In magnetic resonance imaging (MRI), the MR signal intensity can vary spatially and this spatial variation is usually referred to as MR intensity nonuniformity. Although the main source of intensity nonuniformity arises from B1 inhomogeneity of the coil acting as a receiver and/or transmitter, geometric distortion also alters the MR signal intensity. It is useful on some occasions to have these two different sources be separately measured and analyzed. In this paper, we present a practical method for a detailed measurement of the MR intensity nonuniformity. This method is based on the same three-dimensional geometric phantom that was recently developed for a complete measurement of the geometric distortion in MR systems. In this paper, the contribution to the intensity nonuniformity from the geometric distortion can be estimated and thus, it provides a mechanism for estimation of the intensity nonuniformity that reflects solely the spatial characteristics arising from B1. Additionally, a comprehensive scheme for characterization of the intensity nonuniformity based on the new measurement method is proposed. To demonstrate the method, the intensity nonuniformity in a 1.5 T Sonata MR system was measured and is used to illustrate the main features of the method.
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http://dx.doi.org/10.1118/1.1869572 | DOI Listing |
J Imaging Inform Med
January 2025
School of Electrical and Electronic Engineering, Hanoi University of Science and Technology, Hanoi, Vietnam.
The field of medical image segmentation powered by deep learning has recently received substantial attention, with a significant focus on developing novel architectures and designing effective loss functions. Traditional loss functions, such as Dice loss and Cross-Entropy loss, predominantly rely on global metrics to compare predictions with labels. However, these global measures often struggle to address challenges such as occlusion and nonuni-form intensity.
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Anna and Peter Brojde Lung Cancer Center, Sir Mortimer B. Davis Jewish General Hospital, McGill University, Montreal, CAN.
Background A minority of patients receiving stereotactic body radiation therapy (SBRT) for non-small cell lung cancer (NSCLC) are not good responders. Radiomic features can be used to generate predictive algorithms and biomarkers that can determine treatment outcomes and stratify patients to their therapeutic options. This study investigated and attempted to validate the radiomic and clinical features obtained from early-stage and oligometastatic NSCLC patients who underwent SBRT, to predict local response.
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Department of Applied Physics, National Defense Academy, Hashirimizu 1-10-20, Yokosuka 239-0802, Kanagawa, Japan.
Dielectrophoresis (DEP) cell separation technology is an effective means of separating target cells which are only marginally present in a wide variety of cells. To develop highly efficient cell separation devices, detailed analysis of the nonuniform electric field's intensity distribution within the device is needed, as it affects separation performance. Here we analytically expressed the distributions of the electric field and DEP force in a parallel-plate cell separation DEP device by employing electrostatic analysis through the Fourier series method.
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University of Michigan - Shanghai Jiao Tong University Joint Institute, Shanghai Jiao Tong University, Shanghai 200240, China.
Proton conducting electrochemical cells (PCECs) are efficient and clean intermediate-temperature energy conversion devices. The proton concentration across the PCECs is often nonuniform, and characterizing the distribution of proton concentration can help to locate the position of rate-limiting reactions. However, the determination of the local proton concentration under operating conditions remains challenging.
View Article and Find Full Text PDFMed Phys
December 2024
Research Center for Advanced Detection Materials and Medical Imaging Devices, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China.
Background: Recently, the popularity of dual-layer flat-panel detector (DL-FPD) based dual-energy cone-beam CT (CBCT) imaging has been increasing. However, the image quality of dual-energy CBCT remains constrained by the Compton scattered x-ray photons.
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