Background and purpose

This study compares the concordance between arterial spin labeling (ASL) and dynamic susceptibility contrast (DSC) for the identification of regional hypoperfusion and diffusion-perfusion mismatch tissue classification using a quantitative method.

Methods

The inclusion criteria for this retrospective study were as follows: patients with acute ischemic syndrome with symptom onset <24 hours and acquisition of both ASL and DSC MR perfusion. The volumes of infarction and hypoperfused lesions were calculated on ASL and DSC multi-parametric maps. Patients were classified into reperfused, matched, or mismatch groups using time to maximum >6 sec as the reference. In a subset of patients who were successfully recanalized, the identical analysis was performed and the infarction and hypoperfused lesion volumes were used for paired pre- and posttreatment comparisons.

Results

Forty-one patients met our inclusion criteria. Twenty patients underwent successful endovascular revascularization (TICI>2a), resulting in a total of 61 ASL-DSC data pairs for comparison. The hypoperfusion volume on ASL-cerebral blood flow best approximated the DSC-time to peak volume (r=0.83) in pretreatment group and time to maximum (r=0.46) after recanalization. Both ASL-cerebral blood flow and DSC-TTP overestimated the hypoperfusion volume compared with time to maximum volume in pretreatment (F=27.41, P<0.0001) and recanalized patients (F=8.78, P<0.0001).

Conclusions

ASL-cerebral blood flow overestimates the DSC time to maximum hypoperfusion volume and mismatch classification in patients with acute ischemic syndrome. Continued overestimation of hypoperfused volume after recanalization suggests flow pattern and velocity changes in addition to arterial transit delay can affects the performance of ASL.

The electronic health record (EHR) contains a diverse set of clinical observations that are captured as part of routine care, but the incomplete, inconsistent, and sometimes incorrect nature of clinical data poses significant impediments for its secondary use in retrospective studies or comparative effectiveness research. In this work, we describe an ontology-driven approach for extracting and analyzing data from the patient record in a longitudinal and continuous manner. We demonstrate how the ontology helps enforce consistent data representation, integrates phenotypes generated through analyses of available clinical data sources, and facilitates subsequent studies to identify clinical predictors for an outcome of interest. Development and evaluation of our approach are described in the context of studying factors that influence intracranial aneurysm (ICA) growth and rupture. We report our experiences in capturing information on 78 individuals with a total of 120 aneurysms. Two example applications related to assessing the relationship between aneurysm size, growth, gene expression modules, and rupture are described. Our work highlights the challenges with respect to data quality, workflow, and analysis of data and its implications toward a learning health system paradigm.

The human visual pathways that are specialized for object recognition stretch from lateral occipital cortex (LO) to the ventral surface of the temporal lobe, including the fusiform gyrus. Plasticity in these pathways supports the acquisition of visual expertise, but precisely how training affects the different regions remains unclear. We used functional magnetic resonance imaging to measure neural activity in both LO and the fusiform gyrus in radiologists as they detected abnormalities in chest radiographs. Activity in the right fusiform face area (FFA) correlated with visual expertise, measured as behavioral performance during scanning. In contrast, activity in left LO correlated negatively with expertise, and the amount of LO that responded to radiographs was smaller in experts than in novices. Activity in the FFA and LO correlated negatively in experts, whereas in novices, the 2 regions showed no stable relationship. Together, these results suggest that the FFA becomes more engaged and left LO less engaged in interpreting radiographic images over the course of training. Achieving expert visual performance may involve suppressing existing neural representations while simultaneously developing others.

The electronic health record (EHR) contains a diverse set of clinical observations that are captured as part of routine care, but the incomplete, inconsistent, and sometimes incorrect nature of clinical data poses significant impediments for its secondary use in retrospective studies or comparative effectiveness research. In this work, we describe an ontology-driven approach for extracting and analyzing data from the patient record in a longitudinal and continuous manner. We demonstrate how the ontology helps enforce consistent data representation, integrates phenotypes generated through analyses of available clinical data sources, and facilitates subsequent studies to identify clinical predictors for an outcome of interest. Development and evaluation of our approach are described in the context of studying factors that influence intracranial aneurysm (ICA) growth and rupture. We report our experiences in capturing information on 78 individuals with a total of 120 aneurysms. Two example applications related to assessing the relationship between aneurysm size, growth, gene expression modules, and rupture are described. Our work highlights the challenges with respect to data quality, workflow, and analysis of data and its implications towards a learning health system paradigm.Graphical abstract

Purpose

To characterize the relationship between aneurysm size and epidemiologic risk factors with growth and rupture by using computed tomographic (CT) angiography.

Materials and methods

In this HIPAA-compliant, institutional review board approved study, patients with known asymptomatic unruptured intracerebral aneurysms were followed up longitudinally with CT angiographic examinations. Growth was defined as an increase in one or more dimensions above the measurement error, and at least 5% volume by using the ABC/2 method. Associations of epidemiologic factors with aneurysm growth and rupture were analyzed by using logistic regression analysis. Intra- and interobserver agreement coefficients for dimension, volume, and growth were evaluated by using the Pearson correlation coefficient and difference of means with 95% confidence intervals, the agreement statistic, and the McNemar χ(2).

Results

Patients (n = 165) with aneurysms (n = 258) had a mean follow-up time of 2.24 years from time of diagnosis. Forty-six of 258 (18%) aneurysms in 38 patients grew larger. Spontaneous rupture occurred in four of 228 (1.8%) intradural aneurysms of average size (6.2 mm). Risk of aneurysm rupture per patient-year was 2.4% (95% CI: 0.5%, 7.12%) with growth and 0.2% (95% CI: 0.006%, 1.22%) without growth (P = .034). There was a 12-fold higher risk of rupture for growing aneurysms (P < .002), with high intra- and interobserver correlation coefficients for size, volume, and growth. Tobacco smoking (3.806, one degree of freedom; P < .015,) and initial size (5.895, two degrees of freedom; P < .051) were independent covariates, predicting 78.4% of growing aneurysms.

Conclusion

These results support imaging follow-up of all patients with aneurysms, including those whose aneurysms are smaller than the current 7-mm treatment threshold. Aneurysm growth, size, and smoking were associated with increased rupture risk.

Purpose

CT neuroperfusion examinations are capable of delivering high radiation dose to the skin or lens of the eyes of a patient and can possibly cause deterministic radiation injury. The purpose of this study is to: (a) estimate peak skin dose and eye lens dose from CT neuroperfusion examinations based on several voxelized adult patient models of different head size and (b) investigate how well those doses can be approximated by some commonly used CT dose metrics or tools, such as CTDIvol, American Association of Physicists in Medicine (AAPM) Report No. 111 style peak dose measurements, and the ImPACT organ dose calculator spreadsheet.

Methods

Monte Carlo simulation methods were used to estimate peak skin and eye lens dose on voxelized patient models, including GSF's Irene, Frank, Donna, and Golem, on four scanners from the major manufacturers at the widest collimation under all available tube potentials. Doses were reported on a per 100 mAs basis. CTDIvol measurements for a 16 cm CTDI phantom, AAPM Report No. 111 style peak dose measurements, and ImPACT calculations were performed for available scanners at all tube potentials. These were then compared with results from Monte Carlo simulations.

Results

The dose variations across the different voxelized patient models were small. Dependent on the tube potential and scanner and patient model, CTDIvol values overestimated peak skin dose by 26%-65%, and overestimated eye lens dose by 33%-106%, when compared to Monte Carlo simulations. AAPM Report No. 111 style measurements were much closer to peak skin estimates ranging from a 14% underestimate to a 33% overestimate, and with eye lens dose estimates ranging from a 9% underestimate to a 66% overestimate. The ImPACT spreadsheet overestimated eye lens dose by 2%-82% relative to voxelized model simulations.

Conclusions

CTDIvol consistently overestimates dose to eye lens and skin. The ImPACT tool also overestimated dose to eye lenses. As such they are still useful as a conservative predictor of dose for CT neuroperfusion studies. AAPM Report No. 111 style measurements are a better predictor of both peak skin and eye lens dose than CTDIvol and ImPACT for the patient models used in this study. It should be remembered that both the AAPM Report No. 111 peak dose metric and CTDIvol dose metric are dose indices and were not intended to represent actual organ doses.

Background

Advances in diagnostic imaging of stroke include multimodal techniques such as noninvasive angiography and perfusion imaging. We aimed to characterize trends in neuroimaging utilization among acute stroke patients. Utilization of multimodal imaging for acute stroke in the community has remained largely uncharacterized despite its increased adoption at academic medical centers.

Methods

We quantified neuroimaging utilization in the emergency department (ED) for 1,700 hyperacute stroke patients presenting <2 hours after symptom onset who participated in the National Institutes of Health Field Administration of Stroke Therapy-Magnesium (FAST-MAG) study throughout Los Angeles and Orange Counties. FAST-MAG provided no recommendation as to imaging utilization.

Results

A total of 1,700 cases were imaged a median (interquartile range [IQR]) of 92 (74-120) minutes after last known well time and 28 (19-41) minutes after ED arrival. The initial scanner used in the ED was computed tomography (CT) in a preponderance of cases (N = 1,612, 95%), with magnetic resonance imaging (MRI) in 88 cases (5%). CT angiography (CTA) was obtained in 192 (11%) and perfusion CT (CTP) in 91 (5.4%) cases. MRI imaging was universally obtained using diffusion-weighted images, 60% with MR angiography and 33% included perfusion imaging. Rates of concomitant CTA or CTP use increased in the later years of the study from 4% in 2005-2006, 2% in 2007-2008, 8% in 2009-2010, and 26% in 2011-2012 (P for trend < .001).

Conclusions

Among acute stroke patients, noncontrast CT was the most common initial imaging strategy in clinical practice in the 2005-2012 time period, though use of concomitant CTA grew to one-quarter of cases, suggestive of an upward trend.

Purpose

To use machine learning tools and leverage big data informatics to statistically model the variation in the area of lumbar neural foramina in a large asymptomatic population.

Materials and methods

By using an electronic health record and imaging archive, lumbar MRI studies in 645 male (mean age, 50.07 years) and 511 female (mean age, 48.23 years) patients between 20 and 80 years old were identified. Machine learning algorithms were used to delineate lumbar neural foramina autonomously and measure their areas. The relationship between neural foraminal area and patient age, sex, and height was studied by using multivariable linear regression.

Results

Neural foraminal areas correlated directly with patient height and inversely with patient age. The associations involved were statistically significant (P < .01).

Conclusion

By using machine learning and big data techniques, a linear model encoding variation in lumbar neural foraminal areas in asymptomatic individuals has been established. This model can be used to make quantitative assessments of neural foraminal areas in patients by comparing them to the age-, sex-, and height-adjusted population averages.© RSNA, 2019Supplemental material is available for this article.

This cross-sectional study examines the differences in cortical volume and gray-to-white matter contrast (GWC) in first episode schizophrenia patients (SCZ) compared to healthy control participants (HC) and in SCZ patients as a function of exposure to second generation antipsychotic medication. We hypothesize 1) SCZ exhibit regionally lower cortical volumes relative to HCs, 2) cortical volume will be greater with longer exposure to second generation antipsychotics prior to the MRI scan, and 3) lower GWC with longer exposure to second generation antipsychotics prior to the MRI scan, suggesting more blurring from greater intracortical myelin. To accomplish this, MRI scans from 71 male SCZ patients treated with second generation oral risperidone and 42 male HCs were examined. 3D T1-weighted MPRAGE images collected at 1.5T were used to estimate cortical volume and GWC by sampling signal intensity at 30% within the cortical ribbon. Average cortical volume and GWC were calculated and compared between SCZ and HC. Cortical volume and GWC in SCZ patients were correlated with duration of medication exposure for the time period prior to the scan. First-episode SCZ patients had significantly lower cortical volume compared to HCs in bilateral temporal, superior and rostral frontal, postcentral gyral, and parahippocampal regions. In SCZ patients, greater cortical volume was associated with (log-transformed) duration of second-generation antipsychotic medication exposure in bilateral precuneus, right lingual, and right superior parietal regions. Lower GWC was correlated with longer duration of medication exposure bilaterally in the superior frontal lobes. In summary, second generation antipsychotics may increase cortical volume and decrease GWC in first episode SCZ patients.

Purpose

CT neuroperfusion examinations are capable of delivering high radiation dose to the skin or lens of the eyes of a patient and can possibly cause deterministic radiation injury. The purpose of this study is to: (a) estimate peak skin dose and eye lens dose from CT neuroperfusion examinations based on several voxelized adult patient models of different head size and (b) investigate how well those doses can be approximated by some commonly used CT dose metrics or tools, such as CTDIvol, American Association of Physicists in Medicine (AAPM) Report No. 111 style peak dose measurements, and the ImPACT organ dose calculator spreadsheet.

Methods

Monte Carlo simulation methods were used to estimate peak skin and eye lens dose on voxelized patient models, including GSF's Irene, Frank, Donna, and Golem, on four scanners from the major manufacturers at the widest collimation under all available tube potentials. Doses were reported on a per 100 mAs basis. CTDIvol measurements for a 16 cm CTDI phantom, AAPM Report No. 111 style peak dose measurements, and ImPACT calculations were performed for available scanners at all tube potentials. These were then compared with results from Monte Carlo simulations.

Results

The dose variations across the different voxelized patient models were small. Dependent on the tube potential and scanner and patient model, CTDIvol values overestimated peak skin dose by 26%-65%, and overestimated eye lens dose by 33%-106%, when compared to Monte Carlo simulations. AAPM Report No. 111 style measurements were much closer to peak skin estimates ranging from a 14% underestimate to a 33% overestimate, and with eye lens dose estimates ranging from a 9% underestimate to a 66% overestimate. The ImPACT spreadsheet overestimated eye lens dose by 2%-82% relative to voxelized model simulations.

Conclusions

CTDIvol consistently overestimates dose to eye lens and skin. The ImPACT tool also overestimated dose to eye lenses. As such they are still useful as a conservative predictor of dose for CT neuroperfusion studies. AAPM Report No. 111 style measurements are a better predictor of both peak skin and eye lens dose than CTDIvol and ImPACT for the patient models used in this study. It should be remembered that both the AAPM Report No. 111 peak dose metric and CTDIvol dose metric are dose indices and were not intended to represent actual organ doses.