Petersen Holt (epoxyaugust83)

In certain modeling approaches, activation analyses of task-based fMRI data can involve a relatively large number of predictors. For example, in the encoding model approach, complex stimuli are represented in a high-dimensional feature space, resulting in design matrices with many predictors. Similarly, single-trial models and finite impulse response models may also encompass a large number of predictors. In settings where only few of those predictors are expected to be informative, a sparse model fit can be obtained via L1-regularization. However, estimating L1-regularized models requires an iterative fitting procedure, which considerably increases computation time compared to estimating unregularized or L2-regularized models, and complicates the application of L1-regularization on whole-brain data and large sample sizes. Here we provide several functions for estimating L1-regularized models that are optimized for the mass-univariate analysis approach. The package includes a parallel implementation of the coordinate descent algorithm for CPU-only systems and two implementations of the alternating direction method of multipliers algorithm requiring a GPU device. While the core algorithms are implemented in C++/CUDA, data input/output and parameter settings can be conveniently handled via Matlab. The CPU-based implementation is highly memory-efficient and provides considerable speed-up compared to the standard implementation not optimized for the mass-univariate approach. Further acceleration can be achieved on systems equipped with a CUDA-enabled GPU. Using the fastest GPU-based implementation, computation time for whole-brain estimates can be reduced from 9 h to 5 min in an exemplary data setting. Overall, the provided package facilitates the use of L1-regularization for fMRI activation analyses and enables an efficient employment of L1-regularization on whole-brain data and large sample sizes. We aimed to compare the efficiency of prostate cancer (PCa) detection using a radiomics signature based on advanced zoomed diffusion-weighted imaging and conventional full-field-of-view DWI. A total of 136 patients, including 73 patients with PCa and 63 without PCa, underwent multi-parametric magnetic resonance imaging (mp-MRI). Radiomic features were extracted from prostate lesion areas segmented on full-field-of-view DWI with b-value = 1500s/mm (f-DWI ), advanced zoomed DWI images with b-value = 1500s/mm (z-DWI ), calculated zoomed DWI with b-value = 2000s/mm (z-calDWI ), and apparent diffusion coefficient (ADC) maps derived from both sequences (f-ADC and z-ADC). Single-imaging modality radiomics signature, mp-MRI radiomics signature, and a mixed model based on mp-MRI and clinically independent risk factors were built to predict PCa probability. The diagnostic efficacy and the potential net benefits of each model were evaluated. Both z-DWI and z-calDWI had significantly better predictive independent clinical risk factors and the mp-MRI model, the mixed model has the best diagnostic efficiency. • Advanced zoomed DWI technology can improve the diagnostic accuracy of radiomics signatures for PCa. • Radiomics signatures based on z-calDWIb2000 have the best diagnostic performance among individual imaging modalities. • Compared with the independent clinical risk factors and the mp-MRI model, the mixed model has the best diagnostic efficiency.To evaluate the effect of donor-to-recipient sex mismatched (male donor corneas to female recipients) on the incidence of rejection episodes and failures up to 1 year after corneal transplantation. Prospective observational cohort study, with donor corneas randomly assigned and surgeons blind to the sex of donor. A unique eye bank retrieved and selected the donor corneas transplanted in 4 ophthalmic units in patients with clinical indication for primary or repeated keratoplasty for optical reasons, perforating or lamel