Automatic individual arterial input functions calculated from PCA outperform manual and population-averaged approaches for the pharmacokinetic modeling of DCE-MR images

The arterial input function: Spatial dependence within the imaging volume and its influence on 3D quantitative dynamic contrast-enhanced MRI for head and neck cancer

PURPOSE

To evaluate the dependence of the arterial input function (AIF) on the imaging z-axis and its effect on 3D DCE MRI pharmacokinetic parameters as mediated by the SPGR signal equation and Extended Tofts-Kermode model.

THEORY

For SPGR-based 3D DCE MRI acquisition of the head and neck, inflow effects within vessels violate the assumptions underlying the SPGR signal model. Errors in the SPGR-based AIF estimate propagate through the Extended Tofts-Kermode model to affect the output pharmacokinetic parameters.

MATERIALS AND METHODS

3D DCE-MRI data were acquired for six newly diagnosed HNC patients in a prospective single arm cohort study. AIF were selected within the carotid arteries at each z-axis location. A region of interest (ROI) was placed in normal paravertebral muscle and the Extended Tofts-Kermode model solved for each pixel within the ROI for each AIF. Results were compared to those obtained with a published population average AIF.

RESULTS

Due to inflow effect, the AIF showed extreme variation in their temporal shapes. K^trans was most sensitive to the initial bolus concentration and showed more variation over the muscle ROI with AIF taken from the upstream portion of the carotid. k_ep was less sensitive to the peak bolus concentration and showed less variation for AIF taken from the upstream portion of the carotid.

CONCLUSION

Inflow effects may introduce an unknown bias to SPGR-based 3D DCE pharmacokinetic parameters. Variation in the computed parameters depends on the selected AIF location. In the context of high flow, measurements may be limited to relative rather than absolute quantitative parameters.

Influence of Different Measurement Methods of Arterial Input Function on Quantitative Dynamic Contrast-Enhanced MRI Parameters in Head and Neck Cancer

BACKGROUND

Head and neck cancer (HNC) is the sixth most prevalent cancer worldwide. Dynamic contrast-enhanced MRI (DCE-MRI) helps in diagnosis and prognosis. Quantitative DCE-MRI requires an arterial input function (AIF), which affects the values of pharmacokinetic parameters (PKP).

PURPOSE

To evaluate influence of four individual AIF measurement methods on quantitative DCE-MRI parameters values (K^trans , v_e , k_ep , and v_p ), for HNC and muscle.

STUDY TYPE

Prospective.

POPULATION

A total of 34 HNC patients (23 males, 11 females, age range 24-91) FIELD STRENGTH/SEQUENCE: A 3 T; 3D SPGR gradient echo sequence with partial saturation of inflowing spins.

ASSESSMENT

Four AIF methods were applied: automatic AIF (AIFa) with up to 50 voxels selected from the whole FOV, manual AIF (AIFm) with four voxels selected from the internal carotid artery, both conditions without (Mc-) or with (Mc+) motion correction. Comparison endpoints were peak AIF values, PKP values in tumor and muscle, and tumor/muscle PKP ratios.

STATISTICAL TESTS

Nonparametric Friedman test for multiple comparisons. Nonparametric Wilcoxon test, without and with Benjamini Hochberg correction, for pairwise comparison of AIF peak values and PKP values for tumor, muscle and tumor/muscle ratio, P value ≤ 0.05 was considered statistically significant.

RESULTS

Peak AIF values differed significantly for all AIF methods, with mean AIFmMc+ peaks being up to 66.4% higher than those for AIFaMc+. Almost all PKP values were significantly higher for AIFa in both, tumor and muscle, up to 76% for mean K^trans values. Motion correction effect was smaller. Considering tumor/muscle parameter ratios, most differences were not significant (0.068 ≤ Wilcoxon P value ≤ 0.8).

DATA CONCLUSION

We observed important differences in PKP values when using either AIFa or AIFm, consequently choice of a standardized AIF method is mandatory for DCE-MRI on HNC. From the study findings, AIFm and inflow compensation are recommended. The use of the tumor/muscle PKP ratio should be of interest for multicenter studies.

EVIDENCE LEVEL

2 TECHNICAL EFFICACY: Stage 1.

Estimation of the capillary level input function for dynamic contrast-enhanced MRI of the breast using a deep learning approach

PURPOSE

To develop a deep learning approach to estimate the local capillary-level input function (CIF) for pharmacokinetic model analysis of DCE-MRI.

METHODS

A deep convolutional network was trained with numerically simulated data to estimate the CIF. The trained network was tested using simulated lesion data and used to estimate voxel-wise CIF for pharmacokinetic model analysis of breast DCE-MRI data using an abbreviated protocol from women with malignant (n = 25) and benign (n = 28) lesions. The estimated parameters were used to build a logistic regression model to detect the malignancy.

RESULT

The pharmacokinetic parameters estimated using the network-predicted CIF from our breast DCE data showed significant differences between the malignant and benign groups for all parameters. Testing the diagnostic performance with the estimated parameters, the conventional approach with arterial input function (AIF) showed an area under the curve (AUC) between 0.76 and 0.87, and the proposed approach with CIF demonstrated similar performance with an AUC between 0.79 and 0.81.

CONCLUSION

This study shows the feasibility of estimating voxel-wise CIF using a deep neural network. The proposed approach could eliminate the need to measure AIF manually without compromising the diagnostic performance to detect the malignancy in the clinical setting.

Processing of Affective Pictures: A Study Based on Functional Connectivity Network in the Cerebral Cortex

Emotion plays an important role in people's life. However, the existing researches do not give a unified conclusion on the brain function network under different emotional states. In this study, pictures from the international affective picture system (IAPS) of different valences were presented to subjects with a fixed frequency blinking frequency to induce stable state visual evoked potential (SSVEP). With the source location method, the cerebral cortex source signal was reconstructed based on EEG signals, and then the difference in SSVEP amplitudes in key brain areas under different emotional states and the difference in brain function network connections among different brain areas were analysed in cortical space. The results of the study show that positive and negative emotions evoked greater activation intensities in the prefrontal, temporal, and parietal lobes compared with those of neutral emotion. The network connections with a significant difference between emotional states mainly appear in the alpha and gamma bands, and the network connections with significant differences between positive emotion and negative emotion mainly exist in the right middle temporal gyrus and the superior frontal gyrus on both sides. In addition, the long-range connections play an important role in the process of emotional processing, especially the connections among frontal gyrus, middle temporal gyrus, and middle occipital gyrus. The results of this study provide a reliable scientific basis for revealing and elucidating the neural mechanism of emotion processing and the selection of brain regions and frequency bands in emotion recognition based on EEG signals.

Impact of qualitative, semi-quantitative, and quantitative analyses of dynamic contrast-enhanced magnet resonance imaging on prostate cancer detection

Dynamic contrast enhanced imaging (DCE) as an integral part of multiparametric prostate magnet resonance imaging (mpMRI) can be evaluated using qualitative, semi-quantitative, or quantitative assessment methods. Aim of this study is to analyze the clinical benefits of these evaluations of DCE regarding clinically significant prostate cancer (csPCa) detection and grading. 209 DCE data sets of 103 consecutive patients with mpMRI (T2, DWI, and DCE) and subsequent MRI-(in-bore)-biopsy were retrospectively analyzed. Qualitative DCE evaluation according to PI-RADS v2.1, semi-quantitative (curve type; DCE score according to PI-RADS v1), and quantitative Tofts analyses (Ktrans, kep, and ve) as well as PI-RADS v1 and v2.1 overall classification of 209 lesions (92 PCa, 117 benign lesions) were performed. Of each DCE assessment method, cancer detection, discrimination of csPCa, and localization were assessed and compared to histopathology findings. All DCE analyses (p<0.01-0.05), except ve (p = 0.02), showed significantly different results for PCa and benign lesions in the peripheral zone (PZ) with area under the curve (AUC) values of up to 0.92 for PI-RADS v2.1 overall classification. In the transition zone (TZ) only the qualitative DCE evalulation within PI-RADS (v1 and v2.1) could distinguish between PCa and benign lesions (p<0.01; AUC = 0.95). None of the DCE parameters could differentiate csPCa from non-significant (ns) PCa (p ≥ 0.1). Qualitative analysis of DCE within mpMRI according to PI-RADS version 2.1 showed excellent results regarding (cs)PCa detection. Semi-quantitative and quantitative parameters provided no additional improvements. DCE alone wasn't able to discriminate csPCa from nsPCa.

Assessment of metastatic lymph nodes in head and neck squamous cell carcinomas using simultaneous 18F-FDG-PET and MRI

In this study, we investigate the feasibility of using dynamic contrast enhanced magnetic resonance imaging (DCE-MRI), diffusion weighted imaging (DWI), and dynamic positron emission tomography (PET) for detection of metastatic lymph nodes in head and neck squamous cell carcinoma (HNSCC) cases. Twenty HNSCC patients scheduled for lymph node dissection underwent DCE-MRI, dynamic PET, and DWI using a PET-MR scanner within one week prior to their planned surgery. During surgery, resected nodes were labeled to identify their nodal levels and sent for routine clinical pathology evaluation. Quantitative parameters of metastatic and normal nodes were calculated from DCE-MRI (v_e, v_p, PS, F_p, K^trans), DWI (ADC) and PET (K_i, K₁, k₂, k₃) to assess if an individual or a combination of parameters can classify normal and metastatic lymph nodes accurately. There were 38 normal and 11 metastatic nodes covered by all three imaging methods and confirmed by pathology. 34% of all normal nodes had volumes greater than or equal to the smallest metastatic node while 4 normal nodes had SUV > 4.5. Among the MRI parameters, the median v_p, F_p, PS, and K^trans values of the metastatic lymph nodes were significantly lower (p = <0.05) than those of normal nodes. v_e and ADC did not show any statistical significance. For the dynamic PET parameters, the metastatic nodes had significantly higher k₃ (p value = 8.8 × 10^-8) and K_i (p value = 5.3 × 10^-8) than normal nodes. K₁ and k₂ did not show any statistically significant difference. K_i had the best separation with accuracy = 0.96 (sensitivity = 1, specificity = 0.95) using a cutoff of K_i = 5.3 × 10^-3 mL/cm³/min, while k₃ and volume had accuracy of 0.94 (sensitivity = 0.82, specificity = 0.97) and 0.90 (sensitivity = 0.64, specificity = 0.97) respectively. 100% accuracy can be achieved using a multivariate logistic regression model of MRI parameters after thresholding the data with K_i < 5.3 × 10^-3 mL/cm³/min. The results of this preliminary study suggest that quantitative MRI may provide additional value in distinguishing metastatic nodes, particularly among small nodes, when used together with FDG-PET.

Estimation of cellular-interstitial water exchange in dynamic contrast enhanced MRI using two flip angles

PURPOSE

To investigate the feasibility of using multiple flip angles in dynamic contrast enhanced (DCE) MRI to reduce the uncertainty in estimation of intracellular water lifetime (τ_i ).

METHODS

Numerical simulation studies were conducted to assess the uncertainty in estimation of τ_i using dynamic contrast enhanced MRI with one or two flip angles. In vivo experiments with a murine brain tumor model were conducted at 7T using two flip angles. The in vivo data were used to compare τ_i estimation using the single-flip-angle (SFA) protocol with that using the double-flip-angle (DFA) protocol. Data analysis was conducted using the two-compartment exchange model combined with the three-site-two-exchange model for water exchange.

RESULTS

In the numerical simulation studies with a range of contrast kinetic parameters and signal-to-noise ratio = 20, the median bias of τ_i estimation decreased from 72 ms with SFA to 65 ms with DFA, and the corresponding median inter-quartile range reduced from 523 ms to 156 ms. In the in vivo studies, τ_i estimation with SFA was not successful in most voxels in the tumors, as the estimated τ_i values reached the upper limit of the parameter range (2 s). In contrast, the estimated τ_i values with DFA were mostly between 0.2 and 1.5 s and homogeneously distributed spatially across the tumor. The τ_i estimation with DFA was less sensitive to arterial input function scaling but more sensitive to pre-contrast T₁ than the other contrast kinetic parameters.

CONCLUSION

This study results demonstrate the feasibility of using multiple flip angles to encode the post-contrast time-intensity curve with different weighting of water exchange effect to reduce the uncertainty in τ_i estimation.

Use of Indicator Dilution Principle to Evaluate Accuracy of Arterial Input Function Measured With Low-Dose Ultrafast Prostate Dynamic Contrast-Enhanced MRI

Accurately measuring arterial input function (AIF) is essential for quantitative analysis of dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI). We used the indicator dilution principle to evaluate the accuracy of AIF measured directly from an artery following a low-dose contrast media ultrafast DCE-MRI. In total, 15 patients with biopsy-confirmed localized prostate cancers were recruited. Cardiac MRI (CMRI) and ultrafast DCE-MRI were acquired on a Philips 3 T Ingenia scanner. The AIF was measured at iliac arties following injection of a low-dose (0.015 mmol/kg) gadolinium (Gd) contrast media. The cardiac output (CO) from CMRI (CO_CMRI) was calculated from the difference in ventricular volume at diastole and systole measured on the short axis of heart. The CO from DCE-MRI (CO_DCE) was also calculated from the AIF and dose of the contrast media used. A correlation test and Bland–Altman plot were used to compare CO_CMRI and CO_DCE. The average (±standard deviation [SD]) area under the curve measured directly from local AIF was 0.219 ± 0.07 mM·min. The average (±SD) CO_CMRI and CO_DCE were 6.52 ± 1.47 L/min and 6.88 ± 1.64 L/min, respectively. There was a strong positive correlation (r = 0.82, P < .01) and good agreement between CO_CMRI and CO_DCE. The CO_DCE is consistent with the reference standard CO_CMRI. This indicates that the AIF can be measured accurately from an artery with ultrafast DCE-MRI following injection of a low-dose contrast media.

Dynamic Contrast-Enhanced MRI

Dynamic contrast-enhanced MRI in pre-clinical imaging allows the in-vivo monitoring of vascular, physiological properties in normal and diseased tissue. There is considerable variation in the methods employed owing to the different questions that can be asked and answered about the physiologic alterations as well as morphologic changes in tissue. Here we review the typical decisions in the design and execution of a dynamic contrast-enhanced MRI study in mice although the findings can easily be transferred to other species. Emphasis is placed on highlighting the many pitfalls that wait for the unaware pre-clinical MRI practitioner and that go often unmentioned in the abundant literature dealing with dynamic contrast-enhanced MRI in animal models.

Differentiation of High-Grade from Low-Grade Astrocytoma: Improvement in Diagnostic Accuracy and Reliability of Pharmacokinetic Parameters from DCE MR Imaging by Using Arterial Input Functions Obtained from DSC MR Imaging

Purpose To evaluate whether arterial input functions (AIFs) derived from dynamic susceptibility-contrast (DSC) magnetic resonance (MR) imaging, or AIF_DSC values, improve diagnostic accuracy and reliability of the pharmacokinetic (PK) parameters of dynamic contrast material-enhanced (DCE) MR imaging for differentiating high-grade from low-grade astrocytomas, compared with AIFs obtained from DCE MR imaging (AIF_DCE). Materials and Methods This retrospective study included 226 patients (138 men, 88 women; mean age, 52.27 years ± 15.17; range, 24-84 years) with pathologically confirmed astrocytomas (World Health Organization grade II = 21, III = 53, IV = 152; isocitrate dehydrogenase mutant, 11.95% [27 of 226]; 1p19q codeletion 0% [0 of 226]). All patients underwent both DSC and DCE MR imaging before surgery, and AIF_DSC and AIF_DCE were obtained from each image. Volume transfer constant (K^trans), volume of vascular plasma space (v_p), and volume of extravascular extracellular space (v_e) were processed by using postprocessing software with two AIFs. The diagnostic accuracies of individual parameters were compared by using receiver operating characteristic curve (ROC) analysis. Intraclass correlation coefficients (ICCs) and the Bland-Altman method were used to assess reliability. Results The AIF_DSC-driven mean K^trans and v_e were more accurate for differentiating high-grade from low-grade astrocytoma than those derived by using AIF_DCE (area under the ROC curve: mean K^trans, 0.796 vs 0.645, P = .038; mean v_e, 0.794 vs 0.658, P = .020). All three parameters had better ICCs with AIF_DSC than with AIF_DCE (K^trans, 0.737 vs 0.095; v_p, 0.848 vs 0.728; v_e, 0.875 vs 0.581, respectively). In AIF analysis, maximal signal intensity (0.837 vs 0.524) and wash-in slope (0.800 vs 0.432) demonstrated better ICCs with AIF_DSC than AIF_DCE. Conclusion AIF_DSC-driven DCE MR imaging PK parameters showed better diagnostic accuracy and reliability for differentiating high-grade from low-grade astrocytoma than those derived from AIF_DCE. ^© RSNA, 2017 Online supplemental material is available for this article.

Dynamic contrast-enhanced case-control analysis in 3T MRI of prostate cancer can help to characterize tumor aggressiveness

PURPOSE

The aim of this work is to establish normality and tumor tissue ranges for perfusion parameters from dynamic contrast-enhanced (DCE) MR of the peripheral prostate at 3T and to compare the diagnostic performance of quantitative and semi-quantitative parameters.

MATERIALS AND METHODS

Thirty-six patients with prostate carcinomas (18 Gleason-6, 15 Gleason-7, and 3 Gleason-8) and 33 healthy subjects were included. Image analysis workflow comprised four steps: manual segmentation of whole prostate and lesions, series registration, voxelwise T1 mapping and calculation of pharmacokinetic and semi-quantitative parameters.

RESULTS

K^trans, v_e, upslope and AUC60 showed statistically significant differences between healthy peripheral areas and tumors. Curve type showed no association with healthy/tumor peripheral areas (chi-square=0.702). Areas under the ROC curves were 0.64 (95% CI: 0.54-0.75), 0.70 (0.60-0.80), 0.62 (0.51-0.72) and 0.63 (0.52-0.74) for K^trans, v_e, upslope and AUC60, respectively. The optimal cutoff values were: K^trans=0.21min^-1 (sensitivity=0.61, specificity=0.64), v_e=0.36 (0.63, 0.71), upslope=0.59 (0.59, 0.59) and AUC60=2.4 (0.63, 0.64). Significant differences were found between Gleason scores 6 and 7 for normalized K^trans, upslope and AUC60, with good diagnostic accuracy (area under ROC curve 0.80, 95% CI: 0.60-1.00).

CONCLUSION

Quantitative (K^trans and v_e) and semi-quantitative (upslope and AUC60) perfusion parameters showed significant differences between tumors and control areas in the peripheral prostate. Normalized K^trans, upslope and AUC60 values might characterize tumor aggressiveness.

Gadoxetate-enhanced MR imaging and compartmental modelling to assess hepatocyte bidirectional transport function in rats with advanced liver fibrosis

OBJECTIVES

Changes in the expression of hepatocyte membrane transporters in advanced fibrosis decrease the hepatic transport function of organic anions. The aim of our study was to assess if these changes can be evaluated with pharmacokinetic analysis of the hepatobiliary transport of the MR contrast agent gadoxetate.

METHODS

Dynamic gadoxetate-enhanced MRI was performed in 17 rats with advanced fibrosis and 8 normal rats. After deconvolution, hepatocyte three-compartmental analysis was performed to calculate the hepatocyte influx, biliary efflux and sinusoidal backflux rates. The expression of Oatp1a1, Mrp2 and Mrp3 organic anion membrane transporters was assessed with reverse transcription polymerase chain reaction.

RESULTS

In the rats with advanced fibrosis, the influx and efflux rates of gadoxetate decreased and the backflux rate increased significantly (p = 0.003, 0.041 and 0.010, respectively). Significant correlations were found between influx and Oatp1a1 expression (r = 0.78, p < 0.001), biliary efflux and Mrp2 (r = 0.50, p = 0.016) and sinusoidal backflux and Mrp3 (r = 0.61, p = 0.002).

CONCLUSION

These results show that changes in the bidirectional organic anion hepatocyte transport function in rats with advanced liver fibrosis can be assessed with compartmental analysis of gadoxetate-enhanced MRI.

KEY POINTS

• Expression of hepatocyte transporters is modified in rats with advanced liver fibrosis. • Kinetic parameters at gadoxetate-enhanced MRI are correlated with hepatocyte transporter expression. • Hepatocyte transport function can be assessed with compartmental analysis of gadoxetate-enhanced MRI. • Compartmental analysis of gadoxetate-enhanced MRI might provide biomarkers in advanced liver fibrosis.

Patient-specific pharmacokinetic parameter estimation on dynamic contrast-enhanced MRI of prostate: Preliminary evaluation of a novel AIF-free estimation method

PURPOSE

To develop and evaluate a prostate-based method (PBM) for estimating pharmacokinetic parameters on dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) by leveraging inherent differences in pharmacokinetic characteristics between the peripheral zone (PZ) and transition zone (TZ).

MATERIALS AND METHODS

This retrospective study, approved by the Institutional Review Board, included 40 patients who underwent a multiparametric 3T MRI examination and subsequent radical prostatectomy. A two-step PBM for estimating pharmacokinetic parameters exploited the inherent differences in pharmacokinetic characteristics associated with the TZ and PZ. First, the reference region model was implemented to estimate ratios of K^trans between normal TZ and PZ. Subsequently, the reference region model was leveraged again to estimate values for K^trans and v_e for every prostate voxel. The parameters of PBM were compared with those estimated using an arterial input function (AIF) derived from the femoral arteries. The ability of the parameters to differentiate prostate cancer (PCa) from benign tissue was evaluated on a voxel and lesion level. Additionally, the effect of temporal downsampling of the DCE MRI data was assessed.

RESULTS

Significant differences (P < 0.05) in PBM K^trans between PCa lesions and benign tissue were found in 26/27 patients with TZ lesions and in 33/38 patients with PZ lesions; significant differences in AIF-based K^trans occurred in 26/27 and 30/38 patients, respectively. The 75^th and 100^th percentiles of K^trans and v_e estimated using PBM positively correlated with lesion size (P < 0.05).

CONCLUSION

Pharmacokinetic parameters estimated via PBM outperformed AIF-based parameters in PCa detection. J. Magn. Reson. Imaging 2016;44:1405-1414.

Practical aspects of prostate MRI: hardware and software considerations, protocols, and patient preparation

The use of multiparametric MRI scans for the evaluation of men with prostate cancer has increased dramatically and is likely to continue expanding as new developments come to practice. However, it has not yet gained the same level of acceptance of other imaging tests. Partly, this is because of the use of suboptimal protocols, lack of standardization, and inadequate patient preparation. In this manuscript, we describe several practical aspects of prostate MRI that may facilitate the implementation of new prostate imaging programs or the expansion of existing ones.

Arterial input functions (AIFs) measured directly from arteries with low and standard doses of contrast agent, and AIFs derived from reference tissues

Measurements of arterial input function (AIF) can have large systematic errors at standard contrast agent doses in dynamic contrast enhanced MRI (DCE-MRI). We compared measured AIFs from low dose (AIFLD) and standard dose (AIFSD) contrast agent injections, as well as the AIF derived from a muscle reference tissue and artery (AIFref). Twenty-two prostate cancer patients underwent DCE-MRI. Data were acquired on a 3T scanner using an mDixon sequence. Gadobenate dimeglumine was injected twice, at doses of 0.015 and 0.085 mmol/kg. Directly measured AIFs were fitted with empirical mathematical models (EMMs) and compared to the AIF derived from a muscle reference tissue (AIFref). EMMs accurately fitted the AIFs. The 1st and 2nd pass peaks were visualized in AIFLD, but not in AIFSD, thus the peak and shape of AIFSD could not be accurately measured directly. The average scaling factor between AIFSD and AIFLD in the washout phase was only 56% of the contrast dose ratio (~6:1). The shape and magnitude of AIFref closely approximated that of AIFLD after empirically determined dose-dependent normalization. This suggests that AIFref may be a good approximation of the local AIF.