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Background and purpose To investigate the role of multi-parametric magnetic resonance imaging (MP-MRI) as a biomarker for squamous cell carcinoma of the anal canal (SCCAC). Materials and methods From January 2013 to January 2017, 25 patients with non-metastatic SCCAC were enrolled in a multi-centre prospective clinical trial, of whom 20 completed protocol treatment. MP-MRIs, incorporating diffusion weighted magnetic resonance imaging (DW-MRI) and dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) sequences, were performed before (baseline), during the second and fourth weeks of chemo-radiotherapy (CRT), and 8 weeks following treatment completion. Histogram analysis of multi-parametric maps generated maximum, mean, median, minimum, skewness, kurtosis, and standard deviation metrics. Exact logistic regression and ROC AUC analyses were performed for each metric at every timepoint. An elastic net LASSO logistic regression was also performed using all measures at each timepoint. Results With a median follow up of 17.1 months, 3/20 patients had a local recurrence, and 5/20 had any recurrence. Several apparent diffusion coefficient (ADC) metrics extracted from DW-MRIs correlated with local recurrence and demonstrated excellent discrimination: baseline skewness (p = 0.04, ROC AUC 0.90) and standard deviation (SD) (p = 0.02, ROC AUC 0.90), week 2 skewness (p = 0.02, ROC AUC 0.91) and SD (p = 0.01, ROC AUC 0.94), week 4 kurtosis (p = 0.01, AUC 0.92) and SD (p = 0.01, ROC AUC 0.96). Changes in minimum ADC between baseline and week 2 (p = 0.02, ROC AUC 0.94) and baseline and week 4 (p = 0.02, ROC AUC 0.94) were prognostic for local recurrence. For prediction of any recurrence, ADC minimum (p = 0.02, ROC AUC 0.87) and SD (p = 0.01, ROC AUC 0.85) at baseline, and ADC maximum (p = 0.03, ROC AUC 0.77) and SD (p = 0.02, ROC AUC 0.81) at week 4 were significant. On LASSO logistic regression, ADC minimum and SD at baseline were retained for any recurrence. The only significant finding for DCE-MRI was a correlation of k-trans min at the second follow-up with local recurrence (p = 0.05, AUC 0.84). Conclusion Several ADC parameters at various time points correlate with recurrence suggesting DW-MRI is a potential biomarker for SCCAC.

Accurate, patient-specific measurement of arterial input functions (AIF) may improve model-based analysis of vascular permeability. This study investigated factors affecting AIF measurements from magnetic resonance imaging (MRI) magnitude (AIFMAGN) and phase (AIFPHA) signals, and compared them against computed tomography (CT) (AIFCT), under controlled conditions relevant to clinical protocols using a multimodality flow phantom. The flow phantom was applied at flip angles of 20° and 30°, flow rates (3–7.5 mL/s), and peak bolus concentrations (0.5–10 mM), for in-plane and through-plane flow. Spatial 3D-FLASH signal and variable flip angle T1 profiles were measured to investigate in-flow and radiofrequency-related biases, and magnitude- and phase-derived Gd-DTPA concentrations were compared. MRI AIF performance was tested against AIFCT via Pearson correlation analysis. AIFMAGN was sensitive to imaging orientation, spatial location, flip angle, and flow rate, and it grossly underestimated AIFCT peak concentrations. Conversion to Gd-DTPA concentration using T1 taken at the same orientation and flow rate as the dynamic contrast-enhanced acquisition improved AIFMAGN accuracy, yet, AIFMAGN metrics remained variable and significantly reduced from AIFCT at concentrations above 2.5 mM. AIFPHA performed equivalently within 1 mM to AIFCT across all tested conditions. AIFPHA, but not AIFMAGN, reported equivalent measurements to AIFCT across the range of tested conditions. AIFPHA showed superior robustness.

Previous literature has shown that 4D respiratory-gated positron emission tomography (PET) is beneficial for quantitative analysis and defining targets for boosting therapy. However the case for addition of a phase-matched 4D-computed tomography (CT) for attenuation correction (AC) is less clear. We seek to validate the use of 4D-CT for AC and investigate the impact of motion correction for low signal-to-background PET imaging of hypoxia using radiotracers such as FAZA and FMISO. A new insert for the Modus Medicals' QUASAR™ Programmable Respiratory Motion Phantom was developed in which a 3D-printed sphere was placed within the “lung” compartment while an additional compartment is added to simulate muscle/blood compartment required for hypoxia quantification. Experiments are performed at 4:1 or 2:1 signal-to-background ratio consistent with clinical FAZA and FMISO imaging. Motion blur was significant in terms of SUVmax, mean, and peak for motion ≥1 cm and could be significantly reduced (from 20% to 8% at 2-cm motion) for all 4D-PET-gated reconstructions. The effect of attenuation method on precision was significant (σ2 hCT-AC = 5.5%/4.7%/2.7% vs σ2 4D-CT-AC = 0.5%/0.6%/0.7% [max%/peak%/mean% variance]). The simulated hypoxic fraction also significantly decreased under conditions of 2-cm amplitude motion from 55% to 20% and was almost fully recovered (HF = 0.52 for phase-matched 4D-CT) using gated PET. 4D-gated PET is valuable under conditions of low radiotracer uptake found in hypoxia imaging. This work demonstrates the importance of using 4D-CT for AC when performing gated PET based on its significantly improved precision over helical CT.

Purpose To assess cervical tumor hypoxia using the hypoxia tracer 18F-fluoroazomycin arabinoside (18F-FAZA) and compare different reference tissues and thresholds for quantifying tumor hypoxia. Methods and Materials Twenty-seven patients with cervical cancer were studied prospectively by positron emission tomography (PET) imaging with 18F-FAZA before starting standard chemoradiation. The hypoxic volume was defined as all voxels within a tumor (T) with standardized uptake values (SUVs) greater than 3 standard deviations from the mean gluteus maximus muscle SUV value (M) or SUVs greater than 1 to 1.4 times the mean SUV value of the left ventricle, a blood (B) surrogate. The hypoxic fraction was defined as the ratio of the number of hypoxic voxels to the total number of tumor voxels. Results A 18F-FAZA-PET hypoxic volume could be identified in the majority of cervical tumors (89% when using T/M or T/B > 1.2 as threshold) on the 2-hour static scan. The hypoxic fraction ranged from 0% to 99% (median 31%) when defined using the T/M threshold and from 0% to 78% (median 32%) with the T/B > 1.2 threshold. Hypoxic volumes derived from the different thresholds were highly correlated (Spearman's correlation coefficient ρ between T/M and T/B > 1-1.4 were 0.82-0.91), as were hypoxic fractions (0.75-0.85). Compartmental analysis of the dynamic scans showed k3, the FAZA accumulation constant, to be strongly correlated with hypoxic fraction defined using the T/M (Spearman's ρ=0.72) and T/B > 1.2 thresholds (0.76). Conclusions Hypoxia was detected in the majority of cervical tumors on 18F-FAZA-PET imaging. The extent of hypoxia varied markedly between tumors but not significantly with different reference tissues/thresholds.

Quantitative kinetic parameters derived from dynamic contrast-enhanced (DCE) data are dependent on signal measurement quality and choice of pharmacokinetic model. However, the fundamental optimization analysis method is equally important and its impact on pharmacokinetic parameters has been mostly overlooked. We examine the effects of those choices on accuracy and performance of parameter estimation using both computer processing unit and graphical processing unit (GPU) numerical optimization implementations and evaluate the improvements offered by a novel optimization approach. A test framework was developed where experimentally derived population-average arterial input function and randomly sampled parameter sets {Ktrans, Kep, Vb, τ} were used to generate known tissue curves. Five numerical optimization algorithms were evaluated: sequential quadratic programming, downhill simplex (Nelder–Mead), pattern search, simulated annealing, and differential evolution. This was combined with various objective function implementation details: delay approximation, discretization and varying sampling rates. Then, impact of noise and CPU/GPU implementation was tested for speed and accuracy. Finally, the optimal method was compared to conventional implementation as applied to clinical DCE computed tomography. Nelder–Mead, differential evolution and sequential quadratic programming produced good results on clean and noisy input data outperforming simulated annealing and pattern search in terms of speed and accuracy in the respective order of 10−8%, 10−7%, and ×10−6%). A novel approach for DCE numerical optimization (infinite impulse response with fractional delay approximation) was implemented on GPU for speed increase of at least 2 orders of magnitude. Applied to clinical data, the magnitude of overall parameter error was &lt, 10%.

Dynamic contrast-enhanced (DCE)-MRI metrics are evaluated against volumetric DCE-CT quantitative parameters as a standard for tracer-kinetic validation using a common 4-dimensional temporal dynamic analysis platform in tumor perfusion measurements following stereotactic radiosurgery (SRS) for brain metastases. Patients treated with SRS as part of Research Ethics Board-approved clinical trials underwent volumetric DCE-CT and DCE-MRI at baseline, then at 7 and 21 days after SRS. Temporal dynamic analysis was used to create 3-dimensional pharmacokinetic parameter maps for both modalities. Individual vascular input functions were selected for DCE-CT and a population function was used for DCE-MRI. Semiquantitative and pharmacokinetic DCE parameters were assessed using a modified Tofts model within each tumor at every time point for both modalities for characterization of perfusion and capillary permeability, as well as their dependency on precontrast relaxation times (TRs), T10, and input function. Direct voxel-to-voxel Pearson analysis showed statistically significant correlations between CT and magnetic resonance which peaked at day 7 for Ktrans (R = 0.74, P ≤ .0001). The strongest correlation to DCE-CT measurements was found with DCE-MRI analysis using voxel-wise T10 maps (R = 0.575, P &lt, 001) instead of assigning a fixed T10 value. Comparison of histogram features showed statistically significant correlations between modalities over all tumors for median Ktrans (R = 0.42, P = .01), median area under the enhancement curve (iAUC90) (R = 0.55, P &lt, 01), and median iAUC90 skewness (R = 0.34, P = .03). Statistically significant, strong correlations were found for voxel-wise Ktrans, iAUC90, and ve values between DCE-CT and DCE-MRI. For DCE-MRI, the implementation of voxel-wise T10 maps plays a key role in ensuring the accuracy of heterogeneous pharmacokinetic maps.

6545 Background: Immunotherapy has become a standard of care in the treatment of R/M HNSCC, however only a subset of patients respond, highlighting the need for predictive and prognostic biomarkers. Radiomics is a non-invasive method to quantitatively analyze tumors through conventional imaging. Methods: The pre-treatment and first-on-treatment (after 8 weeks) computed tomography (CT) scans from 132 R/M HNSCC patients treated with single-agent Pembrolizumab (10mg/kg Q2W or 200mg Q3W IV) on the KEYNOTE-012 study were analyzed. Identified target lesions, per RECIST 1.1, were manually contoured, and radiomic features from the tumor and peritumoral region (3 mm expansion of the tumor) were extracted using PyRadiomics. All combinations of image filters and feature classes, not including shape descriptors of peritumoral region, were extracted. Feature space dimensionality was reduced by clustering features (hierarchical clustering using Pearson-based distance and complete linkage) and selecting the medoid of each cluster. Correlation with lesion-level response (LLR) at first-on-treatment CT and overall response (OR) was evaluated using concordance index (CI) with Benjamini-Hochberg multiple testing correction. Results: A total of 406 lesions were included (45 head & neck (HN), 207 lung, 57 liver, 86 lymph nodes (LN), 11 other). 3562 features were extracted from pre-treatment scans (2246 tumor, 1316 peritumor). Considering all lesion sites collectively, 27 of 110 feature clusters were significantly correlated with LLR (false discovery rate (FDR) < 0.05) but not with best overall RECIST response per patient on study. However, when grouped by organ, a number of feature cluster medoids were significantly associated (FDR < 0.05) with LLR (HN: 1, lung: 28, liver: 8, LN: 1) and OR (liver: 18). Feature clusters predictive of LLR and OR included descriptors of both tumor-specific and tumor/peritumoral gray-level intensity and texture (e.g. 74% tumor and 26% peritumoral features in clusters significantly associated with OR in liver). Conclusions: Tumor and peritumoral radiomic features at baseline correlate with LLR and OR to immunotherapy in R/M HNSCC. Despite significant heterogeneity in lesion site, both global and site-specific significant feature clusters could be identified.

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Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has been correlated with prognosis in head and neck squamous cell carcinoma as well as with changes in normal tissues. These studies implement different software, either commercial or in-house, and different scan protocols. Thus, the generalizability of the results is not confirmed. To assist in the standardization of quantitative metrics to confirm the generalizability of these previous studies, this data descriptor delineates in detail the DCE-MRI digital imaging and communications in medicine (DICOM) files with DICOM radiation therapy (RT) structure sets and digital reference objects (DROs), as well as, relevant clinical data that encompass a data set that can be used by all software for comparing quantitative metrics. Variable flip angle (VFA) with six flip angles and DCE-MRI scans with a temporal resolution of 5.5 s were acquired in the axial direction on a 3T MR scanner with a field of view of 25.6 cm, slice thickness of 4 mm, and 256×256 matrix size.

Radiomics is to provide quantitative descriptors of normal and abnormal tissues during classification and prediction tasks in radiology and oncology. Quantitative Imaging Network members are developing radiomic “feature” sets to characterize tumors, in general, the size, shape, texture, intensity, margin, and other aspects of the imaging features of nodules and lesions. Efforts are ongoing for developing an ontology to describe radiomic features for lung nodules, with the main classes consisting of size, local and global shape descriptors, margin, intensity, and texture-based features, which are based on wavelets, Laplacian of Gaussians, Law's features, gray-level co-occurrence matrices, and run-length features. The purpose of this study is to investigate the sensitivity of quantitative descriptors of pulmonary nodules to segmentations and to illustrate comparisons across different feature types and features computed by different implementations of feature extraction algorithms. We calculated the concordance correlation coefficients of the features as a measure of their stability with the underlying segmentation, 68% of the 830 features in this study had a concordance CC of ≥0.75. Pairwise correlation coefficients between pairs of features were used to uncover associations between features, particularly as measured by different participants. A graphical model approach was used to enumerate the number of uncorrelated feature groups at given thresholds of correlation. At a threshold of 0.75 and 0.95, there were 75 and 246 subgroups, respectively, providing a measure for the features' redundancy.

To assess cervical tumor hypoxia using the hypoxia tracerTwenty-seven patients with cervical cancer were studied prospectively by positron emission tomography (PET) imaging withAHypoxia was detected in the majority of cervical tumors on

Development of perfusion imaging as a biomarker requires more robust methodologies for quantification of tumor physiology that allow assessment of volumetric tumor heterogeneity over time. This study proposes a parametric method for automatically analyzing perfused tissue from volumetric dynamic contrast-enhanced (DCE) computed tomography (CT) scans and assesses whether this 4-dimensional (4D) DCE approach is more robust and accurate than conventional, region-of-interest (ROI)-based CT methods in quantifying tumor perfusion with preliminary evaluation in metastatic brain cancer.Functional parameter reproducibility and analysis of sensitivity to imaging resolution and arterial input function were evaluated in image sets acquired from a 320-slice CT with a controlled flow phantom and patients with brain metastases, whose treatments were planned for stereotactic radiation surgery and who consented to a research ethics board-approved prospective imaging biomarker study. A voxel-based temporal dynamic analysis (TDA) methodology was used at baseline, at day 7, and at day 20 after treatment. The ability to detect changes in kinetic parameter maps in clinical data sets was investigated for both 4D TDA and conventional 2D ROI-based analysis methods.A total of 7 brain metastases in 3 patients were evaluated over the 3 time points. The 4D TDA method showed improved spatial efficacy and accuracy of perfusion parameters compared to ROI-based DCE analysis (P.005), with a reproducibility error of less than 2% when tested with DCE phantom data. Clinically, changes in transfer constant from the blood plasma into the extracellular extravascular space (Ktrans) were seen when using TDA, with substantially smaller errors than the 2D method on both day 7 post radiation surgery (±13%; P.05) and by day 20 (±12%; P.04). Standard methods showed a decrease in Ktrans but with large uncertainty (111.6 ± 150.5) %.Parametric voxel-based analysis of 4D DCE CT data resulted in greater accuracy and reliability in measuring changes in perfusion CT-based kinetic metrics, which have the potential to be used as biomarkers in patients with metastatic brain cancer.

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) provides quantitative metrics (e.g. Ktrans, ve) via pharmacokinetic models. We tested inter-algorithm variability in these quantitative metrics with 11 published DCE-MRI algorithms, all implementing Tofts-Kermode or extended Tofts pharmacokinetic models. Digital reference objects (DROs) with known Ktrans and ve values were used to assess performance at varying noise levels. Additionally, DCE-MRI data from 15 head and neck squamous cell carcinoma patients over 3 time-points during chemoradiotherapy were used to ascertain Ktrans and ve kinetic trends across algorithms. Algorithms performed well (less than 3% average error) when no noise was present in the DRO. With noise, 87% of Ktrans and 84% of ve algorithm-DRO combinations were generally in the correct order. Low Krippendorff’s alpha values showed that algorithms could not consistently classify patients as above or below the median for a given algorithm at each time point or for differences in values between time points. A majority of the algorithms produced a significant Spearman correlation in ve of the primary gross tumor volume with time. Algorithmic differences in Ktrans and ve values over time indicate limitations in combining/comparing data from distinct DCE-MRI model implementations. Careful cross-algorithm quality-assurance must be utilized as DCE-MRI results may not be interpretable using differing software.

OBJECTIVE: Early changes in tumour behaviour following stereotactic radiosurgery) are potential biomarkers of response. To-date quantitative model-based measures of dynamic contrast-enhanced (DCE) and diffusion-weighted (DW) MRI parameters have shown widely variable findings, which may be attributable to variability in image acquisition, post-processing and analysis. Big data analytic approaches are needed for the automation of computationally intensive modelling calculations for every voxel, independent of observer interpretation. METHODS: This unified platform is a voxel-based, multimodality architecture that brings complimentary solute transport processes such as perfusion and diffusion into a common framework. The methodology was tested on synthetic data and digital reference objects and consequently evaluated in patients who underwent volumetric DCE-CT, DCE-MRI and DWI-MRI scans before and after treatment. Three-dimensional pharmacokinetic parameter maps from both modalities were compared as well as the correlation between apparent diffusion coefficient (ADC) values and the extravascular, extracellular volume (V(e)). Comparison of histogram parameters was done via Bland–Altman analysis, as well as Student’s t-test and Pearson’s correlation using two-sided analysis. RESULTS: System testing on synthetic Tofts model data and digital reference objects recovered the ground truth parameters with mean relative percent error of 1.07 × 10(−7) and 5.60 × 10(−4) respectively. Direct voxel-to-voxel Pearson’s analysis showed statistically significant correlations between CT and MR which peaked at Day 7 for K(trans) (R = 0.74, p

Simple and robust techniques are lacking to assess performance of flow quantification using dynamic imaging. We therefore developed a method to qualify flow quantification technologies using a physical compartment exchange phantom and image analysis tool. We validate and demonstrate utility of this method using dynamic PET and SPECT. Dynamic image sequences were acquired on two PET/CT and a cardiac dedicated SPECT (with and without attenuation and scatter corrections) systems. A two-compartment exchange model was fit to image derived time-activity curves to quantify flow rates. Flowmeter measured flow rates (20-300 mL/min) were set prior to imaging and were used as reference truth to which image derived flow rates were compared. Both PET cameras had excellent agreement with truth ( [Formula: see text]). High-end PET had no significant bias (p0.05) while lower-end PET had minimal slope bias (wash-in and wash-out slopes were 1.02 and 1.01) but no significant reduction in precision relative to high-end PET (15% vs.14% limits of agreement, p0.3). SPECT (without scatter and attenuation corrections) slope biases were noted (0.85 and 1.32) and attributed to camera saturation in early time frames. Analysis of wash-out rates from non-saturated, late time frames resulted in excellent agreement with truth ( [Formula: see text], slope = 0.97). Attenuation and scatter corrections did not significantly impact SPECT performance. The proposed phantom, software and quality assurance paradigm can be used to qualify imaging instrumentation and protocols for quantification of kinetic rate parameters using dynamic imaging.

Background and purpose We examined the utility of dynamic contrast-enhanced MRI (DCE-MRI), diffusion-weighted MRI (DWI), and FDG-PET imaging for brachytherapy target delineation in patients with locally advanced cervical cancer. Materials and methods Twenty-two patients had DWI, DCE-MRI, and FDG-PET/CT scans after brachytherapy applicator insertion, in addition to standard T2-weighted (T2w) 3T MRI. Gross tumor volume (GTV B ) and high-risk clinical target volume (HRCTV) were contoured first on T2w images, and then modified if indicated upon review of DWI/DCE-MRI/FDG-PET images by two observers. The primary endpoint was utility, determined by the number of patients whose volumes were modified, and interobserver variability. Results Eleven patients' T2w-GTV B were modified based on DWI/DCE-MRI/FDG-PET by observer 1, due to clearer demarcation (7) and residual disease not well visualized on T2w MRI (4). GTV B was modified in 17 patients by observer 2 (11 and 6, respectively). Incorporation of functional imaging improved the conformity index (CI) for GTV B from 0.54 (T2w alone) to 0.65 ( P =0.003). HRCTV was modified in 3 and 8 patients by observers 1 and 2, respectively, with a trend toward higher CI using functional imaging (0.71 to 0.76, P =0.06). Conclusions DWI/DCE-MRI/FDG-PET imaging as a supplement to T2w MRI decreased interobserver variability in GTV B delineation.

Purpose: Conventional (i.e., respiratory-correlated) 4DCT exploits the repetitive nature of breathing to provide an estimate of motion; however, it has limitations due to binning artifacts and irregular breathing in actual patient breathing patterns. The aim of this work was to evaluate the accuracy and image quality of a dynamic volume, CT approach (4D{sub vol}) using a 320-slice CT scanner to minimize these limitations, wherein entire image volumes are acquired dynamically without couch movement. This will be compared to the conventional respiratory-correlated 4DCT approach (RCCT). Methods: 4D{sub vol} CT was performed and characterized on an in-house, programmable respiratory motion phantom containing multiple geometric and morphological ''tumor'' objects over a range of regular and irregular patient breathing traces obtained from 3D fluoroscopy and compared to RCCT. The accuracy of volumetric capture and breathing displacement were evaluated and compared with the ground truth values and with the results reported using RCCT. A motion model was investigated to validate the number of motion samples needed to obtain accurate motion probability density functions (PDF). The impact of 4D image quality on this accuracy was then investigated. Dose measurements using volumetric and conventional scan techniques were also performed and compared. Results: Both conventional and dynamic volumemore » 4DCT methods were capable of estimating the programmed displacement of sinusoidal motion, but patient breathing is known to not be regular, and obvious differences were seen for realistic, irregular motion. The mean RCCT amplitude error averaged at 4 mm (max. 7.8 mm) whereas the 4D{sub vol} CT error stayed below 0.5 mm. Similarly, the average absolute volume error was lower with 4D{sub vol} CT. Under irregular breathing, the 4D{sub vol} CT method provides a close description of the motion PDF (cross-correlation 0.99) and is able to track each object, whereas the RCCT method results in a significantly different PDF from the ground truth, especially for smaller tumors (cross-correlation ranging between 0.04 and 0.69). For the protocols studied, the dose measurements were higher in the 4D{sub vol} CT method (40%), but it was shown that significant mAs reductions can be achieved by a factor of 4-5 while maintaining image quality and accuracy. Conclusions: 4D{sub vol} CT using a scanner with a large cone-angle is a promising alternative for improving the accuracy with which respiration-induced motion can be characterized, particularly for patients with irregular breathing motion. This approach also generates 4DCT image data with a reduced total scan time compared to a RCCT scan, without the need for image binning or external respiration signals within the 16 cm scan length. Scan dose can be made comparable to RCCT by optimization of the scan parameters. In addition, it provides the possibility of measuring breathing motion for more than one breathing cycle to assess stability and obtain a more accurate motion PDF, which is currently not feasible with the conventional RCCT approach.« less

Purpose: Dynamic contrast enhanced CT (DCE-CT) studies with modeling of blood flow and tissue perfusion are becoming more prevalent in the clinic, with advances in wide volume CT scanners allowing the imaging of an entire organ with sub-second image frequency and sub-millimeter accuracy. Wide-spread implementation of perfusion DCE-CT, however, is pending fundamental validation of the quantitative parameters that result from dynamic contrast imaging and perfusion modeling. Therefore, the goal of this work was to design and construct a novel dynamic flow imaging phantom capable of producing typical clinical time-attenuation curves (TACs) with the purpose of developing a framework for the quantification and validation of DCE-CT measurements and kinetic modeling under realistic flow conditions. Methods: The phantom is based on a simple two-compartment model and was printed using a 3D printer. Initial analysis of the phantom involved simple flow measurements and progressed to DCE-CT experiments in order to test the phantoms range and reproducibility. The phantom was then utilized to generate realistic input TACs. A phantom prediction model was developed to compute the input and output TACs based on a given set of five experimental (control) parameters: pump flow rate, injection pump flow rate, injection contrast concentration, and both control valvemore » positions. The prediction model is then inversely applied to determine the control parameters necessary to generate a set of desired input and output TACs. A protocol was developed and performed using the phantom to investigate image noise, partial volume effects and CT number accuracy under realistic flow conditionsResults: This phantom and its surrounding flow system are capable of creating a wide range of physiologically relevant TACs, which are reproducible with minimal error between experiments ({sigma}/{mu} < 5% for all metrics investigated). The dynamic flow phantom was capable of producing input and output TACs using either step function based or typical clinical arterial input function (AIF) inputs. The measured TACs were in excellent agreement with predictions across all comparison metrics with goodness of fit (R{sup 2}) for the input function between 0.95 and 0.98, while the maximum enhancement differed by no more than 3.3%. The predicted output functions were similarly accurate producing R{sup 2} values between 0.92 and 0.99 and maximum enhancement to within 9.0%. The effect of ROI size on the arterial input function (AIF) was investigated in order to determine an operating range of ROI sizes which were minimally affected by noise for small dimensions and partial volume effects for large dimensions. It was possible to establish the measurement sensitivity of both the Toshiba (ROI radius range from 1.5 to 3.2 mm ''low dose'', 1.4 to 3.0 mm ''high dose'') and GE scanner (1.5 to 2.6 mm ''low dose'', 1.1 to 3.4 mm ''high dose''). This application of the phantom also provides the ability to evaluate the effect of the AIF error on kinetic model parameter predictions. Conclusions: The dynamic flow imaging phantom is capable of producing accurate and reproducible results which can be predicted and quantified. This results in a unique tool for perfusion DCE-CT validation under realistic flow conditions which can be applied not only to compare different CT scanners and imaging protocols but also to provide a ground truth across multimodality dynamic imaging given its MRI and PET compatibility.« less

The use of cell-penetrating peptides as transduction vectors is a promising approach to deliver peptides and proteins into cells. However, the uptake and bioavailability of trans-activating transcriptor (TAT)-conjugated molecules vary depending on the cell type and the cargo. This study aimed to determine whether a low-voltage electrical pulse can enhance the TAT-mediated delivery of peptide cargoes in different cell types. In TF-1 and mouse embryonic stem cells, the uptake of a novel detachable TAT-conjugated glycogen synthase kinase-3 (GSK-3) peptide inhibitor was enhanced by an order of magnitude without affecting the cell viability. A similar increase in uptake was achieved in primary mouse bone marrow cells while maintaining >80% of their viability. Interestingly, under these low-voltage conditions, the uptake of a control peptide not conjugated to TAT was not significantly increased. A T-cell factor/lymphoid enhancer factor (TCF/LEF) luciferase reporter assay was also used to assess the bioactivity of the TAT construct. The results indicated that cells loaded with a low-voltage electrical pulse had a twofold increase in TCF/LEF activity, which was equivalent to a level of GSK-3 inhibition similar to that of cells treated with 20 mmol/l lithium or 500 nmol/l (2′Z,3′E)-6-bromoindirubin-3′-oxime. These results demonstrate the usefulness of low-voltage electrical pulses to enhance the uptake and bioactivity of TAT-conjugated molecules in different cell types.

Purpose. Analysis metrics such as tumor-to-background (T/B) ratios that rely indirectly on local noise properties of a PET image are known to be highly variable depending on image acquisition and image reconstruction parameters. Less well understood are the effects on local image noise arising from correction algorithms that compensate for photon attenuation and detector sensitivity (attenuation and sensitivity corrections). This work investigates these geometry-dependent local noise characteristics in positron emission tomography (PET) images and their implications for the analysis metrics. Methods. Two different phantoms containing homogeneous radioactivity concentrations were scanned for various initial activities and scan times and then reconstructed with a range of reconstruction parameters. Local noise in the form of a local standard deviation was computed and its spatial variation quantified across the PET imaging volume. Results. The local noise variation across the PET image arising from attenuation and sensitivity corrections can be understood with relatively simple analytical models. For fixed reconstruction parameters, the local standard deviation varied up to 60% in the axial direction and up to 30% in trans-axial (radial) direction. Varying the initial activity concentration and the scan time confirmed that the local noise obeys almost perfect Poisson statistics. As a consequence, depending on the spatial location of the volumes of interest, the T/B metrics can vary more than 30%. In addition, the local standard deviation showed substantial variation up to an order of magnitude (factor 10) for different reconstruction parameters. Conclusion. Quantifying the spatial variation of the geometry-dependent local noise across the imaging volume of a particular PET scanner allows us to develop quantitative correction algorithms for standardizing image analysis using noise-dependent metrics.

BackgroundThe rabbit VX2 lung cancer model is a large animal model useful for preclinical lung cancer imaging and interventional studies. However, previously reported models had issues in terms of invasiveness of tumor inoculation, control of tumor aggressiveness and incidence of complications.PurposeWe aimed to develop a minimally invasive rabbit VX2 lung cancer model suitable for imaging and transbronchial interventional studies.MethodsNew Zealand white rabbits and VX2 tumors were used in the study. An ultra-thin bronchoscope was inserted through a miniature laryngeal mask airway into the bronchus. Different numbers of VX2 tumor cells were selectively inoculated into the lung parenchyma or subcarinal mediastinum to create a uniform tumor with low incidence of complications. The model was characterized by CT, FDG-PET, and endobronchial ultrasound (EBUS). Liposomal dual-modality contrast agent was used to evaluate liposome drug delivery system in this model.ResultsBoth peripheral and mediastinal lung tumor models were created. The tumor making success rate was 75.8% (25/33) in the peripheral lung tumor model and 60% (3/5) in the mediastinal tumor model. The group of 1.0×10(6) of VX2 tumor cells inoculation showed a linear growth curve with less incidence of complications. Radial probe EBUS visualized the internal structure of the tumor and the size measurement correlated well with CT measurements (r(2) = 0.98). Over 7 days of continuous enhancement of the lung tumor by liposomal contrast in the lung tumor was confirmed both CT and fluorescence imaging.ConclusionOur minimally invasive bronchoscopic rabbit VX2 lung cancer model is an ideal platform for lung cancer imaging and preclinical bronchoscopic interventional studies.

PURPOSE The values in a PET image which represent activity concentrations of a radioactive tracer are influenced by a large number of parameters including patient conditions as well as image acquisition and reconstruction. This work investigates noise characteristics in PET images for various image acquisition and image reconstruction parameters. METHODS Different phantoms with homogeneous activity distributions were scanned using several acquisition parameters and reconstructed with numerous sets of reconstruction parameters. Images from six PET scanners from different vendors were analyzed and compared with respect to quantitative noise characteristics. Local noise metrics, which give rise to a threshold value defining the metric of hypoxic fraction, as well as global noise measures in terms of noise power spectra (NPS) were computed. In addition to variability due to different reconstruction parameters, spatial variability of activity distribution and its noise metrics were investigated. Patient data from clinical trials were mapped onto phantom scans to explore the impact of the scanner's intrinsic noise variability on quantitative clinical analysis. RESULTS Local noise metrics showed substantial variability up to an order of magnitude for different reconstruction parameters. Investigations of corresponding NPS revealed reconstruction dependent structural noise characteristics. For the acquisition parameters, noise metrics were guided by Poisson statistics. Large spatial non-uniformity of the noise was observed in both axial and radial direction of a PET image. In addition, activity concentrations in PET images of homogeneous phantom scans showed intriguing spatial fluctuations for most scanners. The clinical metric of the hypoxic fraction was shown to be considerably influenced by the PET scanner's spatial noise characteristics. CONCLUSION We showed that a hypoxic fraction metric based on noise characteristics requires careful consideration of the various dependencies in order to justify its quantitative validity. This work may result in recommendations for harmonizing QA of PET imaging for multi-institutional clinical trials.

Purpose: Quantification of hypoxia from PET images is of considerable clinical interest. In the absence of dynamic PET imaging the hypoxic fraction (HF) of a tumor has to be estimated from voxel values of activity concentration of a radioactive hypoxia tracer. This work is part of an effort to standardize quantification of tumor hypoxic fraction from PET images. Methods: A simple hypoxia imaging model in the tumor was developed. The distribution of the tracer activity was described as the sum of two different probability distributions, one for the normoxic (and necrotic), the other for the hypoxic voxels. The widths of the distributions arise due to variability of the transport, tumor tissue inhomogeneity, tracer binding kinetics, and due to PET image noise. Quantification of HF was performed for various levels of variability using two different methodologies: a) classification thresholds between normoxic and hypoxic voxels based on a non-hypoxic surrogate (muscle), and b) estimation of the (posterior) probability distributions based on maximizing likelihood optimization that does not require a surrogate. Data from the hypoxia imaging model and from 27 cervical cancer patients enrolled in a FAZA PET study were analyzed. Results: In the model, where the true value of HF is known, thresholds usually underestimate the value for large variability. For the patients, a significant uncertainty of the HF values (an average intra-patient range of 17%) was caused by spatial non-uniformity of image noise which is a hallmark of all PET images. Maximum likelihood estimation (MLE) is able to directly optimize for the weights of both distributions, however, may suffer from poor optimization convergence. For some patients, MLE-based HF values showed significant differences to threshold-based HF-values. Conclusion: HF-values depend critically on the magnitude of the different sources of tracer uptake variability. A measure of confidence should also be reported.

Conventional (i.e., respiratory-correlated) 4DCT exploits the repetitive nature of breathing to provide an estimate of motion; however, it has limitations due to binning artifacts and irregular breathing in actual patient breathing patterns. The aim of this work was to evaluate the accuracy and image quality of a dynamic volume, CT approach (4D(vol)) using a 320-slice CT scanner to minimize these limitations, wherein entire image volumes are acquired dynamically without couch movement. This will be compared to the conventional respiratory-correlated 4DCT approach (RCCT).4D(vol) CT was performed and characterized on an in-house, programmable respiratory motion phantom containing multiple geometric and morphological "tumor" objects over a range of regular and irregular patient breathing traces obtained from 3D fluoroscopy and compared to RCCT. The accuracy of volumetric capture and breathing displacement were evaluated and compared with the ground truth values and with the results reported using RCCT. A motion model was investigated to validate the number of motion samples needed to obtain accurate motion probability density functions (PDF). The impact of 4D image quality on this accuracy was then investigated. Dose measurements using volumetric and conventional scan techniques were also performed and compared.Both conventional and dynamic volume 4DCT methods were capable of estimating the programmed displacement of sinusoidal motion, but patient breathing is known to not be regular, and obvious differences were seen for realistic, irregular motion. The mean RCCT amplitude error averaged at 4 mm (max. 7.8 mm) whereas the 4D(vol) CT error stayed below 0.5 mm. Similarly, the average absolute volume error was lower with 4D(vol) CT. Under irregular breathing, the 4D(vol) CT method provides a close description of the motion PDF (cross-correlation 0.99) and is able to track each object, whereas the RCCT method results in a significantly different PDF from the ground truth, especially for smaller tumors (cross-correlation ranging between 0.04 and 0.69). For the protocols studied, the dose measurements were higher in the 4D(vol) CT method (40%), but it was shown that significant mAs reductions can be achieved by a factor of 4-5 while maintaining image quality and accuracy.4D(vol) CT using a scanner with a large cone-angle is a promising alternative for improving the accuracy with which respiration-induced motion can be characterized, particularly for patients with irregular breathing motion. This approach also generates 4DCT image data with a reduced total scan time compared to a RCCT scan, without the need for image binning or external respiration signals within the 16 cm scan length. Scan dose can be made comparable to RCCT by optimization of the scan parameters. In addition, it provides the possibility of measuring breathing motion for more than one breathing cycle to assess stability and obtain a more accurate motion PDF, which is currently not feasible with the conventional RCCT approach.

Rationale and Objectives We evaluated the accuracy and reproducibility of three-dimensional (3D) measurements of lung phantoms and patient tumors from x-ray computed tomography (CT) and compared these to one-dimensional (1D) and two-dimensional (2D) measurements. Materials and Methods CT images of three spherical and three irregularly shaped tumor phantoms were evaluated by three observers who performed five repeated measurements. Additionally, three observers manually segmented 29 patient lung tumors five times each. Follow-up imaging was performed for 23 tumors and response criteria were compared. For a single subject, imaging was performed on nine occasions over 2 years to evaluate multidimensional tumor response. To evaluate measurement accuracy, we compared imaging measurements to ground truth using analysis of variance. For estimates of precision, intraobserver and interobserver coefficients of variation and intraclass correlations (ICC) were used. Linear regression and Pearson correlations were used to evaluate agreement and tumor response was descriptively compared. Results For spherical shaped phantoms, all measurements were highly accurate, but for irregularly shaped phantoms, only 3D measurements were in high agreement with ground truth measurements. All phantom and patient measurements showed high intra- and interobserver reproducibility (ICC >0.900). Over a 2-year period for a single patient, there was disagreement between tumor response classifications based on 3D measurements and those generated using 1D and 2D measurements. Conclusion Tumor volume measurements were highly reproducible and accurate for irregular, spherical phantoms and patient tumors with nonuniform dimensions. Response classifications obtained from multidimensional measurements suggest that 3D measurements provide higher sensitivity to tumor response.

Purpose: To evaluate the impact of individualized magnitude and phase signal arterial input function (AIF) measurements as well as voxel-based pre-contrast T10 relaxation on tumour perfusion metrics from DCE-MRI compared to DCE-CT using a common 4D temporal dynamic analysis (TDA) method. Methods: Nine patients with 13 brain metastases underwent volumetric DCE-CT (Toshiba, Aquilion ONE) and DCE-MRI (IMRIS 3T Verio) at baseline then 7 and 21 days post-radiosurgery. Voxel-based whole brain TDA was performed on all data using in-house software producing kinetic parameters AUC, Ktrans, Kep, and Vb (using the Modified Tofts model). AIF susceptibility was investigated in DCE-CT by selecting the AIF (from internal carotid artery) or VIF (from Sagittal Sinus). In DCE-MRI an individual Magnitude or Phase-based AIF (Sagittal Sinus) was compared to population-based AIF together with susceptibility to voxel-based T10 maps instead of a constant of 2400 msec. Absolute DCE-MRI values were compared to DCE-CT by Pearson correlation. Results: No significant difference in median Ktrans (0.048 +/−0.03 s-1) or AUC (2785.5 +/−1143.6 HU.s) was found between individual AIF and VIF-based DCE CT analyses. Using individual Magnitude VIF or Phase-based AIF for DCE-MRI (T10 2400 ms) resulted in higher Ktrans values (0.181 +/−0.11 vs. 0.121 +/−0.099 s-1). This is likely resulting from the smaller AIF peak since the population AIF (which more closely resembles CT) correlates better to DCE-CT metrics. Using voxel-based T10 maps caused further statistically-significant increase in Ktrans and AUC (p

TPS9089 Background: In melanoma, tumor hypoxia is associated with invasion, angiogenesis, and metastasis formation, as well as treatment resistance such as immune evasion. Evofosfamide (EVO, TH-302...