Recollections about our 1996 JMR paper on diffusion anisotropy

We provide scientific background information and personal accounts relating to our publication of “Microstructural and Physiological Features of Tissues Elucidated by Quantitative-Diffusion-Tensor MRI” in the Journal of Magnetic Resonance B. This paper provided a framework for measuring and mapping intrinsic features of diffusion anisotropy obtained from diffusion tensor MRI (DTI) data.     

A feasible high spatiotemporal resolution breast DCE-MRI protocol for clinical settings

Three dimensional bilateral imaging is the standard for most clinical breast dynamic contrast-enhanced (DCE) MRI protocols. Because of high spatial resolution (sRes) requirement, the typical 1–2 min temporal resolution (tRes) afforded by a conventional full-k-space-sampling gradient echo (GRE) sequence precludes meaningful and accurate pharmacokinetic analysis of DCE time-course data. The commercially available, GRE-based, k-space undersampling and data sharing TWIST (time-resolved angiography with stochastic trajectories) sequence was used in this study to perform DCE-MRI exams on thirty one patients (with 36 suspicious breast lesions) before their biopsies. The TWIST DCE-MRI was immediately followed by a single-frame conventional GRE acquisition. Blinded from each other, three radiologist readers assessed agreements in multiple lesion morphology categories between the last set of TWIST DCE images and the conventional GRE images. Fleiss’ κ test was used to evaluate inter-reader agreement. The TWIST DCE time-course data were subjected to quantitative pharmacokinetic analyses. With a four-channel phased-array breast coil, the TWIST sequence produced DCE images with 20 s or less tRes and ~ 1.0×1.0×1.4 mm³ sRes. There were no significant differences in signal-to-noise (P=.45) and contrast-to-noise (P=.51) ratios between the TWIST and conventional GRE images. The agreements in morphology evaluations between the two image sets were excellent with the intra-reader agreement ranging from 79% for mass margin to 100% for mammographic density and the inter-reader κ value ranging from 0.54 (P<.0001) for lesion size to 1.00 (P<.0001) for background parenchymal enhancement. Quantitative analyses of the DCE time-course data provided higher breast cancer diagnostic accuracy (91% specificity at 100% sensitivity) than the current clinical practice of morphology and qualitative kinetics assessments. The TWIST sequence may be used in clinical settings to acquire high spatiotemporal resolution breast DCE-MRI images for both precise lesion morphology characterization and accurate pharmacokinetic analysis.     

Practical considerations in T1 mapping of prostate for dynamic contrast enhancement pharmacokinetic analyses

There are many challenges in developing robust imaging biomarkers that can be reliably applied in a clinical trial setting. In the case of dynamic contrast-enhanced (DCE) MRI, one such challenge is to obtain accurate precontrast T₁ maps for subsequent use in two-compartment pharmacokinetic models commonly used to fit the MR enhancement time courses. In the prostate, a convenient and common approach for this task has been to use the same 3D spoiled gradient-echo sequence used to collect the DCE data, but with variable flip angles (VFAs) to collect data suitable for T₁ mapping prior to contrast injection. However, inhomogeneous radiofrequency conditions within the prostate have been found to adversely affect the accuracy of this technique. Herein we demonstrate the sensitivity of DCE pharmacokinetic parameters to precontrast T₁ values and examine methods to improve the accuracy of T₁ mapping with flip angle-corrected VFA SPGR methods, comparing T₁ maps from such methods with “gold standard” reference T₁ maps generated with saturation recovery experiments performed with fast spin-echo (FSE) sequences.     

Diagnostic value of breast MRI for predicting metastatic axillary lymph nodes in breast cancer patients: diffusion-weighted MRI and conventional MRI

Purposes

To evaluate the diagnostic value of diffusion-weighted MRI (DWI) and combination of conventional MRI and DWI to predict metastatic axillary lymph nodes in breast cancer.

Materials and methods

Two hundred fifty-two breast cancer patients with 253 axillae were included. The morphological parameters on axial T2-weighted images without fat saturation and apparent diffusion coefficient (ADC) values were retrospectively analyzed. An independent t-test/chi-square test and receiver operating characteristics (ROC) curve analysis were used.

Results

On conventional MRI, short and long axis length, maximal cortical thickness, relative T2 value, loss of fatty hilum (p < 0.001 for each), and eccentric cortical thickening (p < 0.003) were statistically significantly different between the metastatic and nonmetastatic groups. The short axis to long axis ratio was not a statistically significant parameter. The ADC value was significantly different between the 2 groups, with an AUC that was higher than that of conventional MR parameters (AUC, 0.815; threshold, ≤ 0.986 × 10–3 mm²/sec; sensitivity, 75.8%; specificity, 83.9%). Using the adopted thresholds for each parameter, a total number of findings suggesting malignancy of 4 or higher was determined as the threshold, with high specificity (90.1%).

Conclusion

Using conventional MRI and DWI, we can evaluate the axilla in breast cancer with high specificity.     

Improvement of PI-RADS-dependent prostate cancer classification by quantitative image assessment using radiomics or mean ADC

BackgroundCurrently, interpretation of prostate MRI is performed qualitatively. Quantitative assessment of the mean apparent diffusion coefficient (mADC) is promising to improve diagnostic accuracy while radiomic machine learning (RML) allows to probe complex parameter spaces to identify the most promising multi-parametric models. We have previously developed quantitative RML and ADC classifiers for prediction of clinically significant prostate cancer (sPC) from prostate MRI, however these have not been combined with radiologist PI-RADS assessment.PurposeTo propose and evaluate diagnostic algorithms combining quantitative ADC or RML and qualitative PI-RADS assessment for prediction of sPC.Methods and populationThe previously published quantitative models (RML and mADC) were utilized to construct four algorithms: 1) Down(ADC) and 2) Down(RML): clinically detected PI-RADS positive prostate lesions (defined as either PI-RADS≥3 or ≥4) were downgraded to MRI negative upon negative quantitative assessment; and 3) Up(ADC) and 4) Up(RML): MRI-negative lesions were upgraded to MRI-positive upon positive assessment of quantitative parameters. Analyses were performed at the individual lesion level and the patient level in 133 consecutive patients with suspicion for clinically significant prostate cancer (sPC, International Society of Urological Pathology (ISUP) grade group≥2), the test set subcohort of a previously published patient population. McNemar test was used to compare differences in sensitivity, specificity and accuracy. Differences between lesions of different prostate zones were assessed using ANOVA. Reduction in false positive assessments was assessed as ratios.ResultsCompared to clinical assessment at the PI-RADS≥4 cut-off alone, algorithms Down(ADC/RML) improved specificity from 43% to 65% (p = 0.001)/62% (p = 0.003), while sensitivity did not change significantly at 89% compared to 87% (p = 1.0)/89% (unchanged) on the patient level. Reduction of false positive lesions was 50% [26/52] in the PZ and 53% [15/28] in the TZ. Algorithms Up(ADC/RML) led, on a patient basis, to an unfavorable loss of specificity from 43% to 30% (p = 0.039)/32% (p = 0.106), with insignificant increase of sensitivity from 89% to 96%/96% (both p = 1.0). Compared to clinical assessment at the PI-RADS≥3 cut-off alone, similar results were observed for Down(ADC) with significantly increased specificity from 2% to 23% (p < 0.001) and unchanged sensitivity on the lesion level; patient level specificity increased only non-significantly.ConclusionDowngrading PI-RADS≥3 and ≥ 4 lesions based on quantitative mADC measurements or RML classifiers can increase diagnostic accuracy by enhancing specificity and preserving sensitivity for detection of sPC and reduce false positives.     

Should abbreviated breast MRI be compliant with American College of Radiology requirements for MRI accreditation?

ObjectiveTo evaluate non-inferiority and diagnostic performance of an American College of Radiology compliant abbreviated MRI protocol (AB-MRI) compared with standard-of-care breast MRI (SOC-BMRI) in patients with increased breast cancer risk.Material and methodsWomen with increased lifetime breast cancer risk by American Cancer Society guidelines underwent breast MRI at a single institution between October 2015 and February 2018. AB-MRI was acquired at 3.0 T with T2-weighted extended fast spin echo triple-echo Dixon and pre- and post-contrast 3D dual-echo fast spoiled gradient echo two-point Dixon sequences with an 8-channel breast coil 1–7 days after SOC-BMRI. Three readers independently reviewed AB-MRI and assigned BI-RADS categories for maximum intensity projection images (AB1), dynamic contrast-enhanced (DCE) images (AB2), and DCE and non-contrast T2 and fat-only images (AB3). These scores were compared to those from SOC-BMRI.ResultsCancer yield was 14 per 1000 (women-years) in 73 women aged 26–75 years (mean 53.5 years). AB-MRI acquisition times (mean 9.63 min) and table times (mean 15.07 min) were significantly shorter than those of SOC-BMRI (means 19.46 and 36.3 min, respectively) (p < .001). Accuracy, sensitivity, specificity, and positive and negative predictive values were identical for AB3 and SOC-BMRI (93%, 100%, 93%, 16.7%, and 100%, respectively). AB-MRI with AB1 and AB2 had significantly lower specificity (AB1 = 73.6%, AB2 = 77.8%), positive predictive values (AB1 = 5%, AB2 = 5.9%), and accuracy (AB1 = 74%, AB2 = 78%) than those of SOC-BMRI (p = .002 for AB1, p = .01 for AB2).ConclusionAB-MRI was acquired significantly faster than SOC-BMRI and its diagnostic performance was non-inferior. Inclusion of T2 and fat-only images was necessary to achieve non-inferiority by multireader evaluation.     

Arterial spin labelling as a gadolinium-free alternative in the detection of prostate cancer

PurposeTo determine the capability of Gadolinium-free arterial spin labelling (ASL) sequences as novel, contrast-free, non-invasive alternative perfusion imaging method to differentiate prostate cancer (PCA) from benign prostate tissue compared to conventional DCE MRI.MethodsThirty men with histologically confirmed PCA were included in this prospectively enrolled single center cohort study. All patients received multiparametric MRI (T2, DWI, DCE) at 3 T with additional ASL of the PCA lesion. Primary endpoint was differentiability of PCA versus benign prostate tissue by signal intensities (SI) and contrast ratios (CR) in ASL in comparison to DCE. For DCE also Signal-Enhancement-Ratio (SER) of native and early contrast enhancement SI was assessed. Secondary objectives were differences regarding PCA localisation in peripheral (PZ) or transition zone (TZ) and PCA detection.ResultsIn both, ASL and DCE, average SI of PCA differed significantly from SI in benign tissue in the TZ and PZ (p < 0,01, respectively). ASL had significantly higher CR discerning PCA and benign tissue in PZ and TZ (PZ = 5.19; TZ = 6.45) compared to DCE SI (PZ = 1.61; TZ = 1.43) and DCE SER (PZ = 1.59; TZ = 1.43) (p < 0.01, respectively). In subjective evaluation, PCA could be detected in ASL in 28 patients, compared to 29 in DCE.ConclusionASL had significantly higher CR differentiating PCA from benign tissue in PZ and TZ compared to DCE. Visual detection of PCA does not differ significantly between the two sequences. As perfusion gadolinium-based contrast media is seen more critical in the last few years, ASL seems to be a promising alternative to DCE in PCA detection.     

Investigating brain tumor differentiation with diffusion and perfusion metrics at 3T MRI using pattern recognition techniques

The aim of this study was to evaluate the contribution of diffusion and perfusion MR metrics in the discrimination of intracranial brain lesions at 3T MRI, and to investigate the potential diagnostic and predictive value that pattern recognition techniques may provide in tumor characterization using these metrics as classification features. Conventional MRI, diffusion weighted imaging (DWI), diffusion tensor imaging (DTI) and dynamic-susceptibility contrast imaging (DSCI) were performed on 115 patients with newly diagnosed intracranial tumors (low-and- high grade gliomas, meningiomas, solitary metastases). The Mann–Whitney U test was employed in order to identify statistical differences of the diffusion and perfusion parameters for different tumor comparisons in the intra-and peritumoral region. To assess the diagnostic contribution of these parameters, two different methods were used; the commonly used receiver operating characteristic (ROC) analysis and the more sophisticated SVM classification, and accuracy, sensitivity and specificity levels were obtained for both cases. The combination of all metrics provided the optimum diagnostic outcome. The highest predictive outcome was obtained using the SVM classification, although ROC analysis yielded high accuracies as well. It is evident that DWI/DTI and DSCI are useful techniques for tumor grading. Nevertheless, cellularity and vascularity are factors closely correlated in a non-linear way and thus difficult to evaluate and interpret through conventional methods of analysis. Hence, the combination of diffusion and perfusion metrics into a sophisticated classification scheme may provide the optimum diagnostic outcome. In conclusion, machine learning techniques may be used as an adjunctive diagnostic tool, which can be implemented into the clinical routine to optimize decision making.     

Osteoporosis detection in postmenopausal women using axial transmission multi-frequency bone ultrasonometer: Clinical findings

The objective of this study was to evaluate if the Bone UltraSonic Scanner (BUSS) can detect osteoporosis in postmenopausal women. BUSS is an axial transmission multi-frequency ultrasonometer for acquisition of wave propagation profiles along the proximal anterior tibia. We derived 10 diagnostically significant BUSS parameters that were then compared with the DXA spine T-score, which was used in this study as the “gold standard” for the assessment of osteoporosis (T-score <−2.5). BUSS wave parameters were studied in 331 postmenopausal women examined by 9 trained operators at 3 clinical sites with use of 3 devices. The efficiency of each BUSS parameter in osteoporosis detection was assessed using a receiver operating characteristic curve analysis. Area under the curve (AUC) for each of 10 parameters ranged from 58.1% to 70.2%. Using these parameters a linear classifier was derived which provided at its output 83.0% AUC, 87.7% sensitivity and 63.2% specificity to DXA-identified osteoporosis. The results of this study confirm BUSS’s capability to detect osteoporosis in postmenopausal women.     

Associations between histogram analysis parameters derived from dynamic-contrast enhanced MRI and PD L1-expression in head and neck squamous cell carcinomas. A preliminary study

PurposeProgrammed death 1 ligand (PD-L 1) plays an essential role in oncology. It might be crucial to predict its expression non-invasively by imaging. Dynamic-contrast enhanced MRI (DCE MRI) is one of the important imaging modalities in head and neck squamous cell cancer (HNSCC). The aim of the present study was to analyze possible associations between histogram analysis parameters of DCE MRI and PD-L 1 expression in HNSCCMethodsOverall, 26 patients with primary HNSCC of different localizations were involved in the study. DCE MRI was obtained on a 3 T MRI and analyzed with a whole lesion measurement using a histogram approach. PD-L 1 expression was estimated on bioptic samples before any form of treatment using 3 scores (Tumor positive score (TPS), Immune cell score (ICS) and Combined positive score (CPS)).ResultsCPS correlated with mode derived from Ktrans (r = 0.40, p = .04). Also CPS correlated with P90 derived from Kep (r = 0.40, p = .04). ICS correlated with the maximum derived from Kep (r = 0.41, p = .03) and entropy derived from Kep (r = 0.43, p = .02). There were no associations between DCE MRI parameters and TPS.ConclusionKtrans and Kep related histogram analysis parameters derived from DCE MRI correlated moderately with PD-L 1 expression of immune cells in HNSCC.     

A new approach to estimating the signal dimension of concatenated resting-state functional MRI data sets

Estimating the effective signal dimension of resting-state functional MRI (fMRI) data sets (i.e., selecting an appropriate number of signal components) is essential for data-driven analysis. However, current methods are prone to overestimate the dimensions, especially for concatenated group data sets. This work aims to develop improved dimension estimation methods for group fMRI data generated by data reduction and grouping procedure at multiple levels. We proposed a “noise-blurring” approach to suppress intragroup signal variations and to correct spectral alterations caused by the data reduction, which should be responsible for the group dimension overestimation. This technique was evaluated on both simulated group data sets and in vivo resting-state fMRI data sets acquired from 14 normal human subjects during five different scan sessions. Reduction and grouping procedures were repeated at three levels in either “scan–session–subject” or “scan–subject–session” order. Compared with traditional estimation methods, our approach exhibits a stronger immunity against intragroup signal variation, less sensitivity to group size and a better agreement on the dimensions at the third level between the two grouping orders.     

Differentiation between intra-axial metastatic tumor progression and radiation injury following fractionated radiation therapy or stereotactic radiosurgery using MR spectroscopy, perfusion MR imaging or volume progression modeling

Objective

To determine the accuracy of magnetic resonance spectroscopy (MRS), perfusion MR imaging (MRP), or volume modeling in distinguishing tumor progression from radiation injury following radiotherapy for brain metastasis.

Methods

Twenty-six patients with 33 intra-axial metastatic lesions who underwent MRS (n=41) with or without MRP (n=32) after cranial irradiation were retrospectively studied. The final diagnosis was based on histopathology (n=4) or magnetic resonance imaging (MRI) follow-up with clinical correlation (n=29). Cho/Cr (choline/creatinine), Cho/NAA (choline/N-acetylaspartate), Cho/nCho (choline/contralateral normal brain choline) ratios were retrospectively calculated for the multi-voxel MRS. Relative cerebral blood volume (rCBV), relative peak height (rPH) and percentage of signal-intensity recovery (PSR) were also retrospectively derived for the MRPs. Tumor volumes were determined using manual segmentation method and analyzed using different volume progression modeling. Different ratios or models were tested and plotted on the receiver operating characteristic curve (ROC), with their performances quantified as area under the ROC curve (AUC). MRI follow-up time was calculated from the date of initial radiotherapy until the last MRI or the last MRI before surgical diagnosis.

Results

Median MRI follow-up was 16 months (range: 2-33). Thirty percent of lesions (n=10) were determined to be radiation injury; 70% (n=23) were determined to be tumor progression. For the MRS, Cho/nCho had the best performance (AUC of 0.612), and Cho/nCho >1.2 had 33% sensitivity and 100% specificity in predicting tumor progression. For the MRP, rCBV had the best performance (AUC of 0.802), and rCBV >2 had 56% sensitivity and 100% specificity. The best volume model was percent increase (AUC of 0.891); 65% tumor volume increase had 100% sensitivity and 80% specificity.

Conclusion

Cho/nCho of MRS, rCBV of MRP, and percent increase of MRI volume modeling provide the best discrimination of intra-axial metastatic tumor progression from radiation injury for their respective modalities. Cho/nCho and rCBV appear to have high specificities but low sensitivities. In contrast, percent volume increase of 65% can be a highly sensitive and moderately specific predictor for tumor progression after radiotherapy. Future incorporation of 65% volume increase as a pretest selection criterion may compensate for the low sensitivities of MRS and MRP.     

In vivo multiparametric magnetic resonance imaging study for differentiating the severity of hepatic warm ischemia-reperfusion injury in a rabbit model

ObjectivesTo assess the value of multiparametric magnetic resonance imaging including intravoxel incoherent motion (IVIM), diffusion tensor imaging (DTI) and blood oxygen level dependent (BOLD) MRI in differentiating the severity of hepatic warm ischemia-reperfusion injury (WIRI) in a rabbit model.MethodsFifty rabbits were randomly divided into a sham-operation group and four test groups (n = 10 for each group) according to different hepatic warm ischemia times. IVIM, DTI and BOLD MRI were performed on a 3 T MR scanner with 11 b values (0 to 800 s/mm²), 2 b values (0 and 500 s/mm²) on 12 diffusion directions, multiple-echo gradient echo (GRE) sequences (TR/TE, 75/2.57–24.25 ms), respectively. IVIM, DTI and BOLD MRI parameters, hepatic biochemical and histopathological parameters were compared. Pearson and Spearman correlation methods were performed to assess the correlation between these MRI parameters and laboratory parameters. Furthermore, receiver operating characteristic (ROC) curves were compiled to determine diagnostic efficacies.ResultsTrue diffusion (Dslow), pseudodiffusion (Dfast), perfusion fraction (PF), mean diffusivity (MD) significantly decreased, while R2* significantly increased with prolonged warm ischemia times, and significant differences were found in all of biochemical and histopathological parameters (all P < 0.05). Dslow, PF, and R2* correlated significantly with all of biochemical and histopathological parameters (all |r| = 0.381–0.746, all P < 0.05). ROC analysis showed that the area under the ROC curve (AUC) of IVIM across hepatic WIRI groups was the largest among IVIM, DTI and BOLD.ConclusionsMultiparametric MRI may be helpful with characterization of early changes and determination of severity of hepatic WIRI in a rabbit model.     

Validation of optimal DCE-MRI perfusion threshold to classify at-risk tumor imaging voxels in heterogeneous cervical cancer for outcome prediction

Purpose

To classify tumor imaging voxels at-risk for treatment failure within the heterogeneous cervical cancer using DCE MRI and determine optimal voxel's DCE threshold values at different treatment time points for early prediction of treatment failure.

Material and Method

DCE-MRI from 102 patients with stage IB2–IVB cervical cancer was obtained at 3 different treatment time points: before (MRI 1) and during treatment (MRI 2 at 2–2.5 weeks and MRI 3 at 4–5 weeks). For each tumor voxel, the plateau signal intensity (SI) was derived from its time-SI curve from the DCE MRI. The optimal SI thresholds to classify the at-risk tumor voxels was determined by the maximal area under the curve using ROC analysis when varies SI value from 1.0 to 3.0 and correlates with treatment outcome.

Results

The optimal SI thresholds for MRI 1, 2 and 3 were 2.2, 2.2 and 2.1 for significant differentiation between local recurrence/control, respectively, and 1.8, 2.1 and 2.2 for death/survival, respectively.

Conclusion

Optimal SI thresholds are clinically validated to quantify at-risk tumor voxels which vary with time. A single universal threshold (SI = 1.9) was identified for all 3 treatment time points and remained significant for the early prediction of treatment failure.     

Discriminative analysis of multivariate features from structural MRI and diffusion tensor images

Imaging markers derived from magnetic resonance images, together with machine learning techniques allow for the recognition of unique anatomical patterns and further differentiating Alzheimer's disease (AD) from normal states. T1-based imaging markers, especially volumetric patterns have demonstrated their discriminative potential, however, rely on the tissue abnormalities of gray matter alone. White matter abnormalities and their contribution to AD discrimination have been studied by measuring voxel-based intensities in diffusion tensor images (DTI); however, no systematic study has been done on the discriminative power of either region-of-interest (ROI)-based features from DTI or the combined features extracted from both T1 images and DTI. ROI-based analysis could potentially reduce the feature dimensionality of DTI indices, usually from more than 10e + 5, to 10–150 which is almost equal to the order of magnitude with respect to volumetric features from T1. Therefore it allows for straight forward combination of intensity based landmarks of DTI indices and volumetric features of T1. In the present study, the feasibility of tract-based features related to Alzheimer's disease was first evaluated by measuring its discriminative capability using support vector machine on fractional anisotropy (FA) maps collected from 21 subjects with Alzheimer's disease and 15 normal controls. Then the performance of the tract-based FA + gray matter volumes-combined feature was evaluated by cross-validation. The combined feature yielded good classification result with 94.3% accuracy, 95.0% sensitivity, 93.3% specificity, and 0.96 area under the receiver operating characteristic curve. The tract-based FA and the tract-based FA + gray matter volumes-combined features are certified their feasibilities for the recognition of anatomical features and may serve to complement classification methods based on other imaging markers.     

Dynamic contrast-enhanced MRI with Gd-EOB-DTPA for the quantitative assessment of early-stage liver fibrosis induced by carbon tetrachloride in rabbits

PurposeTo explore quantitative parameters obtained by dynamic contrast-enhanced magnetic resonance imaging (DCE MRI) with Gd-EOB-DTPA in discriminating early-stage liver fibrosis (LF) in a rabbit model.Materials and methodsLF was established in 60 rabbits by the injection of 50% CCl₄ oil solution, whereas 30 rabbits served as the control group. All rabbits underwent pathological examination to determine the LF stage using the METAVIR classification system. DCE MRI was performed, and quantitative parameters, including K^trans, K_ep, V_e, V_p and Re were measured and evaluated among the different LF stages using spearman correlation coefficients and receiver operating characteristic curve.ResultsIn all, 24, 25, and 22 rabbits had stage F0, stage F1, and stage F2 LF, respectively. K^trans (r = 0.803) increased, and K_ep (r = −0.495) and Re (r = −0.701) decreased with LF stage progression (P < 0.001), while no significant correlation was found for V_e or V_p. K^trans and Re were significantly different between all LF stage pairs compared (F0 vs. F1, F0 vs. F2, F1 vs. F2, F0 vs. F1-F2, P < 0.05). With the exception of F0 vs. F1, K_ep differed significantly between stages (P < 0.05). The AUC of K^trans was higher than that of other quantitative parameters, with an AUC of 0.92, 0.99, 0.94 and 0.92 for staging F0 vs. F1, F0 vs. F2, F1 vs. F2, and F0 vs. F1-F2, respectively.ConclusionAmong quantitative parameters of Gd-EOB-DTPA DCE MRI, K^trans was the best predictor for quantitatively differentiating early-stage LF.     

Prediction of survival and progression in glioblastoma patients using temporal perfusion changes during radiochemotherapy

BackgroundThe aim of this study was to investigate changes in structural magnetic resonance imaging (MRI) according to the RANO criteria and perfusion- and permeability related metrics derived from dynamic contrast-enhanced MRI (DCE) and dynamic susceptibility contrast MRI (DSC) during radiochemotherapy for prediction of progression and survival in glioblastoma.MethodsTwenty-three glioblastoma patients underwent biweekly structural and perfusion MRI before, during, and two weeks after a six weeks course of radiochemotherapy. Temporal trends of tumor volume and the perfusion-derived parameters cerebral blood volume (CBV) and blood flow (CBF) from DSC and DCE, in addition to contrast agent capillary transfer constant (K^trans) from DCE, were assessed. The patients were separated in two groups by median survival and differences between the two groups explored. Clinical- and MRI metrics were investigated using univariate and multivariate survival analysis and a predictive survival index was generated.ResultsMedian survival was 19.2 months. A significant decrease in contrast-enhancing tumor size and CBV and CBF in both DCE- and DSC-derived parameters was seen during and two weeks past radiochemotherapy (p < 0.05). A 10%/30% increase in K^trans/CBF two weeks after finishing radiochemotherapy resulted in significant shorter survival (13.9/16.8 vs. 31.5/33.1 months; p < 0.05). Multivariate analysis revealed an index using change in K^trans and relative CBV from DSC significantly corresponding with survival time in months (r² = 0.843; p < 0.001).ConclusionsSignificant temporal changes are evident during radiochemotherapy in tumor size (after two weeks) and perfusion-weighted MRI-derived parameters (after four weeks) in glioblastoma patients. While DCE-based metrics showed most promise for early survival prediction, a multiparametric combination of both DCE- and DSC-derived metrics gave additional information.     

Comparison of diffusion tensor,dynamic susceptibility contrast MRI and Tc-Tetrofosmin brain SPECT for the detection of recurrent high-grade glioma

Introduction

Treatment induced necrosis is a relatively frequent finding in patients treated for high-grade glioma. Differentiation by imaging modalities between glioma recurrence and treatment induced necrosis is not always straightforward. This is a comparative study of diffusion tensor imaging (DTI), dynamic susceptibility contrast MRI and ^99mTc-Tetrofosmin brain single-photon emission computed tomography (SPECT) for differentiation of recurrent glioma from treatment induced necrosis.

Methods

A prospective study was made of 30 patients treated for high-grade glioma who had suspected recurrent tumor on follow-up MRI. All had been treated by surgical resection of the tumor followed by standard postoperative radiotherapy with chemotherapy. No residual tumor had been found on brain imaging immediately after the initial treatment. All the patients were studied with dynamic susceptibility contrast brain MRI and, within a week, ^99mTc-Tetrofosmin brain SPECT.

Results

Both ^99mTc-Tetrofosmin brain SPECT and dynamic susceptibility contrast MRI could discriminate between tumor recurrence and treatment induced necrosis with 100% sensitivity and 100% specificity. An apparent diffusion coefficient (ADC) ratio cut-off value of 1.27 could differentiate recurrence from treatment induced necrosis with 65% sensitivity and 100% specificity and a fractional anisotropy (FA) ratio cut-off value of 0.47 could differentiate recurrence from treatment induced necrosis with 57% sensitivity and 100% specificity. A significant correlation was demonstrated between ^99mTc-Tetrofosmin uptake ratio and rCBV (P = 0.003).

Conclusions

Dynamic susceptibility contrast MRI and brain SPECT with ^99mTc-Tetrofosmin had the same accuracy and may be used to detect recurrent tumor following treatment for glioma. DTI also showed promise for the detection of recurrent tumor, but was inferior to both dynamic susceptibility contrast MRI and brain SPECT.     

A comparison of two methods for estimating DCE-MRI parameters via individual and cohort based AIFs in prostate cancer: A step towards practical implementation

Multi-parametric Magnetic Resonance Imaging, and specifically Dynamic Contrast Enhanced (DCE) MRI, play increasingly important roles in detection and staging of prostate cancer (PCa). One of the actively investigated approaches to DCE MRI analysis involves pharmacokinetic (PK) modeling to extract quantitative parameters that may be related to microvascular properties of the tissue. It is well-known that the prescribed arterial blood plasma concentration (or Arterial Input Function, AIF) input can have significant effects on the parameters estimated by PK modeling. The purpose of our study was to investigate such effects in DCE MRI data acquired in a typical clinical PCa setting. First, we investigated how the choice of a semi-automated or fully automated image-based individualized AIF (iAIF) estimation method affects the PK parameter values; and second, we examined the use of method-specific averaged AIF (cohort-based, or cAIF) as a means to attenuate the differences between the two AIF estimation methods.     

Entanglement,superselection rules and supersymmetric quantum mechanics

In this paper we show that the energy eigenstates of supersymmetric quantum mechanics (SUSYQM) with non-definite “fermion” number are entangled states. They are “physical states” of the model provided that observables with odd number of spin variables are allowed in the theory like it happens in the Jaynes–Cummings model. Those states generalize the so-called “spin-spring” states of the Jaynes–Cummings model which have played an important role in the study of entanglement.