Correlation of tumor characteristics derived from DCE‐MRI and DW‐MRI with histology in murine models of breast cancer

The purpose of this work was to determine the relationship between the apparent diffusion coefficient (ADC, from diffusion‐weighted (DW) MRI), the extravascular, extracellular volume fraction (v_e, from dynamic contrast‐enhanced (DCE) MRI), and histological measurement of the extracellular space fraction. Athymic nude mice were injected with either human epidermal growth factor receptor 2 positive (HER2+) BT474 (n = 15) or triple negative MDA‐MB‐231 (n = 20) breast cancer cells, treated with either Herceptin (n = 8), Abraxane (low dose n = 7, high dose n = 6), or saline (n = 7 for each cell line), and imaged using DW‐ and DCE‐MRI before, during, and after treatment. After the final imaging acquisition, the tissue was resected and evaluated by histological analysis. H&E‐stained central slices were scanned using a digital brightfield microscope and evaluated with thresholding techniques to calculate the extracellular space. For both BT474 and MDA‐MB‐231, the median ADC of the central slice exhibited a significantly positive correlation with the corresponding central slice extracellular space as measured by H&E (p = 0.03, p < 0.01, respectively). Median v_e calculated from the central slice showed differing results between the two cell lines. For BT474, a significant correlation between v_e and extracellular space was calculated (p = 0.02), while MDA‐MB‐231 tumors did not demonstrate a significant correlation (p = 0.64). Additionally, there was no correlation discovered between ADC and v_e with either whole tumor analysis or central slice analysis (p > 0.05). While ADC correlates well with the histologically determined fraction of extracellular space, these data add to the growing body of literature that suggests that v_e derived from DCE‐MRI is not a reliable biomarker of extracellular space for a range of physiological conditions. Copyright © 2015 John Wiley & Sons, Ltd.     

Early detection of human glioma sphere xenografts in mouse brain using diffusion MRI at 14.1 T

Glioma models have provided important insights into human brain cancers. Among the investigative tools, MRI has allowed their characterization and diagnosis. In this study, we investigated whether diffusion MRI might be a useful technique for early detection and characterization of slow‐growing and diffuse infiltrative gliomas, such as the proposed new models, LN‐2669GS and LN‐2540GS glioma sphere xenografts. Tumours grown in these models are not visible in conventional T₂‐weighted or contrast‐enhanced T₁‐weighted MRI at 14.1 T. Diffusion‐weighted imaging and diffusion tensor imaging protocols were optimized for contrast by exploring long diffusion times sensitive for probing the microstructural alterations induced in the normal brain by the slow infiltration of glioma sphere cells. Compared with T₂‐weighted images, tumours were properly identified in their early stage of growth using diffusion MRI, and confirmed by localized proton MR spectroscopy as well as immunohistochemistry. The first evidence of tumour presence was revealed for both glioma sphere xenograft models three months after tumour implantation, while no necrosis, oedema or haemorrhage were detected either by MRI or by histology. Moreover, different values of diffusion indices, such as mean diffusivity and fractional anisotropy, were obtained in tumours grown from LN‐2669GS and LN‐2540GS glioma sphere lines. These observations highlighted diverse tumour microstructures for both xenograft models, which were reflected in histology. This study demonstrates the ability of diffusion MRI techniques to identify and investigate early stages of slow‐growing, invasive tumours in the mouse brain, thus providing a potential imaging biomarker for early detection of tumours in humans.     

Phase contrast MRI is an early marker of micrometastatic breast cancer development in the rat brain

The early growth of micrometastatic breast cancer in the brain often occurs through vessel co‐option and is independent of angiogenesis. Remodeling of the existing vasculature is an important step in the evolution of co‐opting micrometastases into angiogenesis‐dependent solid tumor masses. The purpose of this study was to determine whether phase contrast MRI, an intrinsic source of contrast exquisitely sensitive to the magnetic susceptibility properties of deoxygenated hemoglobin, could detect vascular changes occurring independent of angiogenesis in a rat model of breast cancer metastases to the brain. Twelve nude rats were administered 10⁶ MDA‐MB‐231BRL ‘brain‐seeking’ breast cancer cells through intracardiac injection. Serial, multiparametric MRI of the brain was performed weekly until metastatic disease was detected. The results demonstrated that images of the signal phase (area under the receiver operating characteristic curve, 0.97) were more sensitive than T₂* gradient echo magnitude images (area under the receiver operating characteristic curve, 0.73) to metastatic brain lesions. The difference between the two techniques was probably the result of the confounding effects of edema on the magnitude of the signal. A region of interest analysis revealed that vascular abnormalities detected with phase contrast MRI preceded tumor permeability measured with contrast‐enhanced MRI by 1–2 weeks. Tumor size was correlated with permeability (R² = 0.23, p < 0.01), but phase contrast was independent of tumor size (R² = 0.03). Histopathologic analysis demonstrated that capillary endothelial cells co‐opted by tumor cells were significantly enlarged, but less dense, relative to the normal brain vasculature. Although co‐opted vessels were vascular endothelial growth factor‐negative, vessels within larger tumor masses were vascular endothelial growth factor‐positive. In conclusion, phase contrast MRI is believed to be sensitive to vascular remodeling in co‐opting brain tumor metastases independent of sprouting angiogenesis, and may therefore aid in preclinical studies of angiogenic‐independent tumors or in the monitoring of continued tumor growth following anti‐angiogenic therapy. Published 2011. This article is a US Government work and is in the public domain in the USA.     

Quantitative T2* imaging of metastatic human breast cancer to brain in the nude rat at 3 T

This study uses quantitative T₂* imaging to track ferumoxides–protamine sulfate (FEPro)‐labeled MDA‐MB‐231BR‐Luc (231BRL) human breast cancer cells that metastasize to the nude rat brain. Four cohorts of nude rats were injected intracardially with FEPro‐labeled, unlabeled or tumor necrosis factor‐related apoptosis‐inducing ligand(TRAIL)‐treated (to induce apoptosis) 231BRL cells, or saline, in order to develop metastatic breast cancer in the brain. The heads of the rats were imaged serially over 3–4 weeks using gradient multi‐echo and turbo spin‐echo pulse sequences at 3 T with a solenoid receive‐only 4‐cm‐diameter coil. Quantitative T₂* maps of the whole brain were obtained by the application of single‐exponential fitting to the signal intensity of T₂* images, and the distribution of T₂* values in brain voxels was calculated. MRI findings were correlated with Prussian blue staining and immunohistochemical staining for iron in breast cancer and macrophages. Quantitative analysis of T₂* from brain voxels demonstrated a significant shift to lower values following the intracardiac injection of FEPro‐labeled 231BRL cells, relative to animals receiving unlabeled cells, apoptotic cells or saline. Quartile analysis based on the T₂* distribution obtained from brain voxels demonstrated significant differences (p < 0.0083) in the number of voxels with T₂* values in the ranges 10–35 ms (Q1), 36–60 ms (Q2) and 61–86 ms (Q3) from 1 day to 3 weeks post‐infusion of labeled 231BRL cells, compared with baseline scans. There were no significant differences in the distribution of T₂* obtained from serial MRI in rats receiving unlabeled or TRAIL‐treated cells or saline. Histologic analysis demonstrated isolated Prussian blue‐positive breast cancer cells scattered in the brains of rats receiving labeled cells, relative to animals receiving unlabeled or apoptotic cells. Quantitative T₂* analysis of FEPro‐labeled metastasized cancer cells was possible even after the hypointense voxels were no longer visible on T₂*‐weighted images. Published in 2010 by John Wiley & Sons, Ltd.     

A multi‐compartmental SE‐BOLD interpretation for stimulus‐related signal changes in diffusion‐weighted functional MRI

A new interpretation is proposed for stimulus‐induced signal changes in diffusion‐weighted functional MRI. T₂‐weighted spin‐echo echo‐planar images were acquired at different diffusion‐weightings while visual stimulation was presented to human volunteers. The amplitudes of the positive stimulus‐correlated response and post‐stimulus undershoot (PSU) in the functional time‐courses were found to follow different trends as a function of b‐value. Data were analysed using a three‐compartment signal model, with one compartment being purely vascular and the other two dominated by fast‐ and slow‐diffusing molecules in the brain tissue. The diffusion coefficients of the tissue were assumed to be constant throughout the experiments. It is shown that the stimulus‐induced signal changes can be decomposed into independent contributions originating from each of the three compartments. After decomposition, the fast‐diffusion phase displays a substantial PSU, while the slow‐diffusion phase demonstrates a highly reproducible and stimulus‐correlated time‐course with minimal undershoot. The decomposed responses are interpreted in terms of the spin‐echo blood oxygenation level dependent (SE‐BOLD) effect, and it is proposed that the signal produced by fast‐ and slow‐diffusing molecules reflect a sensitivity to susceptibility changes in arteriole/venule‐ and capillary‐sized vessels, respectively. This interpretation suggests that diffusion‐weighted SE‐BOLD imaging may provide subtle information about the haemodynamic and neuronal responses. Copyright © 2009 John Wiley & Sons, Ltd.     

MicroRNA‐127 is Downregulated by Tudor‐SN Protein and Contributes to Metastasis and Proliferation in Breast Cancer Cell Line MDA‐MB‐231

Tudor‐SN is a multifunctional protein that is highly expressed in multiple cancers including breast cancer. Tudor‐SN, as a component in RNA‐induced splicing complex, was recently reported to regulate gene expression in a microRNA (miRNA)‐dependent manner, such as let‐7, miR‐34a and miR‐221. However, how Tudor‐SN is associated with cancer development still remains largely elusive. In the present study, we explored the role of Tudor‐SN in breast cancer. Stable knockdown of endogenous Tudor‐SN, performed on the breast cancer cell line MDA‐MB‐231 by small hairpin RNA expression vectors, suppressed the in vitro migration and invasion ability of the metastatic breast cancer cell line. Interestingly, we found Tudor‐SN as a miRNA regulator according to microarray analysis, and further identified that Tudor‐SN negatively regulated the expression of miR‐127, and consequently increased the expression of the proto‐oncogene BCL6 which was a convincing target of miR‐127. Moreover, overexpression of miR‐127 reduced the in vitro migration and proliferation ability of breast cancer cell MDA‐MB‐231. Collectively, our results suggested a novel mechanism that Tudor‐SN promoted metastasis and proliferation of breast cancer cells via downregulating the miR‐127 expression. Anat Rec, 296:1842–1849, 2013. © 2013 Wiley Periodicals, Inc.     

DCE‐ and DW‐MRI as early imaging biomarkers of treatment response in a preclinical model of triple negative breast cancer

This work evaluates quantitative dynamic contrast‐enhanced magnetic resonance imaging (DCE‐MRI) and diffusion‐weighted MRI (DW‐MRI) parameters as early biomarkers of response in a preclinical model of triple negative breast cancer (TNBC). The standard Tofts' model of DCE‐MRI returns estimates of the volume transfer constant (K^trans) and the extravascular extracellular volume fraction (v_e). DW‐MRI returns estimates of the apparent diffusion coefficient (ADC). Mice (n = 38) were injected subcutaneously with MDA‐MB‐231. Tumors were grown to approximately 275 mm³ and sorted into the following groups: saline controls, low‐dose Abraxane (15 mg/kg) and high‐dose Abraxane (25 mg/kg). Animals were imaged at days zero, one and three. On day three, tumors were extracted for immunohistochemistry. The positive percentage change in ADC on day one was significantly higher in both treatment groups relative to the control group (p < 0.05). In addition, the positive percentage change in K^trans was significantly higher than controls (p < 0.05) on day one for the high‐dose group and on days one and three for the low‐dose group. The percentage change in tumor volume was significantly different between the high‐dose and control groups on day three (p = 0.006). Histology confirmed differences at day three through reduced numbers of proliferating cells (Ki67 staining) in the high‐dose group (p = 0.03) and low‐dose group (p = 0.052) compared with the control group. Co‐immunofluorescent staining of vascular maturity [using von Willebrand Factor (vWF) and α‐smooth muscle actin (α‐SMA)] indicated significantly higher vascular maturation in the low‐dose group compared with the controls on day three (p = 0.03), and trending towards significance in the high‐dose group compared with controls on day three (p = 0.052). These results from quantitative imaging with histological validation indicate that ADC and K^trans have the potential to serve as early biomarkers of treatment response in murine studies of TNBC.     

Difference in the intratumoral distributions of extracellular‐fluid and intravascular MR contrast agents in glioblastoma growth

Contrast enhancement by an extracellular‐fluid contrast agent (CA) (Gd‐DOTA) depends primarily on the blood–brain‐barrier permeability (bp ), and transverse‐relaxation change caused by intravascular T₂ CA (superparamagnetic iron oxide nanoparticles, SPIONs) is closely associated with the blood volume (BV). Pharmacokinetic (PK) vascular characterization based on single‐CA‐using dynamic contrast‐enhanced MRI (DCE‐MRI) has shown significant measurement variation according to the molecular size of the CA. Based on this recognition, this study used a dual injection of Gd‐DOTA and SPIONs for tracing the changes of bp and BV in C6 glioma growth (Days 1 and 7 after the tumor volume reached 2 mL). bp was quantified according to the non‐PK parameters of Gd‐DOTA‐using DCE‐MRI (wash‐in rate, maximum enhancement ratio and initial area under the enhancement curve (IAUC)). BV was estimated by SPION‐induced ΔR₂* and ΔR₂. With validated measurement reliability of all the parameters (coefficients of variation ≤10%), dual‐contrast MRI demonstrated a different region‐oriented distribution between Gd‐DOTA and SPIONs within a tumor as follows: (a) the BV increased stepwise from the tumor center to the periphery; (b) the tumor periphery maintained the augmented BV to support continuous tumor expansion from Day 1 to Day 7; (c) the internal tumor area underwent significant vascular shrinkage (i.e. decreased ΔR₂ and ΔR₂) as the tumor increased in size; (d) the tumor center showed greater bp ‐indicating parameters, i.e. wash‐in rate, maximum enhancement ratio and IAUC, than the periphery on both Days 1 and 7 and (e) the tumor center showed a greater increase of bp than the tumor periphery in tumor growth, as suggested to support tumor viability when there is insufficient blood supply. In the MRI–histologic correlation, a prominent BV increase in the tumor periphery seen in MRI was verified with increased fluorescein isothiocyanate–dextran signals and up‐regulated immunoreactivity of CD31–VEGFR. In conclusion, the spatiotemporal alterations of BV and bp in glioblastoma growth, i.e. augmented BV in the tumor periphery and increased bp in the center, can be sufficiently evaluated by MRI with dual injection of extracellular‐fluid Gd chelates and intravascular SPION.     

The critical role of the ZNF217 oncogene in promoting breast cancer metastasis to the bone

Bone metastasis affects >70% of patients with advanced breast cancer. However, the molecular mechanisms underlying this process remain unclear. On the basis of analysis of clinical datasets, and in vitro and in vivo experiments, we report that the ZNF217 oncogene is a crucial mediator and indicator of bone metastasis. Patients with high ZNF217 mRNA expression levels in primary breast tumours had a higher risk of developing bone metastases. MDA‐MB‐231 breast cancer cells stably transfected with ZNF217 (MDA‐MB‐231‐ZNF217) showed the dysregulated expression of a set of genes with bone‐homing and metastasis characteristics, which overlapped with two previously described ‘osteolytic bone metastasis’ gene signatures, while also highlighting the bone morphogenetic protein (BMP) pathway. The latter was activated in MDA‐MB‐231‐ZNF217 cells, and its silencing by inhibitors (Noggin and LDN‐193189) was sufficient to rescue ZNF217‐dependent cell migration, invasion or chemotaxis towards the bone environment. Finally, by using non‐invasive multimodal in vivo imaging, we found that ZNF217 increases the metastatic growth rate in the bone and accelerates the development of severe osteolytic lesions. Altogether, the findings of this study highlight ZNF217 as an indicator of the emergence of breast cancer bone metastasis; future therapies targeting ZNF217 and/or the BMP signalling pathway may be beneficial by preventing the development of bone metastases. Copyright © 2017 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.     

Time‐dependent diffusion MRI as a probe of microstructural changes in a mouse model of Duchenne muscular dystrophy

Dystrophic muscles show a high variability of fibre sizes and altered sarcolemmal integrity, which are typically assessed by histology. Time‐dependent diffusion MRI is sensitive to tissue microstructure and its investigation through age‐related changes in dystrophic and healthy muscles may help the understanding of the onset and progression of Duchenne muscular dystrophy (DMD). We investigated the capability of time‐dependent diffusion MRI to quantify age and disease‐related changes in hind‐limb muscle microstructure between dystrophic (mdx) and wild‐type (WT) mice of three age groups (7.5, 22 and 44 weeks). Diffusion time‐dependent apparent diffusion coefficients (ADCs) of the gastrocnemius and tibialis anterior muscles were determined versus age and diffusion‐gradient orientation at six diffusion times (Δ; range: 25–350 ms). Mean muscle ADCs were compared between groups and ages, and correlated with T₂, using Student's t test, one‐way analysis of variance and Pearson correlation, respectively. Muscle fibre sizes and sarcolemmal integrity were evaluated by histology and compared with diffusion measurements. Hind‐limb muscle ADC showed characteristic restricted diffusion behaviour in both mdx and WT animals with decreasing ADC values at longer Δ. Significant differences in ADC were observed at long Δ values (≥ 250 ms; p < 0.05, comparison between groups; p < 0.01, comparison between ages) with ADC increased by 5–15% in dystrophic muscles, indicative of reduced diffusion restriction. No significant correlation was found between T₂ and ADC. Additionally, muscle fibre size distributions showed higher variability and lower mean fibre size in mdx than WT animals (p < 0.001). The extensive Evans Blue Dye uptake shown in dystrophic muscles revealed substantial sarcolemmal damage, suggesting diffusion measurements as more consistent with altered permeability rather than changes in muscle fibre sizes. This study shows the potential of diffusion MRI to non‐invasively discriminate between dystrophic and healthy muscles with enhanced sensitivity when using long Δ.     

Noninvasive mapping of endothelial dysfunction in myocardial ischemia by magnetic resonance imaging using an albumin‐based contrast agent

Noninvasive preclinical methods for the characterization of myocardial vascular function are crucial to an understanding of the dynamics of ischemic cardiac disease. Ischemic heart disease is associated with myocardial endothelial dysfunction, resulting in leakage of plasma albumin into the extravascular space. These features can be harnessed in a novel noninvasive three‐dimensional magnetic resonance imaging method to measure fractional blood volume (fBV) and vascular permeability (permeability–surface area product, PS) using labeled albumin as a blood pool contrast agent. C57BL/6 mice were imaged before and 3 days after myocardial infarction (MI). Following the quantification of endogenous myocardial R₁, the dynamics of intravenously injected albumin‐based contrast agent, extravasating from permeable myocardial blood vessels, were tracked on short‐axis magnetic resonance images of the entire heart. This study successfully discriminated between infarcted and remote regions at 3 days post‐infarct, based on a reduced fBV and increased PS in the infarcted region. These findings were confirmed using ex vivo fluorescence imaging and histology. We have demonstrated a novel method to quantify blood volume and permeability in the infarcted myocardium, providing an imaging biomarker for the assessment of endothelial dysfunction. This method has the potential to three‐dimensionally visualize subtle changes in myocardial permeability and to track endothelial function for longitudinal cardiac studies determining pathophysiological processes during infarct healing.     

Using microbubbles as an MRI contrast agent for the measurement of cerebral blood volume

The susceptibility differences at the gas–liquid interface of microbubbles (MBs) allow their use as an intravascular susceptibility contrast agent for in vivo MRI. However, the characteristics of MBs are very different from those of the standard gadolinium‐diethylenetriaminepentaacetic acid (Gd‐DPTA) contrast agent, including the size distribution and hemodynamic properties, which could influence MRI outcomes. Here, we investigate quantitatively the correlation between the relative cerebral blood volume (rCBV) derived from Gd‐DTPA (rCBV_Gd) and the MB‐induced susceptibility effect (ΔR₂*_MB) by conventional dynamic susceptibility contrast MRI (DSC‐MRI). Custom‐made MBs had a mean diameter of 0.92 µm and were capable of inducing 4.68 ± 3.02% of the maximum signal change (MSC). The MB‐associated ΔR₂*_MB was compared with rCBV_Gd in 16 rats on 4.7‐T MRI. We observed a significant effect of the time to peak (TTP) on the correlation between ΔR₂*_MB and rCBV_Gd, and also found a noticeable dependence between TTP and MSC. Our findings suggest that MBs with longer TTPs can be used for the estimation of rCBV by DSC‐MRI, and emphasize the critical effect of TTP on MB‐based contrast MRI. Copyright © 2013 John Wiley & Sons, Ltd.     

Multiparametric MR for non‐invasive evaluation of tumour tissue histological characteristics after radionuclide therapy

Early non‐invasive tumour therapy response assessment requires methods sensitive to biological and physiological tumour characteristics. The aim of this study was to find and evaluate magnetic resonance imaging (MRI) derived tumour tissue parameters that correlate with histological parameters and that reflect effects of radionuclide therapy. Mice bearing a subcutaneous human small‐intestine neuroendocrine tumour were i.v. injected with ¹⁷⁷Lu‐octreotate. MRI was performed (7 T Bruker Biospec) on different post‐therapy intervals (1 and 13 days) using T2‐weighted imaging, mapping of T2* and T1 relaxation time constants, as well as diffusion and dynamic contrast enhancement (DCE‐MRI) techniques. After MRI, animals were killed and tumours excised. Four differently stained histological sections of the most central imaged tumour plane were digitized, and segmentation techniques were used to produce maps reflecting fibrotic and vascular density, apoptosis, and proliferation. Histological maps were aligned with MRI‐derived parametric maps using landmark‐based registration. Correlations and predictive power were evaluated using linear mixed‐effects models and cross‐validation, respectively. Several MR parameters showed statistically significant correlations with histological parameters. In particular, three DCE‐MRI‐derived parameters reflecting capillary function additionally showed high predictive power regarding apoptosis (2/3) and proliferation (1/3). T1 could be used to predict vascular density, and perfusion fraction derived from diffusion MRI could predict fibrotic density, although with lower predictive power. This work demonstrates the potential to use multiparametric MRI to retrieve important information on the tumour microenvironment after radiotherapy. The non‐invasiveness of the method also allows longitudinal tumour tissue characterization. Further investigation is warranted to evaluate the parameters highlighted in this study longitudinally, in larger studies, and with additional histological methods.     

High‐field (11.75T) multimodal MR imaging of exercising hindlimb mouse muscles using a non‐invasive combined stimulation and force measurement device

We have designed and constructed an experimental set‐up allowing electrical stimulation of hindlimb mouse muscles and the corresponding force measurements at high‐field (11.75T). We performed high‐resolution multimodal MRI (including T₂‐weighted imaging, angiography and diffusion) and analysed the corresponding MRI changes in response to a stimulation protocol. Mice were tested twice over a 1‐week period to investigate the reliability of mechanical measurements and T₂ changes associated with the stimulation protocol. Additionally, angiographic images were obtained before and immediately after the stimulation protocol. Finally, multislice diffusion imaging was performed before, during and immediately after the stimulation session. Apparent diffusion coefficient (ADC) maps were calculated on the basis of diffusion weighted images (DWI). Both force production and T₂ values were highly reproducible as illustrated by the low coefficient of variation (<8%) and high intraclass correlation coefficient (≥0.75) values. Maximum intensity projection angiographic images clearly showed a strong vascular effect resulting from the stimulation protocol. Although a motion sensitive imaging sequence was used (echo planar imaging) and in spite of the strong muscle contractions, motion artifacts were minimal for DWI recorded under exercising conditions, thereby underlining the robustness of the measurements. Mean ADC values increased under exercising conditions and were higher during the recovery period as compared with the corresponding control values. The proposed experimental approach demonstrates accurate high‐field multimodal MRI muscle investigations at a preclinical level which is of interest for monitoring the severity and/or the progression of neuromuscular diseases but also for assessing the efficacy of potential therapeutic interventions. Copyright © 2014 John Wiley & Sons, Ltd.     

Automated pixel‐wise brain tissue segmentation of diffusion‐weighted images via machine learning

The diffusion‐weighted (DW) MR signal sampled over a wide range of b‐values potentially allows for tissue differentiation in terms of cellularity, microstructure, perfusion, and T₂ relaxivity. This study aimed to implement a machine learning algorithm for automatic brain tissue segmentation from DW‐MRI datasets, and to determine the optimal sub‐set of features for accurate segmentation. DWI was performed at 3 T in eight healthy volunteers using 15 b‐values and 20 diffusion‐encoding directions. The pixel‐wise signal attenuation, as well as the trace and fractional anisotropy (FA) of the diffusion tensor, were used as features to train a support vector machine classifier for gray matter, white matter, and cerebrospinal fluid classes. The datasets of two volunteers were used for validation. For each subject, tissue classification was also performed on 3D T₁‐weighted data sets with a probabilistic framework. Confusion matrices were generated for quantitative assessment of image classification accuracy in comparison with the reference method. DWI‐based tissue segmentation resulted in an accuracy of 82.1% on the validation dataset and of 82.2% on the training dataset, excluding relevant model over‐fitting. A mean Dice coefficient (DSC) of 0.79 ± 0.08 was found. About 50% of the classification performance was attributable to five features (i.e. signal measured at b‐values of 5/10/500/1200 s/mm² and the FA). This reduced set of features led to almost identical performances for the validation (82.2%) and the training (81.4%) datasets (DSC = 0.79 ± 0.08). Machine learning techniques applied to DWI data allow for accurate brain tissue segmentation based on both morphological and functional information.     

Simultaneous multi‐slice spin‐ and gradient‐echo dynamic susceptibility‐contrast perfusion‐weighted MRI of gliomas

Although combined spin‐ and gradient‐echo (SAGE) dynamic susceptibility‐contrast (DSC) MRI can provide perfusion quantification that is sensitive to both macrovessels and microvessels while correcting for T₁‐shortening effects, spatial coverage is often limited in order to maintain a high temporal resolution for DSC quantification. In this work, we combined a SAGE echo‐planar imaging (EPI) sequence with simultaneous multi‐slice (SMS) excitation and blipped controlled aliasing in parallel imaging (blipped CAIPI) at 3 T to achieve both high temporal resolution and whole brain coverage. Two protocols using this sequence with multi‐band (MB) acceleration factors of 2 and 3 were evaluated in 20 patients with treated gliomas to determine the optimal scan parameters for clinical use. ΔR₂*(t) and ΔR₂(t) curves were derived to calculate dynamic signal‐to‐noise ratio (dSNR), ΔR₂*‐ and ΔR₂‐based relative cerebral blood volume (rCBV), and mean vessel diameter (mVD) for each voxel. The resulting SAGE DSC images acquired using MB acceleration of 3 versus 2 appeared visually similar in terms of image distortion and contrast. The difference in the mean dSNR from normal‐appearing white matter (NAWM) and that in the mean dSNR between NAWM and normal‐appearing gray matter were not statistically significant between the two protocols. ΔR₂*‐ and ΔR₂‐rCBV maps and mVD maps provided unique contrast and spatial heterogeneity within tumors.     

Imaging the intratumoral–peritumoral extracellular pH gradient of gliomas

Solid tumors have an acidic extracellular pH (pH_e) but near neutral intracellular pH (pH_i). Because acidic pH_e milieu is conducive to tumor growth and builds resistance to therapy, simultaneous mapping of pH_e inside and outside the tumor (i.e., intratumoral‐peritumoral pH_e gradient) fulfills an important need in cancer imaging. We used B iosensor I maging of R edundant D eviation in S hifts (BIRDS), which utilizes shifts of non‐exchangeable protons from macrocyclic chelates (e.g., 1,4,7,10‐tetraazacyclododecane‐1,4,7,10‐tetrakis(methylene phosphonate) or DOTP^8?) complexed with paramagnetic thulium (Tm³⁺) ion, to generate in vivo pH_e maps in rat brains bearing 9L and RG2 tumors. Upon TmDOTP^5? infusion, MRI identified the tumor boundary by enhanced water transverse relaxation and BIRDS allowed imaging of intratumoral‐peritumoral pH_e gradients. The pH_e measured by BIRDS was compared with pH_i measured with ³¹P‐MRS. In normal tissue, pH_e was similar to pH_i, but inside the tumor pH_e was lower than pH_i. While the intratumoral pH_e was acidic for both tumor types, peritumoral pH_e varied with tumor type. The intratumoral–peritumoral pH_e gradient was much larger for 9L than RG2 tumors because in RG2 tumors acidic pH_e was found in distal peritumoral regions. The increased presence of Ki‐67 positive cells beyond the RG2 tumor border suggested that RG2 was more invasive than the 9L tumor. These results indicate that extensive acidic pH_e beyond the tumor boundary correlates with tumor cell invasion. In summary, BIRDS has sensitivity to map the in vivo intratumoral–peritumoral pH_e gradient, thereby creating preclinical applications in monitoring cancer therapeutic responses (e.g., with pH_e‐altering drugs). Copyright © 2016 John Wiley & Sons, Ltd.     

Three‐dimensional textural features of conventional MRI improve diagnostic classification of childhood brain tumours

The aim of this study was to assess the efficacy of three‐dimensional texture analysis (3D TA) of conventional MR images for the classification of childhood brain tumours in a quantitative manner. The dataset comprised pre‐contrast T₁‐ and T₂‐weighted MRI series obtained from 48 children diagnosed with brain tumours (medulloblastoma, pilocytic astrocytoma and ependymoma). 3D and 2D TA were carried out on the images using first‐, second‐ and higher order statistical methods. Six supervised classification algorithms were trained with the most influential 3D and 2D textural features, and their performances in the classification of tumour types, using the two feature sets, were compared. Model validation was carried out using the leave‐one‐out cross‐validation (LOOCV) approach, as well as stratified 10‐fold cross‐validation, in order to provide additional reassurance. McNemar's test was used to test the statistical significance of any improvements demonstrated by 3D‐trained classifiers. Supervised learning models trained with 3D textural features showed improved classification performances to those trained with conventional 2D features. For instance, a neural network classifier showed 12% improvement in area under the receiver operator characteristics curve (AUC) and 19% in overall classification accuracy. These improvements were statistically significant for four of the tested classifiers, as per McNemar's tests. This study shows that 3D textural features extracted from conventional T₁‐ and T₂‐weighted images can improve the diagnostic classification of childhood brain tumours. Long‐term benefits of accurate, yet non‐invasive, diagnostic aids include a reduction in surgical procedures, improvement in surgical and therapy planning, and support of discussions with patients' families. It remains necessary, however, to extend the analysis to a multicentre cohort in order to assess the scalability of the techniques used. Copyright © 2015 John Wiley & Sons, Ltd.     

A data‐driven approach to optimising the encoding for multi‐shell diffusion MRI with application to neonatal imaging

Diffusion MRI has the potential to provide important information about the connectivity and microstructure of the human brain during normal and abnormal development, noninvasively and in vivo. Recent developments in MRI hardware and reconstruction methods now permit the acquisition of large amounts of data within relatively short scan times. This makes it possible to acquire more informative multi‐shell data, with diffusion sensitisation applied along many directions over multiple b‐value shells. Such schemes are characterised by the number of shells acquired, and the specific b‐value and number of directions sampled for each shell. However, there is currently no clear consensus as to how to optimise these parameters. In this work, we propose a means of optimising multi‐shell acquisition schemes by estimating the information content of the diffusion MRI signal, and optimising the acquisition parameters for sensitivity to the observed effects, in a manner agnostic to any particular diffusion analysis method that might subsequently be applied to the data. This method was used to design the acquisition scheme for the neonatal diffusion MRI sequence used in the developing Human Connectome Project (dHCP), which aims to acquire high quality data and make it freely available to the research community. The final protocol selected by the algorithm, and currently in use within the dHCP, consists of 20 b=0 images and diffusion‐weighted images at b = 400, 1000 and 2600 s/mm² with 64, 88 and 128 directions per shell, respectively.