SciPy is an open-source scientific computing library for the Python programming language. Since its initial release in 2001, SciPy has become a de facto standard for leveraging scientific algorithms in Python, with over 600 unique code contributors, thousands of dependent packages, over 100,000 dependent repositories and millions of downloads per year. In this work, we provide an overview of the capabilities and development practices of SciPy 1.0 and highlight some recent technical developments.

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Arterial spin labeling (ASL) data are typically differenced, sometimes after interpolation, as part of preprocessing before statistical analysis in fMRI. While this process can reduce the number of time points by half, it simplifies the subsequent signal and noise models (i.e., smoothed box-car predictors and white noise). In this paper, we argue that ASL data are best viewed in the same data analytic framework as BOLD fMRI data, in that all scans are modeled and colored noise is accommodated. The data are not differenced, but the control/label effect is implicitly built into the model. While the models using differenced data may seem easier to implement, we show that differencing models fit with ordinary least squares either produce biased estimates of the standard errors or suffer from a loss in efficiency. The main disadvantage to our approach is that non-white noise must be modeled in order to yield accurate standard errors, however, this is a standard problem that has been solved for BOLD data, and the very same software can be used to account for such autocorrelated noise.

A fast, two-coil, pseudo-continuous labeling scheme is presented. This new scheme permits the collection of a multislice subtraction pair in <3 s, depending on the subject's arterial transit times. The method consists of acquiring both control and tag images immediately after a labeling period that matches the arterial transit time. Blood oxygenation level-dependent (BOLD) contrast functional MRI (fMRI) is currently the dominant technique for functional imaging, and it has produced a wealth of information about the brain's cognitive function. Although BOLD techniques offer great detection power, they also have limitations; therefore, there is strong interest in the development of perfusion-based functional imaging techniques. These limitations include the complexity of the mechanism that gives rise to the BOLD signal (1,2), the nonlinearity of that signal (3,4), the temporal noise characteristics (5), and the well-known sensitivity to susceptibility artifacts.In contrast, perfusion is a quantifiable physiological parameter that is easier to relate to neuronal metabolism. Furthermore, our ability to dynamically measure cerebral blood flow (CBF) is crucial for understanding the BOLD effect. Recent animal studies conducted at high field and high spatial resolution indicated that CBF changes are more localized to the parenchyma than the BOLD effect, consistent with the notion that the BOLD effect is weighted toward draining veins (6,7).In the light of these considerations, it is not surprising that there has been extensive work toward the development of rapid, noninvasive cerebral perfusion measurement techniques over the last decade. Among these, arterial spin labeling (ASL) (8 -11), which employs magnetically labeled arterial water as an endogenous tracer, is the most promising. One additional advantage of ASL techniques is the nature of the noise present along the temporal dimension. fMRI time series data contain severe low-frequency, auto-correlated drifts, the amplitude of which is much larger than the BOLD signal itself (12). These lowfrequency drifts are not present in ASL time series, which effectively enables the use of very low-frequency functional paradigms (13). Furthermore, Desmond and Glover (14) recently showed that the power of an fMRI study is largely determined by the intersubject variance of the activation response intensity. In a BOLD experiment, this variance stems from both physiological and hardware variability, whereas in an ASL experiment, the variability observed is mostly due to physiological sources, since the hardware effects are largely subtracted out.ASL techniques also present a number of challenges. These techniques suffer from low SNR, since Ͻ10% of the water in a given voxel is contributed by blood (15,16), and the label decays at a quick rate.

Objectives The goal of this study is to develop free-breathing high spatiotemporal resolution DCE liver MRI using non-Cartesian parallel imaging acceleration, and quantitative liver perfusion mapping. Materials and Methods This study is HIPAA-compliant, IRB approved, and written informed consent was obtained from all participants. Ten healthy subjects and five patients were scanned on a Siemens 3T Skyra scanner. A stack-of-spirals trajectory was undersampled in-plane with a reduction factor of 6, and reconstructed using 3D through-time non-Cartesian GRAPPA. High resolution 3D images were acquired with a true temporal resolution of 1.6~1.9 seconds, while the subjects were breathing freely. A dual-input single-compartment model was used to retrieve liver perfusion parameters from DCE-MRI data, which were co-registered using an algorithm designed to reduce the effects of dynamic contrast changes on registration. Image quality evaluation was performed on spiral images and conventional images from five healthy subjects. Results Images with a spatial resolution of 1.9×1.9×3 mm3 were obtained with whole liver coverage. With an imaging speed of better than 2 sec/volume, free-breathing scans were achieved, and dynamic changes in enhancement were captured. The overall image quality of free-breathing spiral images was slightly lower than conventional long breath-hold Cartesian images, but provided clinical acceptable or better image quality. The free-breathing 3D images were registered with almost no residual motion in liver tissue. Following the registration, quantitative whole liver 3D perfusion maps were obtained and the perfusion parameters are all in good agreement with the literature. Conclusions This high spatiotemporal resolution free-breathing 3D liver imaging technique allows voxel-wise quantification of liver perfusion.