scholarly journals PSR: Unified Framework of Parameter-Learning-Based MR Image Superresolution

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Huanyu Liu ◽  
Jiaqi Liu ◽  
Junbao Li ◽  
Jeng-Shyang Pan ◽  
Xiaqiong Yu
Keyword(s):  
Block Size ◽  
Disease Diagnosis ◽  
Training Sample ◽  
Optimal Parameters ◽  
Resonance Imaging ◽  
Mr Images ◽  
Unified Framework ◽  
Mr Image ◽  
Radiation Time ◽  
Construction Parameter

Magnetic resonance imaging has significant applications for disease diagnosis. Due to the particularity of its imaging mechanism, hardware imaging suffers from resolution and reaches its limit, and higher radiation intensity and longer radiation time will cause damage to the human body. The problem is expected to be solved by a superresolution algorithm, especially the image superresolution based on sparse reconstruction has good performance. Dictionary generation is a key issue that affects the performance of superresolution algorithms, and dictionary performance is affected by dictionary construction parameters: balance parameters, dictionary size, overlapping block size, and a number of training sample blocks. In response to this problem, we propose an optimal dictionary construction parameter search method through the experiment to find the optimal dictionary construction parameters on the MR image and compare them with the dictionary obtained by multiple sets of random dictionary construction parameters. The dictionary we searched for the optimal parameters of the dictionary construction training has more powerful feature expressions, which can improve the superresolution effect of MR images.

scholarly journals Assessing MRI susceptibility artefact through an indicator of image distortion

2016 ◽  
Vol 2 (1) ◽  
pp. 427-431 ◽  
Author(s):  
Alfredo Illanes ◽  
Johannes W. Krug ◽  
Michael Friebe
Keyword(s):  
Medical Devices ◽  
Observer Variability ◽  
Resonance Imaging ◽  
Mr Images ◽  
Mr Image ◽  
Automated Algorithm ◽  
Susceptibility Artefact ◽  
Magnetic Resonance Imaging Mri ◽  
Study Participants ◽  
Automatic Algorithm

AbstractSusceptibility artefacts in magnetic resonance imaging (MRI) caused by medical devices can result in a severe degradation of the MR image quality. The quantification of susceptibility artefacts is regulated by the ASTM standard which defines a manual method to assess the size of an artefact. This means that the estimated artefact size can be user dependent. To cope with this problem, we propose an algorithm to automatically quantify the size of such susceptibility artefacts. The algorithm is based on the analysis of a 3D surface generated from the 2D MR images. The results obtained by the automatic algorithm were compared to the manual measurements performed by study participants. The results show that the automatic and manual measurements follow the same trend. The clear advantage of the automated algorithm is the absence of the inter- and intra-observer variability. In addition, the algorithm also detects the slice containing the largest artefact which was not the case for the manual measurements.

Degenerative disc disease diagnosis from lumbar MR images using hybrid features

2021 ◽  
Author(s):  
A. Beulah ◽  
T. Sree Sharmila ◽  
V. K. Pramod
Keyword(s):  
Degenerative Disc Disease ◽  
Disease Diagnosis ◽  
Mr Images ◽  
Disc Disease ◽  
Hybrid Features ◽  
Degenerative Disc

scholarly journals Automated Head Tissue Modelling Based on Structural Magnetic Resonance Images for Electroencephalographic Source Reconstruction

2021 ◽  
Author(s):  
Gaia Amaranta Taberna ◽  
Jessica Samogin ◽  
Dante Mantini
Keyword(s):  
Magnetic Resonance ◽  
Brain Structures ◽  
Magnetic Resonance Images ◽  
Head Model ◽  
Source Reconstruction ◽  
Mr Images ◽  
Tissue Segmentation ◽  
Mr Image ◽  
Eeg Recordings ◽  
Tissue Modelling

AbstractIn the last years, technological advancements for the analysis of electroencephalography (EEG) recordings have permitted to investigate neural activity and connectivity in the human brain with unprecedented precision and reliability. A crucial element for accurate EEG source reconstruction is the construction of a realistic head model, incorporating information on electrode positions and head tissue distribution. In this paper, we introduce MR-TIM, a toolbox for head tissue modelling from structural magnetic resonance (MR) images. The toolbox consists of three modules: 1) image pre-processing – the raw MR image is denoised and prepared for further analyses; 2) tissue probability mapping – template tissue probability maps (TPMs) in individual space are generated from the MR image; 3) tissue segmentation – information from all the TPMs is integrated such that each voxel in the MR image is assigned to a specific tissue. MR-TIM generates highly realistic 3D masks, five of which are associated with brain structures (brain and cerebellar grey matter, brain and cerebellar white matter, and brainstem) and the remaining seven with other head tissues (cerebrospinal fluid, spongy and compact bones, eyes, muscle, fat and skin). Our validation, conducted on MR images collected in healthy volunteers and patients as well as an MR template image from an open-source repository, demonstrates that MR-TIM is more accurate than alternative approaches for whole-head tissue segmentation. We hope that MR-TIM, by yielding an increased precision in head modelling, will contribute to a more widespread use of EEG as a brain imaging technique.

Magnetic resonance imaging of primary cerebral gliosarcoma: a report of 15 cases

2008 ◽  
Vol 49 (9) ◽  
pp. 1058-1067 ◽  
Author(s):  
L. Han ◽  
X. Zhang ◽  
S. Qiu ◽  
X. Li ◽  
W. Xiong ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Clinical Information ◽  
Cerebellar Hemisphere ◽  
Imaging Features ◽  
Resonance Imaging ◽  
Mr Images ◽  
Peripheral Location ◽  
Pathology Reports ◽  
The Right ◽  
Radiologic Features

Background: Gliosarcomas are rare tumors with mixed glial and mesenchymal components. Many of their radiologic features resemble those of other primary brain malignancies. Purpose: To investigate the magnetic resonance (MR) imaging features of gliosarcomas. Material and Methods: We retrospectively reviewed the MR images, pathology reports, and clinical information of 11 male and four female patients aged 15–71 years to evaluate the location, morphology, enhancement, and other features of their pathologically confirmed gliosarcomas. Results: Apart from one tumor in the right cerebellar hemisphere, all were supratentorial. Two tumors were intraventricular, and four involved the corpus callosum. The tumors were well demarcated, with an inhomogeneous or cystic appearance and moderate-to-extensive surrounding edema. Thick walls with strong rim and ring-like enhancement were observed in 13 (87%). Seven (47%) showed intratumoral paliform enhancement. Conclusion: Gliosarcoma demonstrates certain characteristic MR features, such as supratentorial and peripheral location, well-demarcated, abutting a dural surface, uneven and thick-walled rim-like or ring enhancement, as well as intratumoral strip enhancement. These findings, combined with patient age, can aid the differential diagnosis of gliosarcomas from more common primary brain tumors.

Optimization of T2-weighted imaging for shoulder magnetic resonance arthrography by synthetic magnetic resonance imaging

2017 ◽  
Vol 59 (8) ◽  
pp. 959-965
Author(s):  
Seung Hyun Lee ◽  
Young Han Lee ◽  
Seok Hahn ◽  
Jaemoon Yang ◽  
Ho-Taek Song ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Supraspinatus Tendon ◽  
Joint Fluid ◽  
Resonance Imaging ◽  
Mr Images ◽  
Diagnostic Agreement ◽  
Tissue Contrast ◽  
Synthetic Images ◽  
Synthetic Mri

Background Synthetic magnetic resonance imaging (MRI) allows reformatting of various synthetic images by adjustment of scanning parameters such as repetition time (TR) and echo time (TE). Optimized MR images can be reformatted from T1, T2, and proton density (PD) values to achieve maximum tissue contrast between joint fluid and adjacent soft tissue. Purpose To demonstrate the method for optimization of TR and TE by synthetic MRI and to validate the optimized images by comparison with conventional shoulder MR arthrography (MRA) images. Material and Methods Thirty-seven shoulder MRA images acquired by synthetic MRI were retrospectively evaluated for PD, T1, and T2 values at the joint fluid and glenoid labrum. Differences in signal intensity between the fluid and labrum were observed between TR of 500–6000 ms and TE of 80–300 ms in T2-weighted (T2W) images. Conventional T2W and synthetic images were analyzed for diagnostic agreement of supraspinatus tendon abnormalities (kappa statistics) and image quality scores (one-way analysis of variance with post-hoc analysis). Results Optimized mean values of TR and TE were 2724.7 ± 1634.7 and 80.1 ± 0.4, respectively. Diagnostic agreement for supraspinatus tendon abnormalities between conventional and synthetic MR images was excellent (κ = 0.882). The mean image quality score of the joint space in optimized synthetic images was significantly higher compared with those in conventional and synthetic images (2.861 ± 0.351 vs. 2.556 ± 0.607 vs. 2.750 ± 0.439; P < 0.05). Conclusion Synthetic MRI with optimized TR and TE for shoulder MRA enables optimization of soft-tissue contrast.

scholarly journals Radiological Followup of the Evolution of Inflammatory Process in Sacroiliac Joint with Magnetic Resonance Imaging: A Case with Pyogenic Sacroiliitis

2012 ◽  
Vol 2012 ◽  
pp. 1-4 ◽  
Author(s):  
Muhammet Cinar ◽  
Hatice Tugba Sanal ◽  
Sedat Yilmaz ◽  
Ismail Simsek ◽  
Hakan Erdem ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Sacroiliac Joint ◽  
Resonance Imaging ◽  
Mr Images ◽  
Buttock Pain ◽  
Pyogenic Sacroiliitis ◽  
Acute Form ◽  
Active Sacroiliitis ◽  
Bone Structures ◽  
Surrounding Bone

Pyogenic sacroiliitis (PS) is an acute form of sacroiliitis that mostly starts with very painful buttock pain. Here in this case, the followup magnetic resonance (MR) images of a 49-year-old male patient with PS is displayed. After his sacroiliitis was documented by MR images, he was treated with the combination of rifampicin plus streptomycin and moxifloxacin. Serial MR investigations were done to disclose acute and subsequent imaging changes concerning sacroiliac joint and surrounding bone structures. Although after treatment all the symptoms were completely resolved, 20 months later changes suggesting active sacroiliitis on MR images were continuing.

Physiological support and monitoring of critically ill patients during magnetic resonance imaging

1988 ◽  
Vol 68 (2) ◽  
pp. 246-250 ◽  
Author(s):  
Gene H. Barnett ◽  
Allan H. Ropper ◽  
Keith A. Johnson
Keyword(s):  
Magnetic Resonance ◽  
Mr Imaging ◽  
Critically Ill ◽  
Resonance Imaging ◽  
Monitoring Equipment ◽  
Mr Image ◽  
Content Type ◽  
Neurological Patients ◽  
Fine Print ◽  
Electrical Interference

✓ Magnetic resonance (MR) imaging has been largely restricted to patients who are neurologically and hemodynamically stable. The strong magnetic field and radiofrequency transmissions involved in acquiring images are potential sources of interference with monitoring equipment. A method of support and physiological monitoring of critically ill neurosurgical and neurological patients during MR imaging using a 0.6-tesla MR system is reported. This technique has not caused degradation of the MR image due to electrical interference. Adequate preparation and precautions allow many critically ill neurosurgical and neurological patients to safely undergo MR imaging.

An Efficient Light-Weight Network for Fast Reconstruction on MR Images

2021 ◽  
Vol 17 ◽  
Author(s):  
Bowen Zhen ◽  
Yingjie Zheng ◽  
Bensheng Qiu
Keyword(s):  
Feature Extraction ◽  
Image Reconstruction ◽  
Data Consistency ◽  
Training Procedure ◽  
Light Weight ◽  
Mr Images ◽  
High Quality ◽  
Mr Image ◽  
Generalization Ability ◽  
Mr Image Reconstruction

Background: In recent years, deep learning (DL) algorithms have emerged in endlessly and achieved impressive performance, which makes it possible to accelerate magnetic resonance (MR) image reconstruction with DL instead of compressed sensing (CS) methods. However, a DL-based MR image reconstruction method has always suffered from its heavy learning parameters and poor generalization ability so far. Therefore, an efficient light-weight network is still in desperate need of fast MR image reconstruction. Methods: We propose an efficient and light-weight MR reconstruction network (named RecNet) that uses a Convolutional Neural Network (CNN) to fast reconstruct high-quality MR images. Specifically, the network is composed of cascade modules, and each cascade module is further divided into feature extraction blocks and a data consistency layer. The feature extraction block can not only effectively extract the features of MR images, but also do not introduce too many parameters for the whole network. To stabilize the training procedure, the correction information of image frequency is adopted in the data consistency (DC) layer. Results: We have evaluated RecNet on a public dataset and the results show that the image quality reconstructed by RecNet is the best on the peak a signal-to-noise ratio (PSNR) and structural similarity index (SSIM) evaluation standards. In addition, the pre-trained RecNet can also reconstruct high-quality MR images on an unseen dataset. Conclusion: The results demonstrate that the RecNet has superior reconstruction ability in various metrics than comparative methods. The RecNet can quickly generate high-quality MR images in fewer parameters. Furthermore, the RecNet has an excellent generalization ability on pathological images and different sampling rates data.

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