The influence of temporal resolution in determining pharmacokinetic parameters from DCE-MRI data

Blind deconvolution decreases requirements on temporal resolution of DCE-MRI: Application to 2nd generation pharmacokinetic modeling

PURPOSE

Dynamic Contrast-Enhanced (DCE) MRI with 2nd generation pharmacokinetic models provides estimates of plasma flow and permeability surface-area product in contrast to the broadly used 1st generation models (e.g. the Tofts models). However, the use of 2nd generation models requires higher frequency with which the dynamic images are acquired (around 1.5 s per image). Blind deconvolution can decrease the demands on temporal resolution as shown previously for one of the 1st generation models. Here, the temporal-resolution requirements achievable for blind deconvolution with a 2nd generation model are studied.

METHODS

The 2nd generation model is formulated as the distributed-capillary adiabatic-tissue-homogeneity (DCATH) model. Blind deconvolution is based on Parker's model of the arterial input function. The accuracy and precision of the estimated arterial input functions and the perfusion parameters is evaluated on synthetic and real clinical datasets with different levels of the temporal resolution.

RESULTS

The estimated arterial input functions remained unchanged from their reference high-temporal-resolution estimates (obtained with the sampling interval around 1 s) when increasing the sampling interval up to about 5 s for synthetic data and up to 3.6-4.8 s for real data. Further increasing of the sampling intervals led to systematic distortions, such as lowering and broadening of the 1st pass peak. The resulting perfusion-parameter estimation error was below 10% for the sampling intervals up to 3 s (synthetic data), in line with the real data perfusion-parameter boxplots which remained unchanged up to the sampling interval 3.6 s.

CONCLUSION

We show that use of blind deconvolution decreases the demands on temporal resolution in DCE-MRI from about 1.5 s (in case of measured arterial input functions) to 3-4 s. This can be exploited in increased spatial resolution or larger organ coverage.

Dynamic Contrast-Enhanced (DCE) MRI

The non-invasive dynamic contrast-enhanced MRI (DCE-MRI) method provides valuable insights into tissue perfusion and vascularity. Primarily used in oncology, DCE-MRI is typically utilized to assess morphology and contrast agent (CA) kinetics in the tissue of interest. Interpretation of the temporal signatures of DCE-MRI data includes qualitative, semi-quantitative, and quantitative approaches. Recent advances in MRI technology allow simultaneous high spatial and temporal resolutions in DCE-MRI data acquisition on most vendor platforms, enabling the more desirable approach of quantitative data analysis using pharmacokinetic (PK) modeling. Many technical factors, including signal-to-noise ratio, temporal resolution, quantifications of arterial input function and native tissue T1, and PK model selection, need to be carefully considered when performing quantitative DCE-MRI. Standardization in data acquisition and analysis is especially important in multi-center studies.

Deconvolution-Based Pharmacokinetic Analysis to Improve the Prediction of Pathological Information of Breast Cancer

Pharmacokinetic (PK) parameters, revealing changes in the tumor microenvironment, are related to the pathological information of breast cancer. Tracer kinetic models (e.g., Tofts-Kety model) with a nonlinear least square solver are commonly used to estimate PK parameters. However, the method is sensitive to noise in images. To relieve the effects of noise, a deconvolution (DEC) method, which was validated on synthetic concentration-time series, was proposed to accurately calculate PK parameters from breast dynamic contrast-enhanced magnetic resonance imaging. A time-to-peak-based tumor partitioning method was used to divide the whole tumor into three tumor subregions with different kinetic patterns. Radiomic features were calculated from the tumor subregion and whole tumor-based PK parameter maps. The optimal features determined by the fivefold cross-validation method were used to build random forest classifiers to predict molecular subtypes, Ki-67, and tumor grade. The diagnostic performance evaluated by the area under the receiver operating characteristic curve (AUC) was compared between the subregion and whole tumor-based PK parameters. The results showed that the DEC method obtained more accurate PK parameters than the Tofts method. Moreover, the results showed that the subregion-based Ktrans (best AUCs = 0.8319, 0.7032, 0.7132, 0.7490, 0.8074, and 0.6950) achieved a better diagnostic performance than the whole tumor-based Ktrans (AUCs = 0.8222, 0.6970, 0.6511, 0.7109, 0.7620, and 0.5894) for molecular subtypes, Ki-67, and tumor grade. These findings indicate that DEC-based Ktrans in the subregion has the potential to accurately predict molecular subtypes, Ki-67, and tumor grade.

Focal nodular enhancement on DCE MRI of the prostatectomy bed: radiologic-pathologic correlations and prognostic value

OBJECTIVES

To determine the concordance of dynamic contrast-enhanced (DCE) imaging findings with clinico-pathologic characteristics and their prognostic impact for predicting biochemical recurrence (BCR) in patients who underwent radical prostatectomy (RP) for prostate cancer.

METHODS

This retrospective study included patients who underwent MRI within 1 year after RP between November 2019 and October 2020. DCE findings and their concordance with the presence and location of positive surgical margin (PSM) were assessed using RP specimens. Kaplan-Meier and logistic regression analyses were used to evaluate the prognostic impact of DCE findings for BCR.

RESULTS

Among the 272 men (mean age ± standard deviation, 66.6 ± 7.4 years), focal nodular enhancement was more frequently observed in those with PSM compared to those with negative margin (85.4% versus 14.6%; p < 0.001). The sites of focal nodular enhancement were 72.9% (35/48) concordant with the PSM locations. Focal nodular enhancement was associated with a higher Gleason score, higher preoperative PSA (≥ 10 ng/mL), higher Gleason grade at the surgical margin, and non-limited margin involvement (p = 0.002, 0.006, 0.032, and 0.001, respectively). In patients without BCR at the time of MRI, focal nodular enhancement was associated with a shorter time to BCR (p < 0.001) and a significant factor predicting 1-year BCR in both univariate (odds ratio = 8.4 [95% CI: 2.5-28.3]; p = 0.001) and multivariate (odds ratio = 5.49 [1.56-19.3]; p = 0.008) analyses.

CONCLUSIONS

Focal nodular enhancement on post-prostatectomy MRI was associated with adverse clinico-pathologic characteristics of high risk for recurrence and can be a predictor for 1-year BCR in patients undergoing RP.

KEY POINTS

• Focal nodular enhancement (PI-RR DCE score ≥ 4) was 72.9% (35/48) concordant with the site of positive resection margin by radiologic-histologic correlation. • Focal nodular enhancement (PI-RR DCE score ≥ 4) was associated with higher Gleason score ( ≥ 8), preoperative PSA ( > 10 ng/mL), and Gleason grade 4 or 5 at the surgical margin and non-limited margin involvement (p ≤ 0.032). • In patients without BCR at the time of MRI, focal nodular enhancement was a significant factor predicting 1-year BCR (odds ratio = 5.49; 95% CI: 1.56-19.3; p = 0.008).

Determination of hepatic extraction fraction with gadoxetate low‐temporal resolution DCE‐MRI ‐based deconvolution analysis: validation with ALBI score and Child‐Pugh class

INTRODUCTION

In this study, we aimed to investigate the feasibility of gadoxetate low-temporal resolution (LTR) DCE-MRI for voxel-based hepatic extraction fraction (HEF) quantification for liver sparing radiotherapy using a deconvolution analysis (DA) method.

METHODS

The accuracy and consistency of the deconvolution implementation in estimating liver function was first assessed using simulation data. Then, the method was applied to DCE-MRI data collected retrospectively from 64 patients (25 normal liver function and 39 cirrhotic patients) to generate HEF maps. The normal liver function patient data were used to measure the variability of liver function quantification. Next, a correlation between HEF and ALBI score (a new model for assessing the severity of liver dysfunction) was assessed using Pearson's correlation. Differences in HEF between Child-Pugh score classifications were assessed for significance using the Kruskal-Wallis test for all patient groups and Mann-Whitney U-test for inter-groups. A statistical significance was considered at a P-value <0.05 in all tests.

RESULTS

The results showed that the implemented method accurately reproduced simulated liver function; root-mean-square error between estimated and simulated liver response functions was 0.003, and the coefficient-of-variance of HEF was <20%. HEF correlation with ALBI score was r = -0.517, P < 0.0001, and HEF was significantly decreased in the cirrhotic patients compared to normal patients (P < 0.0001). Also, HEF in Child-Pugh B/C was significantly lower than in Child-Pugh A (P = 0.024).

CONCLUSION

The study demonstrated the feasibility of gadoxetate LTR-DCE MRI for voxel-based liver function quantification using DA. HEF could distinguish between different grades of liver function impairment and could potentially be used for functional guidance in radiotherapy.

The Influence of Data-Driven Compressed Sensing Reconstruction on Quantitative Pharmacokinetic Analysis in Breast DCE MRI

Radial acquisition with MOCCO reconstruction has been previously proposed for high spatial and temporal resolution breast DCE imaging. In this work, we characterize MOCCO across a wide range of temporal contrast enhancement in a digital reference object (DRO). Time-resolved radial data was simulated using a DRO with lesions in different PK parameters. The under sampled data were reconstructed at 5 s temporal resolution using the data-driven low-rank temporal model for MOCCO, compressed sensing with temporal total variation (CS-TV) and more conventional low-rank reconstruction (PCB). Our results demonstrated that MOCCO was able to recover curves with K^trans values ranging from 0.01 to 0.8 min⁻¹ and fixed V_e = 0.3, where the fitted results are within a 10% bias error range. MOCCO reconstruction showed less impact on the selection of different temporal models than conventional low-rank reconstruction and the greater error was observed with PCB. CS-TV showed overall underestimation in both K^trans and V_e. For the Monte-Carlo simulations, MOCCO was found to provide the most accurate reconstruction results for curves with intermediate lesion kinetics in the presence of noise. Initial in vivo experiences are reported in one patient volunteer. Overall, MOCCO was able to provide reconstructed time-series data that resulted in a more accurate measurement of PK parameters than PCB and CS-TV.

Comparing and combining MRE, T1ρ, SWI, IVIM, and DCE-MRI for the staging of liver fibrosis in rabbits: Assessment of a predictive model based on multiparametric MRI

PURPOSE

To establish and validate an optimal predictive model based on multiparametric MRI for staging liver fibrosis (LF) in rabbits with magnetic resonance elastography (MRE), spin-lattice relaxation time in the rotating frame (T1ρ imaging), SWI, intravoxel incoherent motion (IVIM), and DCE-MRI.

METHODS

The LF group included 120 rabbits induced by subcutaneous injections of carbon tetrachloride (CCl₄ ); 30 normal rabbits served as the control group. Multiparametric MRI was performed, including MRE, T1ρ, SWI, IVIM, and DCE-MRI. The quantitative parameters were analyzed in two groups, with histopathological results serving as the reference standard. The diagnostic performance of multiparametric MRI and the predictive model established by multivariable logistic regression analysis were evaluated by receiver operating characteristic (ROC) curve analysis.

RESULTS

In total, 32, 67, and 51 rabbits were histologically diagnosed as no fibrosis (stage F0), early-stage LF (F1-F2), and advanced-stage LF (F3-F4), respectively. The LF stages presented a strong correlation with liver stiffness (LS) on MRE (r = 0.90), signal-intensity ratio (SIR) on SWI (r = -0.84), and K^trans on DCE-MRI (r = 0.71; p < 0.05 for all). The LS and SIR parameters had higher AUC values for distinguishing early-stage LF from both no fibrosis (0.94 and 0.93, respectively) and advanced-stage LF (0.95 and 0.87, respectively). The predictive model showed a slightly higher AUC value of 0.97 (0.90-0.99) than LS and SIR in distinguishing early-stage LF from no fibrosis (p > 0.05), a significantly higher AUC value of 0.98 (0.93-0.99) than the SIR in distinguishing early-stage from advanced-stage LF (p < 0.05).

CONCLUSION

SWI, DCE-MRI, and MRE in particular showed improved performance for LF diagnosis and stage. The predictive model based on multiparametric MRI was found to further enhance diagnostic accuracy and could serve as an excellent imaging tool for staging LF.

An in silico validation framework for quantitative DCE-MRI techniques based on a dynamic digital phantom

Quantitative evaluation of an image processing method to perform as designed is central to both its utility and its ability to guide the data acquisition process. Unfortunately, these tasks can be quite challenging due to the difficulty of experimentally obtaining the "ground truth" data to which the output of a given processing method must be compared. One way to address this issue is via "digital phantoms", which are numerical models that provide known biophysical properties of a particular object of interest.  In this contribution, we propose an in silico validation framework for dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) acquisition and analysis methods that employs a novel dynamic digital phantom. The phantom provides a spatiotemporally-resolved representation of blood-interstitial flow and contrast agent delivery, where the former is solved by a 1D-3D coupled computational fluid dynamic system, and the latter described by an advection-diffusion equation. Furthermore, we establish a virtual simulator which takes as input the digital phantom, and produces realistic DCE-MRI data with controllable acquisition parameters. We assess the performance of a simulated standard-of-care acquisition (Protocol A) by its ability to generate contrast-enhanced MR images that separate vasculature from surrounding tissue, as measured by the contrast-to-noise ratio (CNR). We find that the CNR significantly decreases as the spatial resolution (SRA, where the subscript indicates Protocol A) or signal-to-noise ratio (SNRA) decreases. Specifically, with an SNRA / SRA = 75 dB / 30 μm, the median CNR is 77.30, whereas an SNRA / SRA = 5 dB / 300 μm reduces the CNR to 6.40. Additionally, we assess the performance of simulated ultra-fast acquisition (Protocol B) by its ability to generate DCE-MR images that capture contrast agent pharmacokinetics, as measured by error in the signal-enhancement ratio (SER) compared to ground truth (PESER). We find that PESER significantly decreases the as temporal resolution (TRB) increases. Similar results are reported for the effects of spatial resolution and signal-to-noise ratio on PESER. For example, with an SNRB / SRB / TRB = 5 dB / 300 μm / 10 s, the median PESER is 21.00%, whereas an SNRB / SRB / TRB = 75 dB / 60 μm / 1 s, yields a median PESER of 0.90%. These results indicate that our in silico framework can generate virtual MR images that capture effects of acquisition parameters on the ability of generated images to capture morphological or pharmacokinetic features. This validation framework is not only useful for investigations of perfusion-based MRI techniques, but also for the systematic evaluation and optimization new MRI acquisition, reconstruction, and image processing techniques.

Real-Time Quantitative Assessment of Accuracy and Precision of Blood Volume Derived from DCE-MRI in Individual Patients During a Clinical Trial

Accuracy and precision of quantitative imaging (QI) metrics should be assessed in real time in each patient during a clinical trial to support QI-based decision-making. We developed a framework for real-time quantitative assessment of QI metrics and evaluated accuracy and precision of dynamic contrast-enhanced (DCE)-magnetic resonance imaging (MRI)–derived blood volume (BV) in a clinical trial for head and neck cancers. Patients underwent DCE-MRI before and after 2 weeks of radiation therapy (2wkRT). A mean as a reference value and a repeatability coefficient (RC) of BV values established from n patients in cerebellum volumes of interest (VOIs), which were normal and affected little by therapy, served as accuracy and precision measurements. The BV maps of a new patient were called accurate and precise if the values in cerebellum VOIs and the difference between the 2 scans agreed with the respective mean and RC with 95% confidence. The new data could be used to update reference values. Otherwise, the data were flagged for further evaluation before use in the trial. BV maps from 62 patients enrolled on the trial were evaluated. Mean BV values were 2.21 (±0.14) mL/100 g pre-RT and 2.22 (±0.17) mL/100 g at 2wkRT; relative RC was 15.9%. The BV maps from 3 patients were identified to be inaccurate and imprecise before use in the clinical trial. Our framework of real-time quantitative assessment of QI metrics during a clinical trial can be translated to different QI metrics and organ-sites for supporting QI-based decision-making that warrants success of a clinical trial.

Accelerated dynamic contrast enhanced MRI based on region of interest compressed sensing

Magnetic Resonance Imaging (MRI) provides excellent soft tissue contrast with one significant limitation of slow data acquisition. Dynamic Contrast Enhanced MRI (DCE-MRI) is one of the widely employed techniques to estimate tumor tissue physiological parameters using contrast agents. DCE-MRI data acquisition and reconstruction requires high spatiotemporal resolution, especially during the post-contrast phase. The region of Interest Compressed Sensing (ROICS) is based on Compressed Sensing (CS) framework and works on the hypothesis that limiting CS to an ROI can achieve superior CS performance. In this work, ROICS has been demonstrated on breast DCE-MRI data at chosen acceleration factors and the results are compared with conventional CS implementation. Normalized Root Mean Square Error (NRMSE) was calculated to compare ROICS with CS quantitatively. CS and ROICS reconstructed images were used to compare K^trans and v_e values derived using standard Tofts Model (TM). This also validated the superior performance of ROICS over conventional CS. ROICS generated Concentration Time Curves (CTC's) at chosen acceleration factors follow similar trend as the ground truth data as compared to CS. Both qualitative and quantitative analyses show that ROICS outperforms CS particularly at acceleration factors of 5× and above.

Quantitative renal function assessment of atheroembolic renal disease using view-shared compressed sensing based dynamic-contrast enhanced MR imaging: An in vivo study

Atheroembolic renal disease (AERD) is the major cause of renal insufficiency in the elderly, and particularly, the diagnose of AERD is often delayed and even missed due to its nonspecific presentation and the sudden occurrence of an embolic event. To investigate the feasibility of the view-shared compressed sensing (VCS) based dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) in the assessment of AERD in animal models. The reproducibility of VCS DCE-MRI based glomerular filtration rate (GFR) estimation was first evaluated using the three healthy rabbits. Animal models of unilateral AERD were then conducted. All the rabbits underwent VCS DCE-MRI and the GFR maps were estimated by a commonly used cortical-compartment model. The whole kidney and suspicious lesion region GFR values of embolized kidneys were then compared with the corresponding values of normal kidneys. Finally, the suspicious lesion regions were confirmed by the corresponding renal specimens and histological findings. The reproducibility of GFR measurements was analyzed using the coefficient of variation and Bland-Altman analysis. The GFR values of normal and embolized kidneys were compared using the Student t-test. Contrast-enhanced images with sufficient diagnostic quality and reduced motion artifacts are obtained at a temporal resolution of 2.5 s. The Bland-Altman plot indicated close agreement between the GFR values estimated from between-day scans in healthy rabbits. Besides, there existed significant differences between the pixel-wise GFR values of normal and AERD kidneys in region-based comparison(P < 0.0001). The suspicious lesions are consistent well with the renal specimen and histological findings. The preliminary animal study verified the feasibility of VCS DCE-MRI for renal function evaluation, and the strategy could potentially provide a valuable tool to identify AERD.

Dual-input tracer kinetic modeling of dynamic contrast-enhanced MRI in thoracic malignancies

Pulmonary perfusion with dynamic contrast‐enhanced (DCE‐) MRI is typically assessed using a single‐input tracer kinetic model. Preliminary studies based on perfusion CT are indicating that dual‐input perfusion modeling of lung tumors may be clinically valuable as lung tumors have a dual blood supply from the pulmonary and aortic system. This study aimed to investigate the feasibility of fitting dual‐input tracer kinetic models to DCE‐MRI datasets of thoracic malignancies, including malignant pleural mesothelioma (MPM) and nonsmall cell lung cancer (NSCLC), by comparing them to single‐input (pulmonary or systemic arterial input) tracer kinetic models for the voxel‐level analysis within the tumor with respect to goodness‐of‐fit statistics. Fifteen patients (five MPM, ten NSCLC) underwent DCE‐MRI prior to radiotherapy. DCE‐MRI data were analyzed using five different single‐ or dual‐input tracer kinetic models: Tofts‐Kety (TK), extended TK (ETK), two compartment exchange (2CX), adiabatic approximation to the tissue homogeneity (AATH) and distributed parameter (DP) models. The pulmonary blood flow (BF), blood volume (BV), mean transit time (MTT), permeability‐surface area product (PS), fractional interstitial volume (v_I), and volume transfer constant (K^Trans) were calculated for both single‐ and dual‐input models. The pulmonary arterial flow fraction (γ), pulmonary arterial blood flow (BF_PA) and systemic arterial blood flow (BF_A) were additionally calculated for only dual‐input models. The competing models were ranked and their Akaike weights were calculated for each voxel according to corrected Akaike information criterion (cAIC). The optimal model was chosen based on the lowest cAIC value. In both types of tumors, all five dual‐input models yielded lower cAIC values than their corresponding single‐input models. The 2CX model was the best‐fitted model and most optimal in describing tracer kinetic behavior to assess microvascular properties in both MPM and NSCLC. The dual‐input 2CX‐model‐derived BF_A was the most significant parameter in differentiating adenocarcinoma from squamous cell carcinoma histology for NSCLC patients.

Localized Blood-Brain Barrier Opening in Ovine Model Using Image-Guided Transcranial Focused Ultrasound

Transcranial application of focused ultrasound (FUS) combined with vascular introduction of microbubble contrast agents (MBs) has emerged as a non-invasive technique that can temporarily create a localized opening in the blood-brain barrier (BBB). Under image-guidance, we administered FUS to sheep brain after intravenous injection of microbubbles. BBB opening was confirmed by performing dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to detect the extravasated gadolinium-based magnetic resonance contrast agents. Through pharmacokinetic analysis as well as independent component analysis of the DCE-MRI data, we observed localized enhancement in BBB permeability at the area that subjected to acoustic pressure of 0.48 MPa (mechanical index = 0.96). On the other hand, application of a higher pressure at 0.58 MPa resulted in localized, minor cerebral hemorrhage. No animals exhibited abnormal behavior during the post-FUS survival periods up to 2 mo. Our data suggest that monitoring for excessive BBB disruption is important for safe translation of the method to humans.

Evaluation of cortical bone perfusion using dynamic contrast enhanced ultrashort echo time imaging: a feasibility study

Background

Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) has been used to study perfusion in a wide variety of soft tissues including the bone marrow. Study of perfusion in hard tissues such as cortical bone has been much more limited because of the lack of detectable MR signal from them using conventional pulse sequences. However, two-dimensional (2D) ultrashort echo time (UTE) sequences detect signal from cortical bone and allow fast imaging of this tissue. In addition, adiabatic 2D inversion recovery UTE (IR-UTE) sequences can provide excellent signal suppression of soft tissues, such as muscle and marrow, and allow cortical bone to be seen with high contrast and reduced artefacts. We aimed to assess the feasibility of using 2D UTE and 2D IR-UTE sequences to perform DCE-MRI in the cortical bone of rabbits and human volunteers.

Methods

Cortical bone perfusion was studied in rabbits (n=12) and human volunteers (n=3) using 2D UTE and 2D IR-UTE sequences on a clinical 3T scanner. Dynamic data with an in-plane resolution of ~0.5×0.5 mm², single slice thickness of 3 mm for rabbits and 10 mm for human volunteers, and temporal resolution of 23 s for 2D UTE imaging of rabbits, 28 s for 2D UTE imaging of human volunteers, and 60 s for 2D IR-UTE imaging of both the rabbits and human volunteers were acquired before and after the injection of a Gd contrast agent (Gd-BOPTA: Multihance; Bracco Imaging SpA, Milan, Italy). The dose was 0.06 mmol/kg for rabbits and 0.2 mmol/kg for human subjects. Kinetic analyses based on the Brix model, as well as simple calculations of maximum enhancement (ME) and enhancement slope (ES), were performed.

Results

The 12 rabbits showed a mean K^trans of 0.36±0.07 min^-1, K_ep of 8.42±3.17 min^-1, ME of 28.30±6.83, ES of 0.35±0.18 for the femur with the 2D UTE sequence, and a mean K^trans of 0.45±0.10 min^-1, K_ep of 9.80±0.50 min^-1, ME of 48.84±12.12, and ES of 0.69±0.27 for the femur with the 2D IR-UTE sequence. Lower ME and ES values were observed in the tibial midshaft of healthy human volunteers compared to rabbits.

Conclusions

These results show that 2D UTE and 2D IR-UTE sequences are capable of detecting dynamic contrast enhancement in cortical bone in both rabbits and healthy human volunteers. Clinical studies with these techniques are likely to be feasible.

Quantitative imaging biomarkers alliance (QIBA) recommendations for improved precision of DWI and DCE-MRI derived biomarkers in multicenter oncology trials

Physiological properties of tumors can be measured both in vivo and noninvasively by diffusion-weighted imaging and dynamic contrast-enhanced magnetic resonance imaging. Although these techniques have been used for more than two decades to study tumor diffusion, perfusion, and/or permeability, the methods and studies on how to reduce measurement error and bias in the derived imaging metrics is still lacking in the literature. This is of paramount importance because the objective is to translate these quantitative imaging biomarkers (QIBs) into clinical trials, and ultimately in clinical practice. Standardization of the image acquisition using appropriate phantoms is the first step from a technical performance standpoint. The next step is to assess whether the imaging metrics have clinical value and meet the requirements for being a QIB as defined by the Radiological Society of North America's Quantitative Imaging Biomarkers Alliance (QIBA). The goal and mission of QIBA and the National Cancer Institute Quantitative Imaging Network (QIN) initiatives are to provide technical performance standards (QIBA profiles) and QIN tools for producing reliable QIBs for use in the clinical imaging community. Some of QIBA's development of quantitative diffusion-weighted imaging and dynamic contrast-enhanced QIB profiles has been hampered by the lack of literature for repeatability and reproducibility of the derived QIBs. The available research on this topic is scant and is not in sync with improvements or upgrades in MRI technology over the years. This review focuses on the need for QIBs in oncology applications and emphasizes the importance of the assessment of their reproducibility and repeatability. Level of Evidence: 5 Technical Efficacy Stage: 1 J. Magn. Reson. Imaging 2019;49:e101-e121.

Histogram analysis of quantitative pharmacokinetic parameters on DCE-MRI: correlations with prognostic factors and molecular subtypes in breast cancer

BACKGROUND

Breast cancer heterogeneity influences poor prognoses thorough therapy resistance. This study quantitatively evaluated intratumoral heterogeneity through a histogram analysis of dynamic contrast-enhanced MRI (DCE-MRI) pharmacokinetic parameters, and determined correlations with prognostic factors and molecular subtypes.

METHODS

We retrospectively investigated 101 invasive ductal breast cancers from 99 women who underwent preoperative DCE-MRI between July 2012 and November 2014. Pharmacokinetic parameters (K^trans, k_ep, and v_e) were obtained by the Tofts model. For each parameter, the mean, standard deviation, coefficient of variation, skewness, and kurtosis values of tumor were calculated, and prognostic factors and subtypes associations were assessed.

RESULTS

The mean of v_e was lower in cancers with high Ki-67 than in cancers with low Ki-67 (P = 0.002). The coefficient of variation of v_e was higher in cancers with estrogen receptor negativity than in cancers with estrogen receptor positivity (P < 0.001). The coefficient of variation of v_e was also higher in cancers with high Ki-67 than in cancers with low Ki-67 (P < 0.001). The skewness of v_e was higher in cancers with high nuclear grade than in cancers with low nuclear grade (P = 0.006). Triple-negative cancers showed higher v_e coefficient of variation than did those with luminal A (P < 0.001) and B (P = 0.006).

CONCLUSIONS

Various v_e parameters correlated with breast cancer prognostic factors and molecular subtypes.

Cerebral blood perfusion deficits using dynamic susceptibility contrast MRI with gadolinium chelates in rats with post-ischemic reperfusion without significant dynamic contrast-enhanced MRI-derived vessel permeabilities: A cautionary note

In this study, we quantified perfusion deficits using dynamic susceptibility contrast magnetic resonance imaging (DSC-MRI) with an extravasating contrast agent (CA). We also investigated the efficacy of leakage compensation from CA pre-load in brains from post-ischemic rat models without significant dynamic contrast-enhanced MRI (DCE-MRI)-derived vessel wall permeability. DSC measurements were obtained using fast (0.3 s) echo-planar imaging in both normal rats and rats with transient middle carotid artery occlusion (MCAO) (1-h MCAO, 24-h reperfusion) after successive administrations of gadoterate meglumine (Dotarem) and intravascular superparamagnetic iron oxide nanoparticles (SPION). The relative cerebral blood volume (CBV) and cerebral blood flow (CBF) values acquired using Dotarem were significantly underestimated (~20%) when compared to those acquired using SPION in ipsilesional post-ischemic brain regions. A slight overestimation of relative mean transit time was observed. Areas with underestimated CBV and CBF values from the corresponding error maps encompassed the area of infarcted tissue (apparent diffusion coefficient < 500 μm²/s) and mostly coincided with the area wherein conspicuous longitudinal relaxation time differences were observed pre- vs. post-injection of Dotarem. The DSC measurements with significant pre-load (0.3 mmol·kg^-1) of Dotarem displayed minimal perfusion deficits when compared to those determined using the reference intravascular SPION.

A multiparametric analysis combining DCE-MRI- and IVIM -derived parameters to improve differentiation of parotid tumors: a pilot study

AIM

To evaluate dynamic contrast-enhanced (DCE)-MRI and diffusion weighted (DW)-MRI diagnostic value to differentiate Warthin tumors (WT) by pleomorphic adenomas (PA).

MATERIALS & METHODS

Seven WT and seven PA were examined. DCE- and  DW-MRI parameters were extracted from volumes of interest; volume of interest-based averages and standard deviations were calculated. Statistical analysis included: linear discriminant analysis, receiver operating characteristic curves, sensitivity and specificity.

RESULTS

No single feature was able to differentiate WT by PA (p > 0.05); linear discriminant analysis analysis showed that a combination of all features or combinations of feature pairs (namely: K^trans(std) & f(std), K^trans(std) & D(std), k_ep(std) & D(std), MRE(av) & TTP(av)) might achieve sensitivity (SENS), specificity (SPEC) = 100%, with a slight reduction after cross-validation analysis (SENS = 0.875; SPEC = 1).

CONCLUSION

Although preliminary and not conclusive, our results suggest that differentiation between WT and PA is possible through a multiparametric approach based on combination of DCE- and DW-MRI parameters.

Linearization improves the repeatability of quantitative dynamic contrast-enhanced MRI

PURPOSE

The purpose of this study was to compare the repeatabilities of the linear and nonlinear Tofts and reference region models (RRM) for dynamic contrast-enhanced MRI (DCE-MRI).

MATERIALS AND METHODS

Simulated and experimental DCE-MRI data from 12 rats with a flank tumor of C6 glioma acquired over three consecutive days were analyzed using four quantitative and semi-quantitative DCE-MRI metrics. The quantitative methods used were: 1) linear Tofts model (LTM), 2) non-linear Tofts model (NTM), 3) linear RRM (LRRM), and 4) non-linear RRM (NRRM). The following semi-quantitative metrics were used: 1) maximum enhancement ratio (MER), 2) time to peak (TTP), 3) initial area under the curve (iauc64), and 4) slope. LTM and NTM were used to estimate K^trans, while LRRM and NRRM were used to estimate K^trans relative to muscle (R^Ktrans). Repeatability was assessed by calculating the within-subject coefficient of variation (wSCV) and the percent intra-subject variation (iSV) determined with the Gage R&R analysis.

RESULTS

The iSV for R^Ktrans using LRRM was two-fold lower compared to NRRM at all simulated and experimental conditions. A similar trend was observed for the Tofts model, where LTM was at least 50% more repeatable than the NTM under all experimental and simulated conditions. The semi-quantitative metrics iauc64 and MER were as equally repeatable as K^trans and R^Ktrans estimated by LTM and LRRM respectively. The iSV for iauc64 and MER were significantly lower than the iSV for slope and TTP.

CONCLUSION

In simulations and experimental results, linearization improves the repeatability of quantitative DCE-MRI by at least 30%, making it as repeatable as semi-quantitative metrics.

TumourMetrics: a comprehensive clinical solution for the standardization of DCE-MRI analysis in research and routine use

Background

A reliable analysis methodology is needed to provide valuable imaging biomarkers for clinical trials, with particular regards to dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) application using pharmacokinetic (PK) model analysis. In order to address this scientific challenge, we provided a comprehensive analysis solution that could overcome the impediments to clinical research and routine use.

Methods

TumourMetrics has been designed to meet the Quantitative Imaging Biomarkers Alliance (QIBA) v.1.0 profile. The quality performance was assessed using the QIBA test data and our customizable numeric phantom. The analysis workflow is made customizable to facilitate standardization of optimized protocol across centers.

Results

Our quantification workflow estimated the PK model parameters accurately. The method is robust, almost fully automatic and allows a direct integration of the results into the diagnostic workflow.

Conclusions

The analysis is easy-to-use and accessible for routine implementation of DCE-MRI into clinical practice.

Vascular disrupting agent in pancreatic and hepatic tumour allografts: observations of location-dependent efficacy by MRI, microangiography and histomorphology

BACKGROUND

Tumours growing in organs of different vascular environment could exhibit diverse responses to vascular disrupting agent (VDA). This study was aimed to identify in vivo imaging biomarkers for evaluation of pancreatic and hepatic tumours and comparison of their responses to a VDA Combretastatin A4 Phosphate (CA4P) using multiparametric MRI.

METHODS

Male WAG/Rij rats were used for orthotopic pancreatic head tumour and hepatic tumour implantation; tumour growth was monitored by 3D isotropic MRI using a 3.0-T clinic scanner. Therapeutic intervention using CA4P was investigated by in vivo quantitative MRI measurements including T2/T1 relaxation mapping, diffusion kurtosis imaging and dynamic contrast-enhancement (DCE) imaging. Animals were scarified 10 h after CA4P treatment for ex vivo validation using microangiography and histomorphology.

RESULTS

State-of-the-art clinical MRI protocols were successfully adapted for imaging small animal tumour with high reliability. One hour after CA4P injection, marked vascular shutdown was detected with DCE MRI in both pancreatic and hepatic tumours. However, 10 h later, therapeutic necrosis was limited in pancreatic tumours compared with that in hepatic tumours (P<0.01). Heterogeneous therapeutic changes were depicted in tumour lesions using pixel-wise Tofts model, which was generated from dynamic T1 mapping. In addition, tumour responses including haemorrhage, oedema and necrosis were detected using quantitative T2/T1 relaxation maps and diffusion kurtosis images, and were validated using histomorphology.

CONCLUSIONS

Using multiparametric imaging biomarkers, hepatic tumours were found to be significantly more responsive to CA4P than pancreatic tumours, which could be of reference for designing future clinical trials on this agent.

A Multi-Institutional Comparison of Dynamic Contrast-Enhanced Magnetic Resonance Imaging Parameter Calculations

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) provides quantitative metrics (e.g. K^trans, v_e) 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 K^trans and v_e 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 K^trans and v_e kinetic trends across algorithms. Algorithms performed well (less than 3% average error) when no noise was present in the DRO. With noise, 87% of K^trans and 84% of v_e 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 v_e of the primary gross tumor volume with time. Algorithmic differences in K^trans and v_e 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.

Effects of arterial input function selection on kinetic parameters in brain dynamic contrast-enhanced MRI

PURPOSE

Kinetic parameters derived from dynamic contrast-enhanced MRI (DCE-MRI) were suggested as a possible instrument for multi-parametric lesion characterization, but have not found their way into clinical practice yet due to inconsistent results. The quantification is heavily influenced by the definition of an appropriate arterial input functions (AIF). Regarding brain tumor DCE-MRI, there are currently several co-existing methods to determine the AIF frequently including different brain vessels as sources. This study quantitatively and qualitatively analyzes the impact of AIF source selection on kinetic parameters derived from commonly selected AIF source vessels compared to a population-based AIF model.

MATERIAL AND METHODS

74 patients with brain lesions underwent 3D DCE-MRI. Kinetic parameters [transfer constants of contrast agent efflux and reflux K^trans and k_ep and, their ratio, v_e, that is used to measure extravascular-extracellular volume fraction and plasma volume fraction v_p] were determined using extended Tofts model in 821 ROI from 4 AIF sources [the internal carotid artery (ICA), the closest artery to the lesion, the superior sagittal sinus (SSS), the population-based Parker model]. The effect of AIF source alteration on kinetic parameters was evaluated by tissue type selective intra-class correlation (ICC) and capacity to differentiate gliomas by WHO grade [area under the curve analysis (AUC)].

RESULTS

Arterial AIF more often led to implausible v_e >100% values (p<0.0001). AIF source alteration rendered different absolute kinetic parameters (p<0.0001), except for k_ep. ICC between kinetic parameters of different AIF sources and tissues were variable (0.08-0.87) and only consistent >0.5 between arterial AIF derived kinetic parameters. Differentiation between WHO III and II glioma was exclusively possible with v_p derived from an AIF in the SSS (p=0.03; AUC 0.74).

CONCLUSION

The AIF source has a significant impact on absolute kinetic parameters in DCE-MRI, which limits the comparability of kinetic parameters derived from different AIF sources. The effect is also tissue-dependent. The SSS appears to be the best choice for AIF source vessel selection in brain tumor DCE-MRI as it exclusively allowed for WHO grades II/III and III/IV glioma distinction (by v_p) and showed the least number of implausible v_e values.

Biopsy targeting with dynamic contrast-enhanced versus standard neuronavigation MRI in glioma: a prospective double-blinded evaluation of selection benefits

Current biopsy planning based on contrast-enhanced T1W (CET1W) or FLAIR sequences frequently delivers biopsy samples that are not in concordance with the gross tumor diagnosis. This study investigates whether the quantitative information of transfer constant K^trans maps derived from T1W dynamic contrast-enhanced MRI (DCE-MRI) can help enhance the quality of biopsy target selection in glioma. 28 patients with suspected glioma received MRI including DCE-MRI and a standard neuronavigation protocol of 3D FLAIR- and CET1W data sets (0.1 mmol/kg gadobutrol) at 3.0 T. After exclusion of five cases with no K^trans-elevation, 2-6 biopsy targets were independently selected by a neurosurgeon (samples based on standard imaging) and a neuroradiologist (samples based on kinetic parameter K^trans) per case and tissue samples corresponding to these targets were collected by a separate independent neurosurgeon. Standard technique and K^trans-based samples were rated for diagnostic concordance with the gross tumor resection reference diagnosis (67 WHO IV; 24 WHO III and II) by a neuropathologist blinded for selection mode. K^trans-based sample targets differed from standard technique sample targets in 90/91 cases. More K^trans-based than standard imaging-based samples could be extracted. Diagnoses from K^trans-based samples were more frequently concordant with the reference gross tumor diagnoses than those from standard imaging-based samples (WHO IV: 30/39 vs. 11/20; p = 0.08; WHO III/II: 12/13 vs. 6/11; p = 0.06). In 4/5 non-contrast-enhancing gliomas, K^trans-based selection revealed significantly more accurate samples than standard technique sample-selection (10/12 vs. 2/8 samples; p = 0.02). If K^trans elevation is present, K^trans-based biopsy targeting provides significantly more diagnostic tissue samples in non-contrast-enhancing glioma than selection based on CET1W and FLAIR-weighted images alone.

Dynamic contrast-enhanced MRI of the prostate: An intraindividual assessment of the effect of temporal resolution on qualitative detection and quantitative analysis of histopathologically proven prostate cancer

PURPOSE

To assess the effects of temporal resolution (R_T ) in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) on qualitative tumor detection and quantitative pharmacokinetic parameters in prostate cancer.

MATERIALS AND METHODS

This retrospective Institutional Review Board (IRB)-approved study included 58 men (64 ± 7 years). They underwent 3T prostate MRI showing dominant peripheral zone (PZ) tumors (24 with Gleason ≥ 4 + 3), prior to prostatectomy. Continuously acquired DCE utilizing GRASP (Golden-angle RAdial Sparse Parallel) was retrospectively reconstructed at R_T of 1.4 sec, 3.7 sec, 6.0 sec, 9.7 sec, and 14.9 sec. A reader placed volumes-of-interest on dominant tumors and benign PZ, generating quantitative pharmacokinetic parameters (k^trans , v_e ) at each R_T . Two blinded readers assessed each R_T for lesion presence, location, conspicuity, and reader confidence on a 5-point scale. Data were assessed by mixed-model analysis of variance (ANOVA), generalized estimating equation (GEE), and receiver operating characteristic (ROC) analysis.

RESULTS

R_T did not affect sensitivity (R1_all : 69.0%-72.4%, all P_adj  = 1.000; R1_{GS≥4 + 3} : 83.3-91.7%, all P_adj  = 1.000; R2_all : 60.3-69.0%, all P_adj  = 1.000; R2_{GS≥4 + 3} : 58.3%-79.2%, all P_adj  = 1.000). R1 reported greater conspicuity of GS ≥ 4 + 3 tumors at R_T of 1.4 sec vs. 14.9 sec (4.29 ± 1.23 vs. 3.46 ± 1.44; P_adj  = 0.029). No other tumor conspicuity pairwise comparison reached significance (R1_all : 2.98-3.43, all P_adj ≥ 0.205; R2_all : 2.57-3.19, all P_adj ≥ 0.059; R1_{GS≥4 + 3} : 3.46-4.29, all other P_adj ≥ 0.156; R2_{GS≥4 + 3} : 2.92-3.71, all P_adj ≥ 0.439). There was no effect of R_T on reader confidence (R1_all : 3.17-3.34, all P_adj  = 1.000; R2_all : 2.83-3.19, all P_adj ≥ 0.801; R1_{GS≥4 + 3} : 3.79-4.21, all P_adj  = 1.000; R2_{GS≥4 + 3} : 3.13-3.79, all P_adj  = 1.000). k^trans and v_e of tumor and benign tissue did not differ across R_T (all adjusted P values [P_adj ] = 1.000). R_T did not significantly affect area under the curve (AUC) of K^trans or v_e for differentiating tumor from benign (all P_adj  = 1.000).

CONCLUSION

Current PI-RADS recommendations for R_T of 10 seconds may be sufficient, with further reduction to the stated PI-RADS preference of R_T ≤ 7 seconds offering no benefit in tumor detection or quantitative analysis.

LEVEL OF EVIDENCE

3 J. MAGN. RESON. IMAGING 2017;45:1464-1475.

Comparison of linear and nonlinear implementation of the compartmental tissue uptake model for dynamic contrast-enhanced MRI

Purpose

Fitting tracer kinetic models using linear methods is much faster than using their nonlinear counterparts, although this comes often at the expense of reduced accuracy and precision. The aim of this study was to derive and compare the performance of the linear compartmental tissue uptake (CTU) model with its nonlinear version with respect to their percentage error and precision.

Theory and Methods

The linear and nonlinear CTU models were initially compared using simulations with varying noise and temporal sampling. Subsequently, the clinical applicability of the linear model was demonstrated on 14 patients with locally advanced cervical cancer examined with dynamic contrast‐enhanced magnetic resonance imaging.

Results

Simulations revealed equal percentage error and precision when noise was within clinical achievable ranges (contrast‐to‐noise ratio >10). The linear method was significantly faster than the nonlinear method, with a minimum speedup of around 230 across all tested sampling rates. Clinical analysis revealed that parameters estimated using the linear and nonlinear CTU model were highly correlated (ρ ≥ 0.95).

Conclusion

The linear CTU model is computationally more efficient and more stable against temporal downsampling, whereas the nonlinear method is more robust to variations in noise. The two methods may be used interchangeably within clinical achievable ranges of temporal sampling and noise. Magn Reson Med 77:2414–2423, 2017. © 2016 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Accelerated Brain DCE-MRI Using Iterative Reconstruction With Total Generalized Variation Penalty for Quantitative Pharmacokinetic Analysis: A Feasibility Study

PURPOSE

To investigate the feasibility of using undersampled k-space data and an iterative image reconstruction method with total generalized variation penalty in the quantitative pharmacokinetic analysis for clinical brain dynamic contrast-enhanced magnetic resonance imaging.

METHODS

Eight brain dynamic contrast-enhanced magnetic resonance imaging scans were retrospectively studied. Two k-space sparse sampling strategies were designed to achieve a simulated image acquisition acceleration factor of 4. They are (1) a golden ratio-optimized 32-ray radial sampling profile and (2) a Cartesian-based random sampling profile with spatiotemporal-regularized sampling density constraints. The undersampled data were reconstructed to yield images using the investigated reconstruction technique. In quantitative pharmacokinetic analysis on a voxel-by-voxel basis, the rate constant K^trans in the extended Tofts model and blood flow F_B and blood volume V_B from the 2-compartment exchange model were analyzed. Finally, the quantitative pharmacokinetic parameters calculated from the undersampled data were compared with the corresponding calculated values from the fully sampled data. To quantify each parameter's accuracy calculated using the undersampled data, error in volume mean, total relative error, and cross-correlation were calculated.

RESULTS

The pharmacokinetic parameter maps generated from the undersampled data appeared comparable to the ones generated from the original full sampling data. Within the region of interest, most derived error in volume mean values in the region of interest was about 5% or lower, and the average error in volume mean of all parameter maps generated through either sampling strategy was about 3.54%. The average total relative error value of all parameter maps in region of interest was about 0.115, and the average cross-correlation of all parameter maps in region of interest was about 0.962. All investigated pharmacokinetic parameters had no significant differences between the result from original data and the reduced sampling data.

CONCLUSION

With sparsely sampled k-space data in simulation of accelerated acquisition by a factor of 4, the investigated dynamic contrast-enhanced magnetic resonance imaging pharmacokinetic parameters can accurately estimate the total generalized variation-based iterative image reconstruction method for reliable clinical application.

Bolus arrival time and its effect on tissue characterization with dynamic contrast-enhanced magnetic resonance imaging

Matching the bolus arrival time (BAT) of the arterial input function (AIF) and tissue residue function (TRF) is necessary for accurate pharmacokinetic (PK) modeling of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). We investigated the sensitivity of volume transfer constant ([Formula: see text]) and extravascular extracellular volume fraction ([Formula: see text]) to BAT and compared the results of four automatic BAT measurement methods in characterization of prostate and breast cancers. Variation in delay between AIF and TRF resulted in a monotonous change trend of [Formula: see text] and [Formula: see text] values. The results of automatic BAT estimators for clinical data were all comparable except for one BAT estimation method. Our results indicate that inaccuracies in BAT measurement can lead to variability among DCE-MRI PK model parameters, diminish the quality of model fit, and produce fewer valid voxels in a region of interest. Although the selection of the BAT method did not affect the direction of change in the treatment assessment cohort, we suggest that BAT measurement methods must be used consistently in the course of longitudinal studies to control measurement variability.

High-temporospatial-resolution dynamic contrast-enhanced (DCE) wrist MRI with variable-density pseudo-random circular Cartesian undersampling (CIRCUS) acquisition: evaluation of perfusion in rheumatoid arthritis patients

This study is to evaluate highly accelerated three-dimensional (3D) dynamic contrast-enhanced (DCE) wrist MRI for assessment of perfusion in rheumatoid arthritis (RA) patients. A pseudo-random variable-density undersampling strategy, circular Cartesian undersampling (CIRCUS), was combined with k-t SPARSE-SENSE reconstruction to achieve a highly accelerated 3D DCE wrist MRI. Two healthy volunteers and 10 RA patients were studied. Two patients were on methotrexate (MTX) only (Group I) and the other eight were treated with a combination therapy of MTX and anti-tumor necrosis factor (TNF) therapy (Group II). Patients were scanned at baseline and 3 month follow-up. DCE MR images were used to evaluate perfusion in synovitis and bone marrow edema pattern in the RA wrist joints. A series of perfusion parameters was derived and compared with clinical disease activity scores of 28 joints (DAS28). 3D DCE wrist MR images were obtained with a spatial resolution of 0.3 × 0.3 × 1.5 mm(3) and temporal resolution of 5 s (with an acceleration factor of 20). The derived perfusion parameters, most notably transition time (dT) of synovitis, showed significant negative correlations with DAS28-ESR (r = -0.80, p < 0.05) and DAS28-CRP (r = -0.87, p < 0.05) at baseline and also correlated significantly with treatment responses evaluated by clinical score changes between baseline and 3 month follow-up (with DAS28-ESR r = -0.79, p < 0.05, and DAS28-CRP r = -0.82, p < 0.05). Highly accelerated 3D DCE wrist MRI with improved temporospatial resolution has been achieved in RA patients and provides accurate assessment of neovascularization and perfusion in RA joints, showing promise as a potential tool for evaluating treatment responses.

Three-dimensional dynamic contrast-enhanced MRI for the accurate, extensive quantification of microvascular permeability in atherosclerotic plaques

Atherosclerotic plaques that cause stroke and myocardial infarction are characterized by increased microvascular permeability and inflammation. Dynamic contrast-enhanced MRI (DCE-MRI) has been proposed as a method to quantify vessel wall microvascular permeability in vivo. Until now, most DCE-MRI studies of atherosclerosis have been limited to two-dimensional (2D) multi-slice imaging. Although providing the high spatial resolution required to image the arterial vessel wall, these approaches do not allow the quantification of plaque permeability with extensive anatomical coverage, an essential feature when imaging heterogeneous diseases, such as atherosclerosis. To our knowledge, we present the first systematic evaluation of three-dimensional (3D), high-resolution, DCE-MRI for the extensive quantification of plaque permeability along an entire vascular bed, with validation in atherosclerotic rabbits. We compare two acquisitions: 3D turbo field echo (TFE) with motion-sensitized-driven equilibrium (MSDE) preparation and 3D turbo spin echo (TSE). We find 3D TFE DCE-MRI to be superior to 3D TSE DCE-MRI in terms of temporal stability metrics. Both sequences show good intra- and inter-observer reliability, and significant correlation with ex vivo permeability measurements by Evans Blue near-infrared fluorescence (NIRF). In addition, we explore the feasibility of using compressed sensing to accelerate 3D DCE-MRI of atherosclerosis, to improve its temporal resolution and therefore the accuracy of permeability quantification. Using retrospective under-sampling and reconstructions, we show that compressed sensing alone may allow the acceleration of 3D DCE-MRI by up to four-fold. We anticipate that the development of high-spatial-resolution 3D DCE-MRI with prospective compressed sensing acceleration may allow for the more accurate and extensive quantification of atherosclerotic plaque permeability along an entire vascular bed. We foresee that this approach may allow for the comprehensive and accurate evaluation of plaque permeability in patients, and may be a useful tool to assess the therapeutic response to approved and novel drugs for cardiovascular disease.

High Spatiotemporal Resolution Dynamic Contrast-Enhanced MR Enterography in Crohn Disease Terminal Ileitis Using Continuous Golden-Angle Radial Sampling, Compressed Sensing, and Parallel Imaging

OBJECTIVE

The purpose of this article was to assess the feasibility of golden-angle radial acquisition with compress sensing reconstruction (Golden-angle RAdial Sparse Parallel [GRASP]) for acquiring high temporal resolution data for pharmacokinetic modeling while maintaining high image quality in patients with Crohn disease terminal ileitis.

MATERIALS AND METHODS

Fourteen patients with biopsy-proven Crohn terminal ileitis were scanned using both contrast-enhanced GRASP and Cartesian breath-hold (volume-interpolated breath-hold examination [VIBE]) acquisitions. GRASP data were reconstructed with 2.4-second temporal resolution and fitted to the generalized kinetic model using an individualized arterial input function to derive the volume transfer coefficient (K(trans)) and interstitial volume (v(e)). Reconstructions, including data from the entire GRASP acquisition and Cartesian VIBE acquisitions, were rated for image quality, artifact, and detection of typical Crohn ileitis features.

RESULTS

Inflamed loops of ileum had significantly higher K(trans) (3.36 ± 2.49 vs 0.86 ± 0.49 min(-1), p < 0.005) and v(e) (0.53 ± 0.15 vs 0.20 ± 0.11, p < 0.005) compared with normal bowel loops. There were no significant differences between GRASP and Cartesian VIBE for overall image quality (p = 0.180) or detection of Crohn ileitis features, although streak artifact was worse with the GRASP acquisition (p = 0.001).

CONCLUSION

High temporal resolution data for pharmacokinetic modeling and high spatial resolution data for morphologic image analysis can be achieved in the same acquisition using GRASP.

Optimized time-resolved imaging of contrast kinetics (TRICKS) in dynamic contrast-enhanced MRI after peptide receptor radionuclide therapy in small animal tumor models

Anti-tumor efficacy of targeted peptide-receptor radionuclide therapy (PRRT) relies on several factors, including functional tumor vasculature. Little is known about the effect of PRRT on tumor vasculature. With dynamic contrast-enhanced (DCE-) MRI, functional vasculature is imaged and quantified using contrast agents. In small animals DCE-MRI is a challenging application. We optimized a clinical sequence for fast hemodynamic acquisitions, time-resolved imaging of contrast kinetics (TRICKS), to obtain DCE-MRI images at both high spatial and high temporal resolution in mice and rats. Using TRICKS, functional vasculature was measured prior to PRRT and longitudinally to investigate the effect of treatment on tumor vascular characteristics. Nude mice bearing H69 tumor xenografts and rats bearing syngeneic CA20948 tumors were used to study perfusion following PRRT administration with (177) lutetium octreotate. Both semi-quantitative and quantitative parameters were calculated. Treatment efficacy was measured by tumor-size reduction. Optimized TRICKS enabled MRI at 0.032 mm(3) voxel size with a temporal resolution of less than 5 s and large volume coverage, a substantial improvement over routine pre-clinical DCE-MRI studies. Tumor response to therapy was reflected in changes in tumor perfusion/permeability parameters. The H69 tumor model showed pronounced changes in DCE-derived parameters following PRRT. The rat CA20948 tumor model showed more heterogeneity in both treatment outcome and perfusion parameters. TRICKS enabled the acquisition of DCE-MRI at both high temporal resolution (Tres ) and spatial resolutions relevant for small animal tumor models. With the high Tres enabled by TRICKS, accurate pharmacokinetic data modeling was feasible. DCE-MRI parameters revealed changes over time and showed a clear relationship between tumor size and Ktrans .

High temporal resolution dynamic contrast-enhanced MRI using compressed sensing-combined sequence in quantitative renal perfusion measurement

PURPOSE

To demonstrate the feasibility of the improved temporal resolution by using compressed sensing (CS) combined imaging sequence in dynamic contrast-enhanced MRI (DCE-MRI) of kidney, and investigate its quantitative effects on renal perfusion measurements.

MATERIALS AND METHODS

Ten rabbits were included in the accelerated scans with a CS-combined 3D pulse sequence. To evaluate the image quality, the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were compared between the proposed CS strategy and the conventional full sampling method. Moreover, renal perfusion was estimated by using the separable compartmental model in both CS simulation and realistic CS acquisitions.

RESULTS

The CS method showed DCE-MRI images with improved temporal resolution and acceptable image contrast, while presenting significantly higher SNR than the fully sampled images (p<.01) at 2-, 3- and 4-X acceleration. In quantitative measurements, renal perfusion results were in good agreement with the fully sampled one (concordance correlation coefficient=0.95, 0.91, 0.88) at 2-, 3- and 4-X acceleration in CS simulation. Moreover, in realistic acquisitions, the estimated perfusion by the separable compartmental model exhibited no significant differences (p>.05) between each CS-accelerated acquisition and the full sampling method.

CONCLUSION

The CS-combined 3D sequence could improve the temporal resolution for DCE-MRI in kidney while yielding diagnostically acceptable image quality, and it could provide effective measurements of renal perfusion.

An efficient calculation method for pharmacokinetic parameters in brain permeability study using dynamic contrast-enhanced MRI

PURPOSE

To develop an efficient method for calculating pharmacokinetic (PK) parameters in brain DCE-MRI permeability studies.

METHODS

A linear least-squares fitting algorithm based on a derivative expression of the two-compartment PK model was proposed to analytically solve for the PK parameters. Noise in the expression was minimized through low-pass filtering. Simulation studies were conducted in which the proposed method was compared with two existing methods in terms of accuracy and efficiency. Five in vivo brain studies were demonstrated for potential clinical application.

RESULTS

In the simulation studies using chosen parameter values, the calculated percent difference of K(trans) by the proposed method was <5.0% with a temporal resolution (Δt) < 5 s, and the accuracies of all parameter results were better or comparable to existing methods. When analyzed within certain parameter intensity ranges, the proposed method was more accurate than the existing methods and improved the efficiency by a factor of up to 458 for a Δt = 1 s and up to 38 for a Δt = 5 s. In the in vivo study, the calculated parameters using the proposed method were comparable to those using the existing methods with improved efficiencies.

CONCLUSIONS

An efficient method was developed for the accurate and efficient calculation of parameters in brain DCE-MRI permeability studies.

Evaluation of dynamic contrast-enhanced MRI derived microvascular permeability in recurrent glioblastoma treated with bevacizumab

Bevacizumab, an antibody to vascular endothelial growth factor, is commonly used in the setting of recurrent glioblastoma (rGB). The aim of the present study was to evaluate whether dynamic-contrast-enhanced MRI (DCE-MRI) derived microvascular permeability is related to bevacizumab treatment outcome in rGB. Twenty-two patients with rGB underwent DCE-MRI at a median of 2.6 weeks prior initializing bevacizumab therapy. Follow-up MRI-scans (DCE-MRI available for 19/22 patients) were obtained after a median of 9.9 weeks. The volume transfer constant (K(trans))--an estimate related to microvascular permeability--at baseline and voxel-wise-reduction (VWR) in K(trans) at first follow-up were measured from the entire contrast-enhancing tumor (CET) and correlated with progression-free and overall survival (PFS, OS) using uni- and multivariate cox-regression (significance-level p < 0.05). Baseline K(trans) ranged from 0.050 to 0.205 min(-1) (median, 0.109 min(-1)). The VWR in K(trans) ranged from 19.9 to 97.2 % (median, 89.4 %). Patients with lower baseline K(trans) and higher VWR in K(trans) showed significantly longer PFS and OS. Given the strong correlation of VWR in K(trans) and CET-volume changes (Spearman's ρ = -0.73, p < 0.01) both variables were included in a multivariate model. Thereby, neither VWR in K(trans) nor CET-volume changes retained independent significance for PFS or OS. Pre-treatment K(trans) stratifies PFS and OS in patients with bevacizumab-treated rGB. Although early pharmacodynamics changes in K(trans) were not assessed, the VWR in K(trans) at first follow-up had no additional benefit over assessment of CET-volume changes. Further prospective trials are needed to confirm these findings and to elucidate the potential role of pre-treatment K(trans) as a predictive and/or prognostic biomarker.

Review of treatment assessment using DCE-MRI in breast cancer radiation therapy

As a noninvasive functional imaging technique, dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is being used in oncology to measure properties of tumor microvascular structure and permeability. Studies have shown that parameters derived from certain pharmacokinetic models can be used as imaging biomarkers for tumor treatment response. The use of DCE-MRI for quantitative and objective assessment of radiation therapy has been explored in a variety of methods and tumor types. However, due to the complexity in imaging technology and divergent outcomes from different pharmacokinetic approaches, the method of using DCE-MRI in treatment assessment has yet to be standardized, especially for breast cancer. This article reviews the basic principles of breast DCE-MRI and recent studies using DCE-MRI in treatment assessment. Technical and clinical considerations are emphasized with specific attention to assessment of radiation treatment response.

An investigation into the effects of temporal resolution on hepatic dynamic contrast-enhanced MRI in volunteers and in patients with hepatocellular carcinoma

This study investigated the effect of temporal resolution on the dual-input pharmacokinetic (PK) modelling of dynamic contrast-enhanced MRI (DCE-MRI) data from normal volunteer livers and from patients with hepatocellular carcinoma. Eleven volunteers and five patients were examined at 3 T. Two sections, one optimized for the vascular input functions (VIF) and one for the tissue, were imaged within a single heart-beat (HB) using a saturation-recovery fast gradient echo sequence. The data was analysed using a dual-input single-compartment PK model. The VIFs and/or uptake curves were then temporally sub-sampled (at interval ▵t = [2-20] s) before being subject to the same PK analysis. Statistical comparisons of tumour and normal tissue PK parameter values using a 5% significance level gave rise to the same study results when temporally sub-sampling the VIFs to HB < ▵t <4 s. However, sub-sampling to ▵t > 4 s did adversely affect the statistical comparisons. Temporal sub-sampling of just the liver/tumour tissue uptake curves at ▵t ≤ 20 s, whilst using high temporal resolution VIFs, did not substantially affect PK parameter statistical comparisons. In conclusion, there is no practical advantage to be gained from acquiring very high temporal resolution hepatic DCE-MRI data. Instead the high temporal resolution could be usefully traded for increased spatial resolution or SNR.

A review of technical aspects of T1-weighted dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in human brain tumors

In the last few years, several imaging methods, such as magnetic resonance imaging (MRI) and computed tomography, have been used to investigate the degree of blood-brain barrier (BBB) permeability in patients with neurological diseases including multiple sclerosis, ischemic stroke, and brain tumors. One promising MRI method for assessing the BBB permeability of patients with neurological diseases in vivo is T1-weighted dynamic contrast-enhanced (DCE)-MRI. Here we review the technical issues involved in DCE-MRI in the study of human brain tumors. In the first part of this paper, theoretical models for the DCE-MRI analysis will be described, including the Toft-Kety models, the adiabatic approximation to the tissue homogeneity model and the two-compartment exchange model. These models can be used to estimate important kinetic parameters related to BBB permeability. In the second part of this paper, details of the data acquisition, issues related to the arterial input function, and procedures for DCE-MRI image analysis are illustrated.

Variations of dynamic contrast-enhanced magnetic resonance imaging in evaluation of breast cancer therapy response: a multicenter data analysis challenge

Pharmacokinetic analysis of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) time-course data allows estimation of quantitative parameters such as K (trans) (rate constant for plasma/interstitium contrast agent transfer), v e (extravascular extracellular volume fraction), and v p (plasma volume fraction). A plethora of factors in DCE-MRI data acquisition and analysis can affect accuracy and precision of these parameters and, consequently, the utility of quantitative DCE-MRI for assessing therapy response. In this multicenter data analysis challenge, DCE-MRI data acquired at one center from 10 patients with breast cancer before and after the first cycle of neoadjuvant chemotherapy were shared and processed with 12 software tools based on the Tofts model (TM), extended TM, and Shutter-Speed model. Inputs of tumor region of interest definition, pre-contrast T1, and arterial input function were controlled to focus on the variations in parameter value and response prediction capability caused by differences in models and associated algorithms. Considerable parameter variations were observed with the within-subject coefficient of variation (wCV) values for K (trans) and v p being as high as 0.59 and 0.82, respectively. Parameter agreement improved when only algorithms based on the same model were compared, e.g., the K (trans) intraclass correlation coefficient increased to as high as 0.84. Agreement in parameter percentage change was much better than that in absolute parameter value, e.g., the pairwise concordance correlation coefficient improved from 0.047 (for K (trans)) to 0.92 (for K (trans) percentage change) in comparing two TM algorithms. Nearly all algorithms provided good to excellent (univariate logistic regression c-statistic value ranging from 0.8 to 1.0) early prediction of therapy response using the metrics of mean tumor K (trans) and k ep (=K (trans)/v e, intravasation rate constant) after the first therapy cycle and the corresponding percentage changes. The results suggest that the interalgorithm parameter variations are largely systematic, which are not likely to significantly affect the utility of DCE-MRI for assessment of therapy response.

Role of high-field MR in studies of localized prostate cancer

Magnetic resonance imaging is attracting increasing attention from the uroradiological community as a modality to guide the management of prostate cancer. With the high incidence of prostate cancer it might come as a surprise that for a very long time (and in many places even at present) treatment decisions were being made without the use of detailed anatomical and functional imaging of the prostate gland at hand. Although T2 -weighted MRI can provide great anatomical detail, by itself it is not specific enough to discriminate cancer from benign disease, so other functional MRI techniques have been explored to aid in detection, localization, staging and risk assessment of prostate cancer. With the current evolution of clinical MR systems from 1.5 to 3 T it is important to understand the advantages and the challenges of the higher magnetic field strength for the different functional MR techniques most used in the prostate: T2 -weighted MRI, diffusion-weighted MRI, MR spectroscopic imaging and dynamic contrast-enhanced imaging. In addition to this, the use of the endorectal coil at different field strengths is discussed in this review, together with an outlook of the possibilities of ultra-high-field MR for the prostate.

Can DCE-MRI explain the heterogeneity in radiopeptide uptake imaged by SPECT in a pancreatic neuroendocrine tumor model?

Although efficient delivery and distribution of treatment agents over the whole tumor is essential for successful tumor treatment, the distribution of most of these agents cannot be visualized. However, with single-photon emission computed tomography (SPECT), both delivery and uptake of radiolabeled peptides can be visualized in a neuroendocrine tumor model overexpressing somatostatin receptors. A heterogeneous peptide uptake is often observed in these tumors. We hypothesized that peptide distribution in the tumor is spatially related to tumor perfusion, vessel density and permeability, as imaged and quantified by DCE-MRI in a neuroendocrine tumor model. Four subcutaneous CA20948 tumor-bearing Lewis rats were injected with the somatostatin-analog (111)In-DTPA-Octreotide (50 MBq). SPECT-CT and MRI scans were acquired and MRI was spatially registered to SPECT-CT. DCE-MRI was analyzed using semi-quantitative and quantitative methods. Correlation between SPECT and DCE-MRI was investigated with 1) Spearman's rank correlation coefficient; 2) SPECT uptake values grouped into deciles with corresponding median DCE-MRI parametric values and vice versa; and 3) linear regression analysis for median parameter values in combined datasets. In all tumors, areas with low peptide uptake correlated with low perfusion/density/ /permeability for all DCE-MRI-derived parameters. Combining all datasets, highest linear regression was found between peptide uptake and semi-quantitative parameters (R(2)>0.7). The average correlation coefficient between SPECT and DCE-MRI-derived parameters ranged from 0.52-0.56 (p<0.05) for parameters primarily associated with exchange between blood and extracellular extravascular space. For these parameters a linear relation with peptide uptake was observed. In conclusion, the 'exchange-related' DCE-MRI-derived parameters seemed to predict peptide uptake better than the 'contrast amount- related' parameters. Consequently, fast and efficient diffusion through the vessel wall into tissue is an important factor for peptide delivery. DCE-MRI helps to elucidate the relation between vascular characteristics, peptide delivery and treatment efficacy, and may form a basis to predict targeting efficiency.

Comparison of dynamic contrast-enhanced MRI and quantitative SPECT in a rat glioma model

Pharmacokinetic modeling of dynamic contrast-enhanced (DCE) MRI data provides measures of the extracellular-extravascular volume fraction (v(e) ) and the volume transfer constant (K(trans) ) in a given tissue. These parameter estimates may be biased, however, by confounding issues such as contrast agent and tissue water dynamics, or assumptions of vascularization and perfusion made by the commonly used model. In contrast to MRI, radiotracer imaging with SPECT is insensitive to water dynamics. A quantitative dual-isotope SPECT technique was developed to obtain an estimate of v(e) in a rat glioma model for comparison with the corresponding estimates obtained using DCE-MRI with a vascular input function and reference region model. Both DCE-MRI methods produced consistently larger estimates of v(e) in comparison to the SPECT estimates, and several experimental sources were postulated to contribute to these differences.

A reference agent model for DCE MRI can be used to quantify the relative vascular permeability of two MRI contrast agents

Dynamic Contrast Enhancement (DCE) MRI has been used to measure the kinetic transport constant, K(trans), which is used to assess tumor angiogenesis and the effects of anti-angiogenic therapies. Standard DCE MRI methods must measure the pharmacokinetics of a contrast agent in the blood stream, known as the Arterial Input Function (AIF), which is then used as a reference for the pharmacokinetics of the agent in tumor tissue. However, the AIF is difficult to measure in pre-clinical tumor models and in patients. Moreover the AIF is dependent on the Fahraeus effect that causes a highly variable hematocrit (Hct) in tumor microvasculature, leading to erroneous estimates of K(trans). To overcome these problems, we have developed the Reference Agent Model (RAM) for DCE MRI analyses, which determines the relative K(trans) of two contrast agents that are simultaneously co-injected and detected in the same tissue during a single DCE-MRI session. The RAM obviates the need to monitor the AIF because one contrast agent effectively serves as an internal reference in the tumor tissue for the other agent, and it also eliminates the systematic errors in the estimated K(trans) caused by assuming an erroneous Hct. Simulations demonstrated that the RAM can accurately and precisely estimate the relative K(trans) (R(Ktrans)) of two agents. To experimentally evaluate the utility of RAM for analyzing DCE MRI results, we optimized a previously reported multiecho (19)F MRI method to detect two perfluorinated contrast agents that were co-injected during a single in vivo study and selectively detected in the same tumor location. The results demonstrated that RAM determined R(Ktrans) with excellent accuracy and precision.

Comparison between PUN and Tofts models in the quantification of dynamic contrast-enhanced MR imaging

Dynamic contrast-enhanced study in magnetic resonance imaging (DCE-MRI) is an important tool in oncology to visualize tissues vascularization and to define tumour aggressiveness on the basis of an altered perfusion and permeability. Pharmacokinetic models are generally used to extract hemodynamic parameters, providing a quantitative description of the contrast uptake and wash-out. Empirical functions can also be used to fit experimental data without the need of any assumption about tumour physiology, as in pharmacokinetic models, increasing their diagnostic utility, in particular when automatic diagnosis systems are implemented on the basis of an MRI multi-parametric approach. Phenomenological universalities (PUN) represent a novel tool for experimental research and offer a simple and systematic method to represent a set of data independent of the application field. DCE-MRI acquisitions can thus be advantageously evaluated by the extended PUN class, providing a convenient diagnostic tool to analyse functional studies, adding a new set of features for the classification of malignant and benign lesions in computer aided detection systems. In this work the Tofts pharmacokinetic model and the class EU1 generated by the PUN description were compared in the study of DCE-MRI of the prostate, evaluating complexity of model implementation, goodness of fitting results, classification performances and computational cost. The mean R² obtained with the EU1 and Tofts model were equal to 0.96 and 0.90, respectively, and the classification performances achieved by the EU1 model and the Tofts implementation discriminated malignant from benign tissues with an area under the receiver operating characteristic curve equal to 0.92 and 0.91, respectively. Furthermore, the EU1 model has a simpler functional form which reduces implementation complexity and computational time, requiring 6 min to complete a patient elaboration process, instead of 8 min needed for the Tofts model analysis.