The Resilience of Pediatric Nurses in Context: A Mixed Methods Study.

BACKGROUND: Resilience, an individual's ability to cope with and recover from stressors, is supported by contextually specific factors. Factors in the work environment may support or hinder nurses' resilience to the specific stressors present in pediatric nursing, an understudied population. OBJECTIVE: We aimed to explore the contextual factors in the work environment of pediatric nurses with varying levels of resilience, including social support, the work environment, and opportunities for coping from an individual approach. METHODS: This study is a secondary mixed-methods analysis using ordinal logistic regression and a meta-matrix of survey responses and semi-structured interview transcripts from 30 pediatric nurses. RESULTS: 5 themes, 3 supporting and 2 hindering resilience, emerged from the interviews. Nurses described their resilience as supported by sharing the burden, support from administration, and taking a break. Participants described resilience as hindered when they found it challenging to provide quality nursing care and when they felt unappreciated or undervalued. No theme significantly changed the odds of having higher resilience. CONCLUSIONS: Our findings suggest that nurses recognize resources and conditions within their work environment as influencing their resilience. However, workplace resources and conditions are only one contributor to pediatric nurse resilience. Encouraging breaks and informal opportunities for nurses to support each other may support resilience in pediatric nurses. In addition, nursing leaders can support pediatric nurse resilience by regularly spending time connecting with nurses. Finally, health care organizations should consider how changes in the work environment may hinder nurse resilience by adding stress or changing access to supportive factors.

Investigation of a breathing surrogate prediction algorithm for prospective pulmonary gating.

PURPOSE: A major challenge of four dimensional computed tomography (4DCT) in treatment planning and delivery has been the lack of respiration amplitude and phase reproducibility during image acquisition. The implementation of a prospective gating algorithm would ensure that images would be acquired only during user-specified breathing phases. This study describes the development and testing of an autoregressive moving average (ARMA) model for human respiratory phase prediction under quiet respiration conditions. METHODS: A total of 47 4DCT patient datasets and synchronized respiration records was utilized in this study. Three datasets were used in model development and were removed from further evaluation of the ARMA model. The remaining 44 patient datasets were evaluated with the ARMA model for prediction time steps from 50 to 1000 ms in increments of 50 and 100 ms. Thirty-five of these datasets were further used to provide a comparison between the proposed ARMA model and a commercial algorithm with a prediction time step of 240 ms. RESULTS: The optimal number of parameters for the ARMA model was based on three datasets reserved for model development. Prediction error was found to increase as the prediction time step increased. The minimum prediction time step required for prospective gating was selected to be half of the gantry rotation period. The maximum prediction time step with a conservative 95% confidence criterion was found to be 0.3 s. The ARMA model predicted peak inhalation and peak exhalation phases significantly better than the commercial algorithm. Furthermore, the commercial algorithm had numerous instances of missed breath cycles and falsely predicted breath cycles, while the proposed model did not have these errors. CONCLUSIONS: An ARMA model has been successfully applied to predict human respiratory phase occurrence. For a typical CT scanner gantry rotation period of 0.4 s (0.2 s prediction time step), the absolute error was relatively small, 0.06 +/- 0.02 s at peak inhalation and 0.05 +/- 0.04 s at peak exhalation. The application of the ARMA model for prospective pulmonary gating has been demonstrated.

ANIMAL ANALOGIES IN FIRST IMPRESSIONS OF FACES.

Analogies between humans and animals based on facial resemblance have a long history. We report evidence for reverse anthropomorphism and the extension of facial stereotypes to lions, foxes, and dogs. In the stereotype extension, more positive traits were attributed to animals judged more attractive than con-specifics; more childlike traits were attributed to those judged more babyfaced. In the reverse anthropomorphism, human faces with more resemblance to lions, ascertained by connectionist modeling of facial metrics, were judged more dominant, cold, and shrewd, controlling attractiveness, babyfaceness, and sex. Faces with more resemblance to Labradors were judged warmer and less shrewd. Resemblance to foxes did not predict impressions. Results for lions and dogs were consistent with trait impressions of these animals and support the species overgeneralization hypothesis that evolutionarily adaptive reactions to particular animals are overgeneralized, with people perceived to have traits associated with animals their faces resemble. Other possible explanations are discussed.

RETRACTED: Robust Proton Pencil Beam Scanning Treatment Planning for Rectal Cancer Radiation Therapy.

This article has been retracted: please see Elsevier Policy on Article Withdrawal (http://www.elsevier.com/locate/withdrawalpolicy). This article has been retracted for failure to comply with the University of Pennsylvania's standards for publishing team-based research following a formal investigation by that institution.

Geometric validation of MV topograms for patient localization on TomoTherapy.

Our goal was to geometrically validate the use of mega-voltage orthogonal scout images (MV topograms) as a fast and low-dose alternative to mega-voltage computed tomography (MVCT) for daily patient localization on the TomoTherapy system. To achieve this, anthropomorphic head and pelvis phantoms were imaged on a 16-slice kilo-voltage computed tomography (kVCT) scanner to synthesize kilo-voltage digitally reconstructed topograms (kV-DRT) in the Tomotherapy detector geometry. MV topograms were generated for couch speeds of 1-4 cm s(-1) in 1 cm s(-1) increments with static gantry angles in the anterior-posterior and left-lateral directions. Phantoms were rigidly translated in the anterior-posterior (AP), superior-inferior (SI), and lateral (LAT) directions to simulate potential setup errors. Image quality improvement was demonstrated by estimating the noise level in the unenhanced and enhanced MV topograms using a principle component analysis-based noise level estimation algorithm. Average noise levels for the head phantom were reduced by 2.53 HU (AP) and 0.18 HU (LAT). The pelvis phantom exhibited average noise level reduction of 1.98 HU (AP) and 0.48 HU (LAT). Mattes Mutual Information rigid registration was used to register enhanced MV topograms with corresponding kV-DRT. Registration results were compared to the known rigid displacements, which assessed the MV topogram localization's sensitivity to daily positioning errors. Reduced noise levels in the MV topograms enhanced the registration results so that registration errors were <1 mm. The unenhanced head MV topograms had discrepancies < 2.1 mm and the pelvis topograms had discrepancies < 2.7 mm. Result were found to be consistent regardless of couch speed. In total, 64.7% of the head phantom MV topograms and 60.0% of the pelvis phantom MV topograms exactly measured the phantom offsets. These consistencies demonstrated the potential for daily patient positioning using MV topogram pairs in the context bony-anatomy based procedures such as total marrow irradiation, total body irradiation, and cranial spinal irradiation.

Accuracy of routine treatment planning 4-dimensional and deep-inspiration breath-hold computed tomography delineation of the left anterior descending artery in radiation therapy.

PURPOSE: To assess the feasibility of radiation therapy treatment planning 4-dimensional computed tomography (4DCT) and deep-inspiration breath-hold (DIBH) CT to accurately contour the left anterior descending artery (LAD), a primary indicator of radiation-induced cardiac toxicity for patients undergoing radiation therapy. METHODS AND MATERIALS: Ten subjects were prospectively imaged with a cardiac-gated MRI protocol to determine cardiac motion effects, including the displacement of a region of interest comprising the LAD. A series of planar views were obtained and resampled to create a 3-dimensional (3D) volume. A 3D optical flow deformable image registration algorithm determined tissue displacement during the cardiac cycle. The measured motion was then used as a spatial boundary to characterize motion blurring of the radiologist-delineated LAD structure for a cohort of 10 consecutive patients enrolled prospectively on a breast study including 4DCT and DIBH scans. Coronary motion-induced blurring artifacts were quantified by applying an unsharp filter to accentuate the LAD structure despite the presence of motion blurring. The 4DCT maximum inhalation and exhalation respiratory phases were coregistered to determine the LAD displacement during tidal respiration, as visualized in 4DCT. RESULTS: The average 90th percentile heart motion for the region of interest was 0.7 ± 0.1 mm (left-right [LR]), 1.3 ± 0.6 mm (superior-inferior [SI]), and 0.6 ± 0.2 mm (anterior-posterior [AP]) in the cardiac-gated MRI cohort. The average relative increase in the number of voxels comprising the LAD contour was 69.4% ± 4.5% for the DIBH. The LAD volume overestimation had the dosimetric impact of decreasing the reported mean LAD dose by 23% ± 9% on average in the DIBH. During tidal respiration the average relative LAD contour increase was 69.3% ± 5.9% and 67.9% ± 4.6% for inhalation and exhalation respiratory phases, respectively. The average 90th percentile LAD motion was 4.8 ± 1.1 mm (LR), 0.9 ± 0.4 mm (SI), and 1.9 ± 0.6 mm (AP) for the 4DCT cohort, in the absence of cardiac gating. CONCLUSIONS: An anisotropic margin of 2.7 mm (LR), 4.1 mm (SI), and 2.4 mm (AP) was quantitatively determined to account for motion blurring and patient setup error while placing minimum constraint on the plan optimization.

Quantitative early decision making metric for identifying irregular breathing in 4DCT.

PURPOSE: To develop a quantitative early decision making metric for prediction of breathing pattern and irregular breathing and validate the metric in a large patient population receiving clinical phase-sorted four-dimensional computed tomography (4DCT). METHODS: This study employed three patient cohorts. The first cohort contained 47 patients, imaged with a nonclinical tidal volume metric. The second cohort contained a sample of 256 patients who received a clinical 4DCT. The third cohort contained 86 patients who received three 4DCT scans at 1-week increment during the course of radiotherapy. The second and third cohorts did not have tidal volume measurements, as per standard radiation oncology clinical practice. Based on a previously published technique that used a single abdominal surrogate, the ratio of extreme inhalation tidal volume to normal inhalation tidal volume (κ) metric was calculated and the patient breathing pattern was characterized. The use of a single surrogate precluded the use of a κ determined by tidal volume, so a κ(rel) was defined based on the amplitude of the surrogate. Patients were classified as either Type 1 or Type 2, based on a previously published technique, where Type 1 patients were apneic at end of exhalation and Type 2 patients exhibited forced respiration. The Ansari-Bradley test was used to determine the statistical similarity between the Type 1 and Type 2 distributions. A Kruskal-Wallis one way analysis of variance was used to determine the statistical similarities among the classified breathing types, κ(rel), and the qualified medical physicist denoted breathing classification (regular or irregular). Receiver operator characteristic curves were used to quantitatively determine optimal cutoff value j(κ) and efficiency cutoff value (τ(κ)) κ(rel) to provide a quantitative early warning of irregular breathing during 4DCT procedures. RESULTS: The statistical tests show a significant consistency for the breathing pattern classifications between the physiologically measured cohort #1 and the remaining cohorts. The classification types were statistically different between Type 1 and Type 2 patients over all cohorts. Values of κ(rel) in excess of 1.72 indicated a substantial presence of irregular breathing that could negatively affect the quality of a 4DCT image dataset. Values of κ(rel) in lower than 1.45 indicated minimal presence of irregular breathing. For values of κ(rel) such that j(κ) ≤ κ(rel) ≤ τ(κ), the decision to reacquire the 4DCT would be at the discretion of the physician. This accounted for only 11.9% of the patients in this study. The magnitude of κ(rel) held consistent over three weeks of treatment for 73% of the patients in cohort #3. CONCLUSIONS: The decision making metric based on κ was shown to be an accurate classifier of regular and irregular breathing patterns in a large patient population. Breathing type, as defined in a previous published work, was accurately classified by κ(rel) with the use of a single respiratory surrogate compared to the physiological use of multiple respiratory surrogates. This work provided a quantitative early decision making metric to quickly and accurately assess breathing patterns as well as the presence and magnitude of irregular breathing during 4DCT.

Assessing margin expansions of internal target volumes in 3D and 4D PET: a phantom study.

BACKGROUND AND PURPOSE: To quantify tumor volume coverage and excess normal tissue coverage using margin expansions of mobile target internal target volumes (ITVs) in the lung. MATERIALS AND METHODS: FDG-PET list-mode data were acquired for four spheres ranging from 1 to 4 cm as they underwent 1D motion based on four patient breathing trajectories. Both ungated PET images and PET maximum intensity projections (PET-MIPs) were examined. Amplitude-based gating was performed on sequential list-mode files of varying signal-to-background ratios to generate PET-MIPs. ITVs were first post-processed using either a Gaussian filter or a custom two-step module, and then segmented by applying a gradient-based watershed algorithm. Uniform and non-uniform 1 mm margins were added to segmented ITVs until complete target coverage was achieved. RESULTS: PET-MIPs required smaller uniform margins (4.7 vs. 11.3 mm) than ungated PET, with correspondingly smaller over-coverage volumes (OCVs). Non-uniform margins consistently resulted in smaller OCVs when compared to uniform margins. PET-MIPs and ungated PET had comparable OCVs with non-uniform margins, but PET-MIPs required smaller longitudinal margins (4.7 vs. 8.5 mm). Non-uniform margins were independent of sphere size. CONCLUSIONS: Gated PET-MIP images and non-uniform margins result in more accurate ITV delineation while reducing normal tissue coverage.

Modeling and incorporating cardiac-induced lung tissue motion in a breathing motion model.

PURPOSE: The purpose of this work is to develop a cardiac-induced lung motion model to be integrated into an existing breathing motion model. METHODS: The authors' proposed cardiac-induced lung motion model represents the lung tissue's specific response to the subject's cardiac cycle. The model is mathematically defined as a product of a converging polynomial function h of the cardiac phase (c) and the maximum displacement y(X0) of each voxel (X0) among all the cardiac phases. The function h(c) was estimated from cardiac-gated MR imaging of ten healthy volunteers using an Akaike Information Criteria optimization algorithm. For each volunteer, a total of 24 short-axis and 18 radial planar views were acquired on a 1.5 T MR scanner during a series of 12-15 s breath-hold maneuvers. Each view contained 30 temporal frames of equal time-duration beginning with the end-diastolic cardiac phase. The frames in each of the planar views were resampled to create a set of three-dimensional (3D) anatomical volumes representing thoracic anatomy at different cardiac phases. A 3D multiresolution optical flow deformable image registration algorithm was used to quantify the difference in tissue position between the end-diastolic cardiac phase and the remaining cardiac phases. To account for image noise, voxel displacements whose maximum values were less than 0.3 mm, were excluded. In addition, the blood vessels were segmented and excluded in order to eliminate registration artifacts caused by blood-flow. RESULTS: The average cardiac-induced lung motions for displacements greater than 0.3 mm were found to be 0.86 ± 0.74 and 0.97 ± 0.93 mm in the left and right lungs, respectively. The average model residual error for the ten healthy volunteers was found to be 0.29 ± 0.08 mm in the left lung and 0.38 ± 0.14 mm in the right lung for tissue displacements greater than 0.3 mm. The relative error decreased with increasing cardiac-induced lung tissue motion. While the relative error was > 60% for submillimeter cardiac-induced lung tissue motion, the relative error decreased to < 5% for cardiac-induced lung tissue motion that exceeded 10 mm in displacement. CONCLUSIONS: The authors' studies implied that modeling and including cardiac-induced lung motion would improve breathing motion model accuracy for tissues with cardiac-induced motion greater than 0.3 mm.

Physiologically guided approach to characterizing respiratory motion.

PURPOSE: To characterize radiation therapy patient breathing patterns based on measured external surrogate information. METHODS: Breathing surrogate data were collected during 4DCT from a cohort of 50 patients including 28 patients with lung cancer and 22 patients without lung cancer. A spirometer and an abdominal pneumatic bellows were used as the surrogates. The relationship between these measurements was assumed to be linear within a small phase difference. The signals were correlated and drift corrected using a previously published method to convert the signal into tidal volume. The airflow was calculated with a first order time derivative of the tidal volume using a window centered on the point of interest and with a window length equal to the CT gantry rotation period. The airflow was compared against the tidal volume to create ellipsoidal patterns that were binned into 25 ml × 25 ml∕s bins to determine the relative amount of time spent in each bin. To calculate the variability of the maximum inhalation tidal volume within a free-breathing scan timeframe, a metric based on percentile volume ratios was defined. The free breathing variability metric (κ) was defined as the ratio between extreme inhalation tidal volumes (defined as >93 tidal volume percentile of the measured tidal volume) and normal inhalation tidal volume (defined as >80 tidal volume percentile of the measured tidal volume). RESULTS: There were three observed types of volume-flow curves, labeled Types 1, 2, and 3. Type 1 patients spent a greater duration of time during exhalation with κ = 1.37 ± 0.11. Type 2 patients had equal time duration spent during inhalation and exhalation with κ = 1.28 ± 0.09. The differences between the mean peak exhalation to peak inhalation tidal volume, breathing period, and the 85th tidal volume percentile for Type 1 and Type 2 patients were statistically significant at the 2% significance level. The difference between κ and the 98th tidal volume percentile for Type 1 and Type 2 patients was found to be statistically significant at the 1% significance level. Three patients did not display a breathing stability curve that could be classified as Type 1 or Type 2 due to chaotic breathing patterns. These patients were classified as Type 3 patients. CONCLUSIONS: Based on an observed volume-flow curve pattern, the cohort of 50 patients was divided into three categories called Type 1, Type 2, and Type 3. There were statistically significant differences in breathing characteristics between Type 1 and Type 2 patients. The use of volume-flow curves to classify patients has been demonstrated as a physiological characterization metric that has the potential to optimize gating windows in radiation therapy.

Quantification of the thorax-to-abdomen breathing ratio for breathing motion modeling.

PURPOSE: The purpose of this study was to develop a methodology to quantitatively measure the thorax-to-abdomen breathing ratio from a 4DCT dataset for breathing motion modeling and breathing motion studies. METHODS: The thorax-to-abdomen breathing ratio was quantified by measuring the rate of cross-sectional volume increase throughout the thorax and abdomen as a function of tidal volume. Twenty-six 16-slice 4DCT patient datasets were acquired during quiet respiration using a protocol that acquired 25 ciné scans at each couch position. Fifteen datasets included data from the neck through the pelvis. Tidal volume, measured using a spirometer and abdominal pneumatic bellows, was used as breathing-cycle surrogates. The cross-sectional volume encompassed by the skin contour when compared for each CT slice against the tidal volume exhibited a nearly linear relationship. A robust iteratively reweighted least squares regression analysis was used to determine η(i), defined as the amount of cross-sectional volume expansion at each slice i per unit tidal volume. The sum Ση(i) throughout all slices was predicted to be the ratio of the geometric expansion of the lung and the tidal volume; 1.11. The Xiphoid process was selected as the boundary between the thorax and abdomen. The Xiphoid process slice was identified in a scan acquired at mid-inhalation. The imaging protocol had not originally been designed for purposes of measuring the thorax-to-abdomen breathing ratio so the scans did not extend to the anatomy with η(i) = 0. Extrapolation of η(i)-η(i) = 0 was used to include the entire breathing volume. The thorax and abdomen regions were individually analyzed to determine the thorax-to-abdomen breathing ratios. There were 11 image datasets that had been scanned only through the thorax. For these cases, the abdomen breathing component was equal to 1.11 - Ση(i) where the sum was taken throughout the thorax. RESULTS: The average Ση(i) for thorax and abdomen image datasets was found to be 1.20 ± 0.17, close to the expected value of 1.11. The thorax-to-abdomen breathing ratio was 0.32 ± 0.24. The average Ση(i) was 0.26 ± 0.14 in the thorax and 0.93 ± 0.22 in the abdomen. In the scan datasets that encompassed only the thorax, the average Ση(i) was 0.21 ± 0.11. CONCLUSIONS: A method to quantify the relationship between abdomen and thoracic breathing was developed and characterized.

A novel CT acquisition and analysis technique for breathing motion modeling.

To report on a novel technique for providing artifact-free quantitative four-dimensional computed tomography (4DCT) image datasets for breathing motion modeling. Commercial clinical 4DCT methods have difficulty managing irregular breathing. The resulting images contain motion-induced artifacts that can distort structures and inaccurately characterize breathing motion. We have developed a novel scanning and analysis method for motion-correlated CT that utilizes standard repeated fast helical acquisitions, a simultaneous breathing surrogate measurement, deformable image registration, and a published breathing motion model. The motion model differs from the CT-measured motion by an average of 0.65 mm, indicating the precision of the motion model. The integral of the divergence of one of the motion model parameters is predicted to be a constant 1.11 and is found in this case to be 1.09, indicating the accuracy of the motion model. The proposed technique shows promise for providing motion-artifact free images at user-selected breathing phases, accurate Hounsfield units, and noise characteristics similar to non-4D CT techniques, at a patient dose similar to or less than current 4DCT techniques.

A comparison of amplitude-based and phase-based positron emission tomography gating algorithms for segmentation of internal target volumes of tumors subject to respiratory motion.

PURPOSE: To quantitatively compare the accuracy of tumor volume segmentation in amplitude-based and phase-based respiratory gating algorithms in respiratory-correlated positron emission tomography (PET). METHODS AND MATERIALS: List-mode fluorodeoxyglucose-PET data was acquired for 10 patients with a total of 12 fluorodeoxyglucose-avid tumors and 9 lymph nodes. Additionally, a phantom experiment was performed in which 4 plastic butyrate spheres with inner diameters ranging from 1 to 4 cm were imaged as they underwent 1-dimensional motion based on 2 measured patient breathing trajectories. PET list-mode data were gated into 8 bins using 2 amplitude-based (equal amplitude bins [A1] and equal counts per bin [A2]) and 2 temporal phase-based gating algorithms. Gated images were segmented using a commercially available gradient-based technique and a fixed 40% threshold of maximum uptake. Internal target volumes (ITVs) were generated by taking the union of all 8 contours per gated image. Segmented phantom ITVs were compared with their respective ground-truth ITVs, defined as the volume subtended by the tumor model positions covering 99% of breathing amplitude. Superior-inferior distances between sphere centroids in the end-inhale and end-exhale phases were also calculated. RESULTS: Tumor ITVs from amplitude-based methods were significantly larger than those from temporal-based techniques (P=.002). For lymph nodes, A2 resulted in ITVs that were significantly larger than either of the temporal-based techniques (P<.0323). A1 produced the largest and most accurate ITVs for spheres with diameters of ≥2 cm (P=.002). No significant difference was shown between algorithms in the 1-cm sphere data set. For phantom spheres, amplitude-based methods recovered an average of 9.5% more motion displacement than temporal-based methods under regular breathing conditions and an average of 45.7% more in the presence of baseline drift (P<.001). CONCLUSIONS: Target volumes in images generated from amplitude-based gating are larger and more accurate, at levels that are potentially clinically significant, compared with those from temporal phase-based gating.