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 (T2p = 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% (T2p = 100 ms) and 60 % (T2p = 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 T2p = 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.

Characterization of the diffusion signal of breast tissues using multi-exponential models.

PURPOSE: Restriction spectrum imaging (RSI) decomposes the diffusion-weighted MRI signal into separate components of known apparent diffusion coefficients (ADCs). The number of diffusion components and optimal ADCs for RSI are organ-specific and determined empirically. The purpose of this work was to determine the RSI model for breast tissues. METHODS: The diffusion-weighted MRI signal was described using a linear combination of multiple exponential components. A set of ADC values was estimated to fit voxels in cancer and control ROIs. Later, the signal contributions of each diffusion component were estimated using these fixed ADC values. Relative-fitting residuals and Bayesian information criterion were assessed. Contrast-to-noise ratio between cancer and fibroglandular tissue in RSI-derived signal contribution maps was compared to DCE imaging. RESULTS: A total of 74 women with breast cancer were scanned at 3.0 Tesla MRI. The fitting residuals of conventional ADC and Bayesian information criterion suggest that a 3-component model improves the characterization of the diffusion signal over a biexponential model. Estimated ADCs of triexponential model were D1,3 = 0, D2,3 = 1.5 × 10-3 , and D3,3 = 10.8 × 10-3 mm2 /s. The RSI-derived signal contributions of the slower diffusion components were larger in tumors than in fibroglandular tissues. Further, the contrast-to-noise and specificity at 80% sensitivity of DCE and a subset of RSI-derived maps were equivalent. CONCLUSION: Breast diffusion-weighted MRI signal was best described using a triexponential model. Tumor conspicuity in breast RSI model is comparable to that of DCE without the use of exogenous contrast. These data may be used as differential features between healthy and malignant breast tissues.

Exploring the diagnostic potential of adding T2 dependence in diffusion-weighted MR imaging of the prostate.

BACKGROUND: Magnetic resonance imaging (MRI) is essential in the detection and staging of prostate cancer. However, improved tools to distinguish between low-risk and high-risk cancer are needed in order to select the appropriate treatment. PURPOSE: To investigate the diagnostic potential of signal fractions estimated from a two-component model using combined T2- and diffusion-weighted imaging (T2-DWI). MATERIAL AND METHODS: 62 patients with prostate cancer and 14 patients with benign prostatic hyperplasia (BPH) underwent combined T2-DWI (TE = 55 and 73 ms, b-values = 50 and 700 s/mm2) following clinical suspicion of cancer, providing a set of 4 measurements per voxel. Cancer was confirmed in post-MRI biopsy, and regions of interest (ROIs) were delineated based on radiology reporting. Signal fractions of the slow component (SFslow) of the proposed two-component model were calculated from a model fit with 2 free parameters, and compared to conventional bi- and mono-exponential apparent diffusion coefficient (ADC) models. RESULTS: All three models showed a significant difference (p<0.0001) between peripheral zone (PZ) tumor and normal tissue ROIs, but not between non-PZ tumor and BPH ROIs. The area under the receiver operating characteristics curve distinguishing tumor from prostate voxels was 0.956, 0.949 and 0.949 for the two-component, bi-exponential and mono-exponential models, respectively. The corresponding Spearman correlation coefficients between tumor values and Gleason Grade Group were fair (0.370, 0.499 and -0.490), but not significant. CONCLUSION: Signal fraction estimates from a two-component model based on combined T2-DWI can differentiate between tumor and normal prostate tissue and show potential for prostate cancer diagnosis. The model performed similarly to conventional diffusion models.

National indication-based diagnostic reference level values in computed tomography: Preliminary results from Ghana.

PURPOSE: This study was conducted to develop national indication-based DRL values for common indications of adult computed tomography (CT) examinations for clinical application in Ghana. MATERIALS AND METHODS: The methodological approach recommended by the International Commission on Radiological Protection (ICRP), Publication 135, for the development of DRLs, was employed. Studies on CT infrastructure, common indications and quality control tests were first undertaken. A sample of 20 CT dose descriptor/quantity data sets were collected from each centre for each indication. Overall, 3960 data sets were collected for all identified common indications from 71.4% of the total CT scanners in Ghana (25/35). The data were collected from image folders reported and accepted by radiologists. The objective image quality was assessed through a signal to noise ratio (SNR) analysis prior to using the data and extracting DRL values. RESULTS: Clinical indications and their respective DRL values in terms of volume weighted CT dose index (CTDIvol) and dose length product (DLP) were cerebrovascular accident (CVA)/stroke (77 mGy; 1313 mGy.cm), head trauma/injury (76 mGy; 1596 mGy.cm), brain tumour/space occupying lesion (SOL) (77 mGy; 2696 mGy.cm), lung tumour/cancer (12 mGy; 828 mGy.cm) and chest lesion with chronic kidney disease (CKD) (13 mGy; 467 mGy.cm). Others were abdominopelvic lesion (17 mGy; 1299 mGy.cm), kidney stones (15 mGy; 731 mGy.cm), urothelial malignancy/CT-intravenous urogram (CT-IVU) (11 mGy; 1449 mGy.cm) and pulmonary embolism (PE) (14 mGy; 942 mGy.cm). CONCLUSION: National Indication-based DRL values developed in this study are recommended to be used to manage CT radiation dose in Ghana.

Understanding diffusion-weighted MRI analysis: Repeatability and performance of diffusion models in a benign breast lesion cohort.

Diffusion-weighted MRI (DWI) is an important tool for oncology research, with great clinical potential for the classification and monitoring of breast lesions. The utility of parameters derived from DWI, however, is influenced by specific analysis choices. The purpose of this study was to critically evaluate repeatability and curve-fitting performance of common DWI signal representations, for a prospective cohort of patients with benign breast lesions. Twenty informed, consented patients with confirmed benign breast lesions underwent repeated DWI (3 T) using: sagittal single-shot spin-echo echo planar imaging, bipolar encoding, TR/TE: 11,600/86 ms, FOV: 180 x 180 mm, matrix: 90 x 90, slices: 60 x 2.5 mm, iPAT: GRAPPA 2, fat suppression, and 13 b-values: 0-700 s/mm2 . A phase-reversed scan (b = 0 s/mm2 ) was acquired for distortion correction. Voxel-wise repeat-measures coefficients of variation (CoVs) were derived for monoexponential (apparent diffusion coefficient [ADC]), biexponential (intravoxel incoherent motion: f, D, D*) and stretched exponential (α, DDC) across the parameter histograms for lesion regions of interest (ROIs). Goodness-of-fit for each representation was assessed by Bayesian information criterion. The volume of interest (VOI) definition was repeatable (CoV 13.9%). Within lesions, and across both visits and the cohort, there was no dominant best-fit model, with all representations giving the best fit for a fraction of the voxels. Diffusivity measures from the signal representations (ADC, D, DDC) all showed good repeatability (CoV < 10%), whereas parameters associated with pseudodiffusion (f, D*) performed poorly (CoV > 50%). The stretching exponent α was repeatable (CoV < 12%). This pattern of repeatability was consistent over the central part of the parameter percentiles. Assumptions often made in diffusion studies about analysis choices will influence the detectability of changes, potentially obscuring useful information. No single signal representation prevails within or across lesions, or across repeated visits; parameter robustness is therefore a critical consideration. Our results suggest that stretched exponential representation is more repeatable than biexponential, with pseudodiffusion parameters unlikely to provide clinically useful biomarkers.

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.

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 bmax = 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.

Correction to: Semi-automatic segmentation from intrinsically-registered 18F-FDG-PET/MRI for treatment response assessment in a breast cancer cohort: comparison to manual DCE-MRI.

The original version of this article unfortunately contained a mistake in Fig. 6.

Feasibility of contrast-enhanced MRI derived textural features to predict overall survival in locally advanced breast cancer.

BACKGROUND: The prognosis for women with locally advanced breast cancer (LABC) is poor and there is a need for better treatment stratification. Gray-level co-occurrence matrix (GLCM) texture analysis of magnetic resonance (MR) images has been shown to predict pathological response and could become useful in stratifying patients to more targeted treatments. PURPOSE: To evaluate the ability of GLCM textural features obtained before neoadjuvant chemotherapy to predict overall survival (OS) seven years after diagnosis of patients with LABC. MATERIAL AND METHODS: This retrospective study includes data from 55 patients with LABC. GLCM textural features were extracted from segmented tumors in pre-treatment dynamic contrast-enhanced 3-T MR images. Prediction of OS by GLCM textural features was assessed and compared to predictions using traditional clinical variables. RESULTS: Linear mixed-effect models showed significant differences in five GLCM features (f1, f2, f5, f10, f11) between survivors and non-survivors. Using discriminant analysis for prediction of survival, GLCM features from 2 min post-contrast images achieved a classification accuracy of 73% (P < 0.001), whereas traditional prognostic factors resulted in a classification accuracy of 67% (P = 0.005). Using a combination of both yielded the highest classification accuracy (78%, P < 0.001). Median values for features f1, f2, f10, and f11 provided significantly different survival curves in Kaplan-Meier analysis. CONCLUSION: This study shows a clear association between textural features from post-contrast images obtained before neoadjuvant chemotherapy and OS seven years after diagnosis. Further studies in larger cohorts should be undertaken to investigate how this prognostic information can be used to benefit treatment stratification.

Semi-automatic segmentation from intrinsically-registered 18F-FDG-PET/MRI for treatment response assessment in a breast cancer cohort: comparison to manual DCE-MRI.

OBJECTIVES: To investigate the reliability of simultaneous positron emission tomography and magnetic resonance imaging (PET/MRI)-derived biomarkers using semi-automated Gaussian mixture model (GMM) segmentation on PET images, against conventional manual tumor segmentation on dynamic contrast-enhanced (DCE) images. MATERIALS AND METHODS: Twenty-four breast cancer patients underwent PET/MRI (following 18F-fluorodeoxyglucose (18F-FDG) injection) at baseline and during neoadjuvant treatment, yielding 53 data sets (24 untreated, 29 treated). Two-dimensional tumor segmentation was performed manually on DCE-MRI images (manual DCE) and using GMM with corresponding PET images (GMM-PET). Tumor area and mean apparent diffusion coefficient (ADC) derived from both segmentation methods were compared, and spatial overlap between the segmentations was assessed with Dice similarity coefficient and center-of-gravity displacement. RESULTS: No significant differences were observed between mean ADC and tumor area derived from manual DCE segmentation and GMM-PET. There were strong positive correlations for tumor area and ADC derived from manual DCE and GMM-PET for untreated and treated lesions. The mean Dice score for GMM-PET was 0.770 and 0.649 for untreated and treated lesions, respectively. DISCUSSION: Using PET/MRI, tumor area and mean ADC value estimated with a GMM-PET can replicate manual DCE tumor definition from MRI for monitoring neoadjuvant treatment response in breast cancer.

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.

Relative enhanced diffusivity: noise sensitivity, protocol optimization, and the relation to intravoxel incoherent motion.

OBJECTIVE: To explore the relationship between relative enhanced diffusivity (RED) and intravoxel incoherent motion (IVIM), as well as the impact of noise and the choice of intermediate diffusion weighting (b value) on the RED parameter. MATERIALS AND METHODS: A mathematical derivation was performed to cast RED in terms of the IVIM parameters. Noise analysis and b value optimization was conducted by using Monte Carlo calculations to generate diffusion-weighted imaging data appropriate to breast and liver tissue at three different signal-to-noise ratios. RESULTS: RED was shown to be approximately linearly proportional to the IVIM parameter f, inversely proportional to D and to follow an inverse exponential decay with respect to D*. The choice of intermediate b value was shown to be important in minimizing the impact of noise on RED and in maximizing its discriminatory power. RED was shown to be essentially a reparameterization of the IVIM estimates for f and D obtained with three b values. CONCLUSION: RED imaging in the breast and liver should be performed with intermediate b values of 100 and 50 s/mm2, respectively. Future clinical studies involving RED should also estimate the IVIM parameters f and D using three b values for comparison.

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.

PET/MR brain imaging: evaluation of clinical UTE-based attenuation correction.

UNLABELLED: One of the greatest challenges in PET/MR imaging is that of accurate MR-based attenuation correction (AC) of the acquired PET data, which must be solved if the PET/MR modality is to reach its full potential. The aim of this study was to investigate the performance of Siemens' most recent version (VB20P) of MR-based AC of head PET data, by comparing it to CT-based AC. METHODS: (18)F-FDG PET data from seven lymphoma and twelve lung cancer patients examined with a Biograph mMR PET/MR system were reconstructed with both CT-based and MR-based AC, avoiding sources of error arising when comparing PET data from different systems. The resulting images were compared quantitatively by measuring changes in mean SUV in ten different brain regions in both hemispheres, as well as the brainstem. In addition, the attenuation maps (µ maps) were compared regarding volume and localization of cranial bone. RESULTS: The UTE µ maps clearly overestimate the amount of bone in the neck, while slightly underestimating the amount of bone in the cranium, and the localization of bone in the cranial region also differ from the CT µ maps. In air/tissue interfaces in the sinuses and ears, the MRAC method struggles to correctly classify the different tissues. The misclassification of tissue is most likely caused by a combination of artefacts and the insufficiency of the UTE method to accurately separate bone. Quantitatively, this results in a combination of overestimation (0.5-3.6 %) and underestimation (2.7-5.2 %) of PET activity throughout the brain, depending on the proximity to the inaccurate regions. CONCLUSIONS: Our results indicate that the performance of the UTE method as implemented in VB20P is close to the theoretical maximum of such an MRAC method in the brain, while it does not perform satisfactorily in the neck or face/nasal area. Further improvement of the UTE MRAC or other available methods for more accurate segmentation of bone should be incorporated.

Transverse relaxivity of iron oxide nanocrystals clustered in nanoemulsions: Experiment and theory.

PURPOSE: To compare experimental transverse relaxivities of iron oxide nanocrystals (IONC) as a function of clustering and magnetic field strength with different theoretical model predictions. THEORY AND METHODS: Well-defined IONC clusters in nanoemulsions (NEs) of which both size and IONC loading could be judiciously tuned were developed. Transverse relaxivities were measured as a function of NE size and IONC loading at 20 and 300 MHz and compared with four theoretical model predictions. Polydispersity of the NEs was measured and taken into account in the theoretical calculations. RESULTS: Experimentally observed relaxivities were in between theoretical predictions from the fast diffusion regime and the static dephasing regimen. NE polydispersity significantly affected the theoretical T2 relaxivity. The effect of both the number of IONCs inside each droplet as well as the radius of the droplet itself was correctly described by a fast diffusion loose aggregate model, while the effect of increased magnetic field was in agreement with a static dephasing model. CONCLUSION: The results suggest that both fast diffusion, originating from bulk water, and static dephasing phenomena, perhaps originating from water associated with the NE, play a role in transverse relaxivities of IONC aggregates. The developed aggregate system represents a powerful tool to further study these phenomena.

Doxycycline treatment in a neonatal rat model of hypoxia-ischemia reduces cerebral tissue and white matter injury: a longitudinal magnetic resonance imaging study.

Doxycycline may potentially be a neuroprotective treatment for neonatal hypoxic-ischemic brain injury through its anti-inflammatory effects. The aim of this study was to examine any long-term neuroprotection by doxycycline treatment on cerebral gray and white matter. Hypoxic-ischemic brain injury was induced in 7-day-old rats. Pups were treated with either doxycycline (HI+doxy) or saline (HI+vehicle) by intraperitoneal injection at 1 h after hypoxia-ischemia (HI). At 6 h after HI, MnCl(2) was injected intraperitoneally for later manganese-enhanced magnetic resonance imaging (MRI). MRI was performed with diffusion-weighted imaging on day 1 and T(1) -weighted imaging and diffusion tensor imaging at 7, 21 and 42 days after HI. Animals were killed after MRI on day 42 and histological examinations of the brains were performed. There was a tendency towards lower lesion volumes on diffusion maps among HI+doxy than HI+vehicle rats at 1 day after HI. Volumetric MRI showed increasing differences between groups with time after HI, with less cyst formation and less cerebral tissue loss among HI+doxy than HI+vehicle pups. HI+doxy pups had less manganese enhancement on day 7 after HI, indicating reduced inflammation. HI+doxy pups had higher fractional anisotropy on diffusion tensor imaging in major white matter tracts in the injured hemisphere than HI+vehicle pups, indicating less injury to white matter and better myelination. Histological examinations supported the MRI results. Lesion size on early MRI was highly correlated with final injury measures. In conclusion, a single dose of doxycycline reduced long-term cerebral tissue loss and white matter injury after neonatal HI, with an increasing effect of treatment with time after injury.

Monitoring the Effect of Docetaxel Treatment in MCF7 Xenografts Using Multimodal In Vivo and Ex Vivo Magnetic Resonance Methods, Histopathology, and Gene Expression.

The purpose of this study was to evaluate the sensitivity of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), diffusion-weighted (DW)-MRI, in vivo MR spectroscopy (MRS), and ex vivo high-resolution magic angle spinning (HR MAS) MRS for the detection of early treatment effects after docetaxel administration. Docetaxel is an antitumor agent that leads to mitotic arrest, apoptosis, and mitotic catastrophe cell death. Gene expression analysis was performed to detect altered regulation in gene expression pathways related to docetaxel treatment effects. Histopathology was used as a measure of alterations in apoptosis and proliferation due to docetaxel. Experiments were performed using MCF7 mouse xenografts, randomized into a docetaxel (30 mg/kg) treatment group and a control group given saline. MRI/MRS was performed 1 day before treatment and 1, 3, and 6 days after treatment. Parametric images of the extracellular extravascular volume fraction (v(e)) transfer constant (K(trans)) and the apparent diffusion coefficient (ADC) were calculated from the DCE-MRI and DW-MRI data. Biopsies were analyzed by HR MAS MRS, and histopathology and gene expression profiles were determined (Illumina). A significant increase in the ADC 3 and 6 days after treatment and a significant decrease in total choline and a higher v(e) were found in treated tumors 6 days after treatment. No significant difference was found in the K(trans) between the two groups. Our results show that docetaxel induces apoptosis and decreases proliferation in MCF7 xenografts. Further, these phenomena can be monitored by in vivo MRS, DW-MRI, and gene expression.

Characterization of tumor microvascular structure and permeability: comparison between magnetic resonance imaging and intravital confocal imaging.

Solid tumors are characterized by abnormal blood vessel organization, structure, and function. These abnormalities give rise to enhanced vascular permeability and may predict therapeutic responses. The permeability and architecture of the microvasculature in human osteosarcoma tumors growing in dorsal window chambers in athymic mice were measured by confocal laser scanning microscopy (CLSM) and dynamic contrast enhanced magnetic resonance imaging (DCE-MRI). Dextran (40 kDa) and Gadomer were used as molecular tracers for CLSM and DCE-MRI, respectively. A significant correlation was found between permeability indicators. The extravasation rate K(i) as measured by CLSM correlated positively with DCE-MRI parameters, such as the volume transfer constant K(trans) and the initial slope of the contrast agent concentration-time curve. This demonstrates that these two techniques give complementary information. Extravasation was further related to microvascular structure and was found to correlate with the fractal dimension and vascular density. The structural parameter values that were obtained from CLSM images were higher for abnormal tumor vasculature than for normal vessels.