Background: Positron Emission Tomography (PET) is routinely used for cancer staging and treatment follow up. Metabolic active tumor volume (MATV) as well as total MATV (TMATV - including primary tumor, lymph nodes and metastasis) and/or total lesion glycolysis (TLG) derived from PET images have been identified as prognostic factor or for the evaluation of treatment efficacy in cancer patients. To this end, a segmentation approach with high precision and repeatability is important. However, the implementation of a repeatable and accurate segmentation algorithm remains an ongoing challenge. Methods: In this study, we compare two semi-automatic artificial intelligence (AI) based segmentation methods with conventional semi-automatic segmentation approaches in terms of repeatability. One segmentation approach is based on a textural feature (TF) segmentation approach designed for accurate and repeatable segmentation of primary tumors and metastasis. Moreover, a Convolutional Neural Network (CNN) is trained. The algorithms are trained, validated and tested using a lung cancer PET dataset. The segmentation accuracy of both segmentation approaches is compared using the Jaccard Coefficient (JC). Additionally, the approaches are externally tested on a fully independent test-retest dataset. The repeatability of the methods is compared with those of two majority vote (MV2, MV3) approaches, 41%SUV MAX , and a SUV>4 segmentation (SUV4). Repeatability is assessed with test-retest coefficients (TRT%) and intraclass correlation coefficient (ICC). An ICC>0.9 was regarded as representing excellent repeatability. Results: The accuracy of the segmentations with the reference segmentation was good (JC median TF: 0.7, CNN: 0.73) Both segmentation approaches outperformed most other conventional segmentation methods in terms of test-retest coefficient (TRT% mean: TF: 13.0%, CNN: 13.9%, MV2: 14.1%, MV3: 28.1%, 41%SUV MAX : 28.1%, SUV4: 18.1% ) and ICC (TF: 0.98, MV2: 0.97, CNN: 0.99, MV3: 0.73, SUV4: 0.81, and 41%SUV MAX : 0.68). Conclusion: The semi-automatic AI based segmentation approaches used in this study provided better repeatability than conventional segmentation approaches. Moreover, both algorithms lead to accurate segmentations for both primary tumors as well as metastasis and are therefore good candidates for PET tumor segmentation.

In oncology, Positron Emission Tomography (PET) is frequently performed for cancer staging and treatment monitoring. Metabolic active tumor volume (MATV) as well as total MATV (TMATV - including primary tumor, lymph nodes and metastasis) derived from PET images have been identified as prognostic factor or for evaluating treatment efficacy in cancer patients. To this end a segmentation approach with high precision and repeatability is important. Moreover, to derive TMATV, a reliable segmentation of the primary tumor as well as all metastasis is essential. However, the implementation of a repeatable and accurate segmentation algorithm remains a challenge. In this work, we propose an artificial intelligence based segmentation method based on textural features (TF) extracted from the PET image. From a large number of textural features, the most important features for the segmentation task were selected. The selected features are used for training a random forest classifier to identify voxels as tumor or background. The algorithm is trained, validated and tested using a lung cancer PET/CT dataset and, additionally, applied on a fully independent test-retest dataset. The approach is especially designed for accurate and repeatable segmentation of primary tumors and metastasis in order to derive TMATV. The segmentation results are compared with conventional segmentation approaches in terms of accuracy and repeatability. In summary, the TF segmentation proposed in this study provided better repeatability and accuracy than conventional segmentation approaches. Moreover, segmentations were accurate for both primary tumors and metastasis and the proposed algorithm is therefore a good candidate for PET tumor segmentation.

Background PET-based tumor delineation is an error prone and labor intensive part of image analysis. Especially for patients with advanced disease showing bulky tumor FDG load, segmentations are challenging. Reducing the amount of user-interaction in the segmentation might help to facilitate segmentation tasks especially when labeling bulky and complex tumors. Therefore, this study reports on segmentation workflows/strategies that may reduce the inter-observer variability for large tumors with complex shapes with different levels of userinteraction. Methods Twenty PET images of bulky tumors were delineated independently by six observers using four strategies: (I) manual, (II) interactive threshold-based, (III) interactive threshold-based segmentation with the additional presentation of the PET-gradient image and (IV) the selection of the most reasonable result out of four established semi-automatic segmentation algorithms (Select-the-best approach). The segmentations were compared using Jaccard coefficients (JC) and percentage volume differences. To obtain a reference standard, a majority vote (MV) segmentation was calculated including all segmentations of experienced observers. Performed and MV segmentations were compared regarding positive predictive value (PPV), sensitivity (SE), and percentage volume differences. Results The results show that with decreasing user-interaction the inter-observer variability decreases. JC values and percentage volume differences of Select-the-best and a workflow including gradient information were significantly better than the measurements of the other segmentation strategies (p-value<0.01). Interactive threshold-based and manual segmentations also result in significant lower and more variable PPV/SE values when compared with the MV segmentation. Conclusions FDG PET segmentations of bulky tumors using strategies with lower user-interaction showed less inter-observer variability. None of the methods led to good results in all cases, but use of either the gradient or the Select-the-best workflow did outperform the other strategies tested and may be a good candidate for fast and reliable labeling of bulky and heterogeneous tumors.

OBJECTIVE:To correlate changes in the apparent diffusion coefficient (ADC) from diffusion-weighted (DW)-MRI and standardised uptake value (SUV) from fluorothymidine (18FLT)-PET/CT with histopathological estimates of response in patients with non-small cell lung cancer (NSCLC) treated with neoadjuvant chemotherapy and track longitudinal changes in these biomarkers in a multicentre, multivendor setting. METHODS:14 patients with operable NSCLC recruited to a prospective, multicentre imaging trial (EORTC-1217) were treated with platinum-based neoadjuvant chemotherapy. 13 patients had DW-MRI and FLT-PET/CT at baseline (10 had both), 12 were re-imaged at Day 14 (eight dual-modality) and nine after completing chemotherapy, immediately before surgery (six dual-modality). Surgical specimens (haematoxylin-eosin and Ki67 stained) estimated the percentage of residual viable tumour/necrosis and proliferation index. RESULTS:Despite the small numbers,significant findings were possible. ADCmedian increased (p < 0.001) and SUVmean decreased (p < 0.001) significantly between baseline and Day 14; changes between Day 14 and surgery were less marked. All responding tumours (>30% reduction in unidimensional measurement pre-surgery), showed an increase at Day 14 in ADC75th centile and reduction in total lesion proliferation (SUVmean x proliferative volume) greater than established measurement variability. Change in imaging biomarkers did not correlate with histological response (residual viable tumour, necrosis). CONCLUSION:Changes in ADC and FLT-SUV following neoadjuvant chemotherapy in NSCLC were measurable by Day 14 and preceded changes in unidimensional size but did not correlate with histopathological response. However, the magnitude of the changes and their utility in predicting (non-) response (tumour size/clinical outcome) remains to be established. ADVANCES IN KNOWLEDGE:During treatment, ADC increase precedes size reductions, but does not reflect histopathological necrosis.

PET is increasingly used for prostate cancer (PCa) diagnostics. Important PCa radiotracers include 68Ga-prostate-specific membrane antigen HBED-CC (68Ga-PSMA), 18F-DCFPyL, 18F-fluoromethylcholine (18F-FCH), and 18F-dihydrotestosterone (18F-FDHT). Knowledge on the variability of tracer uptake in healthy tissues is important for accurate PET interpretation, because malignancy is suspected only if the uptake of a lesion contrasts with its background. Therefore, the aim of this study was to quantify uptake variability of PCa tracers in healthy tissues and identify stable reference regions for PET interpretation. Methods: A total of 232 PCa PET/CT scans from multiple hospitals was analyzed, including 87 68Ga-PSMA scans, 50 18F-DCFPyL scans, 68 18F-FCH scans, and 27 18F-FDHT scans. Tracer uptake was assessed in the blood pool, lung, liver, bone marrow, and muscle using several SUVs (SUVmax, SUVmean, SUVpeak). Variability in uptake between patients was analyzed using the coefficient of variation (COV%). For all tracers, SUV reference ranges (95th percentiles) were calculated, which could be applicable as image-based quality control for future PET acquisitions. Results: For 68Ga-PSMA, the lowest uptake variability was observed in the blood pool (COV, 19.9%), which was significantly more stable than all other tissues (COV, 29.8%–35.2%; P = 0.001–0.024). For 18F-DCFPyL, the lowest variability was observed in the blood pool and liver (COV, 14.4% and 21.7%, respectively; P = 0.001–0.003). The least variable 18F-FCH uptake was observed in the liver, blood pool, and bone marrow (COV, 16.8%–24.2%; P = 0.001–0.012). For 18F-FDHT, low uptake variability was observed in all tissues, except the lung (COV, 14.6%–23.6%; P = 0.001–0.040). The different SUV types had limited effect on variability (COVs within 3 percentage points). Conclusion: In this multicenter analysis, healthy tissues with limited uptake variability were identified, which may serve as reference regions for PCa PET interpretation. These reference regions include the blood pool for 68Ga-PSMA and 18F-DCFPyL and the liver for 18F-FCH and 18F-FDHT. Healthy tissue SUV reference ranges are presented and applicable as image-based quality control.

141 Objectives: Whole body 18F-fluorodihydrotestosterone positron-emission tomography ([18F]FDHT PET) directly targets the androgen receptor and may serve as a prognostic biomarker in metastatic castration-resistant prostate cancer (mCRPC)[1]. Unfortunately, patients often have painful bone metastases, and a shorter scanning protocol could therefore reduce patient burden, and reduce movement artefacts and imaging costs. We assessed how reducing acquisition time (and hence counts) affects accuracy, repeatability, and lesion detectability of [18F]FDHT PET-CT. Methods: Whole body [18F]FDHT PET-CT scans were acquired on two consecutive days in 14 mCRPC patients harboring a total of 336 FDHT-avid lesions. Images were acquired at 45min post-injection of 200MBq [18F]FDHT on a Philips Gemini TF64 scanner at 3min/bedposition and reconstructed with BLOB-OS-TF. A dynamic PET acquisition covering the thorax region was made from 0-30min post-injection to acquire an image derived input function, with parent plasma sampling at 5, 10 and 30min post-injection for input function calibration. List-mode raw whole body PET data were splitted on an alternating count-wise basis, yielding two statistically independent scans with each 50% of counts. Per lesion we measured SUVpeak, SUVmax, SUVmean, and contrast-to-noise ratio (CNR) from original and splitted data. SUV was normalized to both dose per bodyweight and the parent plasma input curve area-under-the-curve (AUC-PP) separately; SUV normalized to AUC-PP is known to be more accurate compared to bodyweight for 18F-FDHT [2], but might induce larger variability. Variability between splitted scans (ie. intrascan variability) and test-retest variability (ie. interscan variability) were assessed with repeatability coefficients (RC). Results: SUVs of original and splitted data were highly correlated (R2=0.98-1.00). Intrascan variability was volume-dependent and RCs between splitted scans were lowest for SUVmean (9.9%), intermediate for SUVpeak (14.3%) and highest for SUVmax (19.6%). For bodyweight normalization, splitting data increased test-retest RCs non-significantly (p>0.05) from 26.6% to 30.2% for SUVpeak, from 30.8% to 35.9% for SUVmax, and from 25.8% to 28.2% for SUVmean, respectively. AUC-PP normalization resulted in similar non-significant (p>0.05) increases in test-retest variability after splitting data, where RCs increased from 27.7% to 31.3% for SUVpeak, from 31.7% to 36.8% for SUVmax, and from 27.6% to 29.7% for SUVmean. Splitting data reduced CNR from 3.7±0.9 to 3.5±0.9 (-5.2%;p<0.01). Conclusions: Reducing counts of [18F]FDHT PET introduced

PET is increasingly used for prostate cancer (PCa) diagnostics. Important PCa radiotracers include Ga-68-prostate-specific membrane antigen HBED-CC (Ga-68-PSMA), F-18-DCFPyL, F-18-fluoromethylcholine (F-18-FCH), and F-18-dihydrotestosterone (F-18-FDHT). Knowledge on the variability of tracer uptake in healthy tissues is important for accurate PET interpretation, because malignancy is suspected only if the uptake of a lesion contrasts with its background. Therefore, the aim of this study was to quantify uptake variability of PCa tracers in healthy tissues and identify stable reference regions for PET interpretation. Methods: A total of 232 PCa PET/CT scans from multiple hospitals was analyzed, including 87 Ga-68-PSMA scans, 50 F-18-DCFPyL scans, 68 F-18-FCH scans, and 27 F-18-FDHT scans. Tracer uptake was assessed in the blood pool, lung, liver, bone marrow, and muscle using several SUVs (SUVmax, SUVmean, SUVpeak). Variability in uptake between patients was analyzed using the coefficient of variation (COV%). For all tracers, SUV reference ranges (95th percentiles) were calculated, which could be applicable as image-based quality control for future PET acquisitions. Results: For Ga-68-PSMA, the lowest uptake variability was observed in the blood pool (COV, 19.9%), which was significantly more stable than all other tissues (COV, 29.8%-35.2%; P = 0.001-0.024). For F-18-DCFPyL, the lowest variability was observed in the blood pool and liver (COV, 14.4% and 21.7%, respectively; P = 0.001-0.003). The least variable F-18-FCH uptake was observed in the liver, blood pool, and bone marrow (COV, 16.8%-24.2%; P = 0.001-0.012). For F-18-FDHT, low uptake variability was observed in all tissues, except the lung (COV, 14.6%-23.6%; P = 0.001-0.040). The different SUV types had limited effect on variability (COVs within 3 percentage points). Conclusion: In this multicenter analysis, healthy tissues with limited uptake variability were identified, which may serve as reference regions for PCa PET interpretation. These reference regions include the blood pool for Ga-68-PSMA and F-18-DCFPyL and the liver for F-18-FCH and F-18-FDHT. Healthy tissue SUV reference ranges are presented and applicable as image-based quality control.

289 Objectives: We explored the possibility of using SUVpeak (defined as a 1mL sphere with the highest average SUV within a lesion/target) derived from 18F-FDG PET/CT images reconstructed with point-spread-function resolution modelling as a surrogate for SUVmax derived from images reconstructed with an EARL compliant protocol. SUVpeak was already suggested as being a better alternative than SUVmax when derived from PSF images1. This assessment was performed using phantom images and clinical data from stage III and IV non-small cell lung cancer (NSCLC) patients. Methods The National Electrical Manufacturers Association (NEMA) image quality phantom was scanned for 5 minutes with a 10:1 sphere-to-background contrast ratio. Additionally, ten stage III and IV NSCLC patients underwent two baseline 18F-FDG PET/CT scans per day at 60min and 90min post injection, at two separate time-points within one week2. All data were reconstructed following two protocols: one compliant with EANM guidelines (EARL multicentre standard) and another with point-spread-function (PSF) resolution modelling. The latter protocol allows for improved lesion detection, but does not provide EARL compliant quantitative results. Patients’ lesions were segmented using a semi-automatic isocontour at SUV=4.0 g/mL. NEMA spheres were manually delineated based on their diameter. The SUVpeak PSF-derived (SUVpeakPSF) was compared with SUVmax EARL-derived (SUVmaxEARL) with Pearson’s correlations, t-tests and Bland-Altman plots. Results For the phantom scans, the mean relative difference between SUVpeakPSF and SUVmaxEARL was -11.0% (SD=15.0%) and showed a correlation ρ=0.95. Considering only the three biggest spheres (volumes larger than 5 mL), the mean relative difference was 1.8% (SD=3.5%). The clinical dataset (total of 21 lesions analysed) presented similar results, with a perfect correlation between SUVpeakPSF and SUVmaxEARL (ρ=1.00) and a mean relative difference of -4.3% (SD=5.2%). This highlights that, overall, SUVpeakPSF values are slightly lower than SUVmaxEARL values. Only two lesions were identified as smaller than 5 mL using the EARL reconstruction, three with PSF. The relative difference between SUV implementations for those small lesions was -7.1% (SD=4.3%). Moreover, uptake time did not affect the differences seen between SUVpeakPSF and SUVmaxEARL, which were -4.2% (SD=5.0%) and -4.3% (SD=5.6%) for the 60 and 90 minutes post-injection data (p=0.89), respectively. Conclusion This pilot study suggests that SUVpeak derived from high resolution PSF reconstructed 18F-FDG PET/CT images might be used as a surrogate for SUVmaxEARL for lesions bigger than 5mL. Conclusions on smaller lesions cannot be substantiated due to limited sample size. Further research using data from different centres and on larger as well as different patient groups is required to fully evaluate the feasibility and clinical impact of using SUVpeakPSF as a surrogate for SUVmaxEARL. References 1. Mansor, S., Pfaehler, E., Heijtel, D., et al. (2017), Impact of PET/CT system, reconstruction protocol, data analysis method, and repositioning on PET/CT precision: An experimental evaluation using an oncology and brain phantom. Med. Phys., 44, 6413-6424. doi:10.1002/mp.12623 2. Kramer, G. M., Frings, V., Hoetjes, N., et al. (2016). Repeatability of quantitative whole-body 18F-FDG PET/CT uptake measures as function of uptake interval and lesion selection in non-small cell lung cancer patients. Journal of Nuclear Medicine, 57(9), 1343-1349. doi:10.2967/jnumed.115.170225.

Oligometastatic disease represents a clinical and anatomical manifestation between localised and polymetastatic disease. In prostate cancer, as with other cancers, recognition of oligometastatic disease enables focal, metastasis-directed therapies. These therapies potentially shorten or postpone the use of systemic treatment and can delay further metastatic progression, thus increasing overall survival. Metastasis-directed therapies require imaging methods that definitively recognise oligometastatic disease to validate their efficacy and reliably monitor response, particularly so that morbidity associated with inappropriately treating disease subsequently recognised as polymetastatic can be avoided. In this Review, we assess imaging methods used to identify metastatic prostate cancer at first diagnosis, at biochemical recurrence, or at the castration-resistant stage. Standard imaging methods recommended by guidelines have insufficient diagnostic accuracy for reliably diagnosing oligometastatic disease. Modern imaging methods that use PET-CT with tumour-specific radiotracers (choline or prostate-specific membrane antigen ligand), and increasingly whole-body MRI with diffusion-weighted imaging, allow earlier and more precise identification of metastases. The European Organisation for Research and Treatment of Cancer (EORTC) Imaging Group suggests clinical algorithms to integrate modern imaging methods into the care pathway at the various stages of prostate cancer to identify oligometastatic disease. The EORTC proposes clinical trials that use modern imaging methods to evaluate the benefits of metastasis-directed therapies.

643 Objectives: In this study we investigated the effects of uptake time, acquisition protocol, image reconstruction settings and delineation methods on total metabolic active tumor volume (MATV) and total tumor burden (TTB = MATV × SUVmean summed across all lesions) and their repeatability for 18F-FDG PET/CT studies in non-small cell lung cancer (NSCLC) patients. Methods: Ten stage III and IV NSCLC patients underwent 2 baseline 18F-FDG PET/CT studies on separate days. At each time point, scans were obtained at both 60 and 90 minutes post injection. All PET/CT data were reconstructed using EARL compliant (EARL) and resolution modeling with point spread function (PSF) protocols. The reconstructed images were assessed with four semi-automated VOI segmentation methods: isocontours at SUV = 2.5, SUV = 4.0, 41% of SUVmax and a contrast adapted 50% of SUVpeak method. In addition, a consensus VOI was derived on the agreement across the predefined VOIs based on the majority vote method, i.e., when a voxel was selected by 2 or more of those VOIs (MV2). The total MATV and TTB and their repeatability were then calculated for each reconstructed dataset using all of the 5 methods. The results were evaluated using Pearson’s correlations, box-plots and Bland-Altman analyses. The obtained delineation of all cases were visually inspected (by an experienced observer) and qualitatively ranked into four classes: success, under or overestimation of volume and failure. Results: The best success rate was obtained using MV2 at 60min tracer uptake with success on 90% cases, independent of reconstruction settings. For those scans, there was perfect correlation between EARL and PSF derived MATV (r² = 1.00, p = 0.92) at a mean MATV difference of -4.49% and repeatability coefficient (RC = 1.96 × SD) of 20.95%. The MATV test-retest (TRT) mean difference was 0.35% (RC = 24.07%) and -3.73% (RC = 20.39%) for EARL and PSF, respectively. Yet, best repeatability was obtained with 90min uptake scans (with a success on 88% of cases, independent of reconstruction settings), having a mean MATV TRT difference of 1.15% (RC = 6.78%) for EARL and 3.38% (RC = 15.57%) for PSF. The same trend was seen for TTB, with repeatability coefficients of 11.88% for EARL and 16.66% for PSF. Most importantly, MV2 was never the worst segmentation, independent of tracer uptake time and image reconstruction settings (total success of 89%), while 11% of the cases showed an overestimated MATV. The performance of MV2 was not affected by lesion size or location. Conclusion: The use of a consensus delineation approach (MV2) seems to be useful for accurate and reliable MATV and TTB assessments in 18F-FDG PET/CT studies in NSCLC patients and seems to be least affected by variations in tracer uptake time and image reconstruction settings. This reliability is especially important for multicenter and retrospective studies, since these PET/CT scans usually have been collected with different protocols and scanners from different vendors and generations. Further research is needed to explore the use and robustness of a consensus delineation approach for 18F-FDG PET/CT MATV and TTB assessments in other diseases.

18F-fluorodihydrotestosterone (18F-FDHT) is a radiolabeled analog of the androgen receptor's primary ligand that is currently being credentialed as a biomarker for prognosis, response, and pharmacodynamic effects of new therapeutics. As part of the biomarker qualification process, we prospectively assessed its reproducibility and repeatability in men with metastatic castration-resistant prostate cancer. Methods: We conducted a prospective multiinstitutional study of metastatic castration-resistant prostate cancer patients undergoing 2 (test/retest) 18F-FDHT PET/CT scans on 2 consecutive days. Two independent readers evaluated all examinations and recorded SUVs, androgen receptor-positive tumor volumes, and total lesion uptake for the most avid lesion detected in each of 32 predefined anatomic regions. The relative absolute difference and reproducibility coefficient (RC) of each metric were calculated between the test and retest scans. Linear regression analyses, intraclass correlation coefficients (ICCs), and Bland-Altman plots were used to evaluate repeatability of 18F-FDHT metrics. The coefficient of variation and ICC were used to assess interobserver reproducibility. Results: Twenty-seven patients with 140 18F-FDHT-avid regions were included. The best repeatability among 18F-FDHT uptake metrics was found for SUV metrics (SUVmax, SUVmean, and SUVpeak), with no significant differences in repeatability among them. Correlations between the test and retest scans were strong for all SUV metrics (R2 ≥ 0.92; ICC ≥ 0.97). The RCs of the SUV metrics ranged from 21.3% (SUVpeak) to 24.6% (SUVmax). The test and retest androgen receptor-positive tumor volumes and TLU, respectively, were highly correlated (R2 and ICC ≥ 0.97), although variability was significantly higher than that for SUV (RCs > 46.4%). The prostate-specific antigen levels, Gleason score, weight, and age did not affect repeatability, nor did total injected activity, uptake measurement time, or differences in uptake time between the 2 scans. Including the most avid lesion per patient, the 5 most avid lesions per patient, only lesions 4.2 mL or more, only lesions with an SUV of 4 g/mL or more, or normalizing of SUV to area under the parent plasma activity concentration-time curve did not significantly affect repeatability. All metrics showed high interobserver reproducibility (ICC > 0.98; coefficient of variation < 0.2%-10.8%). Conclusion: Uptake metrics derived from 18F-FDHT PET/CT show high repeatability and interobserver reproducibility.

PURPOSE:In this study we systematically investigated different Dynamic Contrast Enhancement (DCE)-MRI protocols in the spine, with the goal of finding an optimal protocol that provides data suitable for quantitative pharmacokinetic modelling (PKM).MATERIALS AND METHODS:In 13 patients referred for MRI of the spine, DCE-MRI of the spine was performed with 2D and 3D MRI protocols on a 3T Philips Ingenuity MR system. A standard bolus of contrast agent (Dotarem - 0.2ml/kg body weight) was injected intravenously at a speed of 3ml/s. Different techniques for acceleration and motion compensation were tested: parallel imaging, partial-Fourier imaging and flow compensation. The quality of the DCE MRI images was scored on the basis of SNR, motion artefacts due to flow and respiration, signal enhancement, quality of the T1 map and of the arterial input function, and quality of pharmacokinetic model fitting to the extended Tofts model.RESULTS:Sagittal 3D sequences are to be preferred for PKM of the spine. Acceleration techniques were unsuccessful due to increased flow or motion artefacts. Motion compensating gradients failed to improve the DCE scans due to the longer echo time and the T2* decay which becomes more dominant and leads to signal loss, especially in the aorta. The quality scoring revealed that the best method was a conventional 3D gradient-echo acquisition without any acceleration or motion compensation technique. The priority in the choice of sequence parameters should be given to reducing echo time and keeping the dynamic temporal resolution below 5s. Increasing the number of acquisition, when possible, helps towards reducing flow artefacts. In our setting we achieved this with a sagittal 3D slab with 5 slices with a thickness of 4.5mm and two acquisitions.CONCLUSION:The proposed DCE protocol, encompassing the spine and the descending aorta, produces a realistic arterial input function and dynamic data suitable for PKM.

BACKGROUND:Pharmacokinetic (PK) models can describe microvascular density and integrity. An essential component of PK models is the arterial input function (AIF) representing the time-dependent concentration of contrast agent (CA) in the blood plasma supplied to a tissue. PURPOSE/HYPOTHESIS:To evaluate a novel method for subject-specific AIF estimation that takes inflow effects into account. STUDY TYPE:Retrospective study. SUBJECTS:Thirteen clinical patients referred for spine-related complaints; 21 patients from a study into luminal Crohn's disease with known Crohn's Disease Endoscopic Index of Severity (CDEIS). FIELD STRENGTH/SEQUENCE:Dynamic fast spoiled gradient echo (FSPGR) at 3T. ASSESSMENT:A population-averaged AIF, AIFs derived from distally placed regions of interest (ROIs), and the new AIF method were applied. Tofts' PK model parameters (including vp and Ktrans ) obtained with the three AIFs were compared. In the Crohn's patients Ktrans was correlated to CDEIS. STATISTICAL TESTS:The median values of the PK model parameters from the three methods were compared using a Mann-Whitney U-test. The associated variances were statistically assessed by the Brown-Forsythe test. Spearman's rank correlation coefficient was computed to test the correlation of Ktrans to CDEIS. RESULTS:The median vp was significantly larger when using the distal ROI approach, compared to the two other methods (P < 0.05 for both comparisons, in both applications). Also, the variances in vp were significantly larger with the ROI approach (P < 0.05 for all comparisons). In the Crohn's disease study, the estimated Ktrans parameter correlated better with the CDEIS (r = 0.733, P < 0.001) when the proposed AIF was used, compared to AIFs from the distal ROI method (r = 0.429, P = 0.067) or the population-averaged AIF (r = 0.567, P = 0.011). DATA CONCLUSION:The proposed method yielded realistic PK model parameters and improved the correlation of the Ktrans parameter with CDEIS, compared to existing approaches. LEVEL OF EVIDENCE:3 Technical Efficacy Stage 1 J. Magn. Reson. Imaging 2018;47:1197-1204.

F-18-fluoroazomycin arabinoside (F-18-FAZA) is a PET tracer of tumor hypoxia. However, as hypoxia often is associated with decreased perfusion, the delivery of F-18-FAZA may be compromised, potentially disturbing the association between tissue hypoxia and F-18-FAZA uptake. The aim of this study was to gain insight into the relationship between tumor perfusion and F-18-FAZA uptake. Methods: Ten patients diagnosed with advanced non-small cell lung cancer underwent subsequent dynamic O-15-H2O and F-18-FAZA PET scans with arterial sampling. Parametric images of both O-15-H2O-derived perfusion (tumor blood flow [TBF]) and volume of distribution (V-T) of F-18-FAZA were generated. Next, multiparametric classification was performed using lesional and global thresholds. Voxels were classified as low or high TBF and F-18-FAZA V-T, respectively. Finally, by combining these initial classifications, voxels were allocated to 4 categories: lowTBF-lowV(T), lowTBF-highV(T), highTBF-lowV(T), and highTBF-highV(T). Results: A total of 13 malignant lesions were identified in the 10 patients. The TBF and F-18-FAZA V-T values (average +/- SD) across all lesions were 0.45 +/- 0.20 mL.cm(-3).min(-1) and 0.94 +/- 0.31 mL.cm(-3), respectively. The averages of all lesional median values for TBF and F-18-FAZA V-T were 0.37 +/- 0.15 mL.cm(-3).min(-1) and 0.85 +/- 0.18 mL.cm(-3), respectively. Multiparametric analysis showed that classified voxels were clustered rather than randomly distributed. Several intralesion areas were identified where F-18-FAZA V-T was inversely related to TBF. On the other hand, there were also distinct areas where TBF as well as F-18-FAZA V-T were decreased or increased. Conclusion: The present data indicate that spatial variation of F-18-FAZA uptake is not necessarily inversely related to TBF. This suggests that decreased TBF may result in flow-limited delivery of F-18-FAZA. Areas with both high F-18-FAZA uptake and high TBF values suggest that high F-18-FAZA uptake, possibly suggesting hypoxia, may occur despite high TBF values. In conclusion, multiparametric evaluation of the spatial distributions of both TBF and F-18-FAZA uptake may be helpful for understanding the F-18-FAZA signal.

18F-fluoroazomycin arabinoside (18F-FAZA) is a PET tracer of tumor hypoxia. However, as hypoxia often is associated with decreased perfusion, the delivery of 18F-FAZA may be compromised, potentially disturbing the association between tissue hypoxia and 18F-FAZA uptake. The aim of this study was to gain insight into the relationship between tumor perfusion and 18F-FAZA uptake. Methods: Ten patients diagnosed with advanced non–small cell lung cancer underwent subsequent dynamic 15O-H2O and 18F-FAZA PET scans with arterial sampling. Parametric images of both 15O-H2O–derived perfusion (tumor blood flow [TBF]) and volume of distribution (VT) of 18F-FAZA were generated. Next, multiparametric classification was performed using lesional and global thresholds. Voxels were classified as low or high TBF and 18F-FAZA VT, respectively. Finally, by combining these initial classifications, voxels were allocated to 4 categories: lowTBF–lowVT, lowTBF–highVT, highTBF–lowVT, and highTBF–highVT. Results: A total of 13 malignant lesions were identified in the 10 patients. The TBF and 18F-FAZA VT values (average ± SD) across all lesions were 0.45 ± 0.20 mL·cm−3·min−1 and 0.94 ± 0.31 mL·cm−3, respectively. The averages of all lesional median values for TBF and 18F-FAZA VT were 0.37 ± 0.15 mL·cm−3·min−1 and 0.85 ± 0.18 mL·cm−3, respectively. Multiparametric analysis showed that classified voxels were clustered rather than randomly distributed. Several intralesion areas were identified where 18F-FAZA VT was inversely related to TBF. On the other hand, there were also distinct areas where TBF as well as 18F-FAZA VT were decreased or increased. Conclusion: The present data indicate that spatial variation of 18F-FAZA uptake is not necessarily inversely related to TBF. This suggests that decreased TBF may result in flow-limited delivery of 18F-FAZA. Areas with both high 18F-FAZA uptake and high TBF values suggest that high 18F-FAZA uptake, possibly suggesting hypoxia, may occur despite high TBF values. In conclusion, multiparametric evaluation of the spatial distributions of both TBF and 18F-FAZA uptake may be helpful for understanding the 18F-FAZA signal.

18 F-fluoroazomycin arabinoside ( 18 F-FAZA) is a PET tracer of tumor hypoxia. However, as hypoxia often is associated with decreased perfusion, the delivery of 18 F-FAZA may be compromised, potentially disturbing the association between tissue hypoxia and 18 F-FAZA uptake. The aim of this study was to gain insight into the relationship between tumor perfusion and 18 F-FAZA uptake. Methods: Ten patients diagnosed with advanced non–small cell lung cancer underwent subsequent dynamic 15O-H2 Oa nd18F-FAZA PET scans with arterial sampling. Parametric images of both 15O-H2O–derived perfusion (tumor blood flow [TBF]) and volume of distribution (VT )o f18F-FAZA were generated. Next, multiparametric classification was performed using lesional and global thresholds. Voxels were classified as low or high TBF and 18F-FAZA VT, respectively. Finally, by combining these initial classifications, voxels were allocated to 4 categories: lowTBF–lowVT, lowTBF–highVT, highTBF–lowVT, and highTBF–highVT. Results: A total of 13 malignant lesions were identified in the 10 patients. The TBF and 18F-FAZA VT values (average ± SD) across all lesions were 0.45 ± 0.20 mLcm�3min�1 and 0.94 ± 0.31 mLcm�3, respectively. The averages of all lesional median values for TBF and 18 F-FAZA VT were 0.37 ± 0.15 mLcm �3 min �1 and 0.85 ± 0.18 mLcm �3 , respectively. Multiparametric analysis showed that classified voxels were clustered rather than randomly distributed. Several intralesion areas were identified where 18 F-FAZA VT was inversely related to TBF. On the other hand, there were also distinct areas where TBF as well as 18 F-FAZA VT were decreased or increased. Conclusion: The present data indicate that spatial variation of 18 F-FAZA uptake is not necessarily inversely related to TBF. This suggests that decreased TBF may result in flow-limited delivery of 18 F-FAZA. Areas with both high 18 F-FAZA uptake and high TBF values suggest that high 18 F-FAZA uptake, possibly suggesting hypoxia, may occur despite high TBF values. In conclusion, multiparametric evaluation of the spatial distributions of both TBF and 18 F-FAZA uptake may be helpful for under