Chemical Exchange Saturation Transfer MRI: What Neuro-Oncology Clinicians Need To Know.

Chemical exchange saturation transfer (CEST) is a relatively novel magnetic resonance imaging (MRI) technique with an image contrast designed for in vivo measurement of certain endogenous molecules with protons that are exchangeable with water protons, such as amide proton transfer commonly used for neuro-oncology applications. Recent technological advances have made it feasible to implement CEST on clinical grade scanners within practical acquisition times, creating new opportunities to integrate CEST in clinical workflow. In addition, the majority of CEST applications used in neuro-oncology are performed without the use gadolinium-based contrast agents which are another appealing feature of this technique. This review is written for clinicians involved in neuro-oncologic care (nonphysicists) as the target audience explaining what they need to know as CEST makes its way into practice. The purpose of this article is to (1) review the basic physics and technical principles of CEST MRI, and (2) review the practical applications of CEST in neuro-oncology.

Chemical Exchange Saturation Transfer MRI for Differentiating Radiation Necrosis From Tumor Progression in Brain Metastasis-Application in a Clinical Setting.

BACKGROUND: High radiation doses of stereotactic radiosurgery (SRS) for brain metastases (BM) can increase the likelihood of radiation necrosis (RN). Advanced MRI sequences can improve the differentiation between RN and tumor progression (TP). PURPOSE: To use saturation transfer MRI methods including chemical exchange saturation transfer (CEST) and magnetization transfer (MT) to distinguish RN from TP. STUDY TYPE: Prospective cohort study. SUBJECTS: Seventy patients (median age 60; 73% females) with BM (75 lesions) post-SRS. FIELD STRENGTH/SEQUENCE: 3-T, CEST imaging using low/high-power (saturation B1  = 0.52 and 2.0 µT), quantitative MT imaging using B1  = 1.5, 3.0, and 5.0 µT, WAter Saturation Shift Referencing (WASSR), WAter Shift And B1 (WASABI), T1 , and T2 mapping. All used gradient echoes except T2 mapping (gradient and spin echo). ASSESSMENT: Voxel-wise metrics included: magnetization transfer ratio (MTR); apparent exchange-dependent relaxation (AREX); MTR asymmetry; normalized MT exchange rate and pool size product; direct water saturation peak width; and the observed T1 and T2 . Regions of interests (ROIs) were manually contoured on the post-Gd T1 w. The mean (of median ROI values) was compared between groups. Clinical outcomes were determined by clinical and radiologic follow-up or histopathology. STATISTICAL TESTS: t-Test, univariable and multivariable logistic regression, receiver operating characteristic, and area under the curve (AUC) with sensitivity/specificity values with the optimal cut point using the Youden index, Akaike information criterion (AIC), Cohen's d. P < 0.05 with Bonferroni correction was considered significant. RESULTS: Seven metrics showed significant differences between RN and TP. The high-power MTR showed the highest AUC of 0.88, followed by low-power MTR (AUC = 0.87). The combination of low-power CEST scans improved the separation compared to individual parameters (with an AIC of 70.3 for low-power MTR/AREX). Cohen's d effect size showed that the MTR provided the largest effect sizes among all metrics. DATA CONCLUSION: Significant differences between RN and TP were observed based on saturation transfer MRI. EVIDENCE LEVEL: 3 Technical Efficacy: Stage 2.

ADC, D, f dataset calculated through the simplified IVIM model, with MGMT promoter methylation, age, and ECOG, in 38 patients with wildtype IDH glioblastoma.

Patients undergoing standard chemoradiation post-resection had MRIs at radiation planning and fractions 10 and 20 of chemoradiation. MRIs were 1.5T and 3D T2-FLAIR, pre- and post-contrast 3D T1-weighted (T1) and echo planar DWI with three b-values (0, 500, and 1000s/mm2) were acquired. T2-FLAIR was coregistered to T1C images. Non-overlapping T1 contrast-enhancing (T1C) and nonenhancing T2-FLAIR hyperintense regions were segmented, with necrotic/cystic regions, the surgical cavity, and large vessels excluded. The simplified IVIM model was used to calculate voxelwise diffusion coefficient (D) and perfusion fraction (f) maps; ADC was calculated using the natural logarithm of b = 1000 over b = 0 images. T1C and T2-FLAIR segmentations were brought into this space, and medians calculated. MGMT promoter methylation status (MGMTPMS), age at diagnosis, and Eastern Cooperative Oncology Group (ECOG) performance status were extracted from electronic medical records. The data were presented, analyzed, and described in the article, "Intravoxel incoherent motion (IVIM) modeling of diffusion MRI during chemoradiation predicts therapeutic response in IDH wildtype Glioblastoma", published in Radiotherapy and Oncology [1].

Intravoxel incoherent motion (IVIM) modeling of diffusion MRI during chemoradiation predicts therapeutic response in IDH wildtype glioblastoma.

BACKGROUND: Prediction of early progression in glioblastoma may provide an opportunity to personalize treatment. Simplified intravoxel incoherent motion (IVIM) MRI offers quantitative estimates of diffusion and perfusion metrics. We investigated whether these metrics, during chemoradiation, could predict treatment outcome. METHODS: 38 patients with newly diagnosed IDH-wildtype glioblastoma undergoing 6-week/30-fraction chemoradiation had standardized post-operative MRIs at baseline (radiation planning), and at the 10th and 20th fractions. Non-overlapping T1-enhancing (T1C) and non-enhancing T2-FLAIR hyperintense regions were independently segmented. Apparent diffusion coefficient (ADCT1C, ADCT2-FLAIR) and perfusion fraction (fT1C, fT2-FLAIR) maps were generated with simplified IVIM modelling. Parameters associated with progression before or after 6.9 months (early vs late progression, respectively), overall survival (OS) and progression-free survival (PFS) were investigated. RESULTS: Higher ADCT2-FLAIR at baseline [Odds Ratio (OR) = 1.06, 95% CI 1.01-1.15, p = 0.025], lower fT2-FLAIR at fraction 10 (OR = 2.11, 95% CI 1.04-4.27, p = 0.018), and lack of increase in ADCT2-FLAIR at fraction 20 compared to baseline (OR = 1.12, 95% CI 1.02-1.22, p = 0.02) were associated with early progression. Combining ADCT2-FLAIR at baseline, fT2-FLAIR at fraction 10, ECOG and MGMT promoter methylation status significantly improved AUC to 90.3% compared to a model with only ECOG and MGMT promoter methylation status (p = 0.001). Using multivariable analysis, neither IVIM metrics were associated with OS but higher fT2-FLAIR at fraction 10 (HR = 0.72, 95% CI 0.56-0.95, p = 0.018) was associated with longer PFS. CONCLUSION: ADCT2-FLAIR at baseline, its lack of increase from baseline to fraction 20, or fT2-FLAIR at fraction 10 significantly predicted early progression. fT2-FLAIR at fraction 10 was associated with PFS.

[18 F]-Sodium Fluoride PET/MR Imaging for Bone-Cartilage Interactions in Hip Osteoarthritis: A Feasibility Study.

This study characterized the distribution of [18 F]-sodium fluoride (NaF) uptake and blood flow in the femur and acetabulum in hip osteoarthritis (OA) patients to find associations between bone remodeling and cartilage composition in the presence of morphological abnormalities using simultaneous positron emission tomography and magnetic resonance imaging (PET/MR), quantitative magnetic resonance imaging (MRI) and femur shape modeling. Ten patients underwent a [18 F]-NaF PET/MR dynamic scan of the hip simultaneously with: (i) fast spin-echo CUBE for morphology grading and (ii) T1ρ /T2 magnetization-prepared angle-modulated partitioned k-space spoiled gradient echo snapshots for cartilage, bone segmentation, bone shape modeling, and T1ρ /T2 quantification. The standardized uptake values (SUVs) and Patlak kinetic parameter (Kpat ) were calculated for each patient as PET outcomes, using an automated post-processing pipeline. Shape modeling was performed to extract the variations in bone shapes in the patients. Pearson's correlation coefficients were used to study the associations between bone shapes, PET outcomes, and patient reported pain. Direct associations between quantitative MR and PET evidence of bone remodeling were established in the acetabulum and femur. Associations of shaft thickness with SUV in the femur (p = 0.07) and Kpat in the acetabulum (p = 0.02), cam deformity with acetabular score (p = 0.09), osteophytic growth on the femur head with Kpat (p = 0.01) were observed. Pain had increased correlations with SUV in the acetabulum (p = 0.14) and femur (p = 0.09) when shaft thickness was accounted for. This study demonstrated the ability of [18 F]-NaF PET-MRI, 3D shape modeling, and quantitative MRI to investigate cartilage-bone interactions and bone shape features in hip OA, providing potential investigative tools to diagnose OA. © 2019 The Authors. Journal of Orthopaedic Research® published by Wiley Periodicals, Inc. on behalf of Orthopaedic Research Society J Orthop Res 37:2671-2680, 2019.

Advanced Magnetic Resonance Imaging Techniques in Management of Brain Metastases.

Brain metastases are the most common intracranial tumors and occur in 20-40% of all cancer patients. Lung cancer, breast cancer, and melanoma are the most frequent primary cancers to develop brain metastases. Treatment options include surgical resection, whole brain radiotherapy, stereotactic radiosurgery, and systemic treatment such as targeted or immune therapy. Anatomical magnetic resonance imaging (MRI) of the tumor (in particular post-Gadolinium T1-weighted and T2-weighted FLAIR) provide information about lesion morphology and structure, and are routinely used in clinical practice for both detection and treatment response evaluation for brain metastases. Advanced MRI biomarkers that characterize the cellular, biophysical, micro-structural and metabolic features of tumors have the potential to improve the management of brain metastases from early detection and diagnosis, to evaluating treatment response. Magnetic resonance spectroscopy (MRS), chemical exchange saturation transfer (CEST), quantitative magnetization transfer (qMT), diffusion-based tissue microstructure imaging, trans-membrane water exchange mapping, and magnetic susceptibility weighted imaging (SWI) are advanced MRI techniques that will be reviewed in this article as they pertain to brain metastases.

Evaluation of Glioblastoma Response to Therapy With Chemical Exchange Saturation Transfer.

PURPOSE: To monitor cellular and metabolic characteristics of glioblastoma (GBM) over the course of standard 6-week chemoradiation treatment with chemical exchange saturation transfer (CEST)-MRI; and to identify the earliest time point CEST could determine subsequent therapeutic response. METHODS AND MATERIALS: Nineteen patients with newly diagnosed GBM were recruited, and CEST-MRI was acquired immediately before (Day0), 2 weeks (Day14) and 4 weeks (Day28) into treatment, and 1 month after the end of treatment (Day70). Several CEST metrics, including magnetization transfer ratio and area under the curve of CEST peaks corresponding to nuclear Overhauser effect (NOE) and amide protons (MTRNOE, MTRAmide, CESTNOE, and CESTAmide respectively), magnetization transfer (MT), and direct water effect were investigated. Lack of early progression was determined as no increase in tumor size or worsening of clinical symptoms according to routine post-chemoradiation serial structural MRI. RESULTS: Changes in MTRNOE (nonprogressors = 1.35 ± 0.18, progressors = 0.97 ± 0.22, P = .006) and MTRAmide (nonprogressors = 1.25 ± 0.17, progressors = 0.99 ± 0.10, P = .017) between baseline (Day0) and Day14 resulted in the best separation of nonprogressors from progressors. Moreover, the baseline (Day0) MTRNOE (nonprogressors = 6.5% ± 1.6%, progressors = 9.1% ± 2.1%, P = .015), MTRAmide (nonprogressors = 6.7% ± 1.7%, progressors = 8.9% ± 1.9%, P = .028), MT (nonprogressors = 3.8% ± 0.9%, progressors = 5.4% ± 1.4%, P = .019), and CESTNOE (nonprogressors = 4.1%cHz ± 1.7%cHz, progressors = 6.1%cHz ± 1.9%cHz, P = .044) were able to identify progressors even before the start of the treatment. CONCLUSIONS: Chemical exchange saturation transfer (CEST) provides imaging-based biomarkers of GBM response as early as 2 weeks into the treatment. Certain CEST metrics can characterize tumor aggressiveness and identify early progressors even before beginning the treatment. Such an early biomarker of response may allow for adjusting the GBM treatment plan for adaptive radiation therapy in early progressors and more confidently continuing standard adjuvant treatment for nonprogressors.

Deformable Registration for Longitudinal Breast MRI Screening.

MRI screening of high-risk patients for breast cancer provides very high sensitivity, but with a high recall rate and negative biopsies. Comparing the current exam to prior exams reduces the number of follow-up procedures requested by radiologists. Such comparison, however, can be challenging due to the highly deformable nature of breast tissues. Automated co-registration of multiple scans has the potential to aid diagnosis by providing 3D images for side-by-side comparison and also for use in CAD systems. Although many deformable registration techniques exist, they generally have a large number of parameters that need to be optimized and validated for each new application. Here, we propose a framework for such optimization and also identify the optimal input parameter set for registration of 3D T1-weighted MRI of breast using Elastix, a widely used and freely available registration tool. A numerical simulation study was first conducted to model the breast tissue and its deformation through finite element (FE) modeling. This model generated the ground truth for evaluating the registration accuracy by providing the deformation of each voxel in the breast volume. An exhaustive search was performed over various values of 7 registration parameters (4050 different combinations of parameters were assessed) and the optimum parameter set was determined. This study showed that there was a large variation in the registration accuracy of different parameter sets ranging from 0.29 mm to 2.50 mm in median registration error and 3.71 mm to 8.90 mm in 95 percentile of the registration error. Mean registration errors of 0.32 mm, 0.29 mm, and 0.30 mm and 95 percentile errors of 3.71 mm, 5.02 mm, and 4.70 mm were obtained by the three best parameter sets. The optimal parameter set was applied to consecutive breast MRI scans of 13 patients. A radiologist identified 113 landmark pairs (~ 11 per patient) which were used to assess registration accuracy. The results demonstrated that using the optimal registration parameter set, a registration accuracy (in mm) of 3.4 [1.8 6.8] was achieved.

Glioblastoma (GBM) effects on quantitative MRI of contralateral normal appearing white matter.

PURPOSE: The objective was to investigate (with quantitative MRI) whether the normal appearing white matter (NAWM) of glioblastoma (GBM) patients on the contralateral side (cNAWM) was different from NAWM of healthy controls. METHODS: Thirteen patients with newly diagnosed GBM and nine healthy age-matched controls were MRI-scanned with quantitative magnetization transfer (qMT), chemical exchange saturation transfer (CEST), and transverse relaxation time (T2)-mapping. MRI scans were performed after surgery and before chemo-radiation treatment. Comprehensive qMT, CEST, T2 data were acquired. A two-pool MT model was fit to qMT data in transient state, to calculate MT model parameters [Formula: see text]. CEST signal was isolated by removing the contributions from the MT and direct water saturation, and CEST signal was calculated for Amide (CESTAmide), Amine (CESTAmine) and nuclear overhauser effect, NOE (CESTNOE). RESULTS: There was no difference between GBM patients and normal controls in the qMT properties of the macromolecular pool [Formula: see text]. However, their free water pool spectrum was different (1/RaT2a,patient = 28.1 ± 3.9, 1/RaT2a,control = 25.0 ± 1.1, p = 0.03). This difference could be attributed to the difference in their T2 time ([Formula: see text] = 83 ± 4, [Formula: see text] = 88 ± 1, p = 0.004). CEST signals were statistically significantly different with the CESTAmide having the largest difference between the two cohorts (CESTAmide,patient = 2.8 ± 0.4, CESTAmide,control = 3.4 ± 0.5, p = 0.009). CONCLUSIONS: CEST in cNAWM of GBM patients was lower than healthy controls which could be caused by modified brain metabolism due to tumor cell infiltration. There was no difference in MT properties of the patients and controls, however, the differences in free water pool properties were mainly due to reduced T2 in cNAWM of the patients (resulting from structural changes and increased cellularity).

Quantitative Magnetization Transfer in Monitoring Glioblastoma (GBM) Response to Therapy.

Quantitative magnetization transfer (qMT) was used as a biomarker to monitor glioblastoma (GBM) response to chemo-radiation and identify the earliest time-point qMT could differentiate progressors from non-progressors. Nineteen GBM patients were recruited and MRI-scanned before (Day0), two weeks (Day14), and four weeks (Day28) into the treatment, and one month after the end of the treatment (Day70). Comprehensive qMT data was acquired, and a two-pool MT model was fit to the data. Response was determined at 3-8 months following the end of chemo-radiation. The amount of magnetization transfer ([Formula: see text]) was significantly lower in GBM compared to normal appearing white matter (p < 0.001). Statistically significant difference was observed in [Formula: see text] at Day0 between non-progressors (1.06 ± 0.24) and progressors (1.64 ± 0.48), with p = 0.006. Changes in several qMT parameters between Day14 and Day0 were able to differentiate the two cohorts with [Formula: see text] providing the best separation (relative [Formula: see text] = 1.34 ± 0.21, relative [Formula: see text] = 1.07 ± 0.08, p = 0.031). Thus, qMT characteristics of GBM are more sensitive to treatment effects compared to clinically used metrics. qMT could assess tumor aggressiveness and identify early progressors even before the treatment. Changes in qMT parameters within the first 14 days of the treatment were capable of separating early progressors from non-progressors, making qMT a promising biomarker to guide adaptive radiotherapy for GBM.

Temporal evolution of perfusion parameters in brain metastases treated with stereotactic radiosurgery: comparison of intravoxel incoherent motion and dynamic contrast enhanced MRI.

Intravoxel incoherent motion (IVIM) is a magnetic resonance imaging (MRI) technique that is seeing increasing use in neuro-oncology and offers an alternative to contrast-enhanced perfusion techniques for evaluation of tumor blood volume after stereotactic radiosurgery (SRS). To date, IVIM has not been validated against contrast enhanced techniques for brain metastases after SRS. In the present study, we measure blood volume for 20 brain metastases (15 patients) at baseline, 1 week and 1 month after SRS using IVIM and dynamic contrast enhanced (DCE)-MRI. Correlation between blood volume measurements made with IVIM and DCE-MRI show poor correlation at baseline, 1 week, and 1 month post SRS (r = 0.33, 0.14 and 0.30 respectively). At 1 week after treatment, no significant change in tumor blood volume was found using IVIM or DCE-MRI (p = 0.81 and 0.41 respectively). At 1 month, DCE-MRI showed a significant decrease in blood volume (p = 0.0002). IVIM, on the other hand, demonstrated the opposite effect and showed a significant increase in blood volume at 1 month (p = 0.03). The results of this study indicate that blood volume measured with IVIM and DCE-MRI are not equivalent. While this may relate to differences in the type of perfusion information each technique is providing, it could also reflect a limitation of tumor blood volume measurements made with IVIM after SRS. IVIM measurements of tumor blood volume in the month after SRS should therefore be interpreted with caution.

Water Exchange Rate Constant as a Biomarker of Treatment Efficacy in Patients With Brain Metastases Undergoing Stereotactic Radiosurgery.

PURPOSE: This study was designed to evaluate whether changes in metastatic brain tumors after stereotactic radiosurgery (SRS) can be seen with quantitative MRI early after treatment. METHODS AND MATERIALS: Using contrast-enhanced MRI, a 3-water-compartment tissue model consisting of intracellular (I), extracellular-extravascular (E), and vascular (V) compartments was used to assess the intra-extracellular water exchange rate constant (kIE), efflux rate constant (kep), and water compartment volume fractions (M0,I, M0,E, M0,V). In this prospective study, 19 patients were MRI-scanned before treatment and 1 week and 1 month after SRS. The change in model parameters between the pretreatment and 1-week posttreatment scans was correlated to the change in tumor volume between pretreatment and 1-month posttreatment scans. RESULTS: At 1 week kIE differentiated (P<.001) tumors that had partial response from tumors with stable and progressive disease, and a high correlation (R=-0.76, P<.001) was observed between early changes in the kIE and tumor volume change 1 month after treatment. Other model parameters had lower correlation (M0,E) or no correlation (kep, M0,V). CONCLUSIONS: This is the first study that measured kIE early after SRS, and it found that early changes in kIE (1 week after treatment) highly correlated with long-term tumor response and could predict the extent of tumor shrinkage at 1 month after SRS.

Differentiation between Radiation Necrosis and Tumor Progression Using Chemical Exchange Saturation Transfer.

Purpose: Stereotactic radiosurgery (SRS) is a common treatment used in patients with brain metastases and is associated with high rates of local control, however, at the risk of radiation necrosis. It is difficult to differentiate radiation necrosis from tumor progression using conventional MRI, making it a major diagnostic dilemma for practitioners. This prospective study investigated whether chemical exchange saturation transfer (CEST) was able to differentiate these two conditions.Experimental Design: Sixteen patients with brain metastases who had been previously treated with SRS were included. Average time between SRS and evaluation was 12.6 months. Lesion type was determined by pathology in 9 patients and the other 7 were clinically followed. CEST imaging was performed on a 3T Philips scanner and the following CEST metrics were measured: amide proton transfer (APT), magnetization transfer (MT), magnetization transfer ratio (MTR), and area under the curve for CEST peaks corresponding to amide and nuclear Overhauser effect (NOE).Results: Five lesions were classified as progressing tumor and 11 were classified as radiation necrosis (using histopathologic confirmation and radiographic follow-up). The best separation was obtained by NOEMTR (NOEMTR,necrosis = 8.9 ± 0.9%, NOEMTR,progression = 12.6 ± 1.6%, P < 0.0001) and AmideMTR (AmideMTR,necrosis = 8.2 ± 1.0%, AmideMTR,progression = 12.0 ± 1.9%, P < 0.0001). MT (MTnecrosis = 4.7 ± 1.0%, MTprogression = 6.7 ± 1.7%, P = 0.009) and NOEAUC (NOEAUC,necrosis = 4.3 ± 2.0% Hz, NOEAUC,progression = 7.2 ± 1.9% Hz, P = 0.019) provided statistically significant separation but with higher P values.Conclusions: CEST was capable of differentiating radiation necrosis from tumor progression in brain metastases. Both NOEMTR and AmideMTR provided statistically significant separation of the two cohorts. However, APT was unable to differentiate the two groups. Clin Cancer Res; 23(14); 3667-75. ©2017 AACR.

Chemical exchange saturation transfer for predicting response to stereotactic radiosurgery in human brain metastasis.

PURPOSE: The purpose of this work was to determine the predictive value of chemical exchange saturation transfer (CEST) metrics in brain metastases treated with stereotactic radiosurgery (SRS). METHODS: CEST spectra at a radiofrequency power of 0.52 µT were collected on a 3 Tesla (T) magnetic resonance imaging from 25 patients at three time points: pretreatment, 1 week, and 1 month post-treatment. Amide proton transfer-weighted images and maps of the amplitude and width of Lorentzian-shaped CEST peaks and the relaxation-compensated AREX metric were constructed at the offset frequencies of amide, amine, and relayed nuclear Overhauser effect (NOE) from aliphatic groups as well as the broad magnetization transfer effect. Pretreatment CEST metrics, as well as CEST metric changes at 1 week post-treatment, were compared to changes in tumor volume at 1 month. RESULTS: Significant (P < 0.05) 1-week predictive metrics included NOE peak amplitude (R = 0.69) in normal-appearing white matter (NAWM) and width (R = -0.55) in tumor. Baseline NOE in contralateral NAWM was negatively correlated (R = -0.69) with volume changes at 1 month. Metrics-defined outside tumor margins had higher correlation with volume changes than tumor regions of interest. CONCLUSION: CEST metrics, in particular, the NOE peak amplitude, can predict volume changes 1 month post-SRS. Magn Reson Med 78:1110-1120, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

Pharmacokinetic analysis of prostate cancer using independent component analysis.

Dynamic contrast enhanced (DCE)-MRI combined with pharmacokinetic (PK) modeling of a tumor provides information about its perfusion and vascular permeability. Most PK models require the time course of contrast agent concentration in blood plasma as an input, which cannot be measured directly at the tissue of interest, and is approximated with an arterial input function (AIF). Variability in methods used in estimating the AIF and inter-observer variability in region of interest selection are major sources of discrepancy between different studies. This study had two aims. The first was to determine whether a local vascular input function (VIF) estimated using an adaptive complex independent component analysis (AC-ICA) algorithm could be used to estimate PK parameters from clinical dynamic contrast enhanced (DCE)-MRI studies. The second aim was to determine whether normalizing the input function using its area under the curve would improve the results of PK analysis. AC-ICA was applied to DCE-MRI of 27 prostate cancer patients and the intravascular signal was estimated. This signal was converted into contrast agent concentration to give a local vascular input function (VIF) which was used as the input function for PK analysis. We compared K(trans) values for normal peripheral zone (PZ) and tumor tissues using the local VIF with those calculated using a conventional AIF obtained from the femoral artery. We also compared the K(trans) values obtained from the un-normalized input functions with the KN(trans) values obtained after normalizing the AIF and local VIF. Normalization of the input function resulted in smaller variation in PK parameters (KN(trans) vs. K(trans) for normal PZ tissue was 0.20±0.04mM.min(-1) vs. 0.87±0.54min(-1) for local VIF and 0.21±0.07mM.min(-1) vs. 0.25±0.29min(-1) for AIF) and better separation of the normal and tumor tissues (effect-size of this separation using KN(trans) vs. K(trans) was 0.89 vs. 0.75 for local VIF and 0.94 vs. 0.41 for AIF). The AC-ICA and AIF-based analyses provided similar (KN(trans)) values in normal PZ tissue of prostate across patients. Normalizing the input function before PK analysis significantly improved the reproducibility of the PK parameters and increased the separation between normal and tumor tissues. Using AC-ICA allows a local VIF to be estimated and the resulting PK parameters are similar to those obtained using a more conventional AIF; this may be valuable in studies where an artery is not available in the field of view.

Calculation of intravascular signal in dynamic contrast enhanced-MRI using adaptive complex independent component analysis.

Assessing tumor response to therapy is a crucial step in personalized treatments. Pharmacokinetic (PK) modeling provides quantitative information about tumor perfusion and vascular permeability that are associated with prognostic factors. A fundamental step in most PK analyses is calculating the signal that is generated in the tumor vasculature. This signal is usually inseparable from the extravascular extracellular signal. It was shown previously using in vivo and phantom experiments that independent component analysis (ICA) is capable of calculating the intravascular time-intensity curve in dynamic contrast enhanced (DCE)-MRI. A novel adaptive complex independent component analysis (AC-ICA) technique is developed in this study to calculate the intravascular time-intensity curve and separate this signal from the DCE-MR images of tumors. The use of the complex-valued DCE-MRI images rather than the commonly used magnitude images satisfied the fundamental assumption of ICA, i.e., linear mixing of the sources. Using an adaptive cost function in ICA through estimating the probability distribution of the tumor vasculature at each iteration resulted in a more robust and accurate separation algorithm. The AC-ICA algorithm provided a better estimate for the intravascular time-intensity curve than the previous ICA-based method. A simulation study was also developed in this study to realistically simulate DCE-MRI data of a leaky tissue mimicking phantom. The passage of the MR contrast agent through the leaky phantom was modeled with finite element analysis using a diffusion model. Once the distribution of the contrast agent in the imaging field of view was calculated, DCE-MRI data was generated by solving the Bloch equation for each voxel at each time point. The intravascular time-intensity curve calculation results were compared to the previously proposed ICA-based intravascular time-intensity curve calculation method that applied ICA to the magnitude of the DCE-MRI data (Mag-ICA) using both simulated and experimental tissue mimicking phantoms. The AC-ICA demonstrated superior performance compared to the Mag-ICA method. AC-ICA provided more accurate estimate of intravascular time-intensity curve, having smaller error between the calculated and actual intravascular time-intensity curves compared to the Mag-ICA. Furthermore, it showed higher robustness in dealing with datasets with different resolution by providing smaller variation between the results of each datasets and having smaller difference between the intravascular time-intensity curves of various resolutions. Thus, AC-ICA has the potential to be used as the intravascular time-intensity curve calculation method in PK analysis and could lead to more accurate PK analysis for tumors.

Arterial input function calculation in dynamic contrast-enhanced MRI: an in vivo validation study using co-registered contrast-enhanced ultrasound imaging.

OBJECTIVES: Developing a method of separating intravascular contrast agent concentration to measure the arterial input function (AIF) in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) of tumours, and validating its performance in phantom and in vivo experiments. METHODS: A tissue-mimicking phantom was constructed to model leaky tumour vasculature and DCE-MR images of this phantom were acquired. An in vivo study was performed using tumour-bearing rabbits. Co-registered DCE-MRI and contrast-enhanced ultrasound (CEUS) images were acquired. An independent component analysis (ICA)-based method was developed to separate the intravascular component from DCE-MRI. Results were validated by comparing the time-intensity curves with the actual phantom and in vivo curves. RESULTS: Phantom study: the AIF extracted using ICA correlated well with the true intravascular curve. In vivo study: the AIFs extracted from DCE-MRI using ICA were very close to the true AIF. Intravascular component images were very similar to the CEUS images. The contrast onset times and initial wash-in slope of the ICA-derived AIF showed good agreement with the CEUS curves. CONCLUSION: ICA has the potential to separate the intravascular component from DCE-MRI. This could eliminate the requirement for contrast medium uptake measurements in a major artery and potentially result in more accurate pharmacokinetic parameters. KEY POINTS: • Tumour response to therapy can be inferred from pharmacokinetic parameters. • Arterial input function (AIF) is required for pharmacokinetic modelling of tumours. • Independent component analysis has the potential to measure AIF inside the tumour. • AIF measurement is validated using contrast enhanced ultrasound and phantoms.

A constrained independent component analysis technique for artery-vein separation of two-photon laser scanning microscopy images of the cerebral microvasculature.

Understanding brain hemodynamics as well as the coupling between microvascular hemodynamics and neural activity is important in pathophysiology of cerebral microvasculature. When local increases in neuronal activity occur, the blood volume changes in the surrounding brain vasculature. Dynamic contrast enhanced imaging (DCE) is a powerful technique that quantifies these changes in the blood flow by repeatedly imaging the vasculature over time. Separating artery, vein and capillaries in the images and extracting their intensity-time curves from the DCE image sequence is an important first step in understanding vascular function. A constrained independent component analysis (ICA) technique is developed to analyze the two photon laser scanning microscopy (2PLSM) images of rat brain microvasculature, where a bolus of fluorescent dye is administered to the vascular system as the contrast agent. A priori information inferred from the gamma variate model of cerebral microvasculature is incorporated with the data driven technique in temporal and spatial domains using two constraints. The constraints are: no independent component (IC) is allowed to have negative contribution in forming the images (positivity constraint) and the component curves follow a gamma variate function (model fitting constraint). Experimental and simulation studies are conducted to demonstrate the improved performance of the proposed constrained ICA (CICA) technique over the most commonly used classical ICA algorithm (fast-ICA) in providing physiologically meaningful ICs and its ability to separate the model following factors from other factors are shown. The efficiency of CICA in handling noise is compared to model based techniques. Its capability in providing improved separation between artery, vein and capillaries compared to the other two techniques is also demonstrated.

A constrained reconstruction technique of hyperelasticity parameters for breast cancer assessment.

In breast elastography, breast tissue usually undergoes large compression resulting in significant geometric and structural changes. This implies that breast elastography is associated with tissue nonlinear behavior. In this study, an elastography technique is presented and an inverse problem formulation is proposed to reconstruct parameters characterizing tissue hyperelasticity. Such parameters can potentially be used for tumor classification. This technique can also have other important clinical applications such as measuring normal tissue hyperelastic parameters in vivo. Such parameters are essential in planning and conducting computer-aided interventional procedures. The proposed parameter reconstruction technique uses a constrained iterative inversion; it can be viewed as an inverse problem. To solve this problem, we used a nonlinear finite element model corresponding to its forward problem. In this research, we applied Veronda-Westmann, Yeoh and polynomial models to model tissue hyperelasticity. To validate the proposed technique, we conducted studies involving numerical and tissue-mimicking phantoms. The numerical phantom consisted of a hemisphere connected to a cylinder, while we constructed the tissue-mimicking phantom from polyvinyl alcohol with freeze-thaw cycles that exhibits nonlinear mechanical behavior. Both phantoms consisted of three types of soft tissues which mimic adipose, fibroglandular tissue and a tumor. The results of the simulations and experiments show feasibility of accurate reconstruction of tumor tissue hyperelastic parameters using the proposed method. In the numerical phantom, all hyperelastic parameters corresponding to the three models were reconstructed with less than 2% error. With the tissue-mimicking phantom, we were able to reconstruct the ratio of the hyperelastic parameters reasonably accurately. Compared to the uniaxial test results, the average error of the ratios of the parameters reconstructed for inclusion to the middle and external layers were 13% and 9.6%, respectively. Given that the parameter ratios of the abnormal tissues to the normal ones range from three times to more than ten times, this accuracy is sufficient for tumor classification.