Ktrans Calculation Using Reference Method Corrected Native T10 for Breast Cancer Diagnosis.

Purpose: The objective of the study is to use multiple tube phantoms to generate correction factor at different spatial locations for each breast coil cuff to correct the native T10 value in the corresponding spatial location of the breast lesion. The corrected T10 value was used to compute Ktrans and analyze its diagnostic accuracy in the classification of target condition, i.e., breast tumors into malignant and benign. Materials and Methods: Both in vitro phantom study (external reference) and patient's studies were acquired on simultaneous positron emission tomography/magnetic resonance imaging (PET/MRI) Biograph molecular magnetic resonance (mMR) system using 4 channel mMR breast coil. The spatial correction factors derived using multiple tube phantom were used for a retrospective analysis of dynamic contrast-enhanced (DCE) MRI data of 39 patients with a mean age of 50 years (31-77 years) having 51 enhancing breast lesions. Results: Corrected and non-corrected receiver operating characteristic (ROC) curve analysis revealed a mean Ktrans value of 0.64 min-1 and 0.60 min-1, respectively. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and overall accuracy for non-corrected data were 86.21%, 81.82%, 86.20%, 81.81%, and 84.31%, respectively, and for corrected data were 93.10%, 86.36%, 90%, 90.47%, and 90.20% respectively. The area under curve (AUC) of corrected data was improved to 0.959 (95% confidence interval [CI] 0.862-0.994) from 0.824 (95% CI 0.694-0.918) of non-corrected data, and for NPV, it was improved to 90.47% from 81.81%, respectively. Conclusion: T10 values were normalized using multiple tube phantom which was used for computation of Ktrans. We found significant improvement in the diagnostic accuracy of corrected Ktrans values that results in better characterization of breast lesions.

Use of Multiple-Tube Phantom: A Method to Globally Correct Native T1 Relaxation Time Inhomogeneity in Dedicated Molecular Magnetic Resonance Breast Coil.

BACKGROUND: Native T1 relaxation time (T10) presents an important prerequisite to reliably quantify pharmacokinetic parameter like Ktrans (volume transfer constant). Native T1 value can be varied because of the inhomogeneity in the breast coil, thus influencing the Ktrans measurement. PURPOSE: The current study aims to design and use a phantom with multiple tubes for both breast cuffs to assess native T1 inhomogeneity across the dedicated molecular magnetic resonance (mMR) breast coil and adopt corrective method to spatially normalize T1 values to improve homogeneity. MATERIALS AND METHODS: Two phantoms with multiple tubes (19 tubes) specially designed and filled with contrast medium with known T1 value were placed in each mMR breast coil cuff. Native T1 at various spatial locations was calculated applying dual flip angle sequence. Correction factors were derived at various spatial locations as a function of deviation of the native T1 value from phantom and applied to correct the native T1 relaxation time. RESULTS: A statistically significant difference between native T1 values of the right and left anterior (P = 0.0095), middle (P = 0.0081), and posterior (P = 0.0004) parts of the breast coil. No significant difference was seen in the corrected T1 values between anterior (P = 0.402), middle (P = 0.305), and posterior (P = 0.349) aspects of both sides of the breast coil. CONCLUSION: Inhomogeneity in the native T1 value exists in dedicated mMR breast coil, and significant improvement can be achieved using specially designed external phantom with multiple tubes.

Association of pharmacokinetic and metabolic parameters derived using simultaneous PET/MRI: Initial findings and impact on response evaluation in breast cancer.

PURPOSE: To study relationships among pharmacokinetic and 18F-fluorodeoxyglucose (18F-FDG) PET parameters obtained through simultaneous PET/MRI in breast cancer patients and evaluate their combined potential for response evaluation. METHODS: The study included 41 breast cancer patients for correlation study and 9 patients (pre and post therapy) for response evaluation. All patients underwent simultaneous PET/MRI with dedicated breast imaging. Pharmacokinetic parameters and PET parameters for tumor were derived using an in- house developed and vendor provided softwares respectively. Relationships between SUV and pharmacokinetic parameters and clinical as well as histopathologic parameters were evaluated using Spearman correlation analysis. Response to chemotherapy was derived as percentage reduction in size and in parameters post therapy. RESULTS: Significant correlations were observed between SUVmean, max, peak, TLG with Ktrans (ρ=0.446, 0.417, 0.491, 0.430; p≤0.01); with Kep(ρ=0.303, ρ=0.315, ρ=0.319; p≤0.05); and with iAUC(ρ=0.401, ρ=0.410, ρ=0.379; p≤0.05, p≤0.01). The ratio of ve/iAUC showed significant negative correlation to SUVmean, max, peak and TLG (ρ=0.420, 0.446, 0.443, 0.426; p≤0.01). Ability of SUV as well as pharmacokinetic parameters to predict response to therapy matched the RECIST criteria in 9 out of 11 lesions in 9 patients. Maximum post therapy quantitative reduction was observed in SUVpeak, TLG and Ktrans. CONCLUSION: Simultaneous PET/MRI enables illustration of close interactions between glucose metabolism and pharmacokinetic parameters in breast cancer patients and potential of their simultaneity in response assessment to therapy.

Computer simulation of Cerebral Arteriovenous Malformation-validation analysis of hemodynamics parameters.

PROBLEM: The purpose of this work is to provide some validation methods for evaluating the hemodynamic assessment of Cerebral Arteriovenous Malformation (CAVM). This article emphasizes the importance of validating noninvasive measurements for CAVM patients, which are designed using lumped models for complex vessel structure. METHODS: The validation of the hemodynamics assessment is based on invasive clinical measurements and cross-validation techniques with the Philips proprietary validated software's Qflow and 2D Perfursion. RESULTS: The modeling results are validated for 30 CAVM patients for 150 vessel locations. Mean flow, diameter, and pressure were compared between modeling results and with clinical/cross validation measurements, using an independent two-tailed Student t test. Exponential regression analysis was used to assess the relationship between blood flow, vessel diameter, and pressure between them. Univariate analysis is used to assess the relationship between vessel diameter, vessel cross-sectional area, AVM volume, AVM pressure, and AVM flow results were performed with linear or exponential regression. DISCUSSION: Modeling results were compared with clinical measurements from vessel locations of cerebral regions. Also, the model is cross validated with Philips proprietary validated software's Qflow and 2D Perfursion. Our results shows that modeling results and clinical results are nearly matching with a small deviation. CONCLUSION: In this article, we have validated our modeling results with clinical measurements. The new approach for cross-validation is proposed by demonstrating the accuracy of our results with a validated product in a clinical environment.

Role of pharmacokinetic parameters derived with high temporal resolution DCE MRI using simultaneous PET/MRI system in breast cancer: A feasibility study.

PURPOSE: To evaluate the reliability of pharmacokinetic parameters like Ktrans, Kep and ve derived through DCE MRI breast protocol using 3T Simultaneous PET/MRI (3Tesla Positron Emission Tomography/Magnetic Resonance Imaging) system in distinguishing benign and malignant lesions. MATERIALS AND METHODS: High temporal resolution DCE (Dynamic Contrast Enhancement) MRI performed as routine breast MRI for diagnosis or as a part of PET/MRI for cancer staging using a 3T simultaneous PET/MRI system in 98 women having 109 breast lesions were analyzed for calculation of pharmacokinetic parameters (Ktrans, ve, and Kep) at 60s time point using an in-house developed computation scheme. RESULTS: Receiver operating characteristic (ROC) curve analysis revealed a cut off value for Ktrans, Kep, ve as 0.50, 2.59, 0.15 respectively which reliably distinguished benign and malignant breast lesions. Data analysis revealed an overall accuracy of 94.50%, 79.82% and 87.16% for Ktrans, Kep, ve respectively. Introduction of native T1 normalization with an externally placed phantom showed a higher accuracy (94.50%) than without native T1 normalization (93.50%) with an increase in specificity of 87% vs 84%. CONCLUSION: Overall the results indicate that reliable measurement of pharmacokinetic parameters with reduced acquisition time is feasible in a 3TMRI embedded PET/MRI system with reasonable accuracy and application may be extended to exploit the potential of simultaneous PET/MRI in further work on breast cancer.

Numerical modeling of vessel geometry to measure hemodynamics parameters non-invasively in cerebral arteriovenous malformation.

Cerebral arteriovenous malformations (CAVM) are congenital lesions that contain a cluster of multiple arteriovenous shunts (NIDUS). Cardiac arrhythmia in CAVM patients causes irregular changes in blood flow and pressure in the NIDUS area. This paper proposes the framework for creating the lumped model of tortuous vessel structure near NIDUS based on radiological images. This lumped model is to analyze flow variations, with various combinations of the transient electrical signals, which simulate similar conditions of cardiac arrhythmia in CAVM patients. This results in flow variation at different nodes of the lumped model. Here we present two AVM patients with evaluation of 150 vessels locations as node points, with an accuracy of 93%, the sensitivity of 95%, and specificity of 94%. The calculated p-value is smaller than the significance level of 0.05.

Role of quantitative pharmacokinetic parameter (transfer constant: K(trans)) in the characterization of breast lesions on MRI.

BACKGROUND: The semi-quantitative analysis of the time-intensity curves in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has a limited specificity due to overlapping enhancement patterns after gadolinium administration. With the advances in technology and faster sequences, imaging of the entire breast can be done in a few seconds, which allows measuring the transit of contrast (transfer constant: K(trans)) through the vascular bed at capillary level that reflects quantitative measure of porosity/permeability of tumor vessels. AIM: Our study aims to evaluate the pharmacokinetic parameter K(trans) for enhancing breast lesions and correlate it with histopathology, and assess accuracy, sensitivity, and specificity of this parameter in discriminating benign and malignant breast lesions. MATERIALS AND METHODS: One hundred and fifty-one women with 216 histologically proved enhancing breast lesions underwent high temporal resolution DCE-MRI for the early dynamic analysis for calculation of pharmacokinetic parameters (K(trans)) using standard two compartment model. The calculated values of K(trans) were correlated with histopathology to calculate the sensitivity, specificity, and accuracy. RESULTS: Receiver operating characteristic (ROC) curve analysis revealed a mean K(trans) value of 0.56, which reliably distinguished benign and malignant breast lesions with a sensitivity of 91.1% and specificity of 90.3% with an overall accuracy of 89.3%. The area under curve (AUC) was 0.907. CONCLUSION: K(trans) is a reliable quantitative parameter for characterizing benign and malignant lesions in routine DCE-MRI of breasts.

Optimizing MRI scan time in the computation of pharmacokinetic parameters (K(trans) ) in breast cancer diagnosis.

PURPOSE: To assess the effects of reduced scan time in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) of breast for the evaluation of pharmacokinetic parameters (K(trans) , ve , and kep ). MATERIALS AND METHODS: High temporal resolution DCE-MRI was performed for calculation of pharmacokinetic parameters (K(trans) , ve , and kep ) at different timepoints using an in-house developed computation scheme adopting the standard model (SM). RESULTS: The receiver operating characteristic (ROC) curve analysis revealed an area under the ROC curve (AUC) of 0.994 for K(trans) at 90 seconds and 0.987 for K(trans) at 60 seconds with a significant decrease in the AUC for K(trans) at 30 seconds (0.669). While ve showed a consistently higher AUC (>0.9) at timepoints ≥40 seconds, the AUC for kep showed a consistent decline with reduced acquisition times. CONCLUSION: Reducing the acquisition time for the K(trans) and ve measurement up to 60 seconds yields reasonable accuracy for both and can be incorporated in the routine DCE-MRI protocol for evaluation of enhancing breast lesions.

Handcrafted fuzzy rules for tissue classification.

This article proposes a handcrafted fuzzy rule-based system for segmentation and identification of different tissue types in magnetic resonance (MR) brain images. The proposed fuzzy system uses a combination of histogram and spatial neighborhood-based features. The intensity variation from one type of tissue to another is gradual at the boundaries due to the inherent nature of the MR signal (MR physics). A fuzzy rule-based approach is expected to better handle these variations and variability in features corresponding to different types of tissues. The proposed segmentation is tested to classify the pixels of the T2-weighted axial MR images of the brain into three primary tissue types: white matter, gray matter and cerebral-spinal fluid. The results are compared with those from manual segmentation by an expert, demonstrating good agreement between them.

A soft-segmentation visualization scheme for magnetic resonance images.

Prevalent visualization tools exploit gray value distribution in images through modified histogram equalization and matching technique, referred to as the window width/window level-based method, to improve visibility and enhance diagnostic value. The window width/window level tool is extensively used in magnetic resonance (MR) images to highlight tissue boundaries during image interpretation. However, the identification of different regions and distinct boundaries between them based on gray-level distribution and displayed intensity levels is extremely difficult because of the large dynamic range of tissue intensities inherent in MR images. We propose a soft-segmentation visualization scheme to generate pixel partitions from the histogram of MR image data using a connectionist approach and then generate selective visual depictions of pixel partitions using pseudo color based on an appropriate fuzzy membership function. By applying the display scheme in clinical examples in this study, we could demonstrate additional overlapping regions between distinct tissue types in healthy and diseased areas (in the brain) that could help improve the tissue characterization ability of MR images.