Synthetic T1-weighted Images Research Articles

Detection of pathological contrast enhancement with synthetic brain imaging from quantitative multiparametric MRI.

We aimed to test whether synthetic T1-weighted imaging derived from a post-contrast Quantitative Transient-state Imaging (QTI) acquisition enabled revealing pathological contrast enhancement in intracranial lesions. The analysis included 141 patients who underwent a 3 Tesla-MRI brain exam with intravenous contrast media administration, with the post-contrast acquisition protocol comprising a three-dimensional fast spoiled gradient echo (FSPGR) sequence and a QTI acquisition. Synthetic T1-weighted images were generated from QTI-derived quantitative maps of relaxation times and proton density. Two neuroradiologists assessed synthetic and conventional post-contrast T1-weighted images for the presence and pattern of pathological contrast enhancement in intracranial lesions. Enhancement volumes were quantitatively compared. Using conventional imaging as a reference, synthetic T1-weighted imaging was 93% sensitive in revealing the presence of contrast enhancing lesions. The agreement for the presence/absence of contrast enhancement was almost perfect both between readers (k = 1 for both conventional and synthetic imaging) and between sequences (k = 0.98 for both readers). In 91% of lesions, synthetic T1-weighted imaging showed the same pattern of contrast enhancement visible in conventional imaging. Differences in enhancement pattern in the remaining lesions can be due to the lower spatial resolution and the longer acquisition delay from contrast media administration of QTI compared to FSPGR. Overall, enhancement volumes appeared larger in synthetic imaging. QTI-derived post-contrast synthetic T1-weighted imaging captures pathological contrast enhancement in most intracranial enhancing lesions. Further comparative studies employing quantitative imaging with higher spatial resolution is needed to support our data and explore possible future applications in clinical trials.

Brain Parcellation Repeatability and Reproducibility Using Conventional and Quantitative 3D MR Imaging.

Automatic brain parcellation is typically performed on dedicated MR imaging sequences, which require valuable examination time. In this study, a 3D MR imaging quantification sequence to retrieve R1 and R2 relaxation rates and proton density maps was used to synthesize a T1-weighted image stack for brain volume measurement, thereby combining image data for multiple purposes. The repeatability and reproducibility of using the conventional and synthetic input data were evaluated. Twelve subjects with a mean age of 54 years were scanned twice at 1.5T and 3T with 3D-QALAS and a conventionally acquired T1-weighted sequence. Using SyMRI, we converted the R1, R2, and proton density maps into synthetic T1-weighted images. Both the conventional T1-weighted and the synthetic 3D-T1-weighted inversion recovery images were processed for brain parcellation by NeuroQuant. Bland-Altman statistics were used to correlate the volumes of 12 brain structures. The coefficient of variation was used to evaluate the repeatability. A high correlation with medians of 0.97 for 1.5T and 0.92 for 3T was found. A high repeatability was shown with a median coefficient of variation of 1.2% for both T1-weighted and synthetic 3D-T1-weighted inversion recovery at 1.5T, and 1.5% for T1-weighted imaging and 4.4% for synthetic 3D-T1-weighted inversion recovery at 3T. However, significant biases were observed between the methods and field strengths. It is possible to perform MR imaging quantification of R1, R2, and proton density maps to synthesize a 3D-T1-weighted image stack, which can be used for automatic brain parcellation. Synthetic parameter settings should be reinvestigated to reduce the observed bias.

Feasibility of Quantitative MRI Using 3D-QALAS for Discriminating Immunohistochemical Status in Invasive Ductal Carcinoma of the Breast.

Two-dimensional synthetic MRI of the breast has limited spatial coverage. Three-dimensional (3D) synthetic MRI could provide volumetric quantitative parameters that may reflect the immunohistochemical (IHC) status in invasive ductal carcinoma (IDC) of the breast. To evaluate the feasibility of 3D synthetic MRI using an interleaved Look-Locker acquisition sequence with a T2 preparation pulse (QALAS) for discriminating the IHC status, including hormone receptor (HR), human epidermal growth factor receptor 2 (HER 2), and Ki-67 expression in IDC. Prospective observational study. A total of 33 females with IDC of the breast (mean, 52.3 years). A 3-T, 3D-QALAS gradient-echo and fat-suppressed T1-weighted 3D fast spoiled gradient-echo sequences. Two radiologists semiautomatically delineated 3D regions of interest (ROIs) of the whole tumors on the dynamic MRI that was registered to the synthetic T1-weighted images acquired from 3D-QALAS. The mean T1 and T2 were measured for each IDC. Intraclass correlation coefficient for assessing interobserver agreement. Mann-Whitney U test to determine the relationship between the mean T1 or T2 and the IHC status. Multivariate logistic regression analysis followed by receiver operating characteristics (ROC) analysis for discriminating IHC status. A P value <0.05 was considered statistically significant. The interobserver agreement was good to excellent. There was a significant difference in the mean T1 between HR-positive and HR-negative lesions, while the mean T2 value differed between HR-positive and HR-negative lesions, between the triple-negative and HR-positive or HER2-positive lesions, and between the Ki-67 level > 14% and ≤ 14%. Multivariate analysis showed that the mean T2 was higher in HR-negative IDC than in HR-positive IDC. ROC analysis revealed that the mean T2 was predictive for discriminating HR status, triple-negative status, and Ki-67 level. 3D synthetic MRI using QALAS may be useful for discriminating IHC status in IDC of the breast. 1. Stage 2.

Image quality using synthetic brain MRI: an age-stratified study.

Synthetic magnetic resonance imaging (MRI) might replace the conventional MR sequences in brain evaluation to shorten scan time and obtain multiple quantitative parameters. To evaluate the image quality of multiple-delay-multiple-echo (MDME) sequence-derived synthetic brain MR images compared to conventional images by considering a multi-age sample. Image sets of conventional and synthetic MRI of 200 participants were included. On the basis of the presence of intracranial lesions, the participants were divided into a normal group and a pathological group. Two neuroradiologists compared the anonymous and unordered images. Image quality, artifacts, and diagnostic performance were analyzed. In the quantitative analysis, comparing with conventional images, MDME sequence-derived synthetic MRI demonstrated an equal/greater signal-to-noise ratio and contrast-to-noise ratio (CNR) in all age groups. Specifically, for participants aged ≤2 years, synthetic T2-fluid-attenuated inversion recovery imaging showed a significantly higher cerebellum gray/white matter CNR (P < 0.05). In the qualitative and artifact analyses, except for the superior sagittal sinus and cranial nerves, synthetic MRI showed good imaging quality (≥3 points) in all brain structures. On synthetic T1-weighted imaging, high signal intensity within the superior sagittal sinus was found in most of our participants (107/118, 90.7%). No difference was observed between synthetic and conventional MRI in diagnosing the lesions. MDME sequence-derived synthetic MRI showed similar image quality and diagnostic performance with a shorter acquisition time than conventional MRI. However, the high signal intensity within the superior sagittal sinus on synthetic T1-weighted images requires consideration.

A Deep Learning Segmentation Pipeline for Cardiac T1 Mapping Using MRI Relaxation-based Synthetic Contrast Augmentation.

To design and evaluate an automated deep learning method for segmentation and analysis of cardiac MRI T1 maps with use of synthetic T1-weighted images for MRI relaxation-based contrast augmentation. This retrospective study included MRI scans acquired between 2016 and 2019 from 100 patients (mean age ± SD, 55 years ± 13; 72 men) across various clinical abnormalities with use of a modified Look-Locker inversion recovery, or MOLLI, sequence to quantify native T1 (T1native), postcontrast T1 (T1post), and extracellular volume (ECV). Data were divided into training (n = 60) and internal (n = 40) test subsets. "Synthetic" T1-weighted images were generated from the T1 exponential inversion-recovery signal model at a range of optimal inversion times, yielding high blood-myocardium contrast, and were used for contrast-based image augmentation during training and testing of a convolutional neural network for myocardial segmentation. Automated segmentation, T1, and ECV were compared with experts with use of Dice similarity coefficients (DSCs), correlation coefficients, and Bland-Altman analysis. An external test dataset (n = 147) was used to assess model generalization. Internal testing showed high myocardial DSC relative to experts (0.81 ± 0.08), which was similar to interobserver DSC (0.81 ± 0.08). Automated segmental measurements strongly correlated with experts (T1native, R = 0.87; T1post, R = 0.91; ECV, R = 0.92), which were similar to interobserver correlation (T1native, R = 0.86; T1post, R = 0.94; ECV, R = 0.95). External testing showed strong DSC (0.80 ± 0.09) and T1native correlation (R = 0.88) between automatic and expert analysis. This deep learning method leveraging synthetic contrast augmentation may provide accurate automated T1 and ECV analysis for cardiac MRI data acquired across different abnormalities, centers, scanners, and T1 sequences.Keywords: MRI, Cardiac, Tissue Characterization, Segmentation, Convolutional Neural Network, Deep Learning Algorithms, Machine Learning Algorithms, Supervised Learning Supplemental material is available for this article. © RSNA, 2022.

Improved synthetic T1-weighted images for cerebral tissue segmentation in neurological diseases

Structural cerebral MRI analysis in patients with neurological diseases usually requires T1-weighted datasets for tissue segmentation. For this purpose, synthetic T1-weighted images which are constructed from quantitative maps of the underlying tissue parameters such as the T1 relaxation time and the proton density (PD) may provide advantages over conventional datasets. However, in some cases synthetic images may suffer from specific artifacts, hampering accurate tissue segmentation.The goal was to improve a previously described method for the calculation of synthetic magnetization-prepared rapid gradient-echo (MP-RAGE) datasets from quantitative T1 and PD maps. Improvements comprise a B0-correction for the water-selective excitation pulses employed in T1-mapping and the use of T1-based pseudo-PD maps.Synthetic T1-weighted MP-RAGE datasets were calculated, using the standard and the improved algorithm, for 10 patients with focal epilepsy (caused by focal cortical dysplasia in 9), 10 patients with multiple sclerosis and 10 healthy control subjects and segmented with the Freesurfer toolbox.Visual inspection disclosed that segmentation of the standard synthetic datasets was inaccurate in 6 out of 10 patients with epilepsy, 7 out of 10 patients with multiple sclerosis and 7 out of 10 healthy control subjects, while the improved synthetic datasets resulted in adequate segmentation outcomes in the majority of cases. Only for one patient with multiple sclerosis and one with epilepsy, segmentation in basal temporal regions was not sufficient.Furthermore, data based on the standard algorithm showed strong signal non-uniformities in basal regions. This effect was not present in the improved synthetic datasets.

Signal Intensity within Cerebral Venous Sinuses on Synthetic MRI.

Purpose:Flowing blood sometimes appears bright on synthetic T1-weighted images, which could be misdiagnosed as a thrombus. This study aimed to investigate the frequency of hyperintensity within cerebral venous sinuses on synthetic MR images and to evaluate the influence of increasing flow rates on signal intensity using a flow phantom.Materials and Methods:Imaging data, including synthetic and conventional MRI scans, from 22 patients were retrospectively analyzed. Signal intensities at eight locations of cerebral venous sinuses on synthetic images were graded using the following three-point scale: 0, “dark vessel”; 1, “hyperintensity within the walls”; and 2, “hyperintensity within the lumen.” A phantom with gadolinium solution inside a U-shaped tube was acquired without flow and then with increasing flow rates (60, 100, 200, 300, 400 ml/min).Results:Considering all sinus locations, the venous signal intensity on synthetic T1-weighted images was graded as 2 in 79.8% of the patients. On synthetic T2-weighted images, all sinuses were graded as 0. On fluid-attenuated inversion recovery (FLAIR) images, sinuses were almost always graded as 0 (99.4%). In the phantom study, the signal initially became brighter on synthetic T1-weighted images as the flow rate increased. Above a certain flow rate, the signal started to decrease.Conclusion:High signal intensity within the cerebral venous sinuses is a frequent finding on synthetic T1-weighted images. This corresponds to the hyperintensity noted at certain flow rates in the phantom experiment.

Comparison of reconstruction and acquisition choices for quantitative T2* maps and synthetic contrasts

Aim and scopeA Gradient Echo Plural Contrast Imaging technique (GEPCI) is a post-processing method, which can be used to obtain quantitative T2* values and generate multiple synthetic contrasts from a single acquisition. However, scan duration and image reconstruction from k-space data present challenges in a clinical workflow. This study aimed at optimizing image reconstruction and acquisition duration to facilitate a post-processing method for synthetic image contrast creation in clinical settings. Materials and methodsThis study consists of tests using the American College of Radiology (ACR) image quality phantom, two healthy volunteers, four mild traumatic brain injury patients and four small vessel disease patients. The measurements were carried out on a 3.0 T scanner with multiple echo times. Reconstruction from k-space data and DICOM data with two different coil-channel combination modes were investigated. Partial Fourier techniques were tested to optimize the scanning time. ConclusionsSum of squares coil-channel combination produced artifacts in phase images, but images created with adaptive combination were artifact-free. The voxel-wise median signed difference of T2* between the vendor’s adaptive channel combination and k-space reconstruction modes was 2.9 ± 0.7 ms for white matter and 4.5 ± 0.6 ms for gray matter. Relative white matter/gray matter contrast of all synthetic images and contrast-to-noise ratio of synthetic T1-weighted images were almost equal between reconstruction modes. Our results indicate that synthetic contrasts can be generated from the vendor’s DICOM data with the adaptive combination mode without affecting the quantitative T2* values or white matter/gray matter contrast.

Application of Synthetic MRI for Direct Measurement of Magnetic Resonance Relaxation Time and Tumor Volume at Multiple Time Points after Contrast Administration: Preliminary Results in Patients with Brain Metastasis.

ObjectiveThe purpose of this study was to investigate the time-dependent effects of contrast medium on multi-dynamic, multi-echo (MDME) sequence in patients with brain metastases.Materials and MethodsThis study included 7 patients with 15 brain metastases who underwent magnetic resonance (MR) examination which included MDME sequences at 1 minute, 10 minutes and 20 minutes after contrast injection. Two volumes of interests, covering an entire tumor (whole tumor) and the enhancing portion of the tumor, were derived from post-contrast synthetic T1-weighted images. Statistical comparisons were performed for three different time delays for histogram parameters of the longitudinal relaxation rate (R1) and the transverse relaxation rate (R2), and lesion volumes.ResultsThe mean and the median of R1 and the mean of R2 in both the whole tumor and the inner enhancing portion were larger on the 10 minutes delayed images than on the 1 minute or 20 minutes delayed images (mean of R1 in the whole tumor on the 1 minute, 10 minutes, and 20 minutes delayed images: 1.26 ms, 1.39 ms, and 1.37 ms; mean of R1 in the inner enhancing portion: 1.43 ms, 1.53 ms and 1.44 ms; all p < 0.017). The volumes of the whole tumor and the inner enhancing portion were significantly larger in the 10 minutes and 20 minutes delayed images than on the 1 minute delayed images (all p < 0.017).ConclusionMagnetic resonance relaxation times and the volumes of the whole tumor and the inner enhancing portion were measured larger on the 10 minutes or 20 minutes delayed images than on the 1 minute delayed images. The MDME sequence immediately after contrast injection cannot fully reflect the effects of gadolinium-based contrast agent leakage in the tissue.

Neurobiological origin of spurious brain morphological changes: A quantitative MRI study.

The high gray‐white matter contrast and spatial resolution provided by T1‐weighted magnetic resonance imaging (MRI) has made it a widely used imaging protocol for computational anatomy studies of the brain. While the image intensity in T1‐weighted images is predominantly driven by T1, other MRI parameters affect the image contrast, and hence brain morphological measures derived from the data. Because MRI parameters are correlates of different histological properties of brain tissue, this mixed contribution hampers the neurobiological interpretation of morphometry findings, an issue which remains largely ignored in the community. We acquired quantitative maps of the MRI parameters that determine signal intensities in T1‐weighted images (R 1 (=1/T1), R 2*, and PD) in a large cohort of healthy subjects (n = 120, aged 18–87 years). Synthetic T1‐weighted images were calculated from these quantitative maps and used to extract morphometry features—gray matter volume and cortical thickness. We observed significant variations in morphometry measures obtained from synthetic images derived from different subsets of MRI parameters. We also detected a modulation of these variations by age. Our findings highlight the impact of microstructural properties of brain tissue—myelination, iron, and water content—on automated measures of brain morphology and show that microstructural tissue changes might lead to the detection of spurious morphological changes in computational anatomy studies. They motivate a review of previous morphological results obtained from standard anatomical MRI images and highlight the value of quantitative MRI data for the inference of microscopic tissue changes in the healthy and diseased brain. Hum Brain Mapp 37:1801–1815, 2016. © 2016 The Authors. Human Brain Mapping Published by Wiley Periodicals, Inc.

Synthetic Mri of the Brain in a Clinical Setting

Conventional magnetic resonance imaging (MRI) has relatively long scan times for routine examinations, and the signal intensity of the images is related to the specific MR scanner settings. Due to scanner imperfections and automatic optimizations, it is impossible to compare images in terms of absolute image intensity. Synthetic MRI, a method to generate conventional images based on MR quantification, potentially both decreases examination time and enables quantitative measurements. To evaluate synthetic MRI of the brain in a clinical setting by assessment of the contrast, the contrast-to-noise ratio (CNR), and the diagnostic quality compared with conventional MR images. Twenty-two patients had synthetic imaging added to their clinical MR examination. In each patient, 12 regions of interest were placed in the brain images to measure contrast and CNR. Furthermore, general image quality, probable diagnosis, and lesion conspicuity were investigated. Synthetic T1-weighted turbo spin echo and T2-weighted turbo spin echo images had higher contrast but also a higher level of noise, resulting in a similar CNR compared with conventional images. Synthetic T2-weighted FLAIR images had lower contrast and a higher level of noise, which led to a lower CNR. Synthetic images were generally assessed to be of inferior image quality, but agreed with the clinical diagnosis to the same extent as the conventional images. Lesion conspicuity was higher in the synthetic T1-weighted images, which also had a better agreement with the clinical diagnoses than the conventional T1-weighted images. Synthetic MR can potentially shorten the MR examination time. Even though the image quality is perceived to be inferior, synthetic images agreed with the clinical diagnosis to the same extent as the conventional images in this study.