Restriction spectrum imaging with elastic image registration for automated evaluation of response to neoadjuvant therapy in breast cancer

Purpose

Dynamic contrast-enhanced MRI (DCE) and apparent diffusion coefficient (ADC) are currently used to evaluate treatment response of breast cancer. The purpose of the current study was to evaluate the three-component Restriction Spectrum Imaging model (RSI3C), a recent diffusion-weighted MRI (DWI)-based tumor classification method, combined with elastic image registration, to automatically monitor breast tumor size throughout neoadjuvant therapy.

Experimental design

Breast cancer patients (n=27) underwent multi-parametric 3T MRI at four time points during treatment. Elastically-registered DWI images were used to generate an automatic RSI3C response classifier, assessed against manual DCE tumor size measurements and mean ADC values. Predictions of therapy response during treatment and residual tumor post-treatment were assessed using non-pathological complete response (non-pCR) as an endpoint.

Results

Ten patients experienced pCR. Prediction of non-pCR using ROC AUC (95% CI) for change in measured tumor size from pre-treatment time point to early-treatment time point was 0.65 (0.38-0.92) for the RSI3C classifier, 0.64 (0.36-0.91) for DCE, and 0.45 (0.16-0.75) for change in mean ADC. Sensitivity for detection of residual disease post-treatment was 0.71 (0.44-0.90) for the RSI3C classifier, compared to 0.88 (0.64-0.99) for DCE and 0.76 (0.50-0.93) for ADC. Specificity was 0.90 (0.56-1.00) for the RSI3C classifier, 0.70 (0.35-0.93) for DCE, and 0.50 (0.19-0.81) for ADC.

Conclusion

The automatic RSI3C classifier with elastic image registration suggested prediction of response to treatment after only three weeks, and showed performance comparable to DCE for assessment of residual tumor post-therapy. RSI3C may guide clinical decision-making and enable tailored treatment regimens and cost-efficient evaluation of neoadjuvant therapy of breast cancer.

Effects of echo time on IVIM quantifications of locally advanced breast cancer in clinical diffusion-weighted MRI at 3 T

PURPOSE

The purpose of this study was to investigate the effects of echo time dependence in IVIM quantification of the pseudo-diffusion fraction in breast cancer and whether correcting for the echo time dependence offers added clinical value.

MATERIALS AND METHODS

Fifteen patients with biopsy-proven breast cancer underwent a 3 T MRI examination with an extended DWI protocol at two different echo times (TE = 53 ms, b = 0, 50 s/mm² ; TE = 77 ms, b = 0, 50, 120, 200, 400, 700 s/mm² ). Volumes of interest were delineated around the tumors. In addition, simulated MRI data were generated for different levels of signal-to-noise ratio and two values for the blood T2 relaxation time (T2_p = 100 ms and 150 ms). The pseudo-diffusion signal fraction was estimated from the simulated and in vivo tumor data using both the standard IVIM model and an extended IVIM model that accounts for the echo time dependence arising from distinct transverse relaxation times.

RESULTS

Simulations showed that the standard IVIM model overestimated the pseudo-diffusion fraction by 25% (T2_p = 100 ms) and 60 % (T2_p = 150 ms) (p < 0.0001 at SNR = 50). In vivo, the estimated apparent T2 value at b = 50 s/mm² was around 8% lower than at b = 0 s/mm² (p = 0.01) demonstrating a removal of the signal contribution from blood with long T2 associated with pseudo-diffusion. Using two different fixed values for T2_p = 100, 150 ms, the pseudo-diffusion fraction was 15% and 46% higher in the standard model compared with the echo-time-corrected model (p < 0.01).

CONCLUSION

The standard IVIM model was found to overestimate the pseudo-diffusion fraction by 15% to 46% compared with the echo-time-corrected model in breast tumor DWI data acquired at 3 T. Our results suggest that a corrected model may give more accurate results in terms of signal fractions, but may not justify the added time needed to acquire the additional data in terms of clinical value.

Discrimination of Breast Cancer from Healthy Breast Tissue Using a Three-component Diffusion-weighted MRI Model

PURPOSE

Diffusion-weighted MRI (DW-MRI) is a contrast-free modality that has demonstrated ability to discriminate between predefined benign and malignant breast lesions. However, how well DW-MRI discriminates cancer from all other breast tissue voxels in a clinical setting is unknown. Here we explore the voxelwise ability to distinguish cancer from healthy breast tissue using signal contributions from the newly developed three-component multi-b-value DW-MRI model.

EXPERIMENTAL DESIGN

Patients with pathology-proven breast cancer from two datasets (n = 81 and n = 25) underwent multi-b-value DW-MRI. The three-component signal contributions C 1 and C 2 and their product, C 1 C 2, and signal fractions F 1, F 2, and F 1 F 2 were compared with the image defined on maximum b-value (DWI max), conventional apparent diffusion coefficient (ADC), and apparent diffusion kurtosis (K app). The ability to discriminate between cancer and healthy breast tissue was assessed by the false-positive rate given a sensitivity of 80% (FPR80) and ROC AUC.

RESULTS

Mean FPR80 for both datasets was 0.016 [95% confidence interval (CI), 0.008-0.024] for C 1 C 2, 0.136 (95% CI, 0.092-0.180) for C 1, 0.068 (95% CI, 0.049-0.087) for C 2, 0.462 (95% CI, 0.425-0.499) for F 1 F 2, 0.832 (95% CI, 0.797-0.868) for F 1, 0.176 (95% CI, 0.150-0.203) for F 2, 0.159 (95% CI, 0.114-0.204) for DWI max, 0.731 (95% CI, 0.692-0.770) for ADC, and 0.684 (95% CI, 0.660-0.709) for K app. Mean ROC AUC for C 1 C 2 was 0.984 (95% CI, 0.977-0.991).

CONCLUSIONS

The C 1 C 2 parameter of the three-component model yields a clinically useful discrimination between cancer and healthy breast tissue, superior to other DW-MRI methods and obliviating predefining lesions. This novel DW-MRI method may serve as noncontrast alternative to standard-of-care dynamic contrast-enhanced MRI.

Noninvasive MRI Native T1 Mapping Detects Response to MYCN-targeted Therapies in the Th-MYCN Model of Neuroblastoma

Noninvasive early indicators of treatment response are crucial to the successful delivery of precision medicine in children with cancer. Neuroblastoma is a common solid tumor of young children that arises from anomalies in neural crest development. Therapeutic approaches aiming to destabilize MYCN protein, such as small-molecule inhibitors of Aurora A and mTOR, are currently being evaluated in early phase clinical trials in children with high-risk MYCN-driven disease, with limited ability to evaluate conventional pharmacodynamic biomarkers of response. T1 mapping is an MRI scan that measures the proton spin-lattice relaxation time T1. Using a multiparametric MRI-pathologic cross-correlative approach and computational pathology methodologies including a machine learning-based algorithm for the automatic detection and classification of neuroblasts, we show here that T1 mapping is sensitive to the rich histopathologic heterogeneity of neuroblastoma in the Th-MYCN transgenic model. Regions with high native T1 corresponded to regions dense in proliferative undifferentiated neuroblasts, whereas regions characterized by low T1 were rich in apoptotic or differentiating neuroblasts. Reductions in tumor-native T1 represented a sensitive biomarker of response to treatment-induced apoptosis with two MYCN-targeted small-molecule inhibitors, Aurora A kinase inhibitor alisertib (MLN8237) and mTOR inhibitor vistusertib (AZD2014). Overall, we demonstrate the potential of T1 mapping, a scan readily available on most clinical MRI scanners, to assess response to therapy and guide clinical trials for children with neuroblastoma. The study reinforces the potential role of MRI-based functional imaging in delivering precision medicine to children with neuroblastoma. SIGNIFICANCE: This study shows that MRI-based functional imaging can detect apoptotic responses to MYCN-targeted small-molecule inhibitors in a genetically engineered murine model of MYCN-driven neuroblastoma.

Modeling the diffusion-weighted imaging signal for breast lesions in the b = 200 to 3000 s/mm2 range: quality of fit and classification accuracy for different representations

PURPOSE

To evaluate different non-Gaussian representations for the diffusion-weighted imaging (DWI) signal in the b-value range 200 to 3000 s/mm² in benign and malignant breast lesions.

METHODS

Forty-three patients diagnosed with benign (n = 18) or malignant (n = 25) tumors of the breast underwent DWI (b-values 200, 600, 1200, 1800, 2400, and 3000 s/mm² ). Six different representations were fit to the average signal from regions of interest (ROIs) at different b-value ranges. Quality of fit was assessed by the corrected Akaike information criterion (AICc), and the Friedman test was used for assessing representation ranks. The area under the curve (AUC) of receiver operating characteristic curves were used to evaluate the power of derived parameters to differentiate between malignant and benign lesions. The lesion ROI was divided in central and peripheral parts to assess potential effect of heterogeneity. Sensitivity to noise-floor correction was also evaluated.

RESULTS

The Padé exponent was ranked as the best based on AICc, whereas 3 models (kurtosis, fractional, and biexponential) achieved the highest AUC = 0.99 for lesion differentiation. The monoexponential model at b_max = 600 s/mm² already provides AUC = 0.96, with considerably shorter acquisition time and simpler analysis. Significant differences between central and peripheral parts of lesions were found in malignant lesions. The mono- and biexponential models were most stable against varying degrees of noise-floor correction.

CONCLUSION

Non-Gaussian representations are required for fitting of the DWI curve at high b-values in breast lesions. However, the added clinical value from the high b-value data for differentiation of benign and malignant lesions is not clear.

Stromal Collagen Content in Breast Tumors Correlates With In Vivo Diffusion-Weighted Imaging: A Comparison of Multi b-Value DWI With Histologic Specimen From Benign and Malignant Breast Lesions

BACKGROUND

Increased deposition and reorientation of stromal collagen fibers are associated with breast cancer progression and invasiveness. Diffusion-weighted imaging (DWI) may be sensitive to the collagen fiber organization in the stroma and could provide important biomarkers for breast cancer characterization.

PURPOSE

To understand how collagen fibers influence water diffusion in vivo and evaluate the relationship between collagen content and the apparent diffusion coefficient (ADC) and the signal fractions of the biexponential model using a high b-value scheme.

STUDY TYPE

Prospective.

SUBJECTS/SPECIMENS

Forty-five patients with benign (n = 8), malignant (n = 36), and ductal carcinoma in situ (n = 1) breast tumors. Lesions and normal fibroglandular tissue (n = 9) were analyzed using sections of formalin-fixed, paraffin-embedded tissue stained with hematoxylin, erythrosine, and saffron.

FIELD STRENGTH/SEQUENCE

MRI (3T) protocols: Protocol I: Twice-refocused spin-echo echo-planar imaging with: echo time (TE) 85 msec; repetition time (TR) 9300/11600 msec; matrix 90 × 90 × 60; voxel size 2 × 2 × 2.5 mm³ ; b-values: 0 and 700 s/mm² . Protocol II: Stejskal-Tanner spin-echo echo-planar imaging with: TE: 88 msec; TR: 10600/11800 msec, matrix 90 × 90 × 60; voxel size 2 × 2 × 2.5 mm³ ; b-values [0, 200, 600, 1200, 1800, 2400, 3000] s/mm² .

ASSESSMENT

Area fractions of cellular and collagen content in histologic sections were quantified using whole-slide image analysis and compared with the corresponding DWI parameters.

STATISTICAL TESTS

Correlations were assessed using Pearson's r. Univariate analysis of group median values was done using the Mann-Whitney U-test.

RESULTS

Collagen content correlated with the fast signal fraction (r = 0.67, P < 0.001) and ADC (r = 0.58, P < 0.001) and was lower (P < 0.05) in malignant lesions than benign and normal tissues. Cellular content correlated inversely with the fast signal fraction (r = -0.67, P < 0.001) and ADC (r = -0.61, P < 0.001) and was different (P < 0.05) between malignant, benign, and normal tissues.

DATA CONCLUSION

Our findings suggest stromal collagen content increases diffusivity observed by MRI and is associated with higher ADC and fast signal fraction of the biexponential model.

LEVEL OF EVIDENCE

3 Technical Efficacy Stage: 3 J. Magn. Reson. Imaging 2020;51:1868-1878.

MRI Imaging of the Hemodynamic Vasculature of Neuroblastoma Predicts Response to Antiangiogenic Treatment

Childhood neuroblastoma is a hypervascular tumor of neural origin, for which antiangiogenic drugs are currently being evaluated; however, predictive biomarkers of treatment response, crucial for successful delivery of precision therapeutics, are lacking. We describe an MRI-pathologic cross-correlative approach using intrinsic susceptibility (IS) and susceptibility contrast (SC) MRI to noninvasively map the vascular phenotype in neuroblastoma Th-MYCN transgenic mice treated with the vascular endothelial growth factor receptor inhibitor cediranib. We showed that the transverse MRI relaxation rate R 2* (second-1) and fractional blood volume (fBV, %) were sensitive imaging biomarkers of hemorrhage and vascular density, respectively, and were also predictive biomarkers of response to cediranib. Comparison with MRI and pathology from patients with MYCN-amplified neuroblastoma confirmed the high degree to which the Th-MYCN model vascular phenotype recapitulated that of the clinical phenotype, thereby supporting further evaluation of IS- and SC-MRI in the clinic. This study reinforces the potential role of functional MRI in delivering precision medicine to children with neuroblastoma. SIGNIFICANCE: This study shows that functional MRI predicts response to vascular-targeted therapy in a genetically engineered murine model of neuroblastoma.

Post-radiotherapy apparent diffusion coefficient (ADC) in children and young adults with high-grade gliomas and diffuse intrinsic pontine gliomas

Objectives: Diffusion-weighted magnetic resonance imaging (DW-MRI) offers potential to monitor response and predict survival in high-grade gliomas (HGG) and diffuse intrinsic pontine gliomas (DIPG). We hypothesized that post-radiotherapy DW-MRI may provide prognostic imaging biomarkers in children and young adults with these tumors. Methods: Patients aged ≤21 years diagnosed between 2005 and 2012 were eligible. The tumor median apparent diffusion coefficient (ADC) and its 5th percentile (C5-ADC) were determined at the first post-radiotherapy scan and at the time of radiological progression. DW-MRI parameters were correlated with survival endpoints, temozolomide use and pseudoprogression, when it occurred. Results: Out of 40 patients (20 HGG, 20 DIPG), 23 had evaluable DW-MRI post-radiotherapy and 25 at radiological progression. There were 6 episodes of pseudoprogression. Hazard ratios (95%CI) for progression-free survival were 0.998 (0.993-1.003) for median ADC and 1.003 (0.996-1.010) for C5-ADC. Hazard ratios (95%CI) for overall survival were 1.0009 (0.996-1.006) for median ADC and 0.998 (0.992-1.004) for C5-ADC. Post-radiotherapy median and C5-ADC values were not significantly different between patients treated with radiotherapy alone versus radiotherapy/temozolomide. The median and C5-ADC values were not significantly different at the time of pseudoprogression compared to those at tumor progression. Conclusions: Post-radiotherapy median ADC and C5-ADC were not prognostic, nor able to differentiate radiosensitization with temozolomide or occurrence of pseudoprogression in this cohort of HGG and DIPG patients. Further exploration of alternative DW parameters, study timepoints or data modeling may contribute to the development of prognostic/predictive imaging biomarkers for children and young adults with HGG or DIPG.

Characterisation of fibrosis in chemically-induced rat mammary carcinomas using multi-modal endogenous contrast MRI on a 1.5T clinical platform

OBJECTIVES

To determine the ability of multi-parametric, endogenous contrast MRI to detect and quantify fibrosis in a chemically-induced rat model of mammary carcinoma.

METHODS

Female Sprague-Dawley rats (n=18) were administered with N-methyl-N-nitrosourea; resulting mammary carcinomas underwent nine-b-value diffusion-weighted (DWI), ultrashort-echo (UTE) and magnetisation transfer (MT) magnetic resonance imaging (MRI) on a clinical 1.5T platform, and associated quantitative MR parameters were calculated. Excised tumours were histologically assessed for degree of necrosis, collagen, hypoxia and microvessel density. Significance level adjusted for multiple comparisons was p=0.0125.

RESULTS

Significant correlations were found between MT parameters and degree of picrosirius red staining (r > 0.85, p < 0.0002 for ka and δ, r < -0.75, p < 0.001 for T1 and T1s, Pearson), indicating that MT is sensitive to collagen content in mammary carcinoma. Picrosirius red also correlated with the DWI parameter fD* (r=0.801, p=0.0004) and conventional gradient-echo T2* (r=-0.660, p=0.0055). Percentage necrosis correlated moderately with ultrashort/conventional-echo signal ratio (r=0.620, p=0.0105). Pimonidazole adduct (hypoxia) and CD31 (microvessel density) staining did not correlate with any MR parameter assessed.

CONCLUSIONS

Magnetisation transfer MRI successfully detects collagen content in mammary carcinoma, supporting inclusion of MT imaging to identify fibrosis, a prognostic marker, in clinical breast MRI examinations.

KEY POINTS

• Magnetisation transfer imaging is sensitive to collagen content in mammary carcinoma. • Magnetisation transfer imaging to detect fibrosis in mammary carcinoma fibrosis is feasible. • IVIM diffusion does not correlate with microvessel density in preclinical mammary carcinoma.

Support vector machine for breast cancer classification using diffusion-weighted MRI histogram features: Preliminary study

BACKGROUND

Diffusion-weighted MRI (DWI) is currently one of the fastest developing MRI-based techniques in oncology. Histogram properties from model fitting of DWI are useful features for differentiation of lesions, and classification can potentially be improved by machine learning.

PURPOSE

To evaluate classification of malignant and benign tumors and breast cancer subtypes using support vector machine (SVM).

STUDY TYPE

Prospective.

SUBJECTS

Fifty-one patients with benign (n = 23) and malignant (n = 28) breast tumors (26 ER+, whereof six were HER2+).

FIELD STRENGTH/SEQUENCE

Patients were imaged with DW-MRI (3T) using twice refocused spin-echo echo-planar imaging with echo time / repetition time (TR/TE) = 9000/86 msec, 90 × 90 matrix size, 2 × 2 mm in-plane resolution, 2.5 mm slice thickness, and 13 b-values.

ASSESSMENT

Apparent diffusion coefficient (ADC), relative enhanced diffusivity (RED), and the intravoxel incoherent motion (IVIM) parameters diffusivity (D), pseudo-diffusivity (D*), and perfusion fraction (f) were calculated. The histogram properties (median, mean, standard deviation, skewness, kurtosis) were used as features in SVM (10-fold cross-validation) for differentiation of lesions and subtyping.

STATISTICAL TESTS

Accuracies of the SVM classifications were calculated to find the combination of features with highest prediction accuracy. Mann-Whitney tests were performed for univariate comparisons.

RESULTS

For benign versus malignant tumors, univariate analysis found 11 histogram properties to be significant differentiators. Using SVM, the highest accuracy (0.96) was achieved from a single feature (mean of RED), or from three feature combinations of IVIM or ADC. Combining features from all models gave perfect classification. No single feature predicted HER2 status of ER + tumors (univariate or SVM), although high accuracy (0.90) was achieved with SVM combining several features. Importantly, these features had to include higher-order statistics (kurtosis and skewness), indicating the importance to account for heterogeneity.

DATA CONCLUSION

Our findings suggest that SVM, using features from a combination of diffusion models, improves prediction accuracy for differentiation of benign versus malignant breast tumors, and may further assist in subtyping of breast cancer.

LEVEL OF EVIDENCE

3 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2018;47:1205-1216.

Extracranial Soft-Tissue Tumors: Repeatability of Apparent Diffusion Coefficient Estimates from Diffusion-weighted MR Imaging

Purpose To assess the repeatability of apparent diffusion coefficient (ADC) estimates in extracranial soft-tissue diffusion-weighted magnetic resonance imaging across a wide range of imaging protocols and patient populations. Materials and Methods Nine prospective patient studies and one prospective volunteer study, performed between 2006 and 2016 with research ethics committee approval and written informed consent from each subject, were included in this single-institution study. A total of 141 tumors and healthy organs were imaged twice (interval between repeated examinations, 45 minutes to 10 days, depending the on study) to assess the repeatability of median and mean ADC estimates. The Levene test was used to determine whether ADC repeatability differed between studies. The Pearson linear correlation coefficient was used to assess correlation between coefficient of variation (CoV) and the year the study started, study size, and volumes of tumors and healthy organs. The repeatability of ADC estimates from small, medium, and large tumors and healthy organs was assessed irrespective of study, and the Levene test was used to determine whether ADC repeatability differed between these groups. Results CoV aggregated across all studies was 4.1% (range for each study, 1.7%-6.5%). No correlation was observed between CoV and the year the study started or study size. CoV was weakly correlated with volume (r = -0.5, P = .1). Repeatability was significantly different between small, medium, and large tumors (P < .05), with the lowest CoV (2.6%) for large tumors. There was a significant difference in repeatability between studies-a difference that did not persist after the study with the largest tumors was excluded. Conclusion ADC is a robust imaging metric with excellent repeatability in extracranial soft tissues across a wide range of tumor sites, sizes, patient populations, and imaging protocol variations. Online supplemental material is available for this article.

Feasibility and applicability of diffusion-weighted and dynamic contrast-enhanced magnetic resonance imaging in routine assessments of children with high-grade gliomas

Diffusion-weighted (DW) and dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) have been used as imaging biomarkers in adults with high-grade gliomas (HGGs). We incorporated free-breathing DW-MRI and DCE-MRI, at a single time point, in the routine follow-up of five children (median age 9 years, range 8-15) with histologically confirmed HGG within a prospective imaging study. It was feasible to incorporate DW-MRI and DCE-MRI in routine assessments of children with HGG. DW and DCE parameters were repeatable in paediatric HGG. Higher median ADC100-1000 significantly correlated with longer survival in our sample.

Repeatability of derived parameters from histograms following non-Gaussian diffusion modelling of diffusion-weighted imaging in a paediatric oncological cohort

OBJECTIVES

To examine repeatability of parameters derived from non-Gaussian diffusion models in data acquired in children with solid tumours.

METHODS

Paediatric patients (<16 years, n = 17) were scanned twice, 24 h apart, using DWI (6 b-values, 0-1000 mm-2 s) at 1.5 T in a prospective study. Tumour ROIs were drawn (3 slices) and all data fitted using IVIM, stretched exponential, and kurtosis models; percentage coefficients of variation (CV) calculated for each parameter at all ROI histogram centiles, including the medians.

RESULTS

The values for ADC, D, DDCα, α, and DDCK gave CV < 10 % down to the 5th centile, with sharp CV increases below 5th and above 95th centile. K, f, and D* showed increased CV (>30 %) over the histogram. ADC, D, DDCα, and DDCK were strongly correlated (ρ > 0.9), DDCα and α were not correlated (ρ = 0.083).

CONCLUSION

Perfusion- and kurtosis-related parameters displayed larger, more variable CV across the histogram, indicating observed clinical changes outside of D/DDC in these models should be interpreted with caution. Centiles below 5th for all parameters show high CV and are unreliable as diffusion metrics. The stretched exponential model behaved well for both DDCα and α, making it a strong candidate for modelling multiple-b-value diffusion imaging data.

KEY POINTS

• ADC has good repeatability as low 5th centile of the histogram distribution. • High CV was observed for all parameters at extremes of histogram. • Parameters from the stretched exponential model showed low coefficients of variation. • The median ADC, D, DDC α , and DDC K are highly correlated and repeatable. • Perfusion/kurtosis parameters showed high CV variations across their histogram distributions.

Extended T2-IVIM model for correction of TE dependence of pseudo-diffusion volume fraction in clinical diffusion-weighted magnetic resonance imaging

The bi-exponential intravoxel-incoherent-motion (IVIM) model for diffusion-weighted MRI (DWI) fails to account for differential T 2 s in the model compartments, resulting in overestimation of pseudodiffusion fraction f. An extended model, T2-IVIM, allows removal of the confounding echo-time (TE) dependence of f, and provides direct compartment T 2 estimates. Two consented healthy volunteer cohorts (n  =  5, 6) underwent DWI comprising multiple TE/b-value combinations (Protocol 1: TE  =  62-102 ms, b  =  0-250 mm-2s, 30 combinations. Protocol 2: 8 b-values 0-800 mm-2s at TE  =  62 ms, with 3 additional b-values 0-50 mm-2s at TE  =  80, 100 ms; scanned twice). Data from liver ROIs were fitted with IVIM at individual TEs, and with the T2-IVIM model using all data. Repeat-measures coefficients of variation were assessed for Protocol 2. Conventional IVIM modelling at individual TEs (Protocol 1) demonstrated apparent f increasing with longer TE: 22.4  ±  7% (TE  =  62 ms) to 30.7  ±  11% (TE  =  102 ms); T2-IVIM model fitting accounted for all data variation. Fitting of Protocol 2 data using T2-IVIM yielded reduced f estimates (IVIM: 27.9  ±  6%, T2-IVIM: 18.3  ±  7%), as well as T 2  =  42.1  ±  7 ms, 77.6  ±  30 ms for true and pseudodiffusion compartments, respectively. A reduced Protocol 2 dataset yielded comparable results in a clinical time frame (11 min). The confounding dependence of IVIM f on TE can be accounted for using additional b/TE images and the extended T2-IVIM model.

T2-adjusted computed diffusion-weighted imaging: A novel method to enhance tumour visualisation

PURPOSE

To introduce T2-adjusted computed DWI (T2-cDWI), a method that provides synthetic images at arbitrary b-values and echo times (TEs) that improve tissue contrast by removing or increasing T2 contrast in diffusion-weighted images.

MATERIALS AND METHODS

In addition to the standard DWI acquisition protocol T2-weighted echo-planar images at multiple (≥2) echo times were acquired. This allows voxelwise estimation of apparent diffusion coefficient (ADC) and T2 values, permitting synthetic images to be generated at any chosen b-value and echo time. An analytical model is derived for the noise properties in T2-cDWI, and validated using a diffusion test-object. Furthermore, we present T2-cDWI in two example clinical case studies: (i) a patient with mesothelioma demonstrating multiple disease tissue compartments and (ii) a patient with primary ovarian cancer demonstrating solid and cystic disease compartments.

RESULTS

Measured image noise in T2-cDWI from phantom experiments conformed to the analytical model and demonstrated that T2-cDWI at high computed b-value/TE combinations achieves lower noise compared with conventional DWI. In patients, T2-cDWI with low b-value and long TE enhanced fluid signal while suppressing solid tumour components. Conversely, large b-values and short TEs overcome T2 shine-through effects and increase the contrast between tumour and fluid compared with conventional high-b-value DW images.

CONCLUSION

T2-cDWI is a promising clinical tool for improving image signal-to-noise, image contrast, and tumour detection through suppression of T2 shine-through effects.

Modulation of renal oxygenation and perfusion in rat kidney monitored by quantitative diffusion and blood oxygen level dependent magnetic resonance imaging on a clinical 1.5T platform

BACKGROUND

To investigate the combined use of intravoxel incoherent motion (IVIM) diffusion-weighted (DW) and blood oxygen level dependent (BOLD) magnetic resonance imaging (MRI) to assess rat renal function using a 1.5T clinical platform.

METHODS

Multiple b-value DW and BOLD MR images were acquired from adult rats using a parallel clinical coil arrangement, enabling quantitation of the apparent diffusion coefficient (ADC), IVIM-derived diffusion coefficient (D), pseudodiffusion coefficient (D*) and perfusion fraction (f), and the transverse relaxation time T2*, for whole kidney, renal cortex, and medulla. Following the acquisition of two baseline datasets to assess measurement repeatability, images were acquired following i.v. administration of hydralazine, furosemide, or angiotensin II for up to 40 min.

RESULTS

Excellent repeatability (CoV <10 %) was observed for ADC, D, f and T2* measured over the whole kidney. Hydralazine induced a marked and significant (p < 0.05) reduction in whole kidney ADC, D, and T2*, and a significant (p < 0.05) increase in D* and f. Furosemide significantly (p < 0.05) increased whole kidney ADC, D, and T2*. A more variable response to angiotensin II was determined, with a significant (p < 0.05) increase in medulla D* and significant (p < 0.05) reduction in whole kidney T2* established.

CONCLUSIONS

Multiparametric MRI, incorporating quantitation of IVIM DWI and BOLD biomarkers and performed on a clinical platform, can be used to monitor the acute effects of vascular and tubular modulating drugs on rat kidney function in vivo. Clinical adoption of such functional imaging biomarkers can potentially inform on treatment effects in patients with renal dysfunction.

Development of a temperature-controlled phantom for magnetic resonance quality assurance of diffusion, dynamic, and relaxometry measurements

PURPOSE

Diffusion-weighted (DW) and dynamic contrast-enhanced magnetic resonance imaging (MRI) are increasingly applied for the assessment of functional tissue biomarkers for diagnosis, lesion characterization, or for monitoring of treatment response. However, these techniques are vulnerable to the influence of various factors, so there is a necessity for a standardized MR quality assurance procedure utilizing a phantom to facilitate the reliable estimation of repeatability of these quantitative biomarkers arising from technical factors (e.g., B1 variation) affecting acquisition on scanners of different vendors and field strengths. The purpose of this study is to present a novel phantom designed for use in quality assurance for multicenter trials, and the associated repeatability measurements of functional and quantitative imaging protocols across different MR vendors and field strengths.

METHODS

A cylindrical acrylic phantom was manufactured containing 7 vials of polyvinylpyrrolidone (PVP) solutions of different concentrations, ranging from 0% (distilled water) to 25% w/w, to create a range of different MR contrast parameters. Temperature control was achieved by equilibration with ice-water. Repeated MR imaging measurements of the phantom were performed on four clinical scanners (two at 1.5 T, two at 3.0 T; two vendors) using the same scanning protocol to assess the long-term and short-term repeatability. The scanning protocol consisted of DW measurements, inversion recovery (IR) T1 measurements, multiecho T2 measurement, and dynamic T1-weighted sequence allowing multiple variable flip angle (VFA) estimation of T1 values over time. For each measurement, the corresponding calculated parameter maps were produced. On each calculated map, regions of interest (ROIs) were drawn within each vial and the median value of these voxels was assessed. For the dynamic data, the autocorrelation function and their variance were calculated; for the assessment of the repeatability, the coefficients of variation (CoV) were calculated.

RESULTS

For both field strengths across the available vendors, the apparent diffusion coefficient (ADC) at 0 °C ranged from (1.12 ± 0.01) × 10(-3) mm(2)/s for pure water to (0.48 ± 0.02) × 10(-3) mm(2)/s for the 25% w/w PVP concentration, presenting a minor variability between the vendors and the field strengths. T2 and IR-T1 relaxation time results demonstrated variability between the field strengths and the vendors across the different acquisitions. Moreover, the T1 values derived from the VFA method exhibited a large variation compared with the IR-T1 values across all the scanners for all repeated measurements, although the calculation of the standard deviation of the VFA-T1 estimate across each ROI and the autocorrelation showed a stability of the signal for three scanners, with autocorrelation of the signal over the dynamic series revealing a periodic variation in one scanner. Finally, the ADC, the T2, and the IR-T1 values exhibited an excellent repeatability across the scanners, whereas for the dynamic data, the CoVs were higher.

CONCLUSIONS

The combination of a novel PVP phantom, with multiple compartments to give a physiologically relevant range of ADC and T1 values, together with ice-water as a temperature-controlled medium, allows reliable quality assurance measurements that can be used to measure agreement between MRI scanners, critical in multicenter functional and quantitative imaging studies.

Childhood extracranial neoplasms: the role of imaging in drug development and clinical trials

Cancer is the leading cause of death in children older than 1 year of age and new drugs are necessary to improve outcomes. Imaging is crucial to the drug development process and assessment of therapeutic response. In adults, tumours are often assessed with CT using size criteria. Unfortunately, techniques established in adults are not necessarily applicable in children due to differing pathophysiology, ability to cooperate and increased susceptibility to ionising radiation. MRI, in particular quantitative MRI, has to date not been fully utilised in children with extracranial neoplasms. The specific challenges of imaging in children, the potential for functional imaging techniques to inform upon and their inclusion in clinical trials are discussed.

Demonstration of the reproducibility of free-breathing diffusion-weighted MRI and dynamic contrast enhanced MRI in children with solid tumours: a pilot study

OBJECTIVES

The objectives are to examine the reproducibility of functional MR imaging in children with solid tumours using quantitative parameters derived from diffusion-weighted (DW-) and dynamic contrast enhanced (DCE-) MRI.

METHODS

Patients under 16-years-of age with confirmed diagnosis of solid tumours (n = 17) underwent free-breathing DW-MRI and DCE-MRI on a 1.5 T system, repeated 24 hours later. DW-MRI (6 b-values, 0-1000 sec/mm(2)) enabled monoexponential apparent diffusion coefficient estimation using all (ADC0-1000) and only ≥100 sec/mm(2) (ADC100-1000) b-values. DCE-MRI was used to derive the transfer constant (K(trans)), the efflux constant (kep), the extracellular extravascular volume (ve), and the plasma fraction (vp), using a study cohort arterial input function (AIF) and the extended Tofts model. Initial area under the gadolinium enhancement curve and pre-contrast T1 were also calculated. Percentage coefficients of variation (CV) of all parameters were calculated.

RESULTS

The most reproducible cohort parameters were ADC100-1000 (CV = 3.26%), pre-contrast T1 (CV = 6.21%), and K(trans) (CV = 15.23%). The ADC100-1000 was more reproducible than ADC0-1000, especially extracranially (CV = 2.40% vs. 2.78%). The AIF (n = 9) derived from this paediatric population exhibited sharper and earlier first-pass and recirculation peaks compared with the literature's adult population average.

CONCLUSIONS

Free-breathing functional imaging protocols including DW-MRI and DCE-MRI are well-tolerated in children aged 6 - 15 with good to moderate measurement reproducibility.

KEY POINTS

• Diffusion MRI protocol is feasible and well-tolerated in a paediatric oncology population. • DCE-MRI for pharmacokinetic evaluation is feasible and well tolerated in a paediatric oncology population. • Paediatric arterial input function (AIF) shows systematic differences from the adult population-average AIF. • Variation of quantitative parameters from paired functional MRI measurements were within 20%.

Use of the temporal median and trimmed mean mitigates effects of respiratory motion in multiple-acquisition abdominal diffusion imaging

Respiratory motion commonly confounds abdominal diffusion-weighted magnetic resonance imaging, where averaging of successive samples at different parts of the respiratory cycle, performed in the scanner, manifests the motion as blurring of tissue boundaries and structural features and can introduce bias into calculated diffusion metrics. Storing multiple averages separately allows processing using metrics other than the mean; in this prospective volunteer study, median and trimmed mean values of signal intensity for each voxel over repeated averages and diffusion-weighting directions are shown to give images with sharper tissue boundaries and structural features for moving tissues, while not compromising non-moving structures. Expert visual scoring of derived diffusion maps is significantly higher for the median than for the mean, with modest improvement from the trimmed mean. Diffusion metrics derived from mono- and bi-exponential diffusion models are comparable for non-moving structures, demonstrating a lack of introduced bias from using the median. The use of the median is a simple and computationally inexpensive alternative to complex and expensive registration algorithms, requiring only additional data storage (and no additional scanning time) while returning visually superior images that will facilitate the appropriate placement of regions-of-interest when analysing abdominal diffusion-weighted magnetic resonance images, for assessment of disease characteristics and treatment response.

Comparison of free-breathing with navigator-controlled acquisition regimes in abdominal diffusion-weighted magnetic resonance images: Effect on ADC and IVIM statistics

PURPOSE

To evaluate the effect on diffusion-weighted image-derived parameters in the apparent diffusion coefficient (ADC) and intra-voxel incoherent motion (IVIM) models from choice of either free-breathing or navigator-controlled acquisition.

MATERIALS AND METHODS

Imaging was performed with consent from healthy volunteers (n = 10) on a 1.5T Siemens Avanto scanner. Parameter-matched free-breathing and navigator-controlled diffusion-weighted images were acquired, without averaging in the console, for a total scan time of ∼10 minutes. Regions of interest were drawn for renal cortex, renal pyramid, whole kidney, liver, spleen, and paraspinal muscle. An ADC diffusion model for these regions was fitted for b-values ≥ 250 s/mm(2) , using a Levenberg-Marquardt algorithm, and an IVIM model was fitted for all images using a Bayesian method.

RESULTS

ADC and IVIM parameters from the two acquisition regimes show no significant differences for the cohort; individual cases show occasional discrepancies, with outliers in parameter estimates arising more commonly from navigator-controlled scans. The navigator-controlled acquisitions showed, on average, a smaller range of movement for the kidneys (6.0 ± 1.4 vs. 10.0 ± 1.7 mm, P = 0.03), but also a smaller number of averages collected (3.9 ± 0.1 vs. 5.5 ± 0.2, P < 0.01) in the allocated time.

CONCLUSION

Navigator triggering offers no advantage in fitted diffusion parameters, whereas free-breathing appears to offer greater confidence in fitted diffusion parameters, with fewer outliers, for matched acquisition periods.

Blood oxygenation level dependent, blood volume, and blood flow responses to carbogen and hypoxic hypoxia in 9L rat gliomas as measured by MRI

PURPOSE

To study vascular responsiveness to hypoxia and hypercarbia together with vessel size index (VSI) in a 9L rat glioma (n = 11) using multimodal MRI.

MATERIALS AND METHODS

VSI was determined using T2 and T2* MRI following AMI-227 contrast agent. Blood oxygenation level dependent (BOLD) signal response was determined using T2 EPI MRI, blood volume changes using AMI-227 and blood flow by means of continuous arterial spin labeling.

RESULTS

VSI in the cortex, tumor rim, and core of 2.2 ± 1.0, 18.2 ± 5.4, and 23.9 ± 14.7 μm, respectively, showing a larger average vessel size in glioma than in the brain parenchyma. BOLD and blood volume signal changes to hypoxia and hypercapnia were much more profound in the tumor rim than the core. Hypoxia led to rim BOLD signal change that was larger in amplitude and it attained the low value much faster than either core or brain cortex. The vasculature in the rim appears more responsive to respiratory challenges in terms of volume adaptation than the core. Blood flow values within the gliomas were much lower than in the contralateral brain. Neither hypercarbia nor hypoxia had an effect on the tumor blood flow.

CONCLUSION

Vascular responses of 9L gliomas to respiratory challenge, in particular hypoxia, are heterogeneous between the core and rim zones, potentially offering a means to classify and separate intratumor tissues with differing hemodynamic characteristics.