FOLFCIS Treatment and Genomic Correlates of Response in Advanced Anal Squamous Cell Cancer

Background

Treatment of advanced anal squamous cell cancer (SCC) is usually with the combination of cisplatin and 5-fluorouracil, which is associated with heterogeneous responses across patients and significant toxicity. We examined the safety and efficacy of a modified schedule, FOLFCIS (leucovorin, fluorouracil, and cisplatin), and performed an integrated clinical and genomic analysis of anal SCC.

Symptomatic palliative care for children with neurodisability

Paediatric palliative care and neurodisability are two relatively new, evolving paediatric sub-specialities that have increasing relevance in the current paediatric landscape. For many people palliative care has been synonymous with end of life care, but in paediatrics it encompasses much more and is for all children with life-threatening or life-limiting conditions, from the point of diagnosis. This breadth of focus is demonstrated well through the interface between paediatric palliative care and paediatric neurodisability. In this article we explore this unique interface through the three domains of complex symptom management, advanced care planning and end of life care. We describe the practicalities involved in all three areas and highlight the importance of early referral and the process of “dual” or “parallel” planning. We cover in more depth the specific management of the symptoms: dystonia/abnormalities of muscle tone, seizures, pain, agitation, secretions, respiratory failure, and gut failure.     

Influence of SENSE on image properties in high-resolution single-shot echo-planar DTI.

Limited spatial resolution is a key obstacle to the study of brain white matter structure with diffusion tensor imaging (DTI). In its frequent implementation with single-excitation spin-echo echo-planar sequences, DTI's ability to resolve small structures is strongly restricted by T2 and T2* decay, B0 inhomogeneity, and limited signal-to-noise ratio (SNR). In this work the influence of sensitivity encoding (SENSE) on diffusion-weighted (DW) image properties is investigated. Computer simulations showed that the PSF becomes narrower with increasing SENSE reduction factors, R, enhancing the intrinsic resolution. After a brief theoretical discussion, we describe the estimation of SNR on a pixel-by-pixel basis as a function of R. The mean image SNR behavior is manifold: SENSE is capable of increasing SNR efficiency by reducing the echo time (TE). Each SNR(R) curve reveals a maximum that depends on the amount of partial Fourier encoding used. The overall best SNR efficiency for an eight-element head coil array and a b-factor of 1000 s/mm2 is achieved at R = 2.1 and partial Fourier encoding of 60%. In vivo tensor maps of volunteers and a patient, with an in-plane resolution of 0.78 x 0.78 mm2, are also presented to demonstrate the practical implementation of the parallel approach.     

Electrodynamics and ultimate SNR in parallel MR imaging.

The purpose of this article is to elucidate inherent limitations to the performance of parallel MRI. The study focuses on the ultimate signal-to-noise ratio (SNR), which refers to the maximum SNR permitted by the electrodynamics of the signal detection process. Using a spherical model object, it is shown that the behavior of the ultimate SNR imposes distinct limits on the acceleration rate in parallel imaging. For low and moderate acceleration, the ultimate SNR performance is nearly optimal, with geometry factors close to 1. However, for high reduction factors beyond a critical value, the ultimate performance deteriorates rapidly, corresponding to exponential growth of the geometry factor. The transition from optimal to deteriorating performance depends on the electrodynamic characteristics of the detected RF fields. In the near-field regime, i.e., for low B0 and small object size, the critical reduction factor is constant and approximately equal to four for 1D acceleration in the sphere. In the far-field wave regime the critical reduction factor is larger and increases both with B0 and object size. Therefore, it is concluded that parallel techniques hold particular promise for human MR imaging at very high field.     

Parallel image reconstruction using B-spline approximation (PROBER).

A new reconstruction method for parallel MRI called PROBER is proposed. The method PROBER works in an image domain similar to methods based on Sensitivity Encoding (SENSE). However, unlike SENSE, which first estimates the spatial sensitivity maps, PROBER approximates the reconstruction coefficients directly by B-splines. Also, B-spline coefficients are estimated at once in order to minimize the reconstruction error instead of estimating the reconstruction in each pixel independently (as in SENSE). This makes the method robust to noise in reference images. No presmoothing of reference images is necessary. The number of estimated parameters is reduced, which speeds up the estimation process. PROBER was tested on simulated, phantom, and in vivo data. The results are compared with commercial implementations of the algorithms SENSE and GRAPPA (Generalized Autocalibrating Partially Parallel Acquisitions) in terms of elapsed time and reconstruction quality. The experiments showed that PROBER is faster than GRAPPA and SENSE for images wider than 150x150 pixels for comparable reconstruction quality. With more basis functions, PROBER outperforms both SENSE and GRAPPA in reconstruction quality at the cost of slightly increased computational time.     

移动式护理机器人的控制系统

介绍自行研制的移动式护理机器人控制系统的功能和结构、开发过程中所面临的主要问题以及安全保护措施。所研制的机器人的控制系统具有高水平命令编译、机器人运动规划及环境设备控制功能。用户发出的任务指令由机器人的高水平集成控制系统分解为一系列低层的机器人部件控制命令或电器设备操作指令，然后由相应的控制器予以执行。     

Non-Cartesian data reconstruction using GRAPPA operator gridding (GROG).

A novel approach that uses the concepts of parallel imaging to grid data sampled along a non-Cartesian trajectory using GRAPPA operator gridding (GROG) is described. GROG shifts any acquired data point to its nearest Cartesian location, thereby converting non-Cartesian to Cartesian data. Unlike other parallel imaging methods, GROG synthesizes the net weight for a shift in any direction from a single basis set of weights along the logical k-space directions. Given the vastly reduced size of the basis set, GROG calibration and reconstruction requires fewer operations and less calibration data than other parallel imaging methods for gridding. Instead of calculating and applying a density compensation function (DCF), GROG requires only local averaging, as the reconstructed points fall upon the Cartesian grid. Simulations are performed to demonstrate that the root mean square error (RMSE) values of images gridded with GROG are similar to those for images gridded using the gold-standard convolution gridding. Finally, GROG is compared to the convolution gridding technique using data sampled along radial, spiral, rosette, and BLADE (a.k.a. periodically rotated overlapping parallel lines with enhanced reconstruction [PROPELLER]) trajectories.     

Array compression for MRI with large coil arrays.

Arrays with large numbers of independent coil elements are becoming increasingly available as they provide increased signal-to-noise ratios (SNRs) and improved parallel imaging performance. Processing of data from a large set of independent receive channels is, however, associated with an increased memory and computational load in reconstruction. This work addresses this problem by introducing coil array compression. The method allows one to reduce the number of datasets from independent channels by combining all or partial sets in the time domain prior to image reconstruction. It is demonstrated that array compression can be very effective depending on the size of the region of interest (ROI). Based on 2D in vivo data obtained with a 32-element phased-array coil in the heart, it is shown that the number of channels can be compressed to as few as four with only 0.3% SNR loss in an ROI encompassing the heart. With twofold parallel imaging, only a 2% loss in SNR occurred using the same compression factor.