T2-weighted Lung Imaging Using a 0.55-T MRI System.
Ontology highlight
ABSTRACT: Purpose
To assess a 0.55-T MRI system for imaging lung disease and to compare image quality with clinical CT scans.Materials and methods
In this prospective study conducted between November 2018 and December 2019, respiratory-triggered T2-weighted turbo spin-echo MRI at 0.55 T was compared with clinical CT scans in 24 participants (mean age, 59 years ± 16 [standard deviation]; 18 women) with common lung abnormalities. MR images were reviewed and scored by experienced readers. Abnormal findings identified with MRI and CT were compared using the Cohen κ statistic.Results
High-quality structural pulmonary MR images were attained with an average acquisition time of 11 minutes ± 3. MRI generated sufficient image quality to robustly detect bronchiectasis (κ = 0.61), consolidative opacities (κ = 1.00), cavitary lesions (κ = 1.00), effusion (κ = 0.64), mucus plug (κ = 0.68), and solid scattered nodularity (κ = 0.82). Diffuse disease, including ground-glass opacities (κ = 0.57) and tree-in-bud nodules (κ = 0.48), were the findings that were most difficult to discern using MRI, with false readings in four of 18 patients for each feature. Nodule size, which was measured independently at CT and MRI, was strongly correlated (R 2 = 0.99) for nodules with a measurement of 10 mm ± 5 (range, 5-23 mm).Conclusion
This initial study indicates that high-performance 0.55-T MRI holds promise in the evaluation of common lung disease.Clinical trials registration no. NCT03331380Supplemental material is available for this article. Keywords: MRI, Pulmonary, Technology Assessment© RSNA, 2021.
SUBMITTER: Campbell-Washburn AE
PROVIDER: S-EPMC8259660 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Current protocol of Amide Proton Transfer-weighted (APTw) imaging commonly starts with the acquisition of high-resolution T2-weighted (T2w) images followed by APTw imaging at particular geometry and locations (i.e. slice) determined by the acquired T2w images. Although many advanced MRI reconstruction methods have been proposed to accelerate MRI, existing methods for APTw MRI lacks the capability of taking advantage of structural information in the acquired T2w images for reconstruction. In this paper, we present a novel APTw image reconstruction framework that can accelerate APTw imaging by reconstructing APTw images directly from highly undersampled k-space data and corresponding T2w image at the same location. The proposed framework starts with a novel sparse representation-based slice matching algorithm that aims to find the matched T2w slice given only the undersampled APTw image. A Recurrent Feature Sharing Reconstruction network (RFS-Rec) is designed to utilize intermediate features extracted from the matched T2w image by a Convolutional Recurrent Neural Network (CRNN), so that the missing structural information can be incorporated into the undersampled APT raw image thus effectively improving the image quality of the reconstructed APTw image. We evaluate the proposed method on two real datasets consisting of brain data from rats and humans. Extensive experiments demonstrate that the proposed RFS-Rec approach can outperform the state-of-the-art methods.
| S-EPMC7577831 | biostudies-literature
Project description:PURPOSE:To develop a whole-body MRI technique at 3T with improved lesion conspicuity for metastatic cancer detection using fast, high-resolution and high SNR T2 -weighted (T2 W) imaging with simultaneous fat and fluid suppression. THEORY AND METHODS:The proposed dual-echo T2 -weighted acquisition for enhanced conspicuity of tumors (DETECT) acquires 4 images, in-phase (IP) and out-of-phase (OP) at a short and a long TE using single-shot turbo spin echo. The IP/OP images at the short and long TEs are reconstructed using the standard Dixon and shared-field-map Dixon reconstruction respectively, for robust fat-water separation. An adaptive complex subtraction between the 2 TE water-only images achieves fluid attenuation. DETECT imaging was optimized and evaluated in whole-body imaging of 5 healthy volunteers, and compared against diffusion-weighted imaging with background suppression (DWIBS) in 5 patients with known metastatic renal cell carcinoma. RESULTS:Robust fat-water separation and fluid attenuation were achieved using the shared-field-map Dixon reconstruction and adaptive complex subtraction, respectively. DETECT imaging technique generated co-registered T2 W images with and without fat suppression, heavily T2 W, and fat and fluid suppressed T2 W whole-body images in <7?min. Compared to DWIBS acquired in 17?min, the DETECT imaging achieved better detection and localization of lesions in patients with metastatic cancer. CONCLUSION:DETECT imaging technique generates T2 W images with high resolution, high SNR, minimal geometric distortions, and provides good lesion conspicuity with robust fat and fluid suppression in <7?min for whole-body imaging, demonstrating efficient and reliable metastatic cancer detection at 3T.
| S-EPMC6092263 | biostudies-literature
Project description:Computer-aided detection and diagnosis (CAD) systems have the potential to improve robustness and efficiency compared to traditional radiological reading of magnetic resonance imaging (MRI). Fully automated segmentation of the prostate is a crucial step of CAD for prostate cancer, but visual inspection is still required to detect poorly segmented cases. The aim of this work was therefore to establish a fully automated quality control (QC) system for prostate segmentation based on T2-weighted MRI. Four different deep learning-based segmentation methods were used to segment the prostate for 585 patients. First order, shape and textural radiomics features were extracted from the segmented prostate masks. A reference quality score (QS) was calculated for each automated segmentation in comparison to a manual segmentation. A least absolute shrinkage and selection operator (LASSO) was trained and optimized on a randomly assigned training dataset (N = 1756, 439 cases from each segmentation method) to build a generalizable linear regression model based on the radiomics features that best estimated the reference QS. Subsequently, the model was used to estimate the QSs for an independent testing dataset (N = 584, 146 cases from each segmentation method). The mean ± standard deviation absolute error between the estimated and reference QSs was 5.47 ± 6.33 on a scale from 0 to 100. In addition, we found a strong correlation between the estimated and reference QSs (rho = 0.70). In conclusion, we developed an automated QC system that may be helpful for evaluating the quality of automated prostate segmentations.
| S-EPMC7555425 | biostudies-literature
Project description:ObjectiveSignal intensity normalization is necessary to reduce heterogeneity in T2-weighted (T2W) magnetic resonance imaging (MRI) for quantitative analysis of multicenter data. AutoRef is an automated dual-reference tissue normalization method that normalizes transversal prostate T2W MRI by creating a pseudo-T2 map. The aim of this study was to evaluate the accuracy of pseudo-T2s and multicenter standardization performance for AutoRef with three pairs of reference tissues: fat/muscle (AutoRefF), femoral head/muscle (AutoRefFH) and pelvic bone/muscle (AutoRefPB).Materials and methodsT2s measured by multi-echo spin echo (MESE) were compared to AutoRef pseudo-T2s in the whole prostate (WP) and zones (PZ and TZ/CZ/AFS) for seven asymptomatic volunteers with a paired Wilcoxon signed-rank test. AutoRef normalization was assessed on T2W images from a multicenter evaluation set of 1186 prostate cancer patients. Performance was measured by inter-patient histogram intersections of voxel intensities in the WP before and after normalization in a selected subset of 80 cases.ResultsAutoRefFH pseudo-T2s best approached MESE T2s in the volunteer study, with no significant difference shown (WP: p = 0.30, TZ/CZ/AFS: p = 0.22, PZ: p = 0.69). All three AutoRef versions increased inter-patient histogram intersections in the multicenter dataset, with median histogram intersections of 0.505 (original data), 0.738 (AutoRefFH), 0.739 (AutoRefF) and 0.726 (AutoRefPB).DiscussionAll AutoRef versions reduced variation in the multicenter data. AutoRefFH pseudo-T2s were closest to experimentally measured T2s.
| S-EPMC9363383 | biostudies-literature
Project description:The value of iron-based MRI changes for the diagnosis and staging of Alzheimer's disease (AD) depends on an association between cortical iron accumulation and AD pathology. Therefore, this study determined the cortical distribution pattern of MRI contrast changes in cortical regions selected based on the known distribution pattern of tau pathology and investigated whether MRI contrast changes reflect the underlying AD pathology in the different lobes. T2*-weighted MRI was performed on postmortem cortical tissue of controls, late-onset AD (LOAD), and early-onset AD (EOAD) followed by histology and correlation analyses. Combining ex vivo high-resolution MRI and histopathology revealed that: 1) LOAD and EOAD have a different distribution pattern of AD pathological hallmarks and MRI contrast changes over the cortex, with EOAD showing more severe MRI changes; 2) per lobe, severity of AD pathological hallmarks correlates with iron accumulation, and hence with MRI. Therefore, iron-sensitive MRI sequences allow detection of the cortical distribution pattern of AD pathology ex vivo.
| S-EPMC6218127 | biostudies-literature
Project description:BackgroundMRI T2* and R2* mapping have gained clinical acceptance for noninvasive assessment of iron overload. Lower field MRI may offer increased measurement dynamic range in patients with high iron concentration and may potentially increase MRI accessibility, but it is compromised by lower signal-to-noise ratio that reduces measurement precision.PurposeTo characterize a high-performance 0.55 T MRI system for evaluating patients with liver iron overload.Study typeProspective.PopulationForty patients with known or suspected iron overload (sickle cell anemia [n = 5], ß-thalassemia [n = 3], and hereditary spherocytosis [n = 2]) and a liver iron phantom.Field strength/sequenceA breath-held multiecho gradient echo sequence at 0.55 T and 1.5 T.AssessmentPatients were imaged with T2*/R2* mapping 0.55 T and 1.5 T within 24 hours, and 16 patients returned for follow-up exams within 6-16 months, resulting in 56 paired studies. Liver T2* and R2* measurements and standard deviations were compared between 0.55 T and 1.5 T and used to validate a predictive model between field strengths. The model was then used to classify iron overload at 0.55 T.Statistical testsLinear regression and Bland-Altman analysis were used for comparisons, and measurement precision was assessed using the coefficient of variation. A P-value < 0.05 was considered statistically significant.ResultsR2* was significantly lower at 0.55 T in our cohort (488 ± 449 s-1 at 1.5 T vs. 178 ± 155 s-1 at 0.55 T, n = 56 studies) and in the patients with severe iron overload (937 ± 369 s-1 at 1.5 T vs. 339 ± 127 s-1 at 0.55 T, n = 23 studies). The coefficient of variation indicated reduced precision at 0.55 T (3.5 ± 2.2% at 1.5 T vs 6.9 ± 3.9% at 0.55 T). The predictive model accurately predicted 1.5 T R2* from 0.55 T R2* (Bland Altman bias = -6.6 ± 20.5%). Using this model, iron overload at 0.55 T was classified as: severe R2* > 185 s-1 , moderate 81 s-1 < R2* < 185 s-1 , and mild 45 s-1 < R2* < 91 s-1 .Data conclusionWe demonstrated that 0.55 T provides T2* and R2* maps that can be used for the assessment of liver iron overload in patients.Evidence level2 TECHNICAL EFFICACY: Stage 2.
| S-EPMC9018883 | biostudies-literature
Project description:BackgroundMR fingerprinting (MRF) is a versatile method for rapid multi-parametric quantification. The application of MRF for lower MRI field could enable multi-contrast imaging and improve exam efficiency on these systems. The purpose of this work is to demonstrate the feasibility of 3D whole-brain T1 and T2 mapping using MR fingerprinting on a contemporary 0.55 T MRI system.Materials and methodsA 3D whole brain stack-of-spirals FISP MRF sequence was implemented for 0.55 T. Quantification was validated using the NIST/ISMRM Quantitative MRI phantom, and T1 and T2 values of white matter, gray matter, and cerebrospinal fluid were measured in 19 healthy subjects. To assess MRF performance in the lower SNR regime of 0.55 T, measurement precision was calculated from 100 simulated pseudo-replicas of in vivo data and within-session measurement repeatability was evaluated.ResultsT1 and T2 values calculated by MRF were strongly correlated to standard measurements in the ISMRM/NIST MRI system phantom (R2 > 0.99), with a small constant bias of approximately 5 ms in T2 values. 3D stack-of-spirals MRF was successfully applied for whole brain quantitative T1 and T2 at 0.55 T, with spatial resolution of 1.2 mm × 1.2 mm × 5 mm, and acquisition time of 8.5 min. Moreover, the T1 and T2 quantifications had precision <5%, despite the lower SNR of 0.55 T.ConclusionA 3D whole-brain stack-of-spirals FISP MRF sequence is feasible for T1 and T2 mapping at 0.55 T.
| S-EPMC8749356 | biostudies-literature
Project description:OBJECTIVE::Prior studies advocate the subjective visual differences between meningioma and schwannoma on T2 weighted images, however objective measurement of signal intensity differences may be useful in certain cases. The aim of this study was to investigate whether an objective evaluation of SIs on T2 weighted images would be useful to differentiate spinal schwannomas from meningiomas. METHODS::The patients with spinal MRIs demonstrating path proven and subsequently treated intradural extramedullary spinal tumors were selected between April 2008 and May 2017. Regions of interest (ROIs) were measured in the tumor and subcutaneous fat on the same image, and we calculated the SI ratio between tumor and fat ROIs. RESULTS::Twenty patients each with meningioma and schwannoma were enrolled. The SI ratios of schwannomas were significantly higher than those of meningiomas (both researcher 1 and 2: p = 0.002). The areas under the curve by researchers 1 and 2 were 0.780. The cutoff value of SI ratio by both of researchers 1 and 2 to differentiate between schwannomas from meningiomas was 0.420 (sensitivity: 80.0%, specificity: 70.0-75.0%). CONCLUSION::The SI ratio, calculated from the SIs of the tumor and fat on T2 weighted images, is useful for differentiating spinal schwannomas from meningiomas to obtain an accurate diagnosis. ADVANCES IN KNOWLEDGE::Signal intensity ratio of the spinal tumor and fat on T2 weighted images is useful for differentiating schwannomas from meningiomas to obtain an accurate diagnosis.
| S-EPMC6319836 | biostudies-literature
Project description:Computer-aided diagnosis (CAD) has been proposed for breast MRI as a tool to standardize evaluation, to automate time-consuming analysis, and to aid the diagnostic decision process by radiologists. T2w MRI findings are diagnostically complementary to T1w DCE-MRI findings in the breast and prior research showed that measuring the T2w intensity of a lesion relative to a tissue of reference improves diagnostic accuracy. The diagnostic value of this information in CAD has not been yet quantified. This study proposes an automatic method of assessing relative T2w lesion intensity without the need to select a reference region. We also evaluate the effect of adding this feature to other T2w and T1w image features in the predictive performance of a breast lesion classifier for differential diagnosis of benign and malignant lesions. An automated feature of relative T2w lesion intensity was developed using a quantitative regression model. The diagnostic performance of the proposed feature in addition to T2w texture was compared to the performance of a conventional breast MRI CAD system based on T1w DCE-MRI features alone. The added contribution of T2w features to more conventional T1w-based features was investigated using classification rules extracted from the lesion classifier. After institutional review board approval that waived informed consent, we identified 627 breast lesions (245 malignant, 382 benign) diagnosed after undergoing breast MRI at our institution between 2007 and 2014. An increase in diagnostic performance in terms of area under the curve (AUC) from the receiver operating characteristic (ROC) analysis was observed with the additional T2w features and the proposed quantitative feature of relative T2w lesion intensity. AUC increased from 0.80 to 0.83 and this difference was statistically significant (adjusted p-value = 0.020).
| S-EPMC5675400 | biostudies-literature
Project description:ObjectivesTo investigate the diagnostic performance of deep transfer learning (DTL) to detect liver cirrhosis from clinical MRI.MethodsThe dataset for this retrospective analysis consisted of 713 (343 female) patients who underwent liver MRI between 2017 and 2019. In total, 553 of these subjects had a confirmed diagnosis of liver cirrhosis, while the remainder had no history of liver disease. T2-weighted MRI slices at the level of the caudate lobe were manually exported for DTL analysis. Data were randomly split into training, validation, and test sets (70%/15%/15%). A ResNet50 convolutional neural network (CNN) pre-trained on the ImageNet archive was used for cirrhosis detection with and without upstream liver segmentation. Classification performance for detection of liver cirrhosis was compared to two radiologists with different levels of experience (4th-year resident, board-certified radiologist). Segmentation was performed using a U-Net architecture built on a pre-trained ResNet34 encoder. Differences in classification accuracy were assessed by the χ2-test.ResultsDice coefficients for automatic segmentation were above 0.98 for both validation and test data. The classification accuracy of liver cirrhosis on validation (vACC) and test (tACC) data for the DTL pipeline with upstream liver segmentation (vACC = 0.99, tACC = 0.96) was significantly higher compared to the resident (vACC = 0.88, p < 0.01; tACC = 0.91, p = 0.01) and to the board-certified radiologist (vACC = 0.96, p < 0.01; tACC = 0.90, p < 0.01).ConclusionThis proof-of-principle study demonstrates the potential of DTL for detecting cirrhosis based on standard T2-weighted MRI. The presented method for image-based diagnosis of liver cirrhosis demonstrated expert-level classification accuracy.Key points• A pipeline consisting of two convolutional neural networks (CNNs) pre-trained on an extensive natural image database (ImageNet archive) enables detection of liver cirrhosis on standard T2-weighted MRI. • High classification accuracy can be achieved even without altering the pre-trained parameters of the convolutional neural networks. • Other abdominal structures apart from the liver were relevant for detection when the network was trained on unsegmented images.
| S-EPMC8523404 | biostudies-literature