Multiple Magnetic Resonance Sequences Research Articles

Multi-sequence MR image-based synthetic CT generation using a generative adversarial network for head and neck MRI-only radiotherapy.

The purpose of this study is to investigate the effect of different magnetic resonance (MR) sequences on the accuracy of deep learning-based synthetic computed tomography (sCT) generation in the complex head and neck region. Four sequences of MR images (T1, T2, T1C, and T1DixonC-water) were collected from 45 patients with nasopharyngeal carcinoma. Seven conditional generative adversarial network (cGAN) models were trained with different sequences (single channel) and different combinations (multi-channel) as inputs. To further verify the cGAN performance, we also used a U-net network as a comparison. Mean absolute error, structural similarity index, peak signal-to-noise ratio, dice similarity coefficient, and dose distribution were evaluated between the actual CTs and sCTs generated from different models. The results show that the cGAN model with multi-channel (i.e., T1+T2+T1C+T1DixonC-water) as input to predict sCT achieves higher accuracy than any single MR sequence model. The T1-weighted MR model achieves better results than T2, T1C, and T1DixonC-water models. The comparison between cGAN and U-net shows that the sCTs predicted by cGAN retains additional image details are less blurred and more similar to the actual CT. Conditional generative adversarial network with multiple MR sequences as model input shows the best accuracy. The T1-weighted MR images provide sufficient image information and are suitable for sCT prediction in clinical scenarios with limited acquisition sequences or limited acquisition time.

Effect of region extraction and assigned mass-density values on the accuracy of dose calculation with magnetic resonance-based volumetric arc therapy planning.

This study aimed to verify the validity of generating treatment plans for volumetric arc therapy (VMAT) for prostate cancer using magnetic resonance (MR) imaging with a dose calculation algorithm in Acuros XB (Eclipse version 13.6; Varian Medical Systems, Palo Alto, CA, USA) based on deterministically solving the linear Boltzmann transport equations. Four different classes were applied to prostate MR images: MRW (all water equivalent); MRW+B (water and bone); MRS+B (soft tissue and bone); and MRS+B+G (soft tissue, bone, and rectal gas). Each of these regions was assigned a mass density for calculating doses. The assigned mass-density values were then altered in three ways. Using initial planning and optimization parameters, MR-based VMAT plans were generated and compared with corresponding forward-calculated computed tomography-based plans for doses to the target volumes and organs at risk using dose-volume histograms and γ analyses. In the MRW plans, the mean doses for TVs were overestimated by approximately 1.3%. The MRW+B plans revealed reduced differences within 0.5%. Further segmentation (MRS+B) did not result in substantial improvement. Dose deviations affected by the changes in the mass densities assigned to soft tissue were as small as approximately 1.0%, whereas larger deviations were revealed in bone and rectal gas, especially those with > 5% error. Assignment of accurate mass-density values acquired from MR images is needed for MR-based radiation treatment planning. Multiple MR sequences should be acquired for segmentation and mass-density conversion purposes. Segmented MR-based VMAT planning is feasible with a density assignment method using Acuros XB.

Day2day: investigating daily variability of magnetic resonance imaging measures over half a year

BackgroundMost studies of brain structure and function, and their relationships to cognitive ability, have relied on inter-individual variability in magnetic resonance (MR) images. Intra-individual variability is often ignored or implicitly assumed to be equivalent to the former. Testing this assumption empirically by collecting enough data on single individuals is cumbersome and costly. We collected a dataset of multiple MR sequences and behavioural covariates to quantify and characterize intra-individual variability in MR images for multiple individuals.Methods and designEight participants volunteered to undergo brain scanning 40–50 times over the course of 6 months. Six participants completed the full set of sessions. T1-weighted, T2*-weighted during rest, T2-weighted high-resolution hippocampus, diffusion-tensor imaging (DTI), and proton magnetic resonance spectroscopy sequences were collected, along with a rich set of stable and time-varying physical, behavioural and physiological variables. Participants did not change their lifestyle or participated in any training programs during the period of data collection.ConclusionThis imaging dataset provides a large number of MRI scans in different modalities for six participants. It enables the analysis of the time course and correlates of intra-individual variability in structural, chemical, and functional aspects of the human brain.

Robust skull stripping using multiple MR image contrasts insensitive to pathology

Automatic skull-stripping or brain extraction of magnetic resonance (MR) images is often a fundamental step in many neuroimage processing pipelines. The accuracy of subsequent image processing relies on the accuracy of the skull-stripping. Although many automated stripping methods have been proposed in the past, it is still an active area of research particularly in the context of brain pathology. Most stripping methods are validated on T1-w MR images of normal brains, especially because high resolution T1-w sequences are widely acquired and ground truth manual brain mask segmentations are publicly available for normal brains. However, different MR acquisition protocols can provide complementary information about the brain tissues, which can be exploited for better distinction between brain, cerebrospinal fluid, and unwanted tissues such as skull, dura, marrow, or fat. This is especially true in the presence of pathology, where hemorrhages or other types of lesions can have similar intensities as skull in a T1-w image. In this paper, we propose a sparse patch based Multi-cONtrast brain STRipping method (MONSTR),22http://www.nitrc.org/projects/monstr. where non-local patch information from one or more atlases, which contain multiple MR sequences and reference delineations of brain masks, are combined to generate a target brain mask.We compared MONSTR with four state-of-the-art, publicly available methods: BEaST, SPECTRE, ROBEX, and OptiBET. We evaluated the performance of these methods on 6 datasets consisting of both healthy subjects and patients with various pathologies. Three datasets (ADNI, MRBrainS, NAMIC) are publicly available, consisting of 44 healthy volunteers and 10 patients with schizophrenia. Other three in-house datasets, comprising 87 subjects in total, consisted of patients with mild to severe traumatic brain injury, brain tumors, and various movement disorders. A combination of T1-w, T2-w were used to skull-strip these datasets. We show significant improvement in stripping over the competing methods on both healthy and pathological brains. We also show that our multi-contrast framework is robust and maintains accurate performance across different types of acquisitions and scanners, even when using normal brains as atlases to strip pathological brains, demonstrating that our algorithm is applicable even when reference segmentations of pathological brains are not available to be used as atlases.

MRI-Only Based Radiotherapy Treatment Planning for the Rat Brain on a Small Animal Radiation Research Platform (SARRP).

Computed tomography (CT) is the standard imaging modality in radiation therapy treatment planning (RTP). However, magnetic resonance (MR) imaging provides superior soft tissue contrast, increasing the precision of target volume selection. We present MR-only based RTP for a rat brain on a small animal radiation research platform (SARRP) using probabilistic voxel classification with multiple MR sequences. Six rat heads were imaged, each with one CT and five MR sequences. The MR sequences were: T1-weighted, T2-weighted, zero-echo time (ZTE), and two ultra-short echo time sequences with 20 μs (UTE1) and 2 ms (UTE2) echo times. CT data were manually segmented into air, soft tissue, and bone to obtain the RTP reference. Bias field corrected MR images were automatically segmented into the same tissue classes using a fuzzy c-means segmentation algorithm with multiple images as input. Similarities between segmented CT and automatic segmented MR (ASMR) images were evaluated using Dice coefficient. Three ASMR images with high similarity index were used for further RTP. Three beam arrangements were investigated. Dose distributions were compared by analysing dose volume histograms. The highest Dice coefficients were obtained for the ZTE-UTE2 combination and for the T1-UTE1-T2 combination when ZTE was unavailable. Both combinations, along with UTE1-UTE2, often used to generate ASMR images, were used for further RTP. Using 1 beam, MR based RTP underestimated the dose to be delivered to the target (range: 1.4%-7.6%). When more complex beam configurations were used, the calculated dose using the ZTE-UTE2 combination was the most accurate, with 0.7% deviation from CT, compared to 0.8% for T1-UTE1-T2 and 1.7% for UTE1-UTE2. The presented MR-only based workflow for RTP on a SARRP enables both accurate organ delineation and dose calculations using multiple MR sequences. This method can be useful in longitudinal studies where CT’s cumulative radiation dose might contribute to the total dose.

Extra tree forests for sub-acute ischemic stroke lesion segmentation in MR sequences.

To analyse the relationship between structure and (dys-)function of the brain after stroke, accurate and repeatable segmentation of the lesion area in magnetic resonance (MR) images is required. Manual delineation, the current gold standard, is time consuming and suffers from high intra- and inter-observer differences. A new approach is presented for the automatic and reproducible segmentation of sub-acute ischemic stroke lesions in MR images in the presence of other pathologies. The proposition is based on an Extra Tree forest framework for voxel-wise classification and mainly intensity derived image features are employed. A thorough investigation of multi-spectral variants, which combine the information from multiple MR sequences, finds the fluid attenuated inversion recovery sequence to be both required and sufficient for a good segmentation result. The accuracy can be further improved by adding features extracted from the T1-weighted and the diffusion weighted sequences. The use of other sequences is discouraged, as they impact negatively on the results. Quantitative evaluation was carried out on 37 clinical cases. With a Dice coefficient of 0.65, the method outperforms earlier published methods. The approach proves especially suitable to differentiate between new stroke and other white matter lesions based on the FLAIR sequence alone. This, and the high overlap, renders it suitable for automatic screening of large databases of MR scans, e.g. for a subsequent neuropsychological investigation. Finally, each feature's importance is assessed in detail and the approach's statistical dependency on clinical and image characteristics is investigated.

Pseudo-CT generation from multiple MR images for small animal irradiation

Introduction. Computed tomography (CT) is the standard imaging modality for radiation therapy treatment planning (RTTP) because of its ability to provide information on electron density. However, magnetic resonance (MR) imaging provides superior soft tissue contrast, especially in small animal imaging, facilitating the precise selection of the target volume. This makes the technique interesting for irradiation of brain tumors. The aim of this study was to present an MR-only based workflow for RTTP on a small animal radiation research platform (SARRP) by investigating the potential of probabilistic classification of voxels using multiple MR sequences. Methods. Six female Fisher rats were anesthetized using isoflurane and individually fixed on an in-house made multimodality bed before starting MR and CT acquisitions. MR measurements were performed on a 7-Tesla system using a rat brain volume coil. Four different MR sequences were acquired for each animal, including a T1-weighted (MDEFT) sequence, a T2-weighted (RARE) sequence, an ultra-short echo time sequence with 20 μs echo time (UTE1) and an ultra- short echo time sequence with 2 ms echo time (UTE2). UTE offers the opportunity to acquire images from proton-poor structures with very short transverse relaxation times, such as bone, by using a rapid readout of the fast decaying signal. Following MR, the animals were moved to the SARRP to start a cone-beam CT (CB-CT) by acquiring 720 projections over 360°. Cone-beam CT projection data were reconstructed by filtered back-projection to obtain the standard-CT for RTTP. Then the images were bias field corrected and manually co-registered to the CB-CT. After that, images were segmented in three tissue classes (air, soft tissue and bone) with k-means for the CB-CT and fuzzy c-means segmentation algorithm (FCM) for the MR images with multiple MR images as input. The membership probability can be between 0 and 1, with one indicating 100% probability and zero indicating 0% probability to belong to a specific tissue class. To obtain a pseudo-CT image, voxels were assigned to the tissue class having the highest membership probability. The dice coefficient was used to evaluate the correctness of the segmentation for soft-tissue and bone. The pseudo-CT images with the highest similarity index were used for further radiotherapy treatment planning (RTTP), in addition, to the standard UTE1-UTE2. The target of the RTTP that was selected in the primary cortex (M1) and three different beam arrangements were investigated to compare CB-CT and MR-based dose calculations. The dose plans were a single static beam of 3x3 mm, using a single arc (3x3 mm beam size, 120° arc, couch at 0°), and three non-coplanar arcs (3x3 mm beam size, 120° arc, couch at 0°, 45° and 90°). Dose distributions were calculated using the TPS of the SARRP and cumulative dose volume histograms (DVHs) of the target and normal brain tissue were obtained for the three dose plans. Results The highest dice coefficient was obtained for the T1-UTE1-T2 combination, which was used for further RTTP. The contribution of bone to the total dice coefficient did not exceed 27%. However, bone accounts for only 2% of the image, therefore a misclassified bone pixel has a bigger effect in the dice coefficient than a misclassified soft tissue pixel. Using only 1 beam, both MR combinations underestimate the dose to be delivered to the target. When more complex beam configurations were used to irradiate the target, very small differences were observed between CB-CT and MR based dose calculations.

HEPATIC AND PANCREATICOBILIARY MRI AND MR CHOLANGIOPANCREATOGRAPHY WITH AND WITHOUT SECRETIN STIMULATION IN NORMAL CATS

Magnetic resonance (MR) cholangiopancreatography is useful in humans to diagnose biliary and pancreatic diseases. Some of these protocols incorporate the use of secretin, which stimulates the exocrine pancreas to release bicarbonate with secondary dilation of the pancreatic duct. We compared the utility and quality of multiple hepatic-pancreaticobiliary MR imaging sequences before and after secretin stimulation of the pancreatic duct in five healthy cats. Multiple MR sequences were evaluated, including fast Spoiled Gradient Recalled in- and out-of-phase, Single Shot Fast Spin Echo (SSFSE), T2 Fast Spin Echo, MR cholangiopancreatography (pre- and postsecretin administration), and Fast Acquisition with Multiphase Efgre (FAME) (postsecretin and before and after gadolinium administration). The MR cholangiopancreatography protocol with secretin stimulation was feasible and yielded high-contrast maps of the biliary ductal anatomy but the pancreactic duct was seen inconsistently. The FAME series most consistently provided visualization of biliary and postsecretin pancreatic ductal anatomy, combined with very good depiction of the liver and pancreas. The remaining sequences each had satisfactory utility and diagnostic quality, with the exception of the SSFSE sequences. Secretin improved the conspicuity of the pancreatic duct.

Cardiovascular magnetic resonance imaging in delivering and evaluating the efficacy of hepatocyte growth factor gene in chronic infarct scar

In an open-chest model of acute infarct, epicardial delivery of hepatocyte growth factor (pCK-HGF-X7) gene improved left ventricle (LV) function. This study was designed to test (a) the efficacy of HGF gene in infarct scar delivered under magnetic resonance (MR) guidance and (b) the potential of multiple MR sequences in assessing the effects of pCK-HGF-X7 (treatment) and pCK-LacZ (control) genes on myocardial structure and function. Swine (six per group) were subjected to myocardial infarct, under X-ray fluoroscopy, and developed LV remodeling at 5 weeks. Multiple clinical magnetic resonance (MR) imaging sequences were performed before delivery of gene (at 5 weeks after infarction) and 5 weeks after delivery of gene. Under MR guidance, the active endovascular catheter was introduced into LV to transendocardially deliver 3.96 × 10(11) viral copies of pCK-HGF-X7 or pCK-LacZ in the border and core of the infarct scar. Histological evaluation of the infarct scar was performed 5 weeks after delivery of gene. At 5 weeks after infarction, there was no significant difference in measured cardiovascular MR parameters between the groups. The pCK-HGF-X7 gene caused significant improvement in the following parameters (P<.05 for these parameters): three-dimensional (3D) strain (radial, circumferential, and longitudinal) and perfusion (maximum upslope, peak signal intensity, and time to peak) compared with control pCK-LacZ at 5 weeks after delivery of the genes. The ejection fraction was higher in pCK-HGF-X7-treated (43 ± 1%) than in pCK-LacZ control (37 ± 1%, P<.05) animals. These changes are associated with a decrease in infarct scar size (11.3 ± 2.0% in pCK-LacZ control and 6.7 ± 1.3% in pCK-HGF-X7-treated animals, P<.01) and infarct transmurality in four out of five infarct scar segments (P<.05) on delayed contrast-enhanced MR imaging. Microscopic study confirmed the increase in capillary (P<.05) and arteriole (P<.05) density of infarct scar in pCK-HGF-X7-treated animals compared with pCK-LacZ control animals. Hepatocyte growth factor gene (pCK-HGF-X7) delivered under MR guidance into infarct scar ameliorated global function and 3D strain, increased regional perfusion and infarct resorption, and enhanced angiogenesis/arteriogenesis. This feasibility study provides novel approach and analysis methods and instrumentation for delivering and evaluating new locally delivered therapies.

A Hybrid Geometric–Statistical Deformable Model for Automated 3-D Segmentation in Brain MRI

We present a novel 3-D deformable model-based approach for accurate, robust, and automated tissue segmentation of brain MRI data of single as well as multiple magnetic resonance sequences. The main contribution of this study is that we employ an edge-based geodesic active contour for the segmentation task by integrating both image edge geometry and voxel statistical homogeneity into a novel hybrid geometric-statistical feature to regularize contour convergence and extract complex anatomical structures. We validate the accuracy of the segmentation results on simulated brain MRI scans of both single T1-weighted and multiple T1/T2/PD-weighted sequences. We also demonstrate the robustness of the proposed method when applied to clinical brain MRI scans. When compared to a current state-of-the-art region-based level-set segmentation formulation, our white matter and gray matter segmentation resulted in significantly higher accuracy levels with a mean improvement in Dice similarity indexes of 8.55% ( p < 0.0001) and 10.18% ( p < 0.0001), respectively.