dixon MRI. Both scans were performed in radiotherapy position. PET AC was performed with three different patient-specific attenuation maps derived from: (1) Dixon MRI using a deep learning network (PETDeep). (2) Dixon MRI using the vendor-provided atlas-based method (PETAtlas). (3) CT, serving as reference (PETCT). We analyzed the effect of the MRI-based AC methods on PET quantification by assessing the average voxelwise error within the entire body, and the error as a function of distance to bone/air. The error in mean uptake within anatomical regions of interest and the tumor was also assessed. RESULTS: The average (± standard deviation) PET voxel error was 0.0 ± 11.4% for PETDeep and -1.3 ± 21.8% for PETAtlas. The error in mean PET uptake in bone/air was much lower for PETDeep (-4%/12%) than for PETAtlas (-15%/84%) and PETDeep also demonstrated a more rapidly decreasing error with distance to bone/air affecting only the immediate surroundings (less than 1 cm). The regions with the largest error in mean uptake were those containing bone (mandible) and air (larynx) for both methods, and the error in tumor mean uptake was -0.6 ± 2.0% for PETDeep and -3.5 ± 4.6% for PETAtlas. CONCLUSION: The deep learning network for deriving MRI-based attenuation maps of head and neck cancer patients demonstrated accurate AC and exceeded the performance of the vendor-provided atlas-based method both overall, on a lesion-level, and in vicinity of challenging regions such as bone and air.

Activity Dose Reduction in 64Cu-DOTATATE PET in Patients with Neuroendocrine Neoplasms: Impact on Image Quality and Lesion Detection Ability.

PURPOSE: Patients with neuroendocrine neoplasms (NEN) engage in lifelong follow-up with frequent somatostatin receptor PET, e.g. [64Cu]Cu-DOTATATE PET, and continued measures to reduce radiation exposures should be in pursued in accordance with the as-low-as-reasonably-achievable (ALARA) principle. We therefore aimed to determine the lowest achievable [64Cu]Cu-DOTATATE dose while maintaining image quality and lesion detection rate. PROCEDURES: We included scans from 38 patients with NEN referred to routine [64Cu]Cu-DOTATATE PET/CT. Using reconstruction of under-sampled PET list-mode data, we simulated [64Cu]Cu-DOTATATE activity dose-reduced PET equivalents with median [range] 142 MBq [127;157], 95 MBq [85;105], and 48 MBq [42;52], corresponding to 75% (PET75%), 50% (PET50%), and 25% (PET25%) of the full-dose 191 MBq [169;209] (PET100%). Three blinded readers independently assessed image quality (scores 1-5), lesion confidence (scores 0-2), and counted lesions grouped by organs and regions. Number of lesions, proportions of patients with diagnostic image quality (reader-median image quality ≥ 4), diagnostic lesion confidence (reader-median lesion confidence ≥ 1), and per-patient sensitivities and specificities for organ-specific disease on PET75-25% were compared with PET100%. RESULTS: The median [64Cu]Cu-DOTATATE activity dose could be reduced from 191 to 142 MBq without decline in diagnostic image quality (P = 0.62), diagnostic lesion confidence (P = 1.0), or number of lesions detected in major organs or regions (P = 0.19-0.71). Sensitivity and specificity for detection of liver disease were 100% (26/26 patients) and 100% (12/12), respectively, for both PET75% and PET50%. Overall sensitivity for detection of NEN was 100% (26/26) for both PET75% and PET50%, and overall specificities were 92% (11/12) and 100% (12/12) for PET75 and PET50, respectively. Following dose-blinded post hoc review, the PET75% specificity was adjusted to 100% (12/12). CONCLUSIONS: The [64Cu]Cu-DOTATATE activity dose can be reduced from 191 MBq to at least 142 MBq without losing image quality or lesion detection ability and further reduced to 95 MBq without loss of clinically relevant information.

Test-retest repeatability and interobserver variation of healthy tissue metabolism using 18F-FDG PET/CT of the thorax among lung cancer patients.

OBJECTIVES: The aim of this study was to assess the test-retest repeatability and interobserver variation in healthy tissue (HT) metabolism using 2-deoxy-2-[18F]fluoro-d-glucose (18F-FDG) PET/computed tomography (PET/CT) of the thorax in lung cancer patients. METHODS: A retrospective analysis was conducted in 22 patients with non-small cell lung cancer who had two PET/CT scans of the thorax performed 3 days apart with no interval treatment. The maximum, mean and peak standardized uptake values (SUVs) in different HTs were measured by a single observer for the test-retest analysis and two observers for interobserver variation. Bland-Altman plots were used to assess the repeatability and interobserver variation. Intrasubject variability was evaluated using within-subject coefficients of variation (wCV). RESULTS: The wCV of test-retest SUVmean measurements in mediastinal blood pool, bone marrow, skeletal muscles and lungs was less than 20%. The left ventricle (LV) showed higher wCV (>60%) in all SUV parameters with wide limits of repeatability. High interobserver agreement was found with wCV of less than 10% in SUVmean of all HT, but up to 22% was noted in the LV. CONCLUSION: HT metabolism is stable in a test-retest scenario and has high interobserver agreement. SUVmean was the most stable metric in organs with low FDG uptake and SUVpeak in HTs with moderate uptake. Test-retest measurements in LV were highly variable irrespective of the SUV parameters used for measurements.

Robustness and Generalizability of Deep Learning Synthetic Computed Tomography for Positron Emission Tomography/Magnetic Resonance Imaging-Based Radiation Therapy Planning of Patients With Head and Neck Cancer.

PURPOSE: Radiotherapy planning based only on positron emission tomography/magnetic resonance imaging (PET/MRI) lacks computed tomography (CT) information required for dose calculations. In this study, a previously developed deep learning model for creating synthetic CT (sCT) from MRI in patients with head and neck cancer was evaluated in 2 scenarios: (1) using an independent external dataset, and (2) using a local dataset after an update of the model related to scanner software-induced changes to the input MRI. METHODS AND MATERIALS: Six patients from an external site and 17 patients from a local cohort were analyzed separately. Each patient underwent a CT and a PET/MRI with a Dixon MRI sequence over either one (external) or 2 (local) bed positions. For the external cohort, a previously developed deep learning model for deriving sCT from Dixon MRI was directly applied. For the local cohort, we adapted the model for an upgraded MRI acquisition using transfer learning and evaluated it in a leave-one-out process. The sCT mean absolute error for each patient was assessed. Radiotherapy dose plans based on sCT and CT were compared by assessing relevant absorbed dose differences in target volumes and organs at risk. RESULTS: The MAEs were 78 ± 13 HU and 76 ± 12 HU for the external and local cohort, respectively. For the external cohort, absorbed dose differences in target volumes were within ± 2.3% and within ± 1% in 95% of the cases. Differences in organs at risk were <2%. Similar results were obtained for the local cohort. CONCLUSIONS: We have demonstrated a robust performance of a deep learning model for deriving sCT from MRI when applied to an independent external dataset. We updated the model to accommodate a larger axial field of view and software-induced changes to the input MRI. In both scenarios dose calculations based on sCT were similar to those of CT suggesting a robust and reliable method.

Deep-learning-based attenuation correction in dynamic [15O]H2O studies using PET/MRI in healthy volunteers.

Quantitative [15O]H2O positron emission tomography (PET) is the accepted reference method for regional cerebral blood flow (rCBF) quantification. To perform reliable quantitative [15O]H2O-PET studies in PET/MRI scanners, MRI-based attenuation-correction (MRAC) is required. Our aim was to compare two MRAC methods (RESOLUTE and DeepUTE) based on ultrashort echo-time with computed tomography-based reference standard AC (CTAC) in dynamic and static [15O]H2O-PET. We compared rCBF from quantitative perfusion maps and activity concentration distribution from static images between AC methods in 25 resting [15O]H2O-PET scans from 14 healthy men at whole-brain, regions of interest and voxel-wise levels. Average whole-brain CBF was 39.9 ± 6.0, 39.0 ± 5.8 and 40.0 ± 5.6 ml/100 g/min for CTAC, RESOLUTE and DeepUTE corrected studies respectively. RESOLUTE underestimated whole-brain CBF by 2.1 ± 1.50% and rCBF in all regions of interest (range -2.4%- -1%) compared to CTAC. DeepUTE showed significant rCBF overestimation only in the occipital lobe (0.6 ± 1.1%). Both MRAC methods showed excellent correlation on rCBF and activity concentration with CTAC, with slopes of linear regression lines between 0.97 and 1.01 and R2 over 0.99. In conclusion, RESOLUTE and DeepUTE provide AC information comparable to CTAC in dynamic [15O]H2O-PET but RESOLUTE is associated with a small but systematic underestimation.

Intratumor heterogeneity is biomarker specific and challenges the association with heterogeneity in multimodal functional imaging in head and neck squamous cell carcinoma.

RATIONALE: Tumor biopsy cannot detect heterogeneity and an association between heterogeneity in functional imaging and molecular biology will have an impact on both diagnostics and treatment possibilities. PURPOSE: Multiparametric imaging can provide 3D information on functional aspects of a tumor and may be suitable for predicting intratumor heterogeneity. Here, we investigate the correlation between intratumor heterogeneity assessed with multiparametric imaging and multiple-biopsy immunohistochemistry. METHODS: In this prospective study, patients with primary or recurrent head and neck squamous cell carcinoma (HNSCC) underwent PET/MRI scanning prior to surgery. Tumors were removed en bloc and six core biopsies were used for immunohistochemical (IHC) staining with a predefined list of biomarkers: p40, p53, EGFR, Ki-67, GLUT1, VEGF, Bcl-2, CAIX, PD-L1. Intratumor heterogeneity of each IHC biomarker was quantified by calculating the coefficient of variation (CV) in tumor proportion score among the six core biopsies within each tumor lesion. The heterogeneity in the imaging biomarkers was assessed by calculating CV in 18F-fluorodeoxyglucose (FDG)-uptake, diffusion and perfusion. Concordance of the two variance measures was quantified using Spearman's rank correlation RESULTS: Twenty-eight patients with a total of 33 lesions were included. There was considerable heterogeneity in most of the IHC biomarkers especially in GLUT1, PD-L1, Ki-67, CAIX and p53, however we only observed a correlation between the heterogeneity in GLUT1 and p53 and between Ki-67 and EGFR. Heterogeneity in FDG uptake and diffusion correlated with heterogeneity in cell density. CONCLUSION: Considerable heterogeneity of IHC biomarkers was found, however, only few and weak correlations between the studied IHC markers were observed. The studied functional imaging biomarkers showed weak associations with heterogeneity in some of the IHC biomarkers. Thus, biological heterogeneity is not a general tumor characteristic but depends on the specific biomarker or imaging modality.

Feasibility of Multiparametric Positron Emission Tomography/Magnetic Resonance Imaging as a One-Stop Shop for Radiation Therapy Planning for Patients with Head and Neck Cancer.

PURPOSE: Multiparametric positron emission tomography (PET)/magnetic resonance imaging (MRI) as a one-stop shop for radiation therapy (RT) planning has great potential but is technically challenging. We studied the feasibility of performing multiparametric PET/MRI of patients with head and neck cancer (HNC) in RT treatment position. As a step toward planning RT based solely on PET/MRI, a deep learning approach was employed to generate synthetic computed tomography (sCT) from MRI. This was subsequently evaluated for dose calculation and PET attenuation correction (AC). METHODS AND MATERIALS: Eleven patients, including 3 pilot patients referred for RT of HNC, underwent PET/MRI in treatment position after a routine fluorodeoxyglucose-PET/CT planning scan. The PET/MRI scan protocol included multiparametric imaging. A convolutional neural network was trained in a leave-one-out process to predict sCT from the Dixon MRI. The clinical CT-based dose plans were recalculated on sCT, and the plans were compared in terms of relative differences in mean, maximum, near-maximum, and near-minimum absorbed doses for different volumes of interest. Comparisons between PET with sCT-based AC and PET with CT-based AC were assessed based on the relative differences in mean and maximum standardized uptake values (SUVmean and SUVmax) from the PET-positive volumes. RESULTS: All 11 patients underwent PET/MRI in RT treatment position. Apart from the 3 pilots, full multiparametric imaging was completed in 45 minutes for 7 out of 8 patients. One patient terminated the examination after 30 minutes. With the exception of 1 patient with an inserted tracheostomy tube, all dosimetric parameters of the sCT-based dose plans were within ±1% of the CT-based dose plans. For PET, the mean difference was 0.4 ± 1.2% for SUVmean and -0.5 ± 1.0% for SUVmax. CONCLUSIONS: Performing multiparametric PET/MRI of patients with HNC in RT treatment position was clinically feasible. The sCT generation resulted in AC of PET and dose calculations sufficiently accurate for clinical use. These results are an important step toward using multiparametric PET/MRI as a one-stop shop for personalized RT planning.

Does multiparametric imaging with 18F-FDG-PET/MRI capture spatial variation in immunohistochemical cancer biomarkers in head and neck squamous cell carcinoma?

BACKGROUND: The purpose of this study is to test if functional multiparametric imaging with 18F-FDG-PET/MRI correlates spatially with immunohistochemical biomarker status within a lesion of head and neck squamous cell carcinoma (HNSCC), and also whether a biopsy with the highest FDG uptake was more likely to have the highest PD-L1 expression or the highest percentage of vital tumour cells (VTC) compared with a random biopsy. METHODS: Thirty-one patients with HNSCC were scanned on an integrated PET/MRI scanner with FDG prior to surgery in this prospective study. Imaging was quantified with SUV, ADC and Ktrans. A 3D-morphometric MRI scan of the specimen was used to co-register the patient and the specimen scans. All specimens were sectioned in consecutive slices, and slices from six different locations were selected randomly from each tumour. Core biopsies were performed to construct TMA blocks for IHC staining with the ten predefined biomarkers. The spatial correlation was assessed with a partial correlation analysis. RESULTS: Twenty-eight patients with a total of 33 lesions were eligible for further analysis. There were significant correlations between the three imaging biomarkers and some of the IHC biomarkers. Moreover, a biopsy taken from the most FDG-avid part of the tumour did not have a statistically significantly higher probability of higher PD-L1 expression or VTC, compared with a random biopsy. CONCLUSION: We found statistically significant correlations between functional imaging parameters and key molecular cancer markers.

Comparison of simultaneous arterial spin labeling MRI and 15O-H2O PET measurements of regional cerebral blood flow in rest and altered perfusion states.

Arterial spin labelling (ASL) is a non-invasive magnetic resonance imaging (MRI) technique that may provide fully quantitative regional cerebral blood flow (rCBF) images. However, before its application in clinical routine, ASL needs to be validated against the clinical gold standard, 15O-H2O positron emission tomography (PET). We aimed to compare the two techniques by performing simultaneous quantitative ASL-MRI and 15O-H2O-PET examinations in a hybrid PET/MRI scanner. Duplicate rCBF measurements were performed in healthy young subjects (n = 14) in rest, during hyperventilation, and after acetazolamide (post-ACZ), yielding 63 combined PET/MRI datasets in total. Average global CBF by ASL-MRI and 15O-H2O-PET was not significantly different in any state (40.0 ± 6.5 and 40.6 ± 4.1 mL/100 g/min, respectively in rest, 24.5 ± 5.1 and 23.4 ± 4.8 mL/100 g/min, respectively, during hyperventilation, and 59.1 ± 10.4 and 64.7 ± 10.0 mL/100 g/min, respectively, post-ACZ). Overall, strong correlation between the two methods was found across all states (slope = 1.01, R2 = 0.82), while the correlations within individual states and of reactivity measures were weaker, in particular in rest (R2 = 0.05, p = 0.03). Regional distribution was similar, although ASL yielded higher perfusion and absolute reactivity in highly vascularized areas. In conclusion, ASL-MRI and 15O-H2O-PET measurements of rCBF are highly correlated across different perfusion states, but with variable correlation within and between hemodynamic states, and systematic differences in regional distribution.

Preparing data for multiparametric PET/MR imaging: Influence of PET point spread function modelling and EPI distortion correction on the spatial correlation of [18F]FDG-PET and diffusion-weighted MRI in head and neck cancer.

PURPOSE: Multiparametric imaging holds great potential for characterization of disease heterogeneity. For integrated PET/MR imaging, the combination of 18F-flourodeoxyglucose (FDG) PET and diffusion weighted imaging (DWI) has been suggested for the assessment of tumor heterogeneity. However, PET image resolution is limited and DWI is prone to image distortions. The aim of this study was to assess the influence of PET point spread function (PSF) modelling and DWI distortion correction on the voxelwise correlation between FDG-PET and DWI. METHODS: Data were collected from 11 patients with head and neck cancer, each undergoing PET/MR imaging twice. PET reconstructions with and without PSF modelling and DWI with and without distortion correction were derived. Tumor SUV was compared between PET reconstructions by linear regression. Geometric distortions of DWI with and without distortion correction were quantified by voxelwise correlation coefficients to an undistorted anatomical reference. The influence of PSF modelling and DWI distortion correction on a multiparametric analysis was assessed as a change of the voxelwise correlation coefficient between FDG-PET and DWI measured in tumors. RESULTS: The inclusion of PSF modelling in the PET reconstruction affected tumor quantification by a 10-20% increase in SUV. Distortion correction reduced DWI geometric distortions significantly. The impact of PET PSF modelling on the spatial correlation with DWI was insignificant. However, distortion correction of DWI had a significant effect on the spatial correlation with PET. CONCLUSIONS: Proper preparation of the imaging modalities is important for a correct analysis and interpretation of multiparametric PET/MR imaging of head and neck cancer.

Feasibility of multi-parametric PET and MRI for prediction of tumour recurrence in patients with glioblastoma.

BACKGROUND: Recurrence in glioblastoma patients often occur close to the original tumour and indicates that the current treatment is inadequate for local tumour control. In this study, we explored the feasibility of using multi-modality imaging at the time of radiotherapy planning. Specifically, we aimed to identify parameters from pre-treatment PET and MRI with potential to predict tumour recurrence. MATERIALS AND METHODS: Sixteen patients were prospectively recruited and treated according to established guidelines. Multi-parametric imaging with 18F-FET PET/CT and 18F-FDG PET/MR including diffusion and dynamic contrast enhanced perfusion MRI were performed before radiotherapy. Correlations between imaging parameters were calculated. Imaging was related to the voxel-wise outcome at the time of tumour recurrence. Within the radiotherapy target, median differences of imaging parameters in recurring and non-recurring voxels were calculated for contrast-enhancing lesion (CEL), non-enhancing lesion (NEL), and normal appearing grey and white matter. Logistic regression models were created to predict the patient-specific probability of recurrence. The most important parameters were identified using standardized model coefficients. RESULTS: Significant median differences between recurring and non-recurring voxels were observed for FDG, FET, fractional anisotropy, mean diffusivity, mean transit time, extra-vascular, extra-cellular blood volume and permeability derived from scans prior to chemo-radiotherapy. Tissue-specific patterns of voxel-wise correlations were observed. The most pronounced correlations were observed for 18F-FDG- and 18F-FET-uptake in CEL and NEL. Voxel-wise modelling of recurrence probability resulted in area under the receiver operating characteristic curve of 0.77 from scans prior to therapy. Overall, FET proved to be the most important parameter for recurrence prediction. CONCLUSION: Multi-parametric imaging before radiotherapy is feasible and significant differences in imaging parameters between recurring and non-recurring voxels were observed. Combining parameters in a logistic regression model enabled patient-specific maps of recurrence probability, where 18F-FET proved to be most important. This strategy could enable risk-adapted radiotherapy planning.

Phase contrast mapping MRI measurements of global cerebral blood flow across different perfusion states - A direct comparison with 15O-H2O positron emission tomography using a hybrid PET/MR system.

Phase-contrast mapping (PCM) magnetic resonance imaging (MRI) provides easy-access non-invasive quantification of global cerebral blood flow (gCBF) but its accuracy in altered perfusion states is not established. We aimed to compare paired PCM MRI and 15O-H2O positron emission tomography (PET) measurements of gCBF in different perfusion states in a single scanning session. Duplicate combined gCBF PCM-MRI and 15O-H2O PET measurements were performed in the resting condition, during hyperventilation and after acetazolamide administration (post-ACZ) using a 3T hybrid PET/MR system. A total of 62 paired gCBF measurements were acquired in 14 healthy young male volunteers. Average gCBF in resting state measured by PCM-MRI and 15O-H2O PET were 58.5 ± 10.7 and 38.6 ± 5.7 mL/100 g/min, respectively, during hyperventilation 33 ± 8.6 and 24.7 ± 5.8 mL/100 g/min, respectively, and post-ACZ 89.6 ± 27.1 and 57.3 ± 9.6 mL/100 g/min, respectively. On average, gCBF measured by PCM-MRI was 49% higher compared to 15O-H2O PET. A strong correlation between the two methods across all states was observed (R2 = 0.72, p < 0.001). Bland-Altman analysis suggested a perfusion dependent relative bias resulting in higher relative difference at higher CBF values. In conclusion, measurements of gCBF by PCM-MRI in healthy volunteers show a strong correlation with 15O-H2O PET, but are associated with a large and non-linear perfusion-dependent difference.

Repeatability of FDG PET/CT metrics assessed in free breathing and deep inspiration breath hold in lung cancer patients.

We measured the repeatability of FDG PET/CT uptake metrics when acquiring scans in free breathing (FB) conditions compared with deep inspiration breath hold (DIBH) for locally advanced lung cancer. Twenty patients were enrolled in this prospective study. Two FDG PET/CT scans per patient were conducted few days apart and in two breathing conditions (FB and DIBH). This resulted in four scans per patient. Up to four FDG PET avid lesions per patient were contoured. The following FDG metrics were measured in all lesions and in all four scans: Standardized uptake value (SUV)peak, SUVmax, SUVmean, metabolic tumor volume (MTV) and total lesion glycolysis (TLG), based on an isocontur of 50% of SUVmax. FDG PET avid volumes were delineated by a nuclear medicine physician. The gross tumor volumes (GTV) were contoured on the corresponding CT scans. Nineteen patients were available for analysis. Test-retest standard deviations of FDG uptake metrics in FB and DIBH were: SUVpeak FB/DIBH: 16.2%/16.5%; SUVmax: 18.2%/22.1%; SUVmean: 18.3%/22.1%; TLG: 32.4%/40.5%. DIBH compared to FB resulted in higher values with mean differences in SUVmax of 12.6%, SUVpeak 4.4% and SUVmean 11.9%. MTV, TLG and GTV were all significantly smaller on day 1 in DIBH compared to FB. However, the differences between metrics under FB and DIBH were in all cases smaller than 1 SD of the day to day repeatability. FDG acquisition in DIBH does not have a clinically relevant impact on the uptake metrics and does not improve the test-retest repeatability of FDG uptake metrics in lung cancer patients.

Reproducibility of MR-Based Attenuation Maps in PET/MRI and the Impact on PET Quantification in Lung Cancer.

Quantitative PET/MRI is dependent on reliable and reproducible MR-based attenuation correction (MR-AC). In this study, we evaluated the quality of current vendor-provided thoracic MR-AC maps and further investigated the reproducibility of their impact on 18F-FDG PET quantification in patients with non-small cell lung cancer. Methods: Eleven patients with inoperable non-small cell lung cancer underwent 2-5 thoracic PET/MRI scan-rescan examinations within 22 d. 18F-FDG PET data were acquired along with 2 Dixon MR-AC maps for each examination. Two PET images (PETA and PETB) were reconstructed using identical PET emission data but with MR-AC from these intrasubject repeated attenuation maps. In total, 90 MR-AC maps were evaluated visually for quality and the occurrence of categorized artifacts by 2 PET/MRI-experienced physicians. Each tumor was outlined by a volume of interest (40% isocontour of maximum) on PETA, which was then projected onto the corresponding PETB SUVmean and SUVmax were assessed from the PET images. Within-examination coefficients of variation and Bland-Altman analyses were conducted for the assessment of SUV variations between PETA and PETBResults: Image artifacts were observed in 86% of the MR-AC maps, and 30% of the MR-AC maps were subjectively expected to affect the tumor SUV. SUVmean and SUVmax resulted in coefficients of variation of 5.6% and 6.6%, respectively, and scan-rescan SUV variations were within ±20% in 95% of the cases. Substantial SUV variations were seen mainly for scan-rescan examinations affected by respiratory motion. Conclusion: Artifacts occur frequently in standard thoracic MR-AC maps, affecting the reproducibility of PET/MRI. These, in combination with other well-known sources of error associated with PET/MRI examinations, lead to inconsistent SUV measurements in serial studies, which may affect the reliability of therapy response assessment. A thorough visual inspection of the thoracic MR-AC map and Dixon images from which it is derived remains crucial for the detection of MR-AC artifacts that may influence the reliability of SUV.

Automatic delineation of brain regions on MRI and PET images from the pig.

BACKGROUND: The increasing use of the pig as a research model in neuroimaging requires standardized processing tools. For example, extraction of regional dynamic time series from brain PET images requires parcellation procedures that benefit from being automated. COMPARISON WITH EXISTING METHODS: Manual inter-modality spatial normalization to a MRI atlas is operator-dependent, time-consuming, and can be inaccurate with lack of cortical radiotracer binding or skull uptake. NEW METHOD: A parcellated PET template that allows for automatic spatial normalization to PET images of any radiotracer. RESULTS: MRI and [11C]Cimbi-36 PET scans obtained in sixteen pigs made the basis for the atlas. The high resolution MRI scans allowed for creation of an accurately averaged MRI template. By aligning the within-subject PET scans to their MRI counterparts, an averaged PET template was created in the same space. We developed an automatic procedure for spatial normalization of the averaged PET template to new PET images and hereby facilitated transfer of the atlas regional parcellation. Evaluation of the automatic spatial normalization procedure found the median voxel displacement to be 0.22±0.08mm using the MRI template with individual MRI images and 0.92±0.26mm using the PET template with individual [11C]Cimbi-36 PET images. We tested the automatic procedure by assessing eleven PET radiotracers with different kinetics and spatial distributions by using perfusion-weighted images of early PET time frames. CONCLUSION: We here present an automatic procedure for accurate and reproducible spatial normalization and parcellation of pig PET images of any radiotracer with reasonable blood-brain barrier penetration.

A failure-type specific risk prediction tool for selection of head-and-neck cancer patients for experimental treatments.

OBJECTIVES: The objective of this work was to develop a tool for decision support, providing simultaneous predictions of the risk of loco-regional failure (LRF) and distant metastasis (DM) after definitive treatment for head-and-neck squamous cell carcinoma (HNSCC). MATERIALS AND METHODS: Retrospective data for 560HNSCC patients were used to generate a multi-endpoint model, combining three cause-specific Cox models (LRF, DM and death with no evidence of disease (death NED)). The model was used to generate risk profiles of patients eligible for/included in a de-intensification study (RTOG 1016) and a dose escalation study (CONTRAST), respectively, to illustrate model predictions versus classic inclusion/exclusion criteria for clinical trials. The model is published as an on-line interactive tool (https://katrin.shinyapps.io/HNSCCmodel/). RESULTS: The final model included pre-selected clinical variables (tumor subsite, T stage, N stage, smoking status, age and performance status) and one additional variable (tumor volume). The treatment failure discrimination ability of the developed model was superior of that of UICC staging, 8th edition (AUCLRF=72.7% vs 64.2%, p<0.001 and AUCDM=70.7% vs 58.8%, p<0.001). Using the model for trial inclusion simulation, it was found that 14% of patients eligible for the de-intensification study had>20% risk of tumor relapse. Conversely, 9 of the 15 dose escalation trial participants had LRF risks<20%. CONCLUSION: A multi-endpoint model was generated and published as an on-line interactive tool. Its potential in decision support was illustrated by generating risk profiles for patients eligible for/included in clinical trials for HNSCC.