Dynamic Susceptibility-weighted Contrast-enhanced Research Articles

Assessment of PSR as a Novel Parameter in Perfusion Imaging for CNS Tumor Characterization: An Observational Study

Introduction: Magnetic resonance imaging (MRI) has emerged as the primary modality for diagnosing and evaluating central nervous system (CNS) tumors, transitioning from structural to functional assessment. Advanced MRI techniques, including MR spectroscopy and perfusion imaging, offer insights into functional, metabolic, and hemodynamic changes in addition to structural abnormalities. The study aims to assess the role of PSR (Post-Contrast Signal Recovery) alongside rCBV (Relative Cerebral Blood Volume) and rPSR (Relative Percentage of Signal Recovery) in differentiating grade IV glioma, solitary brain metastasis, and primary CNS lymphoma. Materials and Methods: An observational study was conducted on 38 patients with lymphoma, metastases, and grade IV glioma, utilizing DSC (Dynamic Susceptibility-Weighted Contrast-Enhanced) perfusion imaging and histopathology reports. Sociodemographic, clinical, and MRI parameters were collected. PSR measurements were obtained from perfusion maps using dedicated software. Statistical analysis was performed using SPSS version 27.0. Results: Among the study population, the mean age was 54.16±12.47, with male predominance (63.2%). Conventional imaging characteristics revealed significant differences, including T2 hypo intensity in lymphoma and FLAIR hyperintensity in all gliomas. Perfusion parameters showed higher rCBV in metastases and grade IV glioma compared to lymphoma, whereas PSR and rPSR were higher in lymphoma. PSR and rPSR effectively differentiated among the tumor types (p<0.001). Conclusion: Our study underscores the critical role of advanced MRI techniques, particularly DSC perfusion imaging, in differentiating between grade IV gliomas, solitary brain metastases, and primary CNS lymphomas. PSR emerges as a valuable parameter alongside rCBV and rPSR, demonstrating potential for accurate tumor characterization.

Perfusion-weighted software written in Python for DSC-MRI analysis.

Dynamic susceptibility-weighted contrast-enhanced (DSC) perfusion studies in magnetic resonance imaging (MRI) provide valuable data for studying vascular cerebral pathophysiology in different rodent models of brain diseases (stroke, tumor grading, and neurodegenerative models). The extraction of these hemodynamic parameters via DSC-MRI is based on tracer kinetic modeling, which can be solved using deconvolution-based methods, among others. Most of the post-processing software used in preclinical studies is home-built and custom-designed. Its use being, in most cases, limited to the institution responsible for the development. In this study, we designed a tool that performs the hemodynamic quantification process quickly and in a reliable way for research purposes. The DSC-MRI quantification tool, developed as a Python project, performs the basic mathematical steps to generate the parametric maps: cerebral blood flow (CBF), cerebral blood volume (CBV), mean transit time (MTT), signal recovery (SR), and percentage signal recovery (PSR). For the validation process, a data set composed of MRI rat brain scans was evaluated: i) healthy animals, ii) temporal blood-brain barrier (BBB) dysfunction, iii) cerebral chronic hypoperfusion (CCH), iv) ischemic stroke, and v) glioblastoma multiforme (GBM) models. The resulting perfusion parameters were then compared with data retrieved from the literature. A total of 30 animals were evaluated with our DSC-MRI quantification tool. In all the models, the hemodynamic parameters reported from the literature are reproduced and they are in the same range as our results. The Bland-Altman plot used to describe the agreement between our perfusion quantitative analyses and literature data regarding healthy rats, stroke, and GBM models, determined that the agreement for CBV and MTT is higher than for CBF. An open-source, Python-based DSC post-processing software package that performs key quantitative perfusion parameters has been developed. Regarding the different animal models used, the results obtained are consistent and in good agreement with the physiological patterns and values reported in the literature. Our development has been built in a modular framework to allow code customization or the addition of alternative algorithms not yet implemented.

Comparison of Diffusion Tensor Imaging and Magnetic Resonance Perfusion Imaging in Differentiating Recurrent Brain Neoplasm From Radiation Necrosis

To compare differences in diffusion tensor imaging (DTI) and dynamic susceptibility-weighted contrast-enhanced (DSC) magnetic resonance (MR) perfusion imaging characteristics of recurrent neoplasm and radiation necrosis in patients with brain tumors previously treated with radiotherapy with or without surgery and chemotherapy. Patients with a history of brain neoplasm previously treated with radiotherapy with or without chemotherapy and surgery who developed a new enhancing lesion on posttreatment surveillance MRI were enrolled. DSC perfusion MRI and DTI were performed. Region of interest cursors were manually drawn in the contrast-enhancing lesions, in the perilesional white matter edema, and in the contralateral normal-appearing frontal lobe white matter. DTI and DSC perfusion MR indices were compared in recurrent tumor versus radiation necrosis. Twenty-two patients with 24 lesions were included. Sixteen (67%) lesions were placed into the recurrent neoplasm group and eight (33%) lesions were placed into the radiation necrosis group using biopsy results as the gold standard in all but three patients. Mean apparent diffusion coefficient values, mean parallel eigenvalues, and mean perpendicular eigenvalues in the contrast-enhancing lesion were significantly lower, and relative cerebral blood volume was significantly higher for the recurrent neoplasm group compared to the radiation necrosis group (P < 0.01, P = 0.03, P < 0.01, and P < 0.01, respectively). The combined assessment of DTI and DSC MR perfusion properties of new contrast-enhancing lesions is helpful in distinguishing recurrent neoplasm from radiation necrosis in patients with a history of brain neoplasm previously treated with radiotherapy with or without surgery and chemotherapy.

Accuracy of percentage of signal intensity recovery and relative cerebral blood volume derived from dynamic susceptibility-weighted, contrast-enhanced MRI in the preoperative diagnosis of cerebral tumours.

Conventional magnetic resonance imaging (MRI) is the technique of choice for diagnosis of cerebral tumours, and has become an increasingly powerful tool for their evaluation; however, the diagnosis of common contrast-enhancing lesions can be challenging, as it is sometimes impossible to differentiate them using conventional imaging. Histopathological analysis of biopsy specimens is the gold standard for diagnosis; however, there are significant risks associated with the invasive procedure and definitive diagnosis is not always achieved. Early accurate diagnosis is important, as management differs accordingly. Advanced MRI techniques have increasing utility for aiding diagnosis in a variety of clinical scenarios. Dynamic susceptibility-weighted contrast-enhanced (DSC) MRI is a perfusion imaging technique and a potentially important tool for the characterisation of cerebral tumours. The percentage of signal intensity recovery (PSR) and relative cerebral blood volume (rCBV) derived from DSC MRI provide information about tumour capillary permeability and neoangiogenesis, which can be used to characterise tumour type and grade, and distinguish tumour recurrence from treatment-related effects. Therefore, PSR and rCBV potentially represent a non-invasive means of diagnosis; however, the clinical utility of these parameters has yet to be established. We present a review of the literature to date.

Pro-angiogenic cellular and genomic expression patterns within glioblastoma influences dynamic susceptibility weighted perfusion MRI

To investigate whether quantitative dynamic susceptibility-weighted contrast-enhanced (DSC) perfusion magnetic resonance imaging (MRI) metrics are influenced by cellular and genomic expression patterns of glioblastoma angiogenesis. Twenty-five stereotactic neurosurgical tissue samples were prospectively obtained from enhancing and non-enhancing tumour regions from 10 patients with treatment-naïve glioblastoma. Using monoclonal antibodies, histopathological features of angiogenesis were examined: total microvascular density, vascular morphology, and hypoxia. Angiogenic expression patterns of tissue samples were investigated using RNA microarrays. DSC perfusion MRI metrics were measured from the tissue sampling sites. MRI and histopathological variables were compared using Pearson's correlations. Microarray analysis was performed using false discovery rate (FDR) statistics. Thirteen enhancing and 12 non-enhancing MR image-guided tissue specimens were prospectively obtained. Enhancing tumour regions demonstrated a significant difference in DSC perfusion and histopathological metrics of angiogenesis when compared to non-enhancing regions. Four angiogenic pathways (vascular endothelial growth factor [VEGF], hypoxia inducible factor [HIF], platelet-derived growth factor [PDGF], fibroblast growth factor [FGF]; 25 individual genes) were significantly up-regulated within enhancing regions when compared to non-enhancing regions (adjusted p<0.05, FDR <0.05). A statistically significant correlation was observed between VEGF-A expression, microvascular density, microvascular morphology, and DSC perfusion MRI metrics (p<0.05). Pro-angiogenic genomic and cellular expression patterns of treatment-naïve primary glioblastoma significantly influences morphological and physiological DSC perfusion metrics suggesting that expression levels of therapeutically relevant genetic signatures can be quantified using MRI.

Development and validation of an open source quantification tool for DSC-MRI studies

MotivationThis work presents the development of an open source tool for the quantification of dynamic susceptibility-weighted contrast-enhanced (DSC) perfusion studies. The development of this tool is motivated by the lack of open source tools implemented on open platforms to allow external developers to implement their own quantification methods easily and without the need of paying for a development license. Materials and methodsThis quantification tool was developed as a plugin for the ImageJ image analysis platform using the Java programming language. A modular approach was used in the implementation of the components, in such a way that the addition of new methods can be done without breaking any of the existing functionalities. For the validation process, images from seven patients with brain tumors were acquired and quantified with the presented tool and with a widely used clinical software package. The resulting perfusion parameters were then compared. ResultsPerfusion parameters and the corresponding parametric images were obtained. When no gamma-fitting is used, an excellent agreement with the tool used as a gold-standard was obtained (R2>0.8 and values are within 95% CI limits in Bland–Altman plots). ConclusionAn open source tool that performs quantification of perfusion studies using magnetic resonance imaging has been developed and validated using a clinical software package. It works as an ImageJ plugin and the source code has been published with an open source license.

Outcome of radiosurgery for recurrent malignant gliomas: assessment of treatment response using relative cerebral blood volume.

Gamma knife radiosurgery (GKS) is efficacious for treating recurrent malignant gliomas as a salvage treatment. However, contrast enhancement alone on MR imaging remains difficult to determine the treatment response following GKS. The purpose of this study was to evaluate the radiosurgical effect for recurrent malignant gliomas and to clarify if relative cerebral blood volume (rCBV) derived from dynamic susceptibility-weighted contrast-enhanced (DSC) perfusion MR imaging could represent the treatment response. Between March 2006 and December 2008, 38 patients underwent GKS for recurrent malignant gliomas. Before and after GKS, DSC perfusion MR imaging datasets were retrospectively reprocessed and regions of interest were drawn around the contrast-enhancing region targeted with GKS. DSC-perfusion MR scans were assessed at a regular interval of two months. Following GKS for the recurrent lesions, MR images showed response (stable disease or partial response) in 26 of 38 patients (68.4 %) at post-GKS 2 months and 18 of 38 patients (47.3 %) at post-GKS 4 months. Initial mean rCBV value was 2.552 (0.586-6.178) at the pre-GKS MRI. In the response group, mean rCBV value was significantly decreased (P < 0.05) at the follow up of 2 and 4 months. However, in the treatment-failure group, mean rCBV value had no significant change. We suggest that GKS is an alternative treatment choice for the recurrent glioma. DSC-perfusion MR images are helpful to predict the treatment response after GKS.

Benefits of dynamic susceptibility-weighted contrast-enhanced perfusion MRI for glioma diagnosis and therapy.

Glioma are the most common supra-tentorial brain tumor in the USA with an estimated annual incidence of 17,000 new cases per year. Dynamic susceptibility-weighted contrast-enhanced (DSC) perfusion MRI noninvasively characterizes tumor biology allowing for the diagnosis and therapeutic monitoring of glioma. This MRI technique utilizes the rapid changes in signal intensity caused by a rapid intravascular bolus of paramagnetic contrast agent to calculate physiologic perfusion metrics. DSC perfusion MRI has increasingly become an integrated part of glioma imaging. The specific aim of this article is to review the benefits of DSC perfusion MRI in the therapy of glioma.

Integrating diffusion kurtosis imaging, dynamic susceptibility-weighted contrast-enhanced MRI, and short echo time chemical shift imaging for grading gliomas

We assessed the diagnostic accuracy of diffusion kurtosis imaging (DKI), dynamic susceptibility-weighted contrast-enhanced (DSC) MRI, and short echo time chemical shift imaging (CSI) for grading gliomas. In this prospective study, 35 patients with cerebral gliomas underwent DKI, DSC, and CSI on a 3 T MR scanner. Diffusion parameters were mean diffusivity (MD), fractional anisotropy, and mean kurtosis (MK). Perfusion parameters were mean relative regional cerebral blood volume (rrCBV), mean relative regional cerebral blood flow (rrCBF), mean transit time, and relative decrease ratio (rDR). The diffusion and perfusion parameters along with 12 CSI metabolite ratios were compared among 22 high-grade gliomas and 14 low-grade gliomas (Mann-Whitney U-test, P < .05). Classification accuracy was determined with a linear discriminant analysis for each MR modality independently. Furthermore, the performance of a multimodal analysis is reported, using a decision-tree rule combining the statistically significant DKI, DSC-MRI, and CSI parameters with the lowest P-value. The proposed classifiers were validated on a set of subsequently acquired data from 19 clinical patients. Statistically significant differences among tumor grades were shown for MK, MD, mean rrCBV, mean rrCBF, rDR, lipids over total choline, lipids over creatine, sum of myo-inositol, and sum of creatine. DSC-MRI proved to be the modality with the best performance when comparing modalities individually, while the multimodal decision tree proved to be most accurate in predicting tumor grade, with a performance of 86%. Combining information from DKI, DSC-MRI, and CSI increases diagnostic accuracy to differentiate low- from high-grade gliomas, possibly providing diagnosis for the individual patient.

Combined value of susceptibility-weighted and perfusion-weighted imaging in assessing who grade for brain astrocytomas.

To assess the value of combining susceptibility-weighted imaging (SWI) and dynamic susceptibility weighted contrast-enhanced (DSC) perfusion-weighted MRI (PWI) in assessing World Health Organization (WHO)grade for brain astrocytoma . A total of 94 patients with pathologically confirmed astrocytomas underwent SWI and DSC scans. The evaluation included intratumoral susceptibility signal intensity (ITSS) and relative cerebral blood volume (rCBV) max. The receiver operating characteristic curve (ROC) was used to assess the efficacy of combining two sequences in astrocytoma grading. ITSS within astrocytomas showed significant correlations with rCBV max (r ¼ 0.72; P < 0.01) and with tumor grades (r ¼ 0.92; P < 0.01), and there was also a significant correlation between rCBV and tumor grade (r= 0.77; P < 0.001). The area under the ROC, SWI, PWI,SWI, and PWI, in differentiation of the grades II and III astrocytomas were 0.995, 0.942, and 1.000, respectively;identifying grades III and IV were 0.773, 0.919, and 0.978, respectively; and identifying high and low-grade astrocytomas were 0.999, 0.992, 1.000, respectively. ITSS was useful for assessing the WHO tumor grade in this cohort of patients with astrocytoma.The combination of SWI and PWI may improve the diagnostic accuracy of astrocytoma grading.

Differentiation of Primary Central Nervous System Lymphomas from High-Grade Gliomas by rCBV and Percentage of Signal Intensity Recovery Derived from Dynamic Susceptibility-Weighted Contrast-Enhanced Perfusion MR Imaging

Primary central nervous system lymphoma (PCNSL) and high-grade glioma (HGG) may have similar enhancement patterns on magnetic resonance imaging (MRI), making the differential diagnosis difficult or even impractical. Relative cerebral blood volume (rCBV) and percentage of signal intensity recovery derived from dynamic susceptibility-weighted contrast-enhanced (DSC) perfusion MR imaging may help distinguish PCNSL from HGG. The purpose of this study was to evaluate the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy of these two imaging parameters used alone or in combination for differentiating PCNSL from HGG. A total of 12 patients with PCNSL and 26 patients with HGG were examined using a 3T scanner. rCBV and percentage of signal intensity recovery were obtained and receiver operating characteristic (ROC) analysis was performed to determine optimum thresholds for tumor differentiation. Sensitivity, specificity, PPV, NPV, and accuracy for identifying the tumor types were also calculated. The optimum threshold of 2.56 for rCBV provided sensitivity, specificity, PPV, NPV, and accuracy of 96.2, 90, 92.6, 94.7, and 93.5%, respectively, for determining PCNSL. A threshold value of 0.89 for percentage of signal intensity recovery optimized differentiation of PCNSL and HGG with a sensitivity, specificity, PPV, NPV, and accuracy of 100, 88.5, 87, 100, and 93.5%, respectively. Combining rCBV with the percentage of signal intensity recovery further improved the differentiation of PCNSL and HGG with a specificity of 98.5% and an accuracy of 95.7%. The combination of rCBV measurement with percentage of signal intensity recovery can help in more accurate differentiation of PCNSL from HGG.

Intravascular contrast agent T2* relaxivity in brain tissue

Dynamic susceptibility-weighted contrast-enhanced (DSC) MRI perfusion measurements depend on estimating intravascular contrast agent (CA) concentrations (C) from signal intensity changes in T2*-weighted images after bolus injection. Generally, linearity is assumed between relaxation and C, but previous studies have shown that compartmentalization of CA and secondary magnetic field perturbations generate deviations from linearity. Physical phantoms using bulk blood have been used to empirically determine the relationship between relaxation rate and C in large vessels. However, the relaxivity of CA in the microvasculature is not easily measured since constructing appropriate phantoms is difficult. Instead, theoretical relaxivity models have been developed. In this study, we empirically tested a non-linear expression based on static dephasing regime (SDR) and linear approximation. Signal-time curves in white (WM) and grey matter (GM) were converted to concentration time curves (CTCs) using both expressions. Parameters for both linear and non-linear formulations were adjusted to give a best agreement between cerebral blood volumes (CBV) calculated from WM and arterial CTCs in a group of normal subjects scanned at 3T. Optimized parameters were used to calculate blood volume in WM and GM in healthy subjects scanned at 3T and in meningioma patients scanned at 1.5T. Results from this study showed that a non-linear SDR formulation gave an acceptable functional form for tissue relaxivity, giving reliable CBV estimates at different field strengths and echo times.

Combined value of susceptibility weighted imaging and dynamic susceptibility-weighted contrast-enhanced MR perfusion-weighted imaging in brain astrocytoma grading

Objective To assess the value of combination of susceptibility weighted imaging (SWI)and dynamic susceptibility-weighted contrast-enhanced (DSC) perfusion-weighted magnetic resonance imaging in astrocytoma grading.Methods SWI and DSC scans were performed in 82 patients with pathologically confirmed astrocytoma.The patient group consisted of grade Ⅱ (15),grade Ⅲ (10),and grade Ⅳ (57).The intratumoral susceptibility signal intensity (ITSS) and relative cerebral blood volume (rCBV) max were used to determine the grade of astrocytomas by Kruskal Wallis test,Welch test,Spearman correlation coefficients,Pearson correlation coefficients,and receiver operating characteristic curve (ROC)statistic methods.Results There were no ITSS in 14 cases of low-grade astrocytomas,the degree of ITSS were grade 1 to 3 in anaplastic astrocytomas,the degree of ITSS were grade 3 in all of the glioblastomas,the degree of ITSS were significant difference in all grades (H =71.96,P ＜ 0.01).rCBV max in grade Ⅱ,grade Ⅲ and grade Ⅳ astrocytomas were 1.26 ± 0.42,3.59 ± 2.09 and 8.34 ± 1.16 respectively,rCBV max were significant difference in all grades (F' =681.72,P ＜ 0.01).ITSS showed significant correlation with rCBV max (r =0.72,P ＜0.01) and tumor grades (r =0.89,P ＜0.01),and rCBV and tumor grades showed significant correlation (r =0.78,P ＜ 0.01).Area under the ROC curve application SWI,DSC,SWI and DSC in differentiation of the grade Ⅱ and grade Ⅲ astrocytomas were 0.99,0.93,1.00,differentiate grade Ⅲ from grade Ⅳ were 0.70,0.94,0.94,and differentiate high-grade from low-grade astrocytomas were 1.00,0.99,1.00.Conclusions ITSS is helpful to determine the grade of astrocytomas.The use of SWI in combination with DSC may improve the diagnostic accuracy of astrocytoma grading. Key words: Astrocytoma; Magnetic resonance imaging; Perfusion ; Diagnosis, differential

Comparison of the effectiveness of MRI perfusion and fluorine-18 FDG PET-CT for differentiating radiation injury from viable brain tumor: a preliminary retrospective analysis with pathologic correlation in all patients

ObjectivesDifferentiating radiation injury from viable tumor is important for optimizing patient care. Our aim was to directly compare the effectiveness of fluorine-18 fluorodeoxyglucose (FDG) positron emission tomography–computed tomography (PET-CT) and dynamic susceptibility-weighted contrast-enhanced (DSC) magnetic resonance (MR) perfusion in differentiating radiation effects from tumor growth in patients with increased enhancement following radiotherapy for primary or secondary brain tumors. Materials and methodsWe retrospectively identified 12 consecutive patients with primary and secondary brain tumors over a 1-year period that demonstrated indeterminate enhancing lesions after radiotherapy and that had undergone DSC MR perfusion, FDG PET-CT, and subsequent histopathologic diagnosis. The maximum standardized uptake value (SUV) of the lesion (SUVlesion max), SUVratio (SUVlesion max/SUVnormal brain), maximum relative cerebral blood volume, percentage of signal intensity recovery, and relative peak height were calculated from the positron emission tomography and MR perfusion studies. A prediction of tumor or radiation injury was made based on these variables while being blinded to the results of the surgical pathology. ResultsSUVratio had the highest predictive value (area under the curve=0.943) for tumor progression, although this was not statistically better than any MR perfusion metric (area under the curve=0.757–0.829). ConclusionsThis preliminary study suggests that FDG PET-CT and DSC MR perfusion may demonstrate similar effectiveness for distinguishing tumor growth from radiation injury. Assessment of the SUVratio may increase the sensitivity and specificity of FDG PET-CT for differentiating tumor and radiation injury. Further analysis is needed to help define which modality has greater predictive capabilities.

Comparison between CT and MR in perfusion imaging assessment of high-grade gliomas

The aim of our study was to compare the relative cerebral blood volume (CBV) values obtained by first-pass dynamic susceptibility-weighted contrast-enhanced (DSC) T2-weighted magnetic resonance (MR) and perfusion computed tomography (P-CT) imaging in high-grade gliomas (HGG) in the same patient population. Sixteen patients with histologically proven HGG underwent P-CT and DSC-MR brain imaging. P-CT studies were obtained using a four-row multislice CT scanner and postprocessed with a commercial software package based on a deconvolution-based technique. DSC-MR images were obtained at 1.5 T with a first-pass dynamic susceptibility contrast-enhanced T2-weighted sequence. P-CT and DSC-MR images were obtained within 4 days of each another, always before surgery. Maximum CBV ratios normalised with contralateral white matter (rCBV) were calculated. Statistical analysis was performed with the classical parametric statistic procedure. A linear correlation between maximum rCBV values obtained with P-CT and DSC-MR imaging was evident. The best linear model is CT=slope×MR+error and provides a highly significant estimate of the slope equal to 1.08. Thus CT results can be predicted from MR values. Therefore, it is also possible to predict MR results from CT values by estimating the linear model MR=slope×CT+error. DSC-MR imaging gave lower rCBV average values (4.92±1.52) compared with P-CT (5.56±1.55). In our population of patients, P-CT and DSC-MR imaging showed proportional results in rCBV assessment of HGGs, and thus both modalities may be used interchangeably in HGG of the brain.

Combined Diffusion and Perfusion MRI As Biomarkers of Prognosis in Immunocompetent Patients with Primary CNS Lymphoma

Abstract 2655 Background and Purpose:The prospect for advances in the treatment and improvement in the prognosis of patients with primary central nervous system lymphoma (PCNSL) is likely dependent on the systemic evaluation of its pathobiology and identification of biomarkers that can guide and monitor response to therapy. The validation of noninvasive imaging-based biomarkers of prognosis and therapeutic response may have significant impact on the management of patients with PCNSL. Recent work suggests that apparent diffusion coefficient (ADC) measurements derived from diffusion-weighted magnetic resonance imaging (DW-MRI) may predict prognosis in immunocompetent patients with PCNSL. The purpose of our study was to validate ADC measurement as a prognostic biomarker in patients with PCNSL and to evaluate whether relative cerebral blood volume (rCBV) measurement derived from dynamic susceptibility-weighted contrast-enhanced (DSC) perfusion MRI may also serve as predictor of outcome. Materials and Methods:We studied pre-therapy imaging of 26 immunocompetent patients with PCNSL that were treated homogeneously with methotrexate-based chemotherapy. We measured anatomic tumor volume and peri-tumoral edema volume from post-contrast T1-weighted and T2-weighted images, respectively. ADC map and rCBV map were generated from DW-MRI and DSC perfusion MRI, respectively. Post-contrast T1-weighted images containing regions of interest (ROIs) outlining contrast enhancing tumor were co-registered with the ADC and rCBV maps and minimum and mean ADC and rCBV values were measured. Progression-free survival (PFS) and overall survival (OS) were measured. The Welch t-test assessed differences between groups. Multivariate Cox survival analysis was performed. Results:Anatomic tumor volume or peri-tumoral edema volume at initial diagnosis did not correlate with OS or PFS. However, baseline ADC and rCBV measurements of tumor had significant association with clinical outcome measurements: patients with low minimum ADC (ADCmin) or low mean rCBV (rCBVmean) had worse OS and PFS. The high ADCmin-high rCBVmean group had the best and low ADCmin-low rCBVmean group had the worst PFS and OS. Multivariate Cox survival analysis identified no other imaging-based variables besides ADC and rCBV that significantly affected survival. Conclusion:Our study further supports the potential of non-invasive ADC measurement as a valid and reliable prognostic biomarker in patients with PCNSL. In addition, our study is the first evaluation of perfusion MRI as a prognostic biomarker in PCNSL. We have generated evidence that reduced perfusion, suggestive of hypoxic tumor microenvironment, is associated with adverse prognosis in patients with PCNSL. In addition, our data suggests that perfusion-based parameters enhance the prognostic utility of ADC measurements in the evaluation of newly diagnosed patients with CNS lymphoma.Supported by NIH RO1CA139-83-01A1 and Pathways to Careers in Clinical and Translational Research (PACCTR) Program (FEV). Disclosures:No relevant conflicts of interest to declare.

An improved model for describing the contrast bolus in perfusion MRI

Quantification of perfusion measurements using dynamic, susceptibility-weighted contrast-enhanced (DSC) MRI depends on estimating the size and shape of the tracer bolus. Typically, the bolus is described as a gamma variate function (GV) fitted to the bolus portion of tracer concentration time curve (CTC). However, the last point to fit is arbitrary which can lead to considerable variation in the fitted curve in the presence of noise. In this technical note, we present a model which takes into account recirculation explicitly and fits robustly to the entire CTC in the presence of noise. Signal data measurements from ten DSC MRI patients were fitted with our new model and a GV function using four different methods of estimating the end of the bolus. Estimates of the area under the curves (AUC) and first moments (FMs) of the bolus were compared at different noise levels. The new model gave errors similar to or smaller than those of the most effective methods for fitting a GV. The single compartment recirculation (SCR) model is the most robust fitting technique with respect to noise both for bias and variability.

Treatment Monitoring in Gliomas

To evaluate whether dynamic susceptibility-weighted contrast-enhanced (DSC), dynamic contrast-enhanced (DCE), and proton spectroscopic imaging ((1)H-MRSI) can identify progression and predict treatment failure during follow-up before tumor size changes, contrast agent uptake, or when new lesions become obvious. The aim was also to find out which of the aforementioned techniques had the best diagnostic performance compared with each other and standard magnetic resonance imaging (MRI). Thirty-seven patients with gliomas (21 women, 16 men; mean age at inclusion, 48 ± 14 years [standard deviation]) were assessed prospectively by (1)H-MRSI (point-resolved spectroscopy), DCE, and DSC perfusion MRI, each after a single dose of gadobenate dimeglumine during follow-up. Histology was available in all cases (resection, N = 18; biopsy, N = 19). All patients with low-grade gliomas (n = 20) did not receive any radio- or chemotherapy after partial resection (n = 7) or biopsy (n = 13), whereas 17 patients with high-grade gliomas had received adjuvant radiotherapy immediately after surgery. Tumor progression (progressive disease, PD) was defined as increase in longest glioma diameter by at least 20% (Response Evaluation Criteria in Solid Tumors), appearance of new lesions, or new contrast-enhancement. DSC, DCE, and MRSI image analyses comprised a detailed semiquantitative region of interest (ROI) analysis of the different parameters. Wilcoxon signed-rank test, Wilcoxon rank sum test, and Cox regression were used for statistical analysis. The median follow-up time was 607 days. Twenty patients showed PD (54%), 8 of 20 with low-grade (40%) and 12 of 17 with high-grade gliomas (71%). In PD, significant positive differences between log2-transformed ROI ratios at the last measurement in comparison to the first measurement (baseline) could be detected for tumor blood flow (P < 0.006) and volume (P < 0.001) derived from DSC and for maximum choline within tumor tissue (P = 0.0029) and Cho/Cr (P = 0.032) but not choline/N-acetyl-aspartate (P = 0.37) derived from MRSI. In contrast, these parameters were not significantly higher at last measurement in stable disease. Also, the differences between last value and baseline were significantly different between PD and stable disease for tumor blood flow (P < 0.004) and volume (P < 0.002) as well as for maximum choline within tumor tissue (P = 0.0011). The best prognostic parameter for PD at Cox analysis was time-dependent difference to baseline of log2 of relative regional cerebral blood flow normalized on gray matter (hazard ratio, 2.67; 95% confidence interval, 1.25-6.08; P = 0.01), while a prognostic value of MRS parameters could not be demonstrated. DSC perfusion imaging can identify progression and can predict treatment failure during follow-up of gliomas with the best diagnostic performance.

Infiltrative cerebellar ganglioglioma: conventional and advanced MRI, proton MR spectroscopic, and FDG PET findings in an 18-month-old child

Advanced magnetic resonance imaging (MRI) techniques, such as diffusion tensor imaging (DTI), dynamic susceptibility-weighted contrast-enhanced (DSC) perfusion, and proton magnetic resonance spectroscopy (MRS) have proven to be useful in predicting tumour grade and outcome in glial brain tumours. 1 Law M. Yang S. Wang H. et al. Glioma grading: sensitivity, specificity, and predictive values of perfusion MR imaging and proton MR spectroscopic imaging compared with conventional MR imaging. AJNR Am J Neuroradiol. 2003; 24: 1989-1998 PubMed Google Scholar , 2 Provenzale J.M. Mukundan S. Barboriak D.P. Diffusion-weighted and perfusion MR imaging for brain tumor characterization and assessment of treatment response. Radiology. 2006; 239: 632-649 Crossref PubMed Scopus (307) Google Scholar , 3 Stadlbauer A. Gruber S. Nimsky C. et al. Preoperative grading of gliomas by using metabolite quantification with high-spatial-resolution proton MR spectroscopic imaging. Radiology. 2006; 238: 958-969 Crossref PubMed Scopus (159) Google Scholar , 4 Weber M.A. Zoubaa S. Schlieter M. et al. Diagnostic performance of spectroscopic and perfusion MRI for distinction of brain tumors. Neurology. 2006; 66: 1899-1906 Crossref PubMed Scopus (182) Google Scholar , 5 Young R.J. Knopp E.A. Brain MRI: tumor evaluation. J Magn Reson Imag. 2006; 24: 709-724 Crossref PubMed Scopus (59) Google Scholar However, discrepancies do occur when advanced techniques suggest a biologically more malignant behaviour in low-grade lesions such as gangliogliomas. 6 Kincaid P.K. El-Saden S.M. Park S.H. et al. Cerebral gangliogliomas: preoperative grading using FDG-PET and Tl-201-SPECT. AJNR Am J Neuroradiol. 1998; 19: 801-806 PubMed Google Scholar , 7 Koeller K.K. Henry J.M. Superficial gliomas: radiologic–pathologic correlation. RadioGraphics. 2001; 21: 1533-1556 Crossref PubMed Scopus (111) Google Scholar , 8 Kumabe T. Shimizu H. Sonoda Y. et al. Thallium-201 single-photon emission computed tomographic and proton magnetic resonance spectroscopic characteristics of intracranial ganglioglioma: three technical case reports. Neurosurgery. 1999; 45: 183-187 Crossref PubMed Scopus (27) Google Scholar , 9 Provenzale J.M. Arata M.A. Turkington T.G. et al. Gangliogliomas: characterization by registered positron emission tomography MR images. AJR Am J Roentgenol. 1999; 172: 1103-1107 Crossref PubMed Scopus (22) Google Scholar , 10 Wang Z.Y. Sutton L.N. Cnaan A. et al. Proton MR spectroscopy of pediatric cerebellar tumors. AJNR Am J Neuroradiol. 1995; 16: 1821-1833 PubMed Google Scholar Recently, we had the opportunity to diagnose and follow a child with infiltrative cerebellar ganglioglioma. The purpose of this paper is to contribute to the existing body of knowledge about this rare entity by reporting and discussing the results of conventional and advanced MRI, including DTI, DSC, MRS, susceptibility-weighted imaging (SWI) and 2-[18F]-fluoro-2-deoxy-d-glucose positron emission tomography (FDG PET) with respect to the histopathological features of the tumour, and reviewing currently available literature.

Magnetic Resonance Dynamic Susceptibility-Weighted Contrast-Enhanced Perfusion Imaging in the Diagnosis of Posterior Fossa Hemangioblastomas and Pilocytic Astrocytomas

The purpose of this study was to compare the dynamic susceptibility-weighted contrast-enhanced (DSC) magnetic resonance (MR) perfusion and MR imaging findings between hemangioblastomas and pilocytic astrocytoma (PA). We retrospectively identified 6 patients with hemangioblastomas and 8 patients with PAs who underwent MR imaging before resection. Using fluid-attenuated inversion-recovery imaging, we graded peritumoral edema as absent, minimal, mild, moderate, or severe. In addition, 3 patients with hemangioblastomas and 4 patients with PAs underwent DSC-MR imaging before resection. We observed moderate to severe peritumoral edema in 6 patients with hemangioblastomas and none or minimal peritumoral edema in 8 patients with PAs. The mean relative cerebral blood volume was 7.7 (SD, 1.0) in patients with hemangioblastomas and 1.8 (SD, 1.8) in patients with PAs. Our preliminary findings demonstrate significantly higher DSC-MR imaging relative cerebral blood volumes in patients with hemangioblastomas when compared with patients with PAs. In addition, moderate to severe peritumoral edema was associated with hemangioblastomas.