Wavelet domain de-noising of time-courses in MR image sequences

The National Institute of Health defined cardiomyopathy as diseases of the heart muscle. These myocardial diseases have different etiology, structure and treatment. This review highlights the key imaging features of different myocardial diseases. It provides information on myocardial structure/orientation, perfusion, function and viability in diseases related to cardiomyopathy. The standard cardiac magnetic resonance imaging (MRI) sequences can reveal insight on left ventricular (LV) mass, volumes and regional contractile function in all types of cardiomyopathy diseases. Contrast enhanced MRI sequences allow visualization of different infarct patterns and sizes. Enhancement of myocardial inflammation and infarct (location, transmurality and pattern) on contrast enhanced MRI have been used to highlight the key differences in myocardial diseases, predict recovery of function and healing. The common feature in many forms of cardiomyopathy is the presence of diffuse-fibrosis. Currently, imaging sequences generating the most interest in cardiomyopathy include myocardial strain analysis, tissue mapping (T1, T2, T2*) and extracellular volume (ECV) estimation techniques. MRI sequences have the potential to decode the etiology by showing various patterns of infarct and diffuse fibrosis in myocarditis, amyloidosis, sarcoidosis, hypertrophic cardiomyopathy due to aortic stenosis, restrictive cardiomyopathy, arrythmogenic right ventricular dysplasia and hypertension. Integrated PET/MRI system may add in the future more information for the diagnosis and progression of cardiomyopathy diseases. With the promise of high spatial/temporal resolution and 3D coverage, MRI will be an indispensible tool in diagnosis and monitoring the benefits of new therapies designed to treat myocardial diseases.

Evaluation of giant arachnoid granulations with high-resolution 3D-volumetric MR sequences at 3T

To prospectively evaluate the correlation between a three-dimensional (3D) delayed enhancement magnetic resonance (MR) imaging sequence and a two-dimensional (2D) delayed enhancement MR imaging sequence for noninvasive assessment of myocardial viability in pigs and patients. The pig and patient studies were approved by the responsible authorities, and patients gave written informed consent. MR imaging was performed by using a rapid 3D inversion-recovery balanced steady-state free precession sequence and a 2D segmented inversion-recovery fast low-angle shot sequence as the reference standard. Fourteen pigs with reperfused (n=7) or nonreperfused (n=7) myocardial infarction and 17 patients (13 men, four women; mean age, 64.9 years+/-8.6 [standard deviation]) suspected of having myocardial infarction were included. Linear regression analysis and Bland-Altman analysis were used to compare the infarction volumes. In 10 of the 14 pigs the induction of myocardial infarction was successful. In these pigs, altogether 81 segments with myocardial infarction were demonstrated by both MR sequences, and agreement between the two sequences for classification of transmural extent of myocardial infarction was 99.7%. The infarction volume determined by using 3D MR imaging (4.64 cm3+/-2.48) in the pigs highly correlated with that of 2D MR imaging (4.65 cm3+/-2.39, r=0.989, P<.001) and that of staining by using triphenyltetrazolium chloride (4.67 cm3+/-2.44, r=0.996, P<.001). Thirteen of the 17 patients examined showed myocardial infarction in 34 myocardial segments with both sequences, and agreement between the two sequences for classification of transmural extent of myocardial infarction was 98.6%. In the patients, the infarction volume determined with both sequences highly correlated (9.71 cm3+/-7.47 for the 3D sequence vs 10.01 cm3+/-8.04 for the 2D sequence, r=0.982, P<.001). The breath-hold time necessary for the 3D MR imaging (21.0+/-2.3 seconds) was significantly shorter than that for 2D MR imaging (188.3+/-20.2 seconds, P<.001). Myocardial infarction volumes obtained with the 3D MR imaging sequence are highly correlated and in good agreement with volumes obtained with the 2D MR imaging standard approach and reduced the acquisition time by a factor of nine.

This study presents a novel automated algorithm to segment the left atrium (LA) from 2, 3 and 4-chamber long-axis cardiac cine magnetic resonance image (MRI) sequences using deep convolutional neural network (CNN). The objective of the segmentation process is to delineate the boundary between myocardium and endocardium and exclude the mitral valve so that the results could be used for generating clinical measurements such as strain and strain rate. As such, the segmentation needs to be performed using open contours, a more challenging problem than the pixel-wise semantic segmentation performed by existing machine learning-based approaches such as U-net. The proposed neural net is built based on pre-trained CNN Inception V 4 architecture, and it predicts a compressed vector by applying a multi-layer autoencoder, which is then back-projected into the segmentation contour of the LA to perform the delineation using open contours. Quantitative evaluations were performed to compare the performances of the proposed method and the current state-of-the-art U-net method. Both methods were trained using 6195 images acquired from 80 patients and evaluated using 1515 images acquired from 20 patients where the datasets were obtained retrospectively, and ground truth manual segmentation was provided by an expert radiologist. The proposed method yielded an average Dice score of 93.1% and Hausdorff distance of 4.2 mm, whereas the U-net yielded 91.6% and 11.9 mm for Dice score and Hausdorff distance metrics, respectively. The quantitative evaluations demonstrated that the proposed method performed significantly better than U-net in terms of Hausdorff distance in addition to providing open contour-based segmentation for the LA.

Background: The accuracy of stereotaxic frameless neuronavigation for transcranial magnetic stimulation (TMS) may dependent on the spatial validity of functional magnetic resonance imaging (fMRI) sequences. Nearly all fMRI studies use a gradient echo (GRE) sequence which, in contrast to spin echo (SE) and arterial spin labeling (ASL), provides a better contrast-to-noise ratio but is prone to be shifted to veins. We tested the hypothesis (i) that the functional localisation of the motor area depends on the fMRI sequence used to detect neuronal activity and (ii) that TMS of the different peak voxels assessed by the different fMRI sequences impacts on the motor response at the respective peripheral muscle (motor evoked potential, MEP).

In this paper the goal was to measure the contrast to noise ratio (CNR) of fast spin echo (FSE) magnetic resonance imaging (MRI) sequences in detecting thermal lesions created by high intensity focused ul-trasound (HIFU) in rabbit kidney, liver, heart, and brain and lamb pancreas. A spherically focused transducer was used which is navigated inside MRI by a custom made positioning device. A simple simu-lation model was developed which predicts the CNR for the two FSE MRI sequences. The maximum con-trast measured with T1-W FSE ranges from 10 to 25. For all 5 tissues of interest if one uses TR between 400 and 500 ms the contrast is maximized. The T1 and T2 value of lesion depends strongly on the host tissue and is always lower than the host tissue. The greater the difference in T1 value, the greater the CNR. The simulated model for predicting the CNR was proven successful. The CNR measured with T2-W FSE varies between 12 and 15 for all 5 tissues. With T2-W FSE if one uses TE between 40 and 50 ms, the contrast is maximized.

Abnormalities within the spinal arachnoid space are often treated surgically, but they can be challenging to detect with conventional magnetic resonance imaging (MRI) sequences. 3D-CISS sequences are considered superior in evaluating structures surrounded by cerebrospinal fluid (CSF) due to the high signal-to-noise ratio, high contrast-to-noise ratio and intrinsic insensitivity to motion with minimal signal loss due to CSF pulsations. Our objective was to describe findings and advantages in adding 3D-CISS sequences to routine MRI in patients affected by spinal arachnoid diverticula (SAD) or arachnoid adhesions. This article is a retrospective review of medical records of 19 dogs admitted at Fitzpatrick Referrals between 2013 and 2017 that were diagnosed with SAD and confirmed surgically. Inclusion criterions were the presence of clinical signs compatible with compressive myelopathy and an MRI diagnosis, which included the 3D-CISS sequence. Our database was searched for additional 19 dogs diagnosed with other spinal lesions other than SAD that had the same MR sequences. All MR images were anonymized and evaluated by two assessors. 3D-CISS sequence appears to improve confidence in diagnosing and surgical planning (Mann-Whitney U-test: p < 0.0005), delineating SAD from other changes associated with abnormal CSF hydrodynamics and providing more anatomical details than conventional MRI sequences. The clinical data in combination with imaging findings would limit over interpretation, when concurrent pathology within the arachnoid space is present.

The objective of this research was to determine whether a fast 3-dimensional (3-D) gradient echo magnetic resonance imaging (MRI) sequence could be used to acquire images suitable for image guided surgery of the spine. The main difficulty with MRI is that inhomogeneities in the static magnetic field lead to geometric distortions in the images. We used a very fast 3-D MRI sequence with a wide bandwidth and short echo time (TE) to minimize these distortions. Fiducial markers that could be localized in MRI and computed tomography (CT) images and in physical space were attached to a phantom in order to assess the accuracy of a landmark based registration method. The effect of varying the MRI parameters on image contrast was also investigated. The results demonstrate that the registration can be undertaken with an accuracy of 0.4 mm using the 3-D MRI. This is comparable to the accuracy of 0.3 mm obtained with CT and is a significant improvement over the accuracy of the 2-D MRI techniques (> 1.0 mm). In vivo images demonstrating good contrast between the spine and surrounding soft tissues such as fat, intervertebral disks, and cerebrospinal fluid were obtained. The MRI acquired using the sequence described in this article shows promise for use in computer assisted surgery of the spine.

A Case of Cisternal Pilocytic Astrocytoma Diagnosed with the Balanced Steady-State Free Precession Sequence for Magnetic Resonance Imaging: A Rare Cause of Subarachnoid Hemorrhage

Value of the short time inversion recovery sequence of magnetic resonance imaging in the staging of Klippel-Trenaunay syndrome complicated with lymphedema

No. 244 EE: Evaluation of Vascular Stent’s Patency Using Ultra-Short TE MRI

Identification of brain tumors and accurate grading at an early stage are crucial in cancer diagnosis, as a timely diagnosis can increase the chances of survival. Considering the challenges and risks of tumor biopsies, noninvasive imaging procedures such as Magnetic Resonance Imaging (MRI) are extensively used in analyzing brain tumors. Recent advances in the field of medical imaging with deep learning using three dimensional (3D) MRI is aiding the clinical experts significantly in the diagnosis of brain tumor. In this study, three BraTS MRI datasets named BraTS 2018, BraTS 2019 and BraTS 2020 are employed to classify brain tumor into high-grade glioma (HGG) and low-grade glioma (LGG) where each of the datasets contains four different sequences of 3D MRI brain images named T1-weighted MRI (T1), T1-weighted MRI with contrast enhancement (T1ce), T2-weighted MRI (T2), and Fluid Attenuated Inversion Recovery (FLAIR) for a single patient. This research is composed of two approaches where, in the first part, we propose a hybrid deep learning model named TimeDistributed-CNN-LSTM (TD-CNN-LSTM) combining 3D Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) where each layer of the architecture is wrapped with a TimeDistributed function. The objective of developing the hybrid model is to consider all the four 3D MRI sequences of each patient as a single input data because every sequence contains necessary information on tumor that can have an impact on improving cancer detection performance. However, interpreting all the MRI sequences together with optimal performance especially in 3D is quite challenging. Therefore, the model is developed with optimal configuration based on highest accuracy performing ablation study for layer architecture and hyper-parameters. In the second part, a 3D CNN model is trained respectively with each of the MRI sequences to compare the performance with TD-CNN-LSTM model. In this regard, for both of the models, BraTS 2018 and BraTS 2019 are combined to increase the number of images and used as train dataset where BraTS 2020 dataset is employed as the test dataset. Moreover, before training the models the datasets is preprocessed to ensure the highest performance. Our results of these two approaches demonstrate that the TD-CNN-LSTM network outperforms 3D CNN achieving the highest test accuracy of 98.90%. Later, to evaluate the performance consistency, the TD-CNN-LSTM model is evaluated with K-fold cross validation using different k values. The approach of putting together all the MRI sequences at a time using hybrid CNN-LSTM approach with good generalization capability to classify brain tumor can be used effectively in future Computer Aided Diagnosis (CAD) based research which can aid radiologists in medical diagnostics.

The aim of this study was to compare qualitative and quantitative image quality and geometric distortion of 4 magnetic resonance diffusion-weighted imaging (DWI) sequences of the prostate using comparable imaging parameters and similar acquisition times. Axial T2-weighted turbo spin echo images and axial DWI echo-planar imaging (EPI) sequences, including single-shot spin-echo (ss-EPI), readout-segmented multishot (rs-EPI), selective excitation-reduced field of view (sTX-EPI), and prototype single-shot technique applying slice-specific shimming (iShim-EPI) sequences, were acquired at 3 T in 10 healthy volunteers (mean age, 26.1 ± 3.8 years; body mass index, 23.2 ± 3.0 kg/m). Two radiologists, blinded to the type of DWI, independently rated DWIs on a 5-point Likert scale regarding subjective image quality features (resolution, demarcation of prostate capsule, zonal anatomy). Interreader agreement was assessed using the intraclass correlation coefficient. Signal-to-noise ratio (SNR) and apparent diffusion coefficient (ADC) values were assessed separately in the peripheral and transitional zone. For the analysis of geometric distortion, the diameter of the prostate from left to right and from anterior to posterior was measured at the level of the verumontanum on b-1000 DWIs and on T2-weighted turbo spin echo images. Differences were compared using the Wilcoxon rank sum test for qualitative parameters, analysis of variance, and Friedman test for quantitative parameters. A P value of less than 0.05 was considered significant, with correction for multiple comparisons. Interreader agreement was good to excellent (intraclass correlation coefficient, 0.71-0.79) for all qualitative features. Subjective image quality regarding "resolution" was significantly better for ss-EPI than rs-EPI (mean Likert score, 4.25 vs 3.8; P = 0.031) and sTX-EPI (4.25 vs 3.3; P = 0.046) and for iShim-EPI as compared with rs-EPI (4.4 vs 3.8; P = 0.031) and sTX-EPI (4.4 vs 3.3; P = 0.047). There was no significant difference regarding capsule demarcation and zonal anatomy. Signal-to-noise ratio was significantly higher in iShim-EPI than sTX-EPI (SNR ± standard deviation [SD], 28.13 ± 8.21 vs 14.96 ± 2.4; P = 0.015). The ADC values were lower for the peripheral zone in the sTX-EPI than in the ss-EPI (ADC ± SD, 1002.94 ± 83.51 vs 1165.05 ± 115.64; P = 0.013) and the rs-EPI (1002.94 ± 83.51 vs 1244.40 ± 89.95; P = 0.0012) and in the transitional zone in the sTX-EPI compared with the rs-EPI (874.50 ± 200.72 vs 1261.47 ± 179.23; P = 0.0021). There were no statistically significant differences in geometric distortion between all DWI sequences. Single-shot spin-echo EPI and iShim-EPI showed a tendency toward superior image quality and SNR compared with rs-EPI and sTX-EPI with no significant differences in geometric distortion.

Susceptibility-Weighted Imaging (SWI) is a relatively new magnetic resonance imaging (MRI) sequence relying on susceptibility differences between adjacent tissues to produce an image. It is extremely sensitive for detection of blood products (hemosiderin, ferritin), deoxygenated blood, calcium, iron, and small vein depiction. Little information is available in the literature to describe common findings seen using this imaging sequence. This article is an comprehensive review of appearances across the spectrum of brain pathology encountered in routine clinical practice. A retrospective review of 400 MRI Brain examinations was performed by one fellowship trained Neuroradiologist with 5 years experience (SB) and one general radiologist (RR). Agreement was by consensus observing normal and pathological imaging features seen in SWI sequences. Results are presented as a comprehensive pictorial review highlighting the key imaging findings observed and new directions using SWI. SWI is an extremely useful adjunct to current MRI sequences of the brain and is advocated for inclusion into routine Neuroimaging protocols.

The neural correlates of gait improvement by rhythmic sound stimulation in adults with Parkinson's disease – A functional magnetic resonance imaging study