Effects of neonatal lung abnormalities on parenchymal R2 * estimates.

Infants admitted to the neonatal intensive care unit (NICU) often suffer from multifaceted pulmonary morbidities that are not well understood. Ultrashort echo time (UTE) magnetic resonance imaging (MRI) is a promising technique for pulmonary imaging in this population without requiring exposure to ionizing radiation. The aims of this study were to investigate the effect of neonatal pulmonary disease on R2 * and tissue density and to utilize numerical simulations to evaluate the effect of different alveolar structures on predicted R2 *.This was a prospective study, in which 17 neonatal human subjects (five control, seven with bronchopulmonary dysplasia [BPD], five with congenital diaphragmatic hernia [CDH]) were enrolled. Twelve subjects were male and five were female, with postmenstrual age (PMA) at MRI of 39.7 ± 4.7 weeks. A 1.5T/multiecho three-dimensional UTE MRI was used. Pulmonary R2 * and tissue density were compared across disease groups over the whole lung and regionally. A spherical shell alveolar model was used to predict the expected R2 * over a range of tissue densities and tissue susceptibilities. Tests for significantly different mean R2 * and tissue densities across disease groups were evaluated using analysis of variance, with subsequent pairwise group comparisons performed using t tests. Lung tissue density was lower in the ipsilateral lung in CDH compared to both controls and BPD patients (both p < 0.05), while only the contralateral lung in CDH (CDHc) had higher whole-lung R2 * than both controls and BPD (both p < 0.05). R2 * differences were significant between controls and CDHc within all tissue density ranges (all p < 0.05) with the exception of the 80%-90% range (p = 0.17). Simulations predicted an inverse relationship between alveolar tissue density and R2 * that matches empirical human data. Alveolar wall thickness had no effect on R2 * independent of density (p = 1). The inverse relationship between R2 * and tissue density is influenced by the presence of disease globally and regionally in neonates with BPD and CDH in the NICU. LEVEL OF EVIDENCE: 2. TECHNICAL EFFICACY STAGE: 2.

Transverse relaxation rates of pulmonary dissolved-phase Hyperpolarized 129 Xe as a biomarker of lung injury in idiopathic pulmonary fibrosis.

PURPOSE: The MR properties (chemical shifts and R2∗ decay rates) of dissolved-phase hyperpolarized (HP) 129 Xe are confounded by the large magnetic field inhomogeneity present in the lung. This work improves measurements of these properties using a model-based image reconstruction to characterize the R2∗ decay rates of dissolved-phase HP 129 Xe in healthy subjects and patients with idiopathic pulmonary fibrosis (IPF). METHODS: Whole-lung MRS and 3D radial MRI with four gradient echoes were performed after inhalation of HP 129 Xe in healthy subjects and patients with IPF. A model-based image reconstruction formulated as a regularized optimization problem was solved iteratively to measure regional signal intensity in the gas, barrier, and red blood cell (RBC) compartments, while simultaneously measuring their chemical shifts and R2∗ decay rates. RESULTS: The estimation of spectral properties reduced artifacts in images of HP 129 Xe in the gas, barrier, and RBC compartments and improved image SNR by over 20%. R2∗ decay rates of the RBC and barrier compartments were lower in patients with IPF compared to healthy subjects (P < 0.001 and P = 0.005, respectively) and correlated to DLCO (R = 0.71 and 0.64, respectively). Chemical shift of the RBC component measured with whole-lung spectroscopy was significantly different between IPF and normal subjects (P = 0.022). CONCLUSION: Estimates for R2∗ in both barrier and RBC dissolved-phase HP 129 Xe compartments using a regional signal model improved image quality for dissolved-phase images and provided additional biomarkers of lung injury in IPF.

Characterization of R2∗ and tissue density in the human lung: Application to neonatal imaging in the intensive care unit.

PURPOSE: Novel demonstration of R2∗ and tissue density estimation in infant lungs using 3D ultrashort echo time MRI. Differences between adult and neonates with no clinical indication of lung pathology is explored, as well as relationships between parameter estimates and gravitationally dependent position and lung inflation state. This provides a tool for probing physiologic processes that may be relevant to pulmonary disease and progression in newborns. METHODS: R2∗ and tissue density were estimated in a phantom consisting of standards allowing for ground truth comparisons and in human subjects (N = 5 infants, N = 4 adults, no clinical indication of lung dysfunction) using a 3D radial multiecho ultrashort echo time MRI sequence. Whole lung averages were compared between infants and adults. Dependence of the metrics on anterior-posterior position as well as between end-tidal inspiration and expiration were explored, in addition to the general relationship between R2∗ and tissue density. RESULTS: Estimates in the phantom did not differ significantly from ground truth. Neonates had significantly lower mean R2∗ (P = .006) and higher mean tissue density (P = 1.5e-5) than adults. Tissue density and R2∗ were both significantly dependent on anterior-posterior position and lung inflation state (P < .005). An overall inverse relationship was found between R2∗ and tissue density, which was similar in both neonates and adults. CONCLUSION: Estimation of tissue density and R2∗ in free breathing, nonsedated, neonatal patients is feasible using multiecho ultrashort echo time MRI. R2∗ was no different between infants and adults when matched for tissue density, although density of lung parenchyma was, on average, lower in adults than neonates.

A method to extract image noise level from patient images in CT.

PURPOSE: Image quality in computed tomography (CT) is usually not quantified in real time from patient scans. Rather, phantom scans are employed to measure the contrast, noise, and spatial resolution properties of a given system. This approach, however, is not ideal because many factors are difficult to represent using phantoms like variations in patient size, composition, and position within the gantry. Several methods for measuring the noise found within patient scans have been proposed. All of these methods rely on segmenting out relatively uniform regions within the patient in order to differentiate between morphological variations and pixel differences due to photon noise. In order to avoid this segmentation step, we propose to analyze the noise signal in the air surrounding the patient. We will demonstrate that the air signal surrounding the patient acts as a surrogate for the noise within the contours of the patient. METHODS: Our work builds off the global noise index (GNI) method. In the GNI method, adjacent axial CT image slices are subtracted to remove the majority of the morphological variations in the data. Remaining morphological variations are removed with an edge-finding algorithm. Then the image is divided up into small regions of interest (ROI) and the pixel standard deviation is computed for each ROI. Only those ROIs not containing bone or air are then used to make a histogram of the standard deviation within the image. The mode of this histogram is referred to as the GNI. We refer to this as the traditional GNI (tradGNI). Our modification to this workflow is to apply this metric to just the air signal surrounding the patient. We evaluate the correlation between using the air signal and the in-phantom metric value for titration over: dose level, image slice thickness, kernel sharpness, iterative reconstruction level, model based iterative reconstruction, reconstruction field of view, and reconstruction interval. 373 patient abdomen pelvis exams were collected and the air based noise estimation metrics applied. Liver standard deviation values were measured on 40 of the patients and correlated with air GNI calculations. RESULTS: Our results show excellent linear correlation (R2 > 0.99) between the tradGNI being applied inside and outside of a phantom object. Our results are also shown to still be predictive of the noise under all scan parameters studied, including iterative and model based reconstruction. Fits of tradGNI versus dose level exhibited the expected square root dependence. Air based GNI metrics were predictive of human patient noise level and also correlated with manual liver standard deviation measurement (R2 = 0.73). CONCLUSIONS: Our results demonstrate the signal in the air surrounding an imaging object can accurately be used as a surrogate for the image noise within the object. Our method should enable faster and more robust patient specific image quality assessment due to the lack of the need to segment noise from morphological variations within a patient.

Tracking Patterns of Nonadherence to Prescribed CT Protocol Parameters.

PURPOSE: Quantification of the frequency, understanding the motivation, and documentation of the changes made by CT technologists at scan time are important components of monitoring a quality CT workflow. METHODS: CT scan acquisition data were collected from one CT scanner for a period of 1 year. The data included all relevant acquisition parameters needed to define the technical side of a CT protocol. An algorithm was created to sort these data in groups of irradiation events with the same combinations of scan acquisition parameters. For scans modified at scan time, it was hypothesized that these examinations would show up only once in the organized data. A classification scheme was developed to place each "one-off" examination into a category related to what motivated the scan-time change. RESULTS: A total of 132,707 irradiation events were organized into 434 groups of unique scan acquisition parameters. One hundred forty-four irradiation events had acquisition parameters that showed up only once in the data. These "one-offs" were classified as follows: 25% represented rarely used protocols, 17% were due to service scans, 16% were changed for unknown and therefore undesired reasons, 15% were changed by technologists trying to adapt protocol to patient size, 12% were allowable scan-time changes, 8% of scans had tube current maxed out, and 6% of scans were changed to a higher dose mode as requested by radiologists. CONCLUSIONS: The outcome of this study suggests many areas of needed technologist training and chances for optimizing this institution's CT protocols.