Dataset Information

Applying Compressed Sensing Volumetric Interpolated Breath-Hold Examination and Spiral Ultrashort Echo Time Sequences for Lung Nodule Detection in MRI.

ABSTRACT: This prospective study aimed to investigate the ability of spiral ultrashort echo time (UTE) and compressed sensing volumetric interpolated breath-hold examination (CS-VIBE) sequences in magnetic resonance imaging (MRI) compared to conventional VIBE and chest computed tomography (CT) in terms of image quality and small nodule detection. Patients with small lung nodules scheduled for video-assisted thoracoscopic surgery (VATS) for lung wedge resection were prospectively enrolled. Each patient underwent non-contrast chest CT and non-contrast MRI on the same day prior to thoracic surgery. The chest CT was performed to obtain a standard reference for nodule size, location, and morphology. The chest MRI included breath-hold conventional VIBE and CS-VIBE with scanning durations of 11 and 13 s, respectively, and free-breathing spiral UTE for 3.5-5 min. The signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and normal structure visualizations were measured to evaluate MRI quality. Nodule detection sensitivity was evaluated on a lobe-by-lobe basis. Inter-reader and inter-modality reliability analyses were performed using the Cohen κ statistic and the nodule size comparison was performed using Bland-Altman plots. Among 96 pulmonary nodules requiring surgery, the average nodule diameter was 7.7 ± 3.9 mm (range: 4-20 mm); of the 73 resected nodules, most were invasive cancer (74%) or pre-invasive carcinoma in situ (15%). Both spiral UTE and CS-VIBE images achieved significantly higher overall image quality scores, SNRs, and CNRs than conventional VIBE. Spiral UTE (81%) and CS-VIBE (83%) achieved a higher lung nodule detection rate than conventional VIBE (53%). Specifically, the nodule detection rate for spiral UTE and CS-VIBE reached 95% and 100% for nodules >8 and >10 mm, respectively. A 90% detection rate was achieved for nodules of all sizes with a part-solid or solid morphology. Spiral UTE and CS-VIBE under-estimated the nodule size by 0.2 ± 1.4 mm with 95% limits of agreement from -2.6 to 2.9 mm and by 0.2 ± 1.7 mm with 95% limits of agreement from -3.3 to 3.5 mm, respectively, compared to the reference CT. In conclusion, chest CT remains the gold standard for lung nodule detection due to its high image resolutions. Both spiral UTE and CS-VIBE MRI could detect small lung nodules requiring surgery and could be considered a potential alternative to chest CT; however, their clinical application requires further investigation.

SUBMITTER: Huang YS

PROVIDER: S-EPMC8774698 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:PurposeTo evaluate three-dimensional (3D) ultrashort echo time (UTE) MRI regarding image quality and suitability for functional image analysis using gradient-echo sequences in breath-hold and with self-navigation.Materials and methodsIn this prospective exploratory study, 10 patients (mean age, 21 years; age range, 5-58 years; five men) and 10 healthy control participants (mean age, 25 years; age range, 10-39 years; five men) underwent 3D UTE MRI at 3.0 T. Imaging was performed with a prototypical stack-of-spirals 3D UTE sequence during single breath holds (echo time [TE], 0.05 msec) and with a self-navigated "Koosh ball" 3D UTE sequence at free breathing (TE, 0.03 msec). Image quality was rated on a four-point Likert scale. Edge sharpness was calculated. After semiautomated segmentation, fractional ventilation was calculated from MRI signal intensity (FVSI) and volume change (FVVol). The air volume fraction (AVF) was estimated from relative signal intensity (aortic blood signal intensity was used as a reference). Means were compared between techniques and participants. The Wilcoxon signed rank test and Spearman rank correlation were used for statistical analyses.ResultsImage quality ratings were equal for both techniques. However, stack-of-spirals breath-hold UTE was more susceptible to motion and aliasing artifacts. Mean FVSI was higher during breath hold than at free breathing (mean ± standard deviation in milliliters of gas per milliliters of parenchyma, 0.17 mL/mL ± 0.06 [minimum, 0.07; maximum, 0.34] vs 0.11 mL/mL ± 0.03 [minimum, 0.06; maximum, 0.17], P = .016). Mean FVSI and FVVol were in good agreement (mean difference: at breath hold, -0.008 [95% confidence interval {CI}: 0.007, -0.024]; ρ = 0.97 vs free breathing, -0.004 [95% CI: 0.007, -0.016]; ρ = 0.91). AVF correlated between both techniques (ρ = 0.94).ConclusionBreath-hold and self-navigated 3D UTE sequences yield proton density-weighted images of the lungs that are similar in quality, and both techniques are suitable for functional image analysis.Supplemental material is available for this article.© RSNA, 2020.

Project description:PurposeTo develop a highly accelerated phase-contrast cardiac-gated volume flow measurement (four-dimensional [4D] flow) magnetic resonance (MR) imaging technique based on spiral sampling and dynamic compressed sensing and to compare this technique with established phase-contrast imaging techniques for the quantification of blood flow in abdominal vessels.Materials and methodsThis single-center prospective study was compliant with HIPAA and approved by the institutional review board. Ten subjects (nine men, one woman; mean age, 51 years; age range, 30-70 years) were enrolled. Seven patients had liver disease. Written informed consent was obtained from all participants. Two 4D flow acquisitions were performed in each subject, one with use of Cartesian sampling with respiratory tracking and the other with use of spiral sampling and a breath hold. Cartesian two-dimensional (2D) cine phase-contrast images were also acquired in the portal vein. Two observers independently assessed vessel conspicuity on phase-contrast three-dimensional angiograms. Quantitative flow parameters were measured by two independent observers in major abdominal vessels. Intertechnique concordance was quantified by using Bland-Altman and logistic regression analyses.ResultsThere was moderate to substantial agreement in vessel conspicuity between 4D flow acquisitions in arteries and veins (κ = 0.71 and 0.61, respectively, for observer 1; κ = 0.71 and 0.44 for observer 2), whereas more artifacts were observed with spiral 4D flow (κ = 0.30 and 0.20). Quantitative measurements in abdominal vessels showed good equivalence between spiral and Cartesian 4D flow techniques (lower bound of the 95% confidence interval: 63%, 77%, 60%, and 64% for flow, area, average velocity, and peak velocity, respectively). For portal venous flow, spiral 4D flow was in better agreement with 2D cine phase-contrast flow (95% limits of agreement: -8.8 and 9.3 mL/sec, respectively) than was Cartesian 4D flow (95% limits of agreement: -10.6 and 14.6 mL/sec).ConclusionThe combination of highly efficient spiral sampling with dynamic compressed sensing results in major acceleration for 4D flow MR imaging, which allows comprehensive assessment of abdominal vessel hemodynamics in a single breath hold.

Project description:Performing a BOLD functional MRI (fMRI) acquisition during breath-hold (BH) tasks is a non-invasive, robust method to estimate cerebrovascular reactivity (CVR). However, movement and breathing-related artefacts caused by the BH can substantially hinder CVR estimates due to their high temporal collinearity with the effect of interest, and attention has to be paid when choosing which analysis model should be applied to the data. In this study, we evaluate the performance of multiple analysis strategies based on lagged general linear models applied on multi-echo BOLD fMRI data, acquired in ten subjects performing a BH task during ten sessions, to obtain subject-specific CVR and haemodynamic lag estimates. The evaluated approaches range from conventional regression models, i.e. including drifts and motion timecourses as nuisance regressors, applied on single-echo or optimally-combined data, to more complex models including regressors obtained from multi-echo independent component analysis with different grades of orthogonalization in order to preserve the effect of interest, i.e. the CVR. We compare these models in terms of their ability to make signal intensity changes independent from motion, as well as the reliability as measured by voxelwise intraclass correlation coefficients of both CVR and lag maps over time. Our results reveal that a conservative independent component analysis model applied on the optimally-combined multi-echo fMRI signal offers the largest reduction of motion-related effects in the signal, while yielding reliable CVR amplitude and lag estimates, although a conventional regression model applied on the optimally-combined data results in similar estimates. This work demonstrates the usefulness of multi-echo based fMRI acquisitions and independent component analysis denoising for precision mapping of CVR in single subjects based on BH paradigms, fostering its potential as a clinically-viable neuroimaging tool for individual patients. It also proves that the way in which data-driven regressors should be incorporated in the analysis model is not straight-forward due to their complex interaction with the BH-induced BOLD response.

Project description:BackgroundAlthough the breath-hold cine balanced steady state free precession (bSSFP) imaging is well established for assessment of biventricular volumes and function, shorter breath-hold times or no breath-holds are beneficial in children and severely ill or sedated patients.MethodsClinical cardiovascular magnetic resonance (CMR) examinations from September 2019 to October 2019 that included breath-hold (BH) and free-breathing (FB) cine bSSFP imaging accelerated using compressed sensitivity encoding (C-SENSE) factor of 3 in addition to the clinical standard BH cine bSSFP imaging using SENSE factor of 2 were analyzed retrospectively. Patients with structurally normal hearts who could perform consistent BHs were included. Aortic flow measured by phase contrast acquisition was used as a reference for the left ventricular (LV) stroke volume. Comparative analysis was performed for evaluation of biventricular volumes and function, imaging times, quantitative image quality, and qualitative image scoring.ResultsThere were 26 patients who underwent all three cine scans during the study period (16.7 ± 6.4 years, body surface area (BSA) 1.6 ± 0.4 m2, heart rate 83 ± 7 beats/min). BH durations of 8 ± 1 s with C-SENSE = 3 were significantly shorter (p < 0.001) by 33% compared to 12 ± 1 s with SENSE = 2. Actual scan time for BH SENSE (4.9 ± 1.2 min) was comparable to that with FB C-SENSE (5.2 ± 1.5 min; p= NS). Biventricular stroke volume and ejection fraction, and LV mass computed using all three sequences were comparable. There was a small but statistically significant (p < 0.05) difference in LV end-diastolic volume (- 3.0 ± 6.8 ml) between BH SENSE and FB C-SENSE. There was a small but statistically significant (p < 0.005) difference in end-diastolic LV (- 5.0 ± 7.7 ml) and RV (- 6.0 ± 8.5 ml) volume and end-systolic LV (- 3.2 ± 4.3 ml) and RV(- 4.2 ± 6.8 ml) volumes between BH C-SENSE and FB C-SENSE. The LV stroke volumes from all three sequences had excellent correlations (r = 0.96, slope = 0.98-1.02) with aortic flow, with overestimation by 2.7 (5%) to 4.6 (8%) ml/beat. The image quality score was Excellent (16 of 26) to Good (10 of 26) with BH SENSE, Excellent (13 of 26) to Good (13 of 26) with BH C-SENSE, and Excellent (3 of 26) to Good (21 of 26) to Adequate (2 of 26) with FB C-SENSE.ConclusionsImage quality and ventricular volumetric and functional indices using either BH or FB C-SENSE cine bSSFP imaging were comparable to standard BH SENSE cine bSSFP imaging while maintaining nominally identical spatio-temporal resolution. This accelerated image acquisition provides an alternative to accommodate patients with impaired BH capacity.

Dataset Information

Applying Compressed Sensing Volumetric Interpolated Breath-Hold Examination and Spiral Ultrashort Echo Time Sequences for Lung Nodule Detection in MRI.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets