The Boston Marathon bombing: after-action review of the Brigham and Women's Hospital emergency radiology response.

PURPOSE: To analyze imaging utilization and emergency radiology process turnaround times in response to the April 15, 2013, Boston Marathon bombing in order to identify opportunities for improvement in the Brigham and Women's Hospital (BWH) emergency operations plan. MATERIALS AND METHODS: Institutional review board approval was obtained with waivers of informed consent. Patient demographics, injuries, and outcomes were gathered, along with measures of emergency department (ED) imaging utilization and turnaround times, which were compared with operations from the preceding year by using the Wilcoxon rank sum test. Multivariate linear regression was used to assess contributors to examination cancellations. RESULTS: Forty patients presented to BWH after the bombing; 16 were admitted and 24 were discharged home. There were no fatalities. Ten patients required emergent surgery. Blast injury types included 13 (33%) primary, 20 (51%) secondary, three (8%) tertiary, and 19 (49%) quaternary. Thirty-one patients (78%) underwent imaging in the ED; 57 radiographic examinations in 30 patients and 16 computed tomographic (CT) examinations in seven patients. Sixty-two radiographic and 14 CT orders were cancelled. Median time from blast to patient arrival was 97 minutes (interquartile range [IQR], 43-139 minutes), patient arrival to ED examination order, 24 minutes (IQR, 12-50 minutes), order to examination completion, 49 minutes (IQR, 26-70 minutes), and examination completion to available dictated text report, 75 minutes (IQR, 19-147 minutes). Examination completion turnaround times were significantly increased for radiography (52 minutes [IQR, 26-73 minutes] vs annual median, 31 minutes [IQR, 19-48 minutes]; P = .001) and decreased for CT (37 minutes [IQR, 26-50 minutes] vs annual median, 72 minutes [IQR, 40-129 minutes]; P = .001). There were no significant differences in report availability turnaround time (75 minutes [IQR, 19-147 minutes] vs annual median, 74 minutes [IQR, 35-127 minutes]; P = .34). CONCLUSION: The surge in imaging utilization after the Boston Marathon bombing stressed emergency radiology operations. Process analysis enabled identification of successes and opportunities for improvement in ongoing emergency operations planning. © RSNA, 2014.

OASIS is Automated Statistical Inference for Segmentation, with applications to multiple sclerosis lesion segmentation in MRI.

Magnetic resonance imaging (MRI) can be used to detect lesions in the brains of multiple sclerosis (MS) patients and is essential for diagnosing the disease and monitoring its progression. In practice, lesion load is often quantified by either manual or semi-automated segmentation of MRI, which is time-consuming, costly, and associated with large inter- and intra-observer variability. We propose OASIS is Automated Statistical Inference for Segmentation (OASIS), an automated statistical method for segmenting MS lesions in MRI studies. We use logistic regression models incorporating multiple MRI modalities to estimate voxel-level probabilities of lesion presence. Intensity-normalized T1-weighted, T2-weighted, fluid-attenuated inversion recovery and proton density volumes from 131 MRI studies (98 MS subjects, 33 healthy subjects) with manual lesion segmentations were used to train and validate our model. Within this set, OASIS detected lesions with a partial area under the receiver operating characteristic curve for clinically relevant false positive rates of 1% and below of 0.59% (95% CI; [0.50%, 0.67%]) at the voxel level. An experienced MS neuroradiologist compared these segmentations to those produced by LesionTOADS, an image segmentation software that provides segmentation of both lesions and normal brain structures. For lesions, OASIS out-performed LesionTOADS in 74% (95% CI: [65%, 82%]) of cases for the 98 MS subjects. To further validate the method, we applied OASIS to 169 MRI studies acquired at a separate center. The neuroradiologist again compared the OASIS segmentations to those from LesionTOADS. For lesions, OASIS ranked higher than LesionTOADS in 77% (95% CI: [71%, 83%]) of cases. For a randomly selected subset of 50 of these studies, one additional radiologist and one neurologist also scored the images. Within this set, the neuroradiologist ranked OASIS higher than LesionTOADS in 76% (95% CI: [64%, 88%]) of cases, the neurologist 66% (95% CI: [52%, 78%]) and the radiologist 52% (95% CI: [38%, 66%]). OASIS obtains the estimated probability for each voxel to be part of a lesion by weighting each imaging modality with coefficient weights. These coefficients are explicit, obtained using standard model fitting techniques, and can be reused in other imaging studies. This fully automated method allows sensitive and specific detection of lesion presence and may be rapidly applied to large collections of images.