Computerized breast lesions detection using kinetic and morphologic analysis for dynamic contrast-enhanced MRI

To facilitate rapid and accurate assessment, this study proposed a novel fully automatic method to detect and identify focal tumor breast lesions using both kinetic and morphologic features from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). After motion registration of all phases of the DCE-MRI study, three automatically generated lines were used to segment the whole breast region of each slice. The kinetic features extracted from the pixel-based time-signal intensity curve (TIC) by a two-stage detection algorithm was first used, and then three-dimensional (3-D) morphologic characteristics of the detected regions were applied to differentiate between tumor and non-tumor regions. In this study, 95 biopsy-confirmed lesions (28 benign and 67 malignant lesions) in 54 women were used to evaluate the detection efficacy of the proposed system. The detection performance was analyzed using the free-response operating characteristics (FROC) curve and detection rate. The proposed computer-aided detection (CADe) system had a detection rate of 92.63% (88/95) of all tumor lesions, with 6.15 false positives per case. Based on the results, kinetic features extracted by TIC can be used to detect tumor lesions and 3-D morphology can effectively reduce the false positives.     

Intravoxel incoherent motion diffusion-weighted imaging as an adjunct to dynamic contrast-enhanced MRI to improve accuracy of the differential diagnosis of benign and malignant breast lesions

PurposeTo investigate the value of use of intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) as an adjunct to dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to distinguish benign from malignant breast lesions.Materials and methodsRetrospective analysis of data pertaining to 117 patients with breast lesions who underwent DCE-MRI and IVIM-DWI examination with 3.0 T MRI was conducted. A total of 128 lesions were pathologically confirmed (47 benign and 81 malignant). Between-group differences in DCE-MRI parameters (Morphology, enhancement pattern, maximum slope of increase (MSI) and time–signal curve (TIC) type) and IVIM-DWI parameters (f value, D value and D* value) were assessed. Multivariate logistic regression was performed to identify variables that distinguished benign from malignant breast lesions. The diagnostic performance of DCE-MRI and DCE-MRI plus IVIM-DWI, to distinguish benign from malignant breast lesions, was evaluated using pathology results as the gold standard.ResultsLesion morphology, MSI, and TIC type (P < 0.05), but not the enhancement pattern (P > 0.05), were significantly different between the benign and malignant groups. The f (8.53 ± 2.14) and D* (7.64 ± 2.07) values in the malignant group were significantly higher than those in the benign group (7.68 ± 1.97 and 6.83 ± 2.13, respectively), while the D value (0.99 ± 0.22) was significantly lower than that (1.34 ± 0.17) in the benign group (P < 0.05 for all). On logistic regression analysis, the sensitivity, specificity and accuracy of DCE-MRI were 90.1%, 70.2% and 82.8% respectively; the corresponding figures for the combination of IVIM-DWI and DCE-MRI were 88.8%, 85.1%, and 87.5%respectively.ConclusionIVIM-DWI method as an adjunct to DCE-MRI can improve the specificity and accuracy in differential diagnosis of benign and malignant lesions of breast.     

Diagnosis of suspicious breast lesions using an empirical mathematical model for dynamic contrast-enhanced MRI

The purpose of this study was to test whether an empirical mathematical model (EMM) of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can distinguish between benign and malignant breast lesions. A modified clinical protocol was used to improve the sampling of contrast medium uptake and washout. T(1)-weighted DCE magnetic resonance images were acquired at 1.5 T for 22 patients before and after injection of Gd-DTPA. Contrast medium concentration as a function of time was calculated over a small region of interest containing the most rapidly enhancing pixels. Then the curves were fitted with the EMM, which accurately described contrast agent uptake and washout. Results demonstrate that benign lesions had uptake (P<2.0 x 10(-5)) and washout (P<.01) rates of contrast agent significantly slower than those of malignant lesions. In addition, secondary diagnostic parameters, such as time to peak of enhancement, enhancement slope at the peak and curvature at the peak of enhancement, were derived mathematically from the EMM and expressed in terms of primary parameters. These diagnostic parameters also effectively differentiated benign from malignant lesions (P<.03). Conventional analysis of contrast medium dynamics, using a subjective classification of contrast medium kinetics in lesions as "washout," "plateau" or "persistent" (sensitivity=83%, specificity=50% and diagnostic accuracy=72%), was less effective than the EMM (sensitivity=100%, specificity=83% and diagnostic accuracy=94%) for the separation of benign and malignant lesions. In summary, the present research suggests that the EMM is a promising alternative method for evaluating DCE-MRI data with improved diagnostic accuracy.     

Intravoxel incoherent motion diffusion-weighted imaging in head and neck squamous cell carcinoma: Assessment of perfusion-related parameters compared to dynamic contrast-enhanced MRI

Purpose

To investigate the correlation between perfusion-related parameters obtained with intravoxel incoherent motion (IVIM) and classical perfusion parameters obtained with dynamic contrast-enhanced (DCE) magnetic resonance imaging in patients with head and neck squamous cell carcinoma (HNSCC), and to compare direct and asymptotic fitting, the pixel-by-pixel approach, and a region of interest (ROI)-based approach respectively for IVIM parameter calculation.

Materials and methods

Seventeen patients with HNSCC were included in this retrospective study. All magnetic resonance (MR) scanning was performed using a 3 T MR unit. Acquisition of IVIM was performed using single-shot spin-echo echo-planar imaging with three orthogonal gradients with 12 b-values (0, 10, 20, 30, 50, 80, 100, 200, 400, 800, 1000, and 2000). Perfusion-related parameters of perfusion fraction ‘f’ and the pseudo-diffusion coefficient ‘D*’ were calculated from IVIM data by using least square fitting with the two fitting methods of direct and asymptotic fitting, respectively. DCE perfusion was performed in a total of 64 dynamic phases with a 3.2-s phase interval. The two-compartment exchange model was used for the quantification of tumor blood volume (TBV) and tumor blood flow (TBF). Each tumor was delineated with a polygonal ROI for the calculation of f, f ? D* performed using both the pixel-by-pixel approach and the ROI-based approach. In the pixel-by-pixel approach, after fitting each pixel to obtain f, f ? D* maps, the mean value in the delineated ROI on these maps was calculated. In the ROI-based approach, the mean value of signal intensity was calculated within the ROI for each b-value in IVIM images, and then fitting was performed using these values. Correlations between f in a total of four combinations (direct or asymptotic fitting and pixel-by-pixel or ROI-based approach) and TBV were respectively analyzed using Pearson's correlation coefficients. Correlations between f ? D* and TBF were also similarly analyzed.

Results

In all combinations of f and TBV, f ? D* and TBF, there was a significant correlation. In the comparison of f and TBV, a moderate correlation was observed only between f obtained by direct fitting with the pixel-by-pixel approach, whereas a good correlation was observed in the comparisons using the other three combinations. In the comparison of f ? D* and TBF, a good correlation was observed only with f ? D* obtained by asymptotic fitting with the ROI-based approach. In contrast, moderate correlations were observed in the comparisons using the other three combinations.

Conclusion

IVIM was found to be feasible for the analysis of perfusion-related parameters in patients with HNSCC. Especially, the combination of asymptotic fitting with the ROI-based approach was better correlated with DCE perfusion.     

A variational approach to image registration in dynamic contrast-enhanced MRI of the human kidney

Kidney function can be accessed by dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) measurements which yield spatially resolved maps of physiological parameters like perfusion or filtration. The motion of the kidneys during the scan is a dominant limitation of the measurement quality, and image registration is necessary for accurate quantification. We analyzed the feasibility of applying an algorithm, originally developed for multimodal registration, to kidney perfusion time series. The algorithm uses a variational calculation scheme to align the images. In four out of five data sets, kidney motion could be reduced to below the spatial resolution of the images of 1.6 mm while preserving the enhancement pattern of kidney perfusion. Fitting a pharmacokinetic model to the data showed an average reduction of the Akaike fit error of 10% for the registered data, suggesting more stable parameters. We conclude that this image registration algorithm is feasible for correcting kidney motion in renal DCE-MRI.     

High-resolution,three-dimensional diffusion-weighted breast imaging using DESS

PurposeTo evaluate the use of the double-echo steady-state (DESS) sequence for acquiring high-resolution breast images with diffusion and T2 weighting.Materials and MethodsPhantom scans were used to verify the T2 and diffusion weighting of the DESS sequence. Image distortion was evaluated in volunteers by comparing DESS images and conventional diffusion-weighted images (DWI) to spoiled gradient-echo images. The DESS sequence was added to a standard clinical protocol, and the resulting patient images were used to evaluate overall image quality and image contrast in lesions.ResultsThe diffusion weighting of the DESS sequence can be easily modulated by changing the spoiler gradient area and flip angle. Radiologists rated DESS images as having higher resolution and less distortion than conventional DWI. Lesion-to-tissue contrast ratios are strongly correlated between DWI and DESS images (R = 0.83) and between T2-weighted fast spin-echo and DESS images (R = 0.80).ConclusionThe DESS sequence is able to acquire high-resolution 3D diffusion- and T2-weighted images in short scan times, with image quality that facilitates morphological assessment of lesions.     

Free-breathing radial volumetric interpolated breath-hold examination sequence and dynamic contrast-enhanced MRI combined with diffusion-weighted imaging for assessment of solitary pulmonary nodules

ObjectiveTo test the performance of free-breathing Dynamic Contrast-Enhanced MRI (DCE-MRI) using a radial volumetric interpolated breath-hold examination (VIBE) sequence combined with diffusion-weighted imaging (DWI) for quantitative solitary pulmonary nodule (SPN) assessment.MethodsA total of 67 SPN cases receiving routine MRI routine scans, DWI, and dynamic-enhanced MRI in our hospital from May 2017 to November 2018 were collected. These cases were divided into a malignant group and a benign group according to the characteristics of the SPNs. The quantitative DCE-MRI parameters (K^trans, K_ep, V_e) and apparent diffusion coefficient (ADC) values of the nodules were measured.ResultsThe K^trans and K_ep values in the malignant group were higher than those in the benign group, while the ADC values in the malignant group were lower than those in the benign group. Furthermore, the K^trans value of adenocarcinoma was higher than that of squamous cell carcinoma and small cell carcinoma (P < 0.05). The V_e value was significantly different between non-small cell carcinoma and small cell carcinoma (P < 0.05). With an ADC value of 0.98 × 10⁻³ mm²/s as the threshold, the specificity and sensitivity to diagnose benign and malignant nodules was 90.6% and 80%, respectively.ConclusionHigh-temporal-resolution DCE-MRI using the r-VIBE technique in combination with DWI could contribute to pulmonary nodule analysis and possibly serve as a potential alternative to distinguish malignant from benign nodules as well as differentiate different types of malignancies.     

Assessment of the link between quantitative biexponential diffusion-weighted imaging and contrast-enhanced MRI in the liver

PurposeTo investigate if intravoxel incoherent motion (IVIM) modeled diffusion-weighted imaging (DWI) can be linked to contrast-enhanced (CE-)MRI in liver parenchyma and liver lesions.MethodsTwenty-five patients underwent IVIM-DWI followed by multiphase CE-MRI using Gd-EOB-DTPA (n = 20) or Gd-DOTA (n = 5) concluded with IVIM-DWI. Diffusion (D_slow), microperfusion (D_fast), its fraction (f_fast), wash-in-rate (R_early) and late-enhancement-rate (R_late) of Gd-EOB-DTPA were calculated voxel-wise for the liver. Parenchyma and lesions were segmented. Pre-contrast IVIM was compared 1) between low, medium and high R_early for parenchyma 2) to post-contrast IVIM substantiated with simulations 3) between low and high R_late per lesion type.ResultsD_fast and f_fast increased (P < 0.001) with 25.6% and 33.8% between low and high R_early of Gd-EOB-DTPA. D_slow decreased (− 15.0%; P < 0.001) with increasing R_early. Gd-DOTA demonstrated similar observations. f_fast (+ 10%; P < 0.001) and D_fast (+ 6.6%; P < 0.001) increased after Gd-EOB-DTPA, while decreasing after Gd-DOTA (− 4.2% and − 5.7%, P < 0.001) and were confirmed by simulations. For focal nodular hyperplasia lesions (n = 5) D_fast and f_fast increased (P < 0.001) with increasing R_late, whereas for hepatocellular carcinoma (n = 4) and adenoma (n = 7) no differences were found.ConclusionMicroperfusion measured by IVIM reflects perfusion in a way resembling CE-MRI. Also IVIM separated intra- and extracellular MR contrast media. This underlines the potential of IVIM in quantitative liver imaging.     

Detection of small intrahepatic metastases of hepatocellular carcinomas using diffusion-weighted imaging: comparison with conventional dynamic MRI

Purpose

The purpose of our study was to compare diffusion-weighted MR imaging (DWI) with conventional dynamic MRI in terms of the assessment of small intrahepatic metastases from hepatocellular carcinoma (HCC).

Materials and Methods

In 24 patients with multifocal, small (≤2 cm) intrahepatic metastatic foci of advanced HCC, a total of 134 lesions (≤1 cm, n=81; >1 cm, n=53) were subjected to a comparative analysis of hepatic MRI including static and gadopentetate dimeglumine-enhanced dynamic imaging, and DWI using a single-shot spin-echo echo-planar MRI (b values=50, 400 and 800 s/mm²), by two independent reviewers.

Results

A larger number of the lesions were detected and diagnosed as intrahepatic metastases on DWI [Reviewer 1, 121 (90%); Reviewer 2, 117 (87%)] than on dynamic imaging [Reviewer 1, 107 (80%); Reviewer 2, 105 (78%)] (P<.05). For the 81 smaller lesions (≤1 cm), DWI was able to detect more lesions than dynamic imaging [Reviewer 1, 68 (84%) vs. 56 (69%), P=.008; Reviewer 2, 65 (80%) vs. 55 (68%), P=.031], but there was no statistically significant difference between the two image sets for larger (>1 cm) lesions.

Conclusion

Due to its higher detection rate of subcentimeter lesions, DWI could be considered complementary to dynamic MRI in the diagnosis of intrahepatic metastases of HCCs.     

Quantitative diffusion-weighted imaging and dynamic contrast-enhanced MR imaging for assessment of tumor aggressiveness in prostate cancer at 3T

PurposeTo compare diffusion-weighted imaging (DWI) and dynamic contrast-enhanced MR imaging (DCE-MRI) for characterization of prostate cancer (PC).Methods104 PC patients who underwent prostate multiparametric MRI at 3T including DWI and DCE-MRI before MRI-guided biopsy or radical prostatectomy. Apparent diffusion coefficient (ADC) with histogram analysis (mean, 0–25th percentile, skewness, and kurtosis), intravoxel incoherent motion model including D and f; stretched exponential model including distributed diffusion coefficient (DDC) and a; and permeability parameters including K^trans, K_ep, and V_e were obtained from a region of interest placed on the dominant tumor of each patient.ResultsADC_mean, ADC_0–25, D, DDC, and V_e were significantly lower and K_ep was significantly higher in GS ≥ 3 + 4 tumors (n = 89) than in GS = 3 + 3 tumors (n = 15), and also in GS ≥ 4 + 3 tumors (n = 57) than in GS ≤ 3 + 4 tumors (n = 47) (P < 0.001 to P = 0.040). f was significantly lower in GS ≥ 4 + 3 tumors than in GS ≤ 3 + 4 tumors (P = 0.022), but there was no significant difference between GS = 3 + 3 tumors and GS ≥ 3 + 4 tumors, or between the remaining metrics in both comparisons. In metrics with area under the curve (AUC) >0.80, there was a significant difference in AUC between ADC_0–25 and D, and DDC for separating GS ≤ 3 + 4 tumors from GS ≥ 4 + 3 tumors (P = 0.040 and P = 0.022, respectively). There were no significant differences between metrics with AUC > 0.80 for separating GS = 3 + 3 tumors from GS ≥ 3 + 4 tumors. ADC_0–25 had the highest correlation with Gleason grade (ρ = −0.625, P < 0.001).ConclusionsDWI and DCE-MRI showed no apparent clinical superiority of non-Gaussian models or permeability MRI over the mono-exponential model for assessment of tumor aggressiveness in PC.     

Assessment of early response to concurrent chemoradiotherapy in cervical cancer: value of diffusion-weighted and dynamic contrast-enhanced MR imaging

Purpose

To investigate diffusion-weighted (DWI) and dynamic contrast-enhanced MR imaging (DCE-MRI) as early response predictors in cervical cancer patients who received concurrent chemoradiotherapy (CCRT).

Materials and methods

Sixteen patients with cervical cancer underwent DWI and DCE-MRI before CCRT (preTx), at 1 week (postT1) and 4 weeks (postT2) after initiating treatment, and 1 month after the end of treatment (postT3). At each point, apparent diffusion coefficient (ADC) and DCE-MRI parameters were measured in tumors and gluteus muscles (GM). Tumor response was correlated with imaging parameters or changes in imaging parameters at each point.

Results

At each point, ADC, K^trans and V_e in tumors showed significant changes (P < 0.05), as compared with those of GM (P > 0.05). PostT1 tumor ADCs showed a significant correlation with tumor size response at postT2 (P = 0.041), and changes in tumor ADCs at postT1 had a significant correlation with tumor size (P = 0.04) and volume response (P = 0.003) at postT2. In tumors, preTx K^trans and V_e showed significant correlations with tumor size at postT3 (P = 0.011) and tumor size response at postT2 (P = 0.019), respectively.

Conclusion

DWI and DCE-MRI, as early biomarkers, have the potential to evaluate therapeutic responses to CCRT in cervical cancers.     

Differentiation of myeloma and metastatic cancer in the spine using dynamic contrast-enhanced MRI

Spinal myeloma and metastatic cancer cause similar symptoms and show similar imaging presentations, thus making them difficult to differentiate. In this study, dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) was performed to differentiate between 9 myelomas and 22 metastatic cancers that present as focal lesions in the spine. The characteristic DCE parameters, including the peak signal enhancement percentage (SE%), the steepest wash-in SE% during the ascending phase and the wash-out SE%, were calculated by normalizing to the precontrast signal intensity. The two-compartmental pharmacokinetic model was used to obtain K^trans and k_ep. All nine myelomas showed the wash-out DCE pattern. Of the 22 metastatic cancers, 12 showed wash-out, 7 showed plateau, and 3 showed persistent enhancing patterns. The fraction of cases that showed the wash-out pattern was significantly higher in the myeloma group than the metastatic cancer group (9/9 = 100% vs. 12/22 = 55%, P = .03). Compared to the metastatic cancer group, the myeloma group had a higher peak SE% (226% ± 72% vs. 165% ± 60%, P = .044), a higher steepest wash-in SE% (169% ± 51% vs. 111% ± 41%, P = .01), a higher K^trans (0.114 ± 0.036 vs. 0.077 ± 0.028 1/min, P = .016) and a higher k_ep (0.88 ± 0.26 vs. 0.49 ± 0.23 1/min, P = .002). The receiver operating characteristic analysis to differentiate between these two groups showed that the area under the curve was 0.798 for K^trans, 0.864 for k_ep and 0.919 for combined K^trans and k_ep. These results show that DCE-MRI may provide additional information for making differential diagnosis to aid in choosing the optimal subsequent procedures or treatments for spinal lesions.     

A simple approach to T2 imaging in MRI

A simple method for obtaining images whose contrast depends only on T₂ is described and tested both on phantoms and in vivo. The method works reliably and effectively under clinically realistic operating conditions using standard imaging protocols. It can result in a substantial reduction in imaging times for T₂ weighted images.     

Deep-learning approach with convolutional neural network for classification of maximum intensity projections of dynamic contrast-enhanced breast magnetic resonance imaging

PurposeWe aimed to evaluate deep learning approach with convolutional neural networks (CNNs) to discriminate between benign and malignant lesions on maximum intensity projections of dynamic contrast-enhanced breast magnetic resonance imaging (MRI).MethodsWe retrospectively gathered maximum intensity projections of dynamic contrast-enhanced breast MRI of 106 benign (including 22 normal) and 180 malignant cases for training and validation data. CNN models were constructed to calculate the probability of malignancy using CNN architectures (DenseNet121, DenseNet169, InceptionResNetV2, InceptionV3, NasNetMobile, and Xception) with 500 epochs and analyzed that of 25 benign (including 12 normal) and 47 malignant cases for test data. Two human readers also interpreted these test data and scored the probability of malignancy for each case using Breast Imaging Reporting and Data System. Sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUC) were calculated.ResultsThe CNN models showed a mean AUC of 0.830 (range, 0.750–0.895). The best model was InceptionResNetV2. This model, Reader 1, and Reader 2 had sensitivities of 74.5%, 72.3%, and 78.7%; specificities of 96.0%, 88.0%, and 80.0%; and AUCs of 0.895, 0.823, and 0.849, respectively. No significant difference arose between the CNN models and human readers (p > 0.125).ConclusionOur CNN models showed comparable diagnostic performance in differentiating between benign and malignant lesions to human readers on maximum intensity projection of dynamic contrast-enhanced breast MRI.     

Quantitative perfusion imaging in carotid artery stenosis using dynamic susceptibility contrast-enhanced magnetic resonance imaging

Quantitative, multislice dynamic susceptibility contrast-enhanced MRI perfusion measurements were used to determine the patterns of cerebral blood flow (CBF), cerebral blood volume (CBV), mean transit time (MTT), and normalized first moment of the tissue deltaR2-time curve (N) in 11 subjects with carotid artery occlusion or stenosis. MTT correlated with degree of carotid stenosis, whereas a range of alterations in CBF and CBV were found presumably reflecting variables degrees of collateral flow. There was no significant correlation between MRI and SPET flow perfusion measurements, with increasing disparity between the two techniques at higher inter-hemispheric flow ratios. The effect of obtaining the arterial input function (AIF) from the middle cerebral artery (MCA) ipsilateral or contralateral to the stenosis was determined. Despite the use of an AIF from the MCA, which is distal to the circle of Willis, and hence the major sources of collateral supply, there was still some extra dispersion of the contrast agent bolus due to differences in arrival time.     

Use of dynamic contrast-enhanced MRI to measure subtle blood-brain barrier abnormalities

There is growing interest in investigating the role of subtle changes in blood-brain barrier (BBB) function in common neurological disorders and the possible use of imaging techniques to assess these abnormalities. Some studies have used dynamic contrast-enhanced MR imaging (DCE-MRI) and these have demonstrated much smaller signal changes than obtained from more traditional applications of the technique, such as in intracranial tumors and multiple sclerosis. In this work, preliminary results are presented from a DCE-MRI study of patients with mild stroke classified according to the extent of visible underlying white matter abnormalities. These data are used to estimate typical signal enhancement profiles in different tissue types and by degrees of white matter abnormality. The effect of scanner noise, drift and different intrinsic tissue properties on signal enhancement data is also investigated and the likely implications for interpreting the enhancement profiles are discussed. No significant differences in average signal enhancement or contrast agent concentration were observed between patients with different degrees of white matter abnormality, although there was a trend towards greater signal enhancement with more abnormal white matter. Furthermore, the results suggest that many of the factors considered introduce uncertainty of a similar magnitude to expected effect sizes, making it unclear whether differences in signal enhancement are truly reflective of an underlying BBB abnormality or due to an unrelated effect. As the ultimate aim is to achieve a reliable quantification of BBB function in subtle disorders, this study highlights the factors which may influence signal enhancement and suggests that further work is required to address the challenging problems of quantifying contrast agent concentration in healthy and diseased living human tissue and of establishing a suitable model to enable quantification of relevant physiological parameters. Meanwhile, it is essential that future studies use an appropriate control group to minimize these influences.     

Quantitative correlation between (1)H MRS and dynamic contrast-enhanced MRI of human breast cancer

Proton magnetic resonance spectroscopy (¹H MRS) and dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) provide functional information, including vascular volume, vascular permeability and choline (Cho) metabolism. In this study, we applied these two imaging modalities to quantitatively characterize 36 malignant breast lesions in 32 patients and analyzed the correlation between them. Cho concentration was quantified by single-voxel ¹H MRS using water as an internal reference. The measured Cho levels ranged from 0.32 to 10.47 mmol/kg, consistent with previously reported values. In 25 mass-type lesions, the Cho concentration was significantly correlated with tumor size (r=.69, P<.0002). In addition, the Cho level was found to be significantly higher in lesions presenting as mass-type lesions compared to non-mass-type diffuse enhancements (P=.035). The enhancement kinetics from tissues covered within each MRS voxel were measured and analyzed with a two-compartmental model to obtain pharmacokinetic parameters K^trans and k_ep. A significant correlation was found between the Cho level and the pharmacokinetic parameter k_ep (r=.62, P<.0001), indicating that tissues with a high Cho level have higher wash-out rates in DCE MRI. The results suggest a correlation between Cho metabolism and angiogenesis activity, which might be explained by the association of Cho with cell replication and angiogenesis required to support tumor growth.     

Effect of gadolinium injection on diffusion-weighted imaging with background body signal suppression (DWIBS) imaging of breast lesions

Objectives

Diffusion-weighted imaging with background body signal suppression (DWIBS) provides both qualitative and quantitative imaging of breast lesions and are usually performed before contrast material injection (CMI). This study aims to assess whether the administration of gadolinium significantly affects DWIBS imaging.

Methods

200 patients were prospectively evaluated by MRI with STIR, TSE-T2, pre-CMI DWIBS, contrast enhanced THRIVE-T1 and post-CMI DWIBS sequences. Pre and post-CMI DWIBS were analyzed searching for the presence of breast lesions and calculating the ADC value. ADC values of ≤ 1.44 × 10^- 3 mm²/s were considered suspicious for malignancy. This analysis was then compared with the histological findings. Sensitivity, specificity, diagnostic accuracy (DA), positive predictive value (PPV) and negative (NPV) were calculated for both sequences and represented by ROC analysis. Pre and post-CMI ADC values were compared by using the paired t test.

Results

In 150/200 (59%) patients, pre and post-CMI DWIBS indicated the presence of breast lesions, 53 (35%) with ADC values of > 1.44 × 10^- 3 mm²/s and 97 (65%) with ADC ≤ 1.44 × 10^- 3 mm²/s. Pre-CMI and post-DWIBS sequences obtained the same sensitivity, specificity, DA, PPV and NPV values of 97%, 83%, 89%, 79% and 98%. The mean ADC value of benign lesions was 1.831 ± 0.18 × 10^- 3 mm²/s before and 1.828 ± 0.18 × 10^- 3 mm²/s after CMI. The mean ADC value of the malignant lesions was 1.146 ± 0.16 × 10^- 3 mm²/s before and 1.144 ± 0.16 × 10^- 3 mm²/s after CMI. No significant difference was found between pre and post CMI ADC values (p > 0.05).

Conclusion

DWIBS imaging is not influenced by CMI. Breast MR protocol could be modified by placing DWIBS after dynamic contrast enhanced sequences in order to maximize patient cooperation.     

Assessment of peripheral tissue perfusion disorder in streptozotocin-induced diabetic rats using dynamic contrast-enhanced MRI

Purpose

To assess peripheral tissue perfusion disorder in streptozotocin (STZ)-induced diabetic rats by using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI).

Materials and Methods

A rat diabetes model was produced by intravenous injection of STZ. Diabetic rats were sustainably treated with either saline or insulin using an Alzet osmotic pump. Hind paw tissue perfusion was measured by signal intensity (SI) enhancement after gadolinium diethylenetriaminepentaacetic acid injection in DCE-MRI study and quantified using the initial area under the SI-time curve (IAUC). Peripheral tissue uptake of [¹⁴C]iodoantipyrine (IAP) was also determined as a marker of tissue blood flow for comparison with the IAUC value indicating tissue perfusion.

Results

STZ caused hyperglycemia at 1 and 2 weeks after injection. Treatment with insulin significantly alleviated hyperglycemia. At 2 weeks after STZ injection, peripheral tissue perfusion was clearly reduced in the diabetic rats and its reduction was significantly improved in the insulin-treated diabetic rats. Tissue perfusion evaluated by DCE-MRI was similar to the tissue blood flow measured by [¹⁴C]IAP uptake.

Conclusion

Our findings demonstrated that DCE-MRI can assess peripheral tissue perfusion disorder in diabetes. DCE-MRI could be suitable for noninvasive evaluation of peripheral tissue perfusion in both preclinical and clinical studies. It may also be useful for developing novel drugs to protect against diabetic vascular complications.     

Prognostic and therapeutic evaluation of nasopharyngeal carcinoma by dynamic contrast-enhanced (DCE), diffusion-weighted (DW) magnetic resonance imaging (MRI) and magnetic resonance spectroscopy (MRS)

Nasopharyngeal carcinoma (NPC) is an aggressive head and neck malignancy, and radiotherapy (with or without chemotherapy) is the primary treatment modality. Reliable tumour assessment during the treatment phase, which can portend the efficacy of radiotherapy and early identification of potential treatment failure in radioresistant disease, has been implicit for better cancer management. Technological advancement in the last decade has fostered the development of functional magnetic resonance imaging (fMRI) techniques into a promising tool for diagnostic and therapeutic assessments in head and neck cancer. Apart from conventional morphological assessment, early detection of the physiological environment by fMRI allows a more thorough investigation in monitoring tumour response. This article discusses the relevant fMRI utilities in NPC as an early prognostic and monitoring tool for treatment. Challenges and future developments of fMRI in radiation oncology are also discussed.