Reward learning and statistical learning independently influence attentional priority of salient distractors in visual search

Existing research demonstrates different ways in which attentional prioritization of salient nontarget stimuli is shaped by prior experience: Reward learning renders signals of high-value outcomes more likely to capture attention than signals of low-value outcomes, whereas statistical learning can produce attentional suppression of the location in which salient distractor items are likely to appear. The current study combined manipulations of the value and location associated with salient distractors in visual search to investigate whether these different effects of selection history operate independently or interact to determine overall attentional prioritization of salient distractors. In Experiment 1, high-value and low-value distractors most frequently appeared in the same location; in Experiment 2, high-value and low-value distractors typically appeared in distinct locations. In both experiments, effects of distractor value and location were additive, suggesting that attention-promoting effects of value and attention-suppressing effects of statistical location-learning independently modulate overall attentional priority. Our findings are consistent with a view that sees attention as mediated by a common priority map that receives and integrates separate signals relating to physical salience and value, with signal suppression based on statistical learning determined by physical salience, but not incentive salience.

Whole-body MRI: detecting bone metastases from prostate cancer

Whole-body magnetic resonance imaging (WB-MRI) is currently used worldwide for detecting bone metastases from prostate cancer. The 5-year survival rate for prostate cancer is > 95%. However, an increase in survival time may increase the incidence of bone metastasis. Therefore, detecting bone metastases is of great clinical interest. Bone metastases are commonly located in the spine, pelvis, shoulder, and distal femur. Bone metastases from prostate cancer are well-known representatives of osteoblastic metastases. However, other types of bone metastases, such as mixed or inter-trabecular type, have also been detected using MRI. MRI does not involve radiation exposure and has good sensitivity and specificity for detecting bone metastases. WB-MRI has undergone gradual developments since the last century, and in 2004, Takahara et al., developed diffusion-weighted Imaging (DWI) with background body signal suppression (DWIBS). Since then, WB-MRI, including DWI, has continued to play an important role in detecting bone metastases and monitoring therapeutic effects. An imaging protocol that allows complete examination within approximately 30 min has been established. This review focuses on WB-MRI standardization and the automatic calculation of tumor total diffusion volume (tDV) and mean apparent diffusion coefficient (ADC) value. In the future, artificial intelligence (AI) will enable shorter imaging times and easier automatic segmentation.

Impaired selection of a previously ignored singleton: Evidence for salience map plastic changes

Spatial suppression of a salient colour distractor is achievable via statistical learning. Distractor suppression attenuates unwanted capture, but at the same time target selection at the most likely distractor location is impaired. This result corroborates the idea that the distractor salience is attenuated via inhibitory signals applied to the corresponding location in the priority map. What is less clear, however, is whether lingering impairment in target selection when the distractor is removed are due to the proactive strategic maintenance of the suppressive signal at the previous most likely distractor location or result from the fact that suppression has induced plastic changes in the priority map, probably changing input weights. Here, we provide evidence that supports the latter possibility, as we found that impairment in target selection persisted even when the singleton distractor in the training phase became the target of search in a subsequent test phase. This manipulation rules out the possibility that the observed impairments at the previous most likely distractor location were caused by a signal suppression maintained at this location. Rather, the results reveal that the inhibitory signals cause long-lasting changes in the priority map, which affect future computation of the target salience at the same location, and therefore the efficiency of attentional selection.

Diffusion-weighted whole-body imaging with background body signal suppression (DWIBS) in detection and characterization of neoplastic breast lesions

Background Breast cancer is a major health problem in women and early detection is of prime importance. Objective We aimed to evaluate the role of diffusion-weighted imaging with background body signal suppression (DWIBS) in detection of breast lesions and characterization of these detected lesions. Patients and methods 40 female patients with suspicious 50 breast lesions detected by sonomammography, in addition to the routine protocol that includes T1Wi's, T2Wi's, STIR, DCE-MRI and DWI sequences (with ADC maps) all participants underwent DWIBS sequence (with ADC maps). The histopathology served as reference standard. First, we compared the detectability of breast lesions on DWIBS with that of the DWI. We then compared the ADCs of the malignant lesions (n = 35) to that of the benign lesions (n = 15) in both DWI and DWIBS. Results Thirty seven lesions were detected via DWIBS (detectability of 74.0%) which was less than that of DWI (detectability of 78.0%). In DWIBS, the mean ADC value of the malignant lesions (0.80 ± 0.27 × 10-3mm2/s) was significantly lower than that of the benign lesions (1.40 ± 0.41 × 10-3mm2/s). With a cut-off value of 1.3 × 10-3mm2/s for ADC, DWIBS achieved 85.7% sensitivity and 80% specificity for differentiating between benign and malignant lesions. Conclusion Although it showed lower detectability for breast lesions than DWI, our study suggests that DWIBS is superior to DWI in the characterization of malignant breast lesions.. Also based on ADC, DWIBS provides additional information that may further increase the specificity of breast lesion characterization.

The Effectiveness of Data Augmentation for Bone Suppression in Chest Radiograph using Convolutional Neural Network

Objective: Bone suppression of chest radiograph holds great promise to improve the localization accuracy in Image-Guided Radiation Therapy (IGRT). However, data scarcity has long been considered as the prime culprit of developing Convolutional Neural Networks (CNNs) models for the task of bone suppression. In this study, we explored the effectiveness of various data augmentation techniques for the task of bone suppression. Methods: In this study, chest radiograph and bone-free chest radiograph are derived from 59 high-resolution CT scans. Two CNN models (U-Net and Generative Adversarial Network (GAN)) were adapted to explore the effectiveness of various data augmentation techniques for bone signal suppression in the chest radiograph. Lung radiograph and bone-free radiograph were used as the input and target label, respectively. Impacts of six typical data augmentation techniques (flip, cropping, noise injection, rotation, shift and zoom) on model performance were investigated. A series of statistical evaluating metrics, including Peak Signal-To-Noise Ratio (PSNR), Structural Similarity (SSIM) and Mean Absolute Error (MAR), were deployed to comprehensively assess the prediction performance of the two networks under the six data augmentation strategies. Quantitative comparative results showed that different data augmentation techniques exhibited a varying degree of influence on the performance of CNN models in the task of CR bone signal suppression. Results: For the U-Net model, flips, rotation (10 to 20 degrees), all the shifts, and zoom (1/8) resulted in improved model prediction accuracy. By contrast, other studied augmentation techniques showed adverse impacts on the model performance. For the GAN model, it was found to be more sensitive to the studied augmentation techniques than the U-Net. Vertical flip was the only augmentation method that yielded enhanced model performance. Conclusion: In this study, we found that different data augmentation techniques resulted in a varying degree of impacts on the prediction performance of U-Net and GAN models in the task of bone suppression in CR. However, it remains challenging to determine the optimal parameter settings for each augmentation technique. In the future, a more comprehensive evaluation is still warranted to evaluate the effectiveness of different augmentation techniques in task-specific image synthesis.