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Posts tagged #DeepResolve

Antje Hellwich
Antje Hellwich
@antjehellwich.bsky.social
3 months ago
This article addresses claustrophobia in #MRI patients and presents strategies to improve the overall patient experience. With its ultra-wide 80 cm bore, the 0.55T MAGNETOM Free.Max scanner is designed to create a less confining environment and enhance comfort. Its compact design and shorter magnet length allow for more flexible patient positioning. Additional measures such as using Contour coils, music during the scan, and cushions for positioning can further increase comfort. The MAGNETOM Free.Max Head/Neck coil has a 12-channel design with 12 integrated pre-amplifiers with three rungs of 4 elements. The coil is tilted to 9 degrees to give patients the feeling of lying on a pillow. The upper part, with 6 elements, can be easily removed. The lower part, also with 6 elements, is usable without the upper part for highly claustrophobic patients. Even with the upper part removed, the AutoAlign option still works. Deep learning-based image reconstruction with Deep Resolve Boost helps denoise the image and achieve good quality. Protocol adjustments, such as selecting prescan normalize, increasing the FOV, and changing the iPAT factor and average, are recommended to increase SNR when scanning without the upper part of the Head/Neck coil.

This article addresses claustrophobia in #MRI patients and presents strategies to improve the overall patient experience. With its ultra-wide 80 cm bore, the 0.55T MAGNETOM Free.Max scanner is designed to create a less confining environment and enhance comfort. Its compact design and shorter magnet length allow for more flexible patient positioning. Additional measures such as using Contour coils, music during the scan, and cushions for positioning can further increase comfort. The MAGNETOM Free.Max Head/Neck coil has a 12-channel design with 12 integrated pre-amplifiers with three rungs of 4 elements. The coil is tilted to 9 degrees to give patients the feeling of lying on a pillow. The upper part, with 6 elements, can be easily removed. The lower part, also with 6 elements, is usable without the upper part for highly claustrophobic patients. Even with the upper part removed, the AutoAlign option still works. Deep learning-based image reconstruction with Deep Resolve Boost helps denoise the image and achieve good quality. Protocol adjustments, such as selecting prescan normalize, increasing the FOV, and changing the iPAT factor and average, are recommended to increase SNR when scanning without the upper part of the Head/Neck coil.

Managing Head Exams in Claustrophobic Patients Undergoing #MRI: Challenges and Strategies Using MAGNETOM Free.Max
by Marcelo Fernandes Arêas (Siemens Healthineers, Germany).

🔗 marketing.webassets.siemens-healthineers.com/61813c8f921b...

#RadSky #NeuroSky #DeepResolve #LowFieldMRI #Claustrophobia

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Antje Hellwich
Antje Hellwich
@antjehellwich.bsky.social
11 months ago
Fast Abdominal MRI with Deep Learning Reconstruction: Practical Experience from Zhongshan Hospital by Mengsu Zeng, MD; Shengxiang Rao, MD; et al. (Zhongshan Hospital, Fudan University, Shanghai Institute of Medical Imaging, Shanghai, China) Abdominal MRI is a key diagnostic modality for evaluating liver, biliary, and pancreatic diseases—its image quality directly impacting diagnostic accuracy. Deep learning-based image reconstruction (DLR) significantly reduces acquisition time while maintaining or enhancing image quality through optimized image acquisition, reconstruction, and denoising. In this article the authors share their practical experience in applying Deep Resolve technology to abdominal MRI, covering T2-weighted imaging (T2WI), magnetic resonance cholangiopancreatography (#MRCP), and diffusion-weighted imaging (DWI). Reduced acquisition times not only increase scanner throughput and reduce operational costs, but also improve patient comfort and minimize motion artifacts.

Fast Abdominal MRI with Deep Learning Reconstruction: Practical Experience from Zhongshan Hospital by Mengsu Zeng, MD; Shengxiang Rao, MD; et al. (Zhongshan Hospital, Fudan University, Shanghai Institute of Medical Imaging, Shanghai, China) Abdominal MRI is a key diagnostic modality for evaluating liver, biliary, and pancreatic diseases—its image quality directly impacting diagnostic accuracy. Deep learning-based image reconstruction (DLR) significantly reduces acquisition time while maintaining or enhancing image quality through optimized image acquisition, reconstruction, and denoising. In this article the authors share their practical experience in applying Deep Resolve technology to abdominal MRI, covering T2-weighted imaging (T2WI), magnetic resonance cholangiopancreatography (#MRCP), and diffusion-weighted imaging (DWI). Reduced acquisition times not only increase scanner throughput and reduce operational costs, but also improve patient comfort and minimize motion artifacts.

Fast Abdominal #MRI with Deep Learning Image Reconstruction. Cuts scan time, improves image quality & boosts patient comfort. Covers T2WI, #MRCP & DWI.

Read more: marketing.webassets.siemens-healthineers.com/fce0d2bcf600...

#MagnetomWorld #DeepResolve #Radiology #FastMRI #AI #MedSky #OncSky

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Antje Hellwich
Antje Hellwich
@antjehellwich.bsky.social
1 year ago
SWI is a useful MRI technique for the detection and characterization of microhemorrhages and small venous structures. In general, SWI profits from high magnetic field strength with respect to susceptibility contrast and spatial resolution. However, methods based on 3D EPI have been implemented at 0.55T and result in similar detection rates of microbleeds compared to 1.5T. Recently, a deep learning (DL)-based reconstruction has been added to this 3D-segmented EPI sequence, enabling susceptibility-weighted images with higher spatial resolution and increased sharpness. Learn more about this research sequence that offers great advantages for diagnostic neuroimaging at lower field strengths, and enabled the authors to visualize a larger volume of the brain parenchyma more accurately, and to avoid common pitfalls and mimics.

SWI is a useful MRI technique for the detection and characterization of microhemorrhages and small venous structures. In general, SWI profits from high magnetic field strength with respect to susceptibility contrast and spatial resolution. However, methods based on 3D EPI have been implemented at 0.55T and result in similar detection rates of microbleeds compared to 1.5T. Recently, a deep learning (DL)-based reconstruction has been added to this 3D-segmented EPI sequence, enabling susceptibility-weighted images with higher spatial resolution and increased sharpness. Learn more about this research sequence that offers great advantages for diagnostic neuroimaging at lower field strengths, and enabled the authors to visualize a larger volume of the brain parenchyma more accurately, and to avoid common pitfalls and mimics.

Susceptibility-Weighted Imaging at Lower Field Strength with Deep Learning Image Reconstruction by Johanna M Lieb, M.D.; et al. (University Hospital Basel, Switzerland).
Learn more at marketing.webassets.siemens-healthineers.com/4aee99d7d80c...

#MagnetomWorld #MRI #Below1T #DeepResolve

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