Periodically rotated overlapping parallel lines with enhanced reconstruction acquisition to improve motion-induced artifacts in bladder cancer imaging

Abstract

Motion-induced artifacts have been a major drawback in bladder cancer imaging. This study is to evaluate the clinical utility of periodically rotated overlapping parallel lines with enhanced reconstruction (PROPELLER) acquisition in improving motion-induced artifacts in T2-weighted (T2W) magnetic resonance imaging (MRI) of bladder cancer at 3T.Sixteen patient MRI exams were included. Using a Likert scale, 2 radiologists independently scored T2W data without and with PROPELLER in terms of artifact severity and tumor visualization. Statistical analysis was done to assess the image quality improvement by PROPELLER and inter-observer variability.Without PROPELLER, the median scores of artifact severity and tumor visualization were 1.5 and 1.5 for reviewer 1, and 2.0 and 2.0 for reviewer 2. With PROPELLER, the scores increased to 3 and 3.5 for reviewer 1, and 3.5 and 3.5 for reviewer 2. Despite the inter-observer variability (κ scores < 0.2), both reviewers found significant improvement in artifacts and visualization (all P < .001).PROPELLER acquisition significantly improved the image quality of T2W-MRI. These initial findings indicate that this technique should be utilized in clinical MRI of the bladder.

Application of deep learning–based image reconstruction in MR imaging of the shoulder joint to improve image quality and reduce scan time

“…The PROPELLER technique is a well-established method for image acquisition when motion reduction is desired especially in the imaging of the abdomen, lung, or shoulder joint [13,16,[43][44][45][46][47]. Blood flow in the axillary vessels could be a potential source of pulsation artifacts in shoulder MRI; therefore, the use of the PROPELLER technique should be considered to minimize motion artifacts.…”

System Analysis of the Learning Behavior Recognition System for Students in a Law Classroom: Based on the Improved SSD Behavior Recognition Algorithm

“…e main mechanism of convolutional neural networks is that when the network is connected to a network, two neurons are related, but convolutional neural networks are only partially protected. I have an N − 1 layer of neurons connected to N layers of neurons [21][22][23][24][25]. As shown in Figure 2, the special part connects layer N − 1 and layer N.…”

“…e behavior recognition model design process was obtained as shown in Figure 3. [2,22,26]. e first step was to prepare student behavior images.…”

Paediatric magnetic resonance imaging adaptations without the use of sedation or anaesthesia: A narrative review

“…Both techniques use data sparsity aiming to decrease scan time without compromising image quality by motion artefacts in awake children. Motion resistant techniques, like PROPELLER, have been shown to substantially improve or eliminate motion artefacts for a number of clinical applications including brain, shoulder and cardiac imaging [48] . The latest state-of-the-art in data acquisition and reconstruction in paediatric imaging will involve a more intense use of artificial intelligence but fully optimised and validated algorithms are needed to ensure this is also clinically useful and meaningful [49 , 50] .…”