Purpose: Ultrashort echotime (UTE) sequences aim to improve the signal yield in pulmonary magnetic resonance imaging (MRI). We demonstrate the initial results of spiral 3-dimensional (3D) UTE-MRI for combined morphologic and functional imaging in pediatric patients. Methods: Seven pediatric patients with pulmonary abnormalities were included in this observational, prospective, single-center study, with the patients having the following conditions: cystic fibrosis (CF) with middle lobe atelectasis, CF with allergic bronchopulmonary aspergillosis, primary ciliary dyskinesia, air trapping, congenital lobar overinflation, congenital pulmonary airway malformation, and pulmonary hamartoma. Patients were scanned during breath-hold in 5 breathing states on a 3-Tesla system using a prototypical 3D stack-of-spirals UTE sequence. Ventilation maps and signal intensity maps were calculated. Morphologic images, ventilation-weighted maps, and signal intensity maps of the lungs of each patient were assessed intraindividually and compared with reference examinations. Results: With a scan time of ∼15 seconds per breathing state, 3D UTE-MRI allowed for sufficient imaging of both “plus” pathologies (atelectasis, inflammatory consolidation, and pulmonary hamartoma) and “minus” pathologies (congenital lobar overinflation, congenital pulmonary airway malformation, and air trapping). Color-coded maps of normalized signal intensity and ventilation increased diagnostic confidence, particularly with regard to “minus” pathologies. UTE-MRI detected new atelectasis in an asymptomatic CF patient, allowing for rapid and successful therapy initiation, and it was able to reproduce atelectasis and hamartoma known from multidetector computed tomography and to monitor a patient with allergic bronchopulmonary aspergillosis. Conclusion: 3D UTE-MRI using a stack-of-spirals trajectory enables combined morphologic and functional imaging of the lungs within ~115 second acquisition time and might be suitable for monitoring a wide spectrum of pulmonary diseases.
Apparent diffusion coefficients (ADCs) obtained with diffusion-weighted imaging (DWI) are highly valuable for the detection and staging of prostate cancer and for assessing the response to treatment. However, DWI suffers from significant anatomic distortions and susceptibility artifacts, resulting in reduced accuracy and reproducibility of the ADC calculations. The current methods for improving the DWI quality are heavily dependent on software, hardware, and additional scan time. Therefore, their clinical application is limited. An accelerated ADC generation method that maintains calculation accuracy and repeatability without heavy dependence on magnetic resonance imaging scanners is of great clinical value.We aimed to establish and evaluate a supervised learning framework for synthesizing ADC images using generative adversarial networks.This prospective study included 200 patients with suspected prostate cancer (training set: 150 patients; test set #1: 50 patients) and 10 healthy volunteers (test set #2) who underwent both full field-of-view (FOV) diffusion-weighted imaging (f-DWI) and zoomed-FOV DWI (z-DWI) with b-values of 50, 1,000, and 1,500 s/mm2. ADC values based on f-DWI and z-DWI (f-ADC and z-ADC) were calculated. Herein we propose an ADC synthesis method based on generative adversarial networks that uses f-DWI with a single b-value to generate synthesized ADC (s-ADC) values using z-ADC as a reference. The image quality of the s-ADC sets was evaluated using the peak signal-to-noise ratio (PSNR), root mean squared error (RMSE), structural similarity (SSIM), and feature similarity (FSIM). The distortions of each ADC set were evaluated using the T2-weighted image reference. The calculation reproducibility of the different ADC sets was compared using the intraclass correlation coefficient. The tumor detection and classification abilities of each ADC set were evaluated using a receiver operating characteristic curve analysis and a Spearman correlation coefficient.The s-ADCb1000 had a significantly lower RMSE score and higher PSNR, SSIM, and FSIM scores than the s-ADCb50 and s-ADCb1500 (all P < 0.001). Both z-ADC and s-ADCb1000 had less distortion and better quantitative ADC value reproducibility for all the evaluated tissues, and they demonstrated better tumor detection and classification performance than f-ADC.The deep learning algorithm might be a feasible method for generating ADC maps, as an alternative to z-ADC maps, without depending on hardware systems and additional scan time requirements.
The value of non-parallel-transmission zoom-diffusion weighted imaging (non-PTX zoom-DWI) compared with conventional DWI in the diagnosis of bladder cancer muscular invasion is investigated. Results show that non-PTX zoom-DWI improves sensitivity, specificity, positive predictive value, negative predictive value, and accuracy. Additionally, it has good diagnostic consistency across different readers, and shows better display of bladder than conventional DWI sequence. Non-PTX zoom-DWI may be superior to conventional DWI in predicting the muscular invasion in bladder cancer.
Monoexponential apparent diffusion coefficient (ADC) and biexponential intravoxel incoherent motion (IVIM) analysis of diffusion-weighted imaging (DWI) is helpful in the characterization of breast tumors. Toward this goal, a novel breast phantom containing tubes of different polyvinylpyrrolidone (PVP) concentrations, water, fat, and sponge flow chambers was utilized. This work tests this breast phantom at two sites employing different vendor MRI scanners to estimate the ADC and IVIM parameters. The results are reproducible within sites, and show progress towards reproducibility across sites and vendors, and can be used in the future in multicenter clinical trials for breast cancer characterization, prediction and prognosis.
Echo planar imaging is highly affected by field map inhomogeneity distortion artifact. Field map inhomogeneity has shown to be motion dependent in the kidneys. In the present work, we propose an alternative method for correction of magnetic field inhomogeneity for renal DWI in respiratory-resolved fashion. Specifically, we collect a series of forward and reverse phase encoded b=0 images to sample kidney motion caused by breathing, map the spatial and respiratory phase dependence of the magnetic field inhomogeneity, and correct each image of free-breathing DWI series according to their respiratory phase.
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