Evaluation of swallowing has been made possible by cine-Magnetic resonance (MR) imaging with high time resolution. However, the spatial resolution in cine-MR imaging remains inadequate for the detection of anatomical structures. Therefore, it is necessary to refer to static MR images in conjunction with cine-MR imaging. The aim of this study was to determine which MR parameters were appropriate for static imaging of the anatomical structures involved in swallowing. MR imaging was carried out, and T1-weighted, T2-weighted and proton-density-weighted MR images were obtained in the sagittal plane in 5 healthy volunteers. Each image was evaluated for anatomic landmark clarity by 3 oral radiologists. The anatomic landmarks selected were the lip, tip of tongue, center of tongue, tongue base, soft palate and epiglottis. Differences in clarity among 3 imaging modalities were evaluated. A 3-point score rating system was used. The results showed that lower TE sequences, i.e., either T1-weighted or proton-density-weighted images, were the most suitable for use in conjunction with cine-MR imaging in diagnosing swallowing disorders.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.2209/tdcpublication.49.113 | DOI Listing |
Med Phys
January 2025
National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Background: Respiratory motion during radiotherapy (RT) may reduce the therapeutic effect and increase the dose received by organs at risk. This can be addressed by real-time tracking, where respiration motion prediction is currently required to compensate for system latency in RT systems. Notably, for the prediction of future images in image-guided adaptive RT systems, the use of deep learning has been considered.
View Article and Find Full Text PDFMed Image Comput Comput Assist Interv
October 2024
Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
Tagged magnetic resonance imaging (MRI) has been successfully used to track the motion of internal tissue points within moving organs. Typically, to analyze motion using tagged MRI, cine MRI data in the same coordinate system are acquired, incurring additional time and costs. Consequently, tagged-to-cine MR synthesis holds the potential to reduce the extra acquisition time and costs associated with cine MRI, without disrupting downstream motion analysis tasks.
View Article and Find Full Text PDFMed Phys
January 2025
Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA.
Background: Respiratory motion irregularities in lung cancer patients are common and can be severe during multi-fractional (∼20 mins/fraction) radiotherapy. However, the current clinical standard of motion management is to use a single-breath respiratory-correlated four-dimension computed tomography (RC-4DCT or 4DCT) to estimate tumor motion to delineate the internal tumor volume (ITV), covering the trajectory of tumor motion, as a treatment target.
Purpose: To develop a novel multi-breath time-resolved (TR) 4DCT using the super-resolution reconstruction framework with TR 4D magnetic resonance imaging (TR-4DMRI) as guidance for patient-specific breathing irregularity assessment, overcoming the shortcomings of RC-4DCT, including binning artifacts and single-breath limitations.
Phys Med Biol
November 2024
Department of Medical Physics, Faculty of Physics, Ludwig-Maximilians-Universität München, Garching, Germany.
Radial cine-MRI allows for sliding window reconstruction at nearly arbitrary frame rate, promising high-speed imaging for intra-fractional motion monitoring in magnetic resonance guided radiotherapy. However, motion within the reconstruction window may determine the location of the reconstructed target to deviate from the true real-time position (target positioning errors), particularly in cases of fast breathing or for anatomical structures affected by the heartbeat. In this work, we present a proof-of-concept study aiming to enhance radial cine-MR imaging by implementing deep-learning-based intra-frame motion compensation techniques.
View Article and Find Full Text PDFProc IEEE Int Symp Biomed Imaging
May 2024
Department of Electrical and Computer Engineering, University of Virginia, USA.
This paper presents a multimodal deep learning framework that utilizes advanced image techniques to improve the performance of clinical analysis heavily dependent on routinely acquired standard images. More specifically, we develop a joint learning network that for the first time leverages the accuracy and reproducibility of myocardial strains obtained from Displacement Encoding with Stimulated Echo (DENSE) to guide the analysis of cine cardiac magnetic resonance (CMR) imaging in late mechanical activation (LMA) detection. An image registration network is utilized to acquire the knowledge of cardiac motions, an important feature estimator of strain values, from standard cine CMRs.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!