Time efficiency analysis for undersampled quantitative MRI acquisitions

To realize Quantitative MRI (QMRI) with clinically acceptable scan time, acceleration factors achieved by conventional parallel imaging techniques are often inadequate. Further acceleration is possible using model-based reconstruction. We propose a theoretical metric called TEUSQA: Time Efficiency for UnderSampled QMRI Acquisitions to inform sequence design and sample pattern optimisation. TEUSQA is designed for a particular class of reconstruction techniques that directly estimate tissue parameters, possibly using prior information to regularize the estimation. TEUSQA can be used to evaluate undersampling patterns for multi-contrast QMRI sequences targeting any tissue parameter. To verify the time efficiency predicted by TEUSQA, we performed Monte Carlo simulations and an accelerated parameter mapping with two sequences (Inversion prepared fast spin echo for $T_1$ and $T_2$ mapping and 3D GRASE for $T_2$ and B0 inhomogeneity mapping). Using TEUSQA, we assessed several ways to generate undersampling patterns in silico, providing insight into the relation between sample distribution and time efficiency for different acceleration factors. The time efficiency predicted by TEUSQA was within 15% of that observed in the Monte Carlo simulations and the prospective acquisition experiment. The assessment of undersampling patterns showed that a class of good patterns could be obtained by low-discrepancy sampling. We believe that TEUSQA offers a valuable instrument for developers of novel QMRI sequences pushing the boundaries of acceleration to achieve clinically feasible protocols. Finally, we applied a time-efficient undersampling pattern selected using TEUSQA for a 32-fold accelerated scan to map $T_1$ & $T_2$ mapping of a healthy volunteer.     

Spatio-temporal directed acyclic graph learning with attention mechanisms on brain functional time series and connectivity

We develop a deep learning framework, spatio-temporal directed acyclic graph with attention mechanisms (ST-DAG-Att), to predict cognition and disease using functional magnetic resonance imaging (fMRI). This ST-DAG-Att framework comprises of two neural networks, (1) spatio-temporal graph convolutional network (ST-graph-conv) to learn the spatial and temporal information of functional time series at multiple temporal and spatial graph scales, where the graph is represented by the brain functional network, the spatial convolution is over the space of this graph, and the temporal convolution is over the time dimension; (2) functional connectivity convolutional network (FC-conv) to learn functional connectivity features, where the functional connectivity is derived from embedded multi-scale fMRI time series and the convolutional operation is applied along both edge and node dimensions of the brain functional network. This framework also consists of an attention component, i.e., functional connectivity-based spatial attention (FC-SAtt), that generates a spatial attention map through learning the local dependency among high-level features of functional connectivity and emphasizing meaningful brain regions. Moreover, both the ST-graph-conv and FC-conv networks are designed as feed-forward models structured as directed acyclic graphs (DAGs). Our experiments employ two large-scale datasets, Adolescent Brain Cognitive Development (ABCD, $n=7693$) and Open Access Series of Imaging Study-3 (OASIS-3, $n=1786$). Our results show that the ST-DAG-Att model is generalizable from cognition prediction to age prediction. It is robust to independent samples obtained from different sites of the ABCD study. It outperforms the existing machine learning techniques, including support vector regression (SVR), elastic net’s mixture with random forest, spatio-temporal graph convolution, and BrainNetCNN.     

Deep model-based magnetic resonance parameter mapping network (DOPAMINE) for fast T1 mapping using variable flip angle method

Quantitative tissue characteristics, which provide valuable diagnostic information, can be represented by magnetic resonance (MR) parameter maps using magnetic resonance imaging (MRI); however, a long scan time is necessary to acquire them, which prevents the application of quantitative MR parameter mapping to real clinical protocols. For fast MR parameter mapping, we propose a deep model-based MR parameter mapping network called DOPAMINE that combines a deep learning network with a model-based method to reconstruct MR parameter maps from undersampled multi-channel k-space data. DOPAMINE consists of two networks: 1) an MR parameter mapping network that uses a deep convolutional neural network (CNN) that estimates initial parameter maps from undersampled k-space data (CNN-based mapping), and 2) a reconstruction network that removes aliasing artifacts in the parameter maps with a deep CNN (CNN-based reconstruction) and an interleaved data consistency layer by an embedded MR model-based optimization procedure. We demonstrated the performance of DOPAMINE in brain $T_1$ map reconstruction with a variable flip angle (VFA) model. To evaluate the performance of DOPAMINE, we compared it with conventional parallel imaging, low-rank based reconstruction, model-based reconstruction, and state-of-the-art deep-learning-based mapping methods for three different reduction factors ($R$ = 3, 5, and 7) and two different sampling patterns (1D Cartesian and 2D Poisson-disk). Quantitative metrics indicated that DOPAMINE outperformed other methods in reconstructing $T_1$ maps for all sampling patterns and reduction factors. DOPAMINE exhibited quantitatively and qualitatively superior performance to that of conventional methods in reconstructing MR parameter maps from undersampled multi-channel k-space data. The proposed method can thus reduce the scan time of quantitative MR parameter mapping that uses a VFA model.     

High-Intensity Walking Time Is a Key Determinant to Increase Physical Fitness and Improve Health Outcomes After Interval Walking Training in Middle-Aged and Older People

ObjectiveTo examine the effects of interval walking training (IWT) on the estimated peak aerobic capacity (e$\dot{V}{\text{O}}_{2\text{peak}}$) and lifestyle-related disease (LSD) score while focusing on exercise intensity and volume in middle-aged and older people.Participants and MethodsMen and women (N=679; mean age, 65±7 SD years) completed 5-month IWT. Participants were instructed to repeat 5 or more sets of fast and slow walking for 3 minutes each at 70% or more and 40% e$\dot{V}{\text{O}}_{2\text{peak}}$ for walking, respectively, per day for 4 or more d/wk. This study was conducted from April 1, 2005, through February 29, 2008.ResultsInterval walking training increased e$\dot{V}{\text{O}}_{2\text{peak}}$ by 14% and decreased LSD score by 17% on average (P<.001). During 5-month IWT, fast and slow walking times were 88±65 SD and 100±86 min/wk, respectively, but varied among participants. We divided participants into approximately 10 bins for 6 minutes each of fast and slow walking times per week up to 60 min/wk, and above this time, approximately 8 bins for 30 or 60 minutes each of fast and slow walking up to the maximal time. We found that both e$\dot{V}{\text{O}}_{2\text{peak}}$ and LSD score improved as fast walking time per week increased up to 50 min/wk (R²=0.94; P<.001 for e$\dot{V}{\text{O}}_{2\text{peak}}$; R²=0.51; P=.03 for LSDS) but plateaued above this time. In contrast, improvement in neither e$\dot{V}{\text{O}}_{2\text{peak}}$ nor LSDS was positively correlated with slow or total walking time per week. Multiple regression analyses confirmed that fast walking time per week was the major determinant of improvements in e$\dot{V}{\text{O}}_{2\text{peak}}$ (P<.001) and LSD score (P=.001).ConclusionHigh-intensity walking time during IWT is a key factor to increase e$\dot{V}{\text{O}}_{2\text{peak}}$ and decrease LSD score in middle-aged and older people.     

Unsupervised PET logan parametric image estimation using conditional deep image prior

Recently, deep learning-based denoising methods have been gradually used for PET images denoising and have shown great achievements. Among these methods, one interesting framework is conditional deep image prior (CDIP) which is an unsupervised method that does not need prior training or a large number of training pairs. In this work, we combined CDIP with Logan parametric image estimation to generate high-quality parametric images. In our method, the kinetic model is the Logan reference tissue model that can avoid arterial sampling. The neural network was utilized to represent the images of Logan slope and intercept. The patient’s computed tomography (CT) image or magnetic resonance (MR) image was used as the network input to provide anatomical information. The optimization function was constructed and solved by the alternating direction method of multipliers (ADMM) algorithm. Both simulation and clinical patient datasets demonstrated that the proposed method could generate parametric images with more detailed structures. Quantification results showed that the proposed method results had higher contrast-to-noise (CNR) improvement ratios (PET/CT datasets: $62.25\%\pm 29.93\%$; striatum of brain PET datasets : $129.51\%\pm 32.13\%$, thalamus of brain PET datasets: $128.24\%\pm 31.18\%$) than Gaussian filtered results (PET/CT datasets: $23.33\%\pm 18.63\%$; striatum of brain PET datasets: $74.71\%\pm 8.71\%$, thalamus of brain PET datasets: $73.02\%\pm 9.34\%$) and nonlocal mean (NLM) denoised results (PET/CT datasets: $37.55\%\pm 26.56\%$; striatum of brain PET datasets: $100.89\%\pm 16.13\%$, thalamus of brain PET datasets: $103.59\%\pm 16.37\%$).     

Investigating the Accumulation of Submicron Phase-Change Droplets in Tumors

Submicron phase-change droplets are an emerging class of ultrasound contrast agent. Compared with microbubbles, their relatively small size and increased stability offer the potential to passively extravasate and accumulate in solid tumors through the enhanced permeability and retention effect. Under exposure to sufficiently powerful ultrasound, these droplets can convert into in situ gas microbubbles and thus be used as an extravascular-specific contrast agent. However, in vivo imaging methods to detect extravasated droplets have yet to be established. Here, we develop an ultrasound imaging pulse sequence within diagnostic safety limits to selectively detect droplet extravasation in tumors. Tumor-bearing mice were injected with submicron perfluorobutane droplets and interrogated with our imaging–vaporization–imaging sequence. By use of a pulse subtraction method, median droplet extravasation signal relative to the total signal within the tumor was estimated to be $E_{\text{tumor}}=37\pm 5\%$ compared with the kidney $E_{\text{kidney}}=-2\pm 8\%$ (p < 0.001). This work contributes toward the advancement of volatile phase-shift droplets as a next-generation ultrasound agent for imaging and therapy.     

Fine-Tuning and training of densenet for histopathology image representation using TCGA diagnostic slides

Feature vectors provided by pre-trained deep artificial neural networks have become a dominant source for image representation in recent literature. Their contribution to the performance of image analysis can be improved through fine-tuning. As an ultimate solution, one might even train a deep network from scratch with the domain-relevant images, a highly desirable option which is generally impeded in pathology by lack of labeled images and the computational expense. In this study, we propose a new network, namely KimiaNet, that employs the topology of the DenseNet with four dense blocks, fine-tuned and trained with histopathology images in different configurations. We used more than 240,000 image patches with $1000\times 1000$ pixels acquired at 20$\times$ magnification through our proposed “high-cellularity mosaic” approach to enable the usage of weak labels of 7126 whole slide images of formalin-fixed paraffin-embedded human pathology samples publicly available through The Cancer Genome Atlas (TCGA) repository. We tested KimiaNet using three public datasets, namely TCGA, endometrial cancer images, and colorectal cancer images by evaluating the performance of search and classification when corresponding features of different networks are used for image representation. As well, we designed and trained multiple convolutional batch-normalized ReLU (CBR) networks. The results show that KimiaNet provides superior results compared to the original DenseNet and smaller CBR networks when used as feature extractor to represent histopathology images.