Feasibility of Quantifying Arterial Cerebral Blood Volume Using Multiphase Alternate Ascending/Descending Directional Navigation (ALADDIN)

Prediction of Lung Shunt Fraction for Yttrium-90 Treatment of Hepatic Tumors Using Dynamic Contrast Enhanced MRI with Quantitative Perfusion Processing

There is no noninvasive method to estimate lung shunting fraction (LSF) in patients with liver tumors undergoing Yttrium-90 (Y90) therapy. We propose to predict LSF from noninvasive dynamic contrast enhanced (DCE) MRI using perfusion quantification. Two perfusion quantification methods were used to process DCE MRI in 25 liver tumor patients: Kety's tracer kinetic modeling with a delay-fitted global arterial input function (AIF) and quantitative transport mapping (QTM) based on the inversion of transport equation using spatial deconvolution without AIF. LSF was measured on SPECT following Tc-99m macroaggregated albumin (MAA) administration via hepatic arterial catheter. The patient cohort was partitioned into a low-risk group (LSF ≤ 10%) and a high-risk group (LSF > 10%). Results: In this patient cohort, LSF was positively correlated with QTM velocity |u| (r = 0.61, F = 14.0363, p = 0.0021), and no significant correlation was observed with Kety's parameters, tumor volume, patient age and gender. Between the low LSF and high LSF groups, there was a significant difference for QTM |u| (0.0760 ± 0.0440 vs. 0.1822 ± 0.1225 mm/s, p = 0.0011), and Kety's Ktrans (0.0401 ± 0.0360 vs 0.1198 ± 0.3048, p = 0.0471) and Ve (0.0900 ± 0.0307 vs. 0.1495 ± 0.0485, p = 0.0114). The area under the curve (AUC) for distinguishing between low LSF and high LSF was 0.87 for |u|, 0.80 for Ve and 0.74 for Ktrans. Noninvasive prediction of LSF is feasible from DCE MRI with QTM velocity postprocessing.

qMTNet: Accelerated quantitative magnetization transfer imaging with artificial neural networks

PURPOSE

To develop a set of artificial neural networks, collectively termed qMTNet, to accelerate data acquisition and fitting for quantitative magnetization transfer (qMT) imaging.

METHODS

Conventional and interslice qMT data were acquired with two flip angles at six offset frequencies from seven subjects for developing the networks and from four young and four older subjects for testing the generalizability. Two subnetworks, qMTNet-acq and qMTNet-fit, were developed and trained to accelerate data acquisition and fitting, respectively. qMTNet-2 is the sequential application of qMTNet-acq and qMTNet-fit to produce qMT parameters (exchange rate, pool fraction) from undersampled qMT data (two offset frequencies rather than six). qMTNet-1 is one single integrated network having the same functionality as qMTNet-2. qMTNet-fit was compared with a Gaussian kernel-based fitting. qMT parameters generated by the networks were compared with those from ground truth fitted with a dictionary-driven approach.

RESULTS

The proposed networks achieved high peak signal-to-noise ratio (>30) and structural similarity index (>97) in reference to the ground truth. qMTNet-fit produced qMT parameters in concordance with the ground truth with better performance than the Gaussian kernel-based fitting. qMTNet-2 and qMTNet-1 could accelerate data acquisition at threefold and accelerate fitting at 5800- and 4218-fold, respectively. qMTNet-1 showed slightly better performance than qMTNet-2, whereas qMTNet-2 was more flexible for applications.

CONCLUSION

The proposed single (qMTNet-1) and two joint neural networks (qMTNet-2) can accelerate qMT workflow for both data acquisition and fitting significantly. qMTNet has the potential to accelerate qMT imaging for clinical applications, which warrants further investigation.

Evaluation of Tumor Blood Flow Using Alternate Ascending/Descending Directional Navigation in Primary Brain Tumors: A Comparison Study with Dynamic Susceptibility Contrast Magnetic Resonance Imaging

Objective

Alternate ascending/descending directional navigation (ALADDIN) is a novel arterial spin labeling technique that does not require a separate spin preparation pulse. We sought to compare the normalized cerebral blood flow (nCBF) values obtained by ALADDIN and dynamic susceptibility contrast (DSC) perfusion magnetic resonance imaging (MRI) in patients with primary brain tumors.

Materials and Methods

Sixteen patients with primary brain tumors underwent MRI scans including contrast-enhanced T1-weighted imaging, DSC perfusion MRI, and ALADDIN. The nCBF values of normal gray matter (GM) and tumor areas were measured by both DSC perfusion MRI and ALADDIN, which were compared by the Wilcoxon signed rank test. Subgroup analyses according to pathology were performed with the Wilcoxon signed rank test.

Results

Higher mean nCBF values of GM regions in the bilateral frontal lobe, temporal lobe, and caudate were detected by ALADDIN than by DSC perfusion MRI (p <0.05). In terms of the mean or median nCBF values and the mean of the top 10% nCBF values from tumors, DSC perfusion MRI and ALADDIN did not statistically significantly differ either overall or in each tumor group.

Conclusion

ALADDIN tended to detect higher nCBF values in normal GM, as well as higher perfusion portions of primary brain tumors, than did DSC perfusion MRI. We believe that the high perfusion signal on ALADDIN can be beneficial in lesion detection and characterization.

MRI techniques to measure arterial and venous cerebral blood volume

The measurement of cerebral blood volume (CBV) has been the topic of numerous neuroimaging studies. To date, however, most in vivo imaging approaches can only measure CBV summed over all types of blood vessels, including arterial, capillary and venous vessels in the microvasculature (i.e. total CBV or CBVtot). As different types of blood vessels have intrinsically different anatomy, function and physiology, the ability to quantify CBV in different segments of the microvascular tree may furnish information that is not obtainable from CBVtot, and may provide a more sensitive and specific measure for the underlying physiology. This review attempts to summarize major efforts in the development of MRI techniques to measure arterial (CBVa) and venous CBV (CBVv) separately. Advantages and disadvantages of each type of method are discussed. Applications of some of the methods in the investigation of flow-volume coupling in healthy brains, and in the detection of pathophysiological abnormalities in brain diseases such as arterial steno-occlusive disease, brain tumors, schizophrenia, Huntington's disease, Alzheimer's disease, and hypertension are demonstrated. We believe that the continual development of MRI approaches for the measurement of compartment-specific CBV will likely provide essential imaging tools for the advancement and refinement of our knowledge on the exquisite details of the microvasculature in healthy and diseased brains.