Quantifying the calcification of abdominal aorta and major side branches with deep learning.

AIM: To explore the possibility of a neural network-based method for quantifying calcifications of the abdominal aorta and its branches. MATERIALS AND METHODS: In total, 58 computed tomography (CT) angiography volumes were selected from a dataset of 609 to represent different stages of sclerosis. The ground truth segmentations of the abdominal aorta, coeliac trunk, superior mesenteric artery, renal arteries, common iliac arteries, and their calcifications were delineated manually. Two V-Net ensemble models were trained, one for segmenting arteries of interest and another for calcifications. The branches of interest were shortened algorithmically. The volumes of calcification were then evaluated from the arteries of interest. RESULTS: The results indicate that automatic detection is possible with a high correlation to the ground truth. The scores for the ensemble calcification model were dice score of 0.69 and volumetric similarity (VS) of 0.80 and for the arteries of interest segmentations: aorta: dice 0.96, VS 0.98; aortic branches: dice 0.74, VS 0.87; and common iliac arteries: dice 0.72, VS 0.91. CONCLUSIONS: The presented neural network model is the first to be capable of automatically segmenting, in addition to calcification, both the aorta and its branches from contrast-enhanced CT angiography. This technology shows promise in addressing limitations inherent in earlier methods that relied solely on plain CT.

Automatic detection of Crohn's disease using quantified motility in magnetic resonance enterography: initial experiences.

AIM: To report initial experiences of automatic detection of Crohn's disease (CD) using quantified motility in magnetic resonance enterography (MRE). MATERIALS AND METHODS: From 302 patients, three datasets with roughly equal proportions of CD and non-CD cases with various illnesses were drawn for testing and neural network training and validation. All datasets had unique MRE parameter configurations and were performed in free breathing. Nine neural networks were devised for automatic generation of three different regions of interests (ROI): small bowel, all bowel, and non-bowel. Additionally, a full-image ROI was tested. The motility in an MRE series was quantified via a registration procedure, which, accompanied with given ROIs, resulted in three motility indices (MI). A subset of the indices was used as an input for a binary logistic regression classifier, which predicted whether the MRE series represented CD. RESULTS: The highest mean area under the curve (AUC) score, 0.78, was reached using the full-image ROI and with the dataset with the highest cine series length. The best AUC scores for the other two datasets were only 0.54 and 0.49. CONCLUSION: The automatic system was able to detect CD in the group of MRE studies with lower temporal resolution and longer cine series showing potential in primary bowel disorder diagnostics. Larger ROI selections and utilising all available cine series for motility registration yielded slight performance improvements.

A Statewide Survey to Understand Barriers to Utilization of the Medical Orders for Scope of Treatment Form among Emergency Medical Service Providers.

Background: Advance care planning allows communication of end-of-life goals. The Kentucky medical orders for scope of treatment (MOST) form became a legal medical order in 2015. The Kentucky MOST Coalition formed in 2017 to implement MOST. Objectives: In 2019, Kentucky MOST Coalition members developed a survey to determine emergency medical services (EMS) providers' understanding and barriers to utilization of MOST. Design: The Kentucky Board of EMS e-mailed the survey to its members. The survey was voluntarily completed by EMS providers. The data were analyzed by Kentucky MOST Coalition. Results: A majority of participants had never (72%) encountered a MOST form. In addition, 69% said they had never received MOST training. However, 60% knew that "the MOST form is a physician order and shall be followed in all settings." Conclusions: The majority of the EMS providers knew basic information about MOST. However, accessibility and education were barriers to utilization of MOST. EMS providers need continuing education. Education surrounding MOST can help to ensure a person-centered approach.

Combined use of non-invasive techniques for improved functional localization for a selected group of epilepsy surgery candidates.

Invasive cortical mapping is conventionally required for preoperative identification of epileptogenic and eloquent cortical regions before epilepsy surgery. The decision on the extent and exact location of the resection is always demanding and multimodal approach is desired for added certainty. The present study describes two non-invasive preoperative protocols, used in addition to the normal preoperative work-up for localization of the epileptogenic and sensorimotor cortical regions, in two young patients with epilepsy. Magnetoencephalography (MEG) was used to determine the primary somatosensory cortex (S1) and the ictal onset zones. Navigated transcranial magnetic stimulation (nTMS) was used to determine the location and the extent of the primary motor representation areas. The localization results from these non-invasive methods were used for guiding the subdural grid deployment and later compared with the results from electrical cortical stimulation (ECS) via subdural grids, and validated by surgery outcome. The results from MEG and nTMS localizations were consistent with the ECS results and provided improved spatial precision. Consistent results of our study suggest that these non-invasive methods can be added to the standard preoperative work-up and may even hold a potential to replace the ECS in a subgroup of patients with epilepsy who have the suspected epileptogenic zone near the sensorimotor cortex and seizures frequent enough for ictal MEG.

Automatic image quality quantification and mapping with an edge-preserving mask-filtering algorithm.

BACKGROUND: Imaging modalities in digital radiology produce large amounts of data for which image quality should be determined in order to validate the diagnostic operation. PURPOSE: To develop an automatic method for image quality assessment. MATERIAL AND METHODS: A filtering algorithm using a moving square mask was applied to create a map of filtered local intensity and noise values. Image quality scores were calculated from the filtered image data. The procedure was applied to technical and anthropomorphic (radiosurgery verification phantom [RSVP] head) phantom images obtained with varying radiation dose, field of view (FOV), and image content. The method was also applied to a clinical computed tomography (CT) brain image. RESULTS: The image quality score (IQs) of the phantom images increased from 0.51 to 0.82 as the radiation dose (CTDIvol) increased from 9.2 to 74.3 mGy. Correlation of the IQs with the pixel noise was R(2)=0.99. The deviation (1 SD) of IQs was 2.8% when the reconstruction FOV was set between 21 and 25 cm. The correlation of IQs with the pixel noise was R(2)=0.98 with variable image contents and dose. Automatic tube current modulation applied to the RSVP phantom scan reduced the variation in the calculated image quality score by about 60% compared to the use of a fixed tube current. CONCLUSION: The image quality score provides an efficient tool for automatic quantification of image quality. The presented method also produces a 2D image quality map, which can be used for further image analysis.

Reversal of cerebral asymmetry in schizophrenia measured with magnetoencephalography.

It has been suggested that schizophrenic patients fail to develop left-hemisphere dominance because of an early disturbance in neuronal development. This hypothesis has been supported by some post-mortem. CT and magnetic resonance imaging (MRI) studies, while other in-vivo studies have given contradicting results. We used 122-channel whole-head magnetoencephalography and MRI to locate the sources of auditory evoked responses in 19 schizophrenic patients and in 20 healthy controls. Auditory evoked responses were detected in all subjects. The left-right hemisphere asymmetry of cerebral sources for auditory evoked responses was markedly dispersed among patients when compared with controls. The source locations for left auditory cortex were clearly anterior with respect to the right hemisphere in 32% of the patients, while the corresponding prevalence of this abnormal asymmetry was 0% in controls (p = 0.008. Fisher's exact test). The reversed asymmetry appeared to be associated with a shorter anterior-posterior distance between the auditory cortex and the anterior tip of the temporal lobe in the left side when compared with the right side. The reversed asymmetry was associated with higher PANSS general psychopathological score, and especially with higher guilt feelings and motor retardation scores. The large 2.5-fold standard deviation in the inter-hemispheric anterior posterior difference in the location of the auditory cortex among patients (p 0.001 for the difference in the magnitude of variance between controls and patients) clearly reflects the dispersion of the left right asymmetry into both direction, and three of the patients with 'normal asymmetry' had a greater left-right asymmetry than any of the controls. Markedly greater reversal of hemispheric asymmetry among patients implies that regulation of the development of brain asymmetry is disturbed among schizophrenic patients. Abnormality in the cerebral asymmetry may be a crucial factor in the development of schizophrenic disorder in a substantial proportion of patients. The results suggest that the reversed asymmetry is associated with the higher severity of general psychopathological symptoms.

Somatosensory evoked magnetic fields from the primary somatosensory cortex (SI) in acute stroke.

We recorded somatosensory evoked magnetic fields (SEFs) to median nerve stimulation from 15 patients in the acute stage (1-15 days from the onset of the symptoms) of their first-ever unilateral stroke involving sensorimotor cortical and/or subcortical structures in the territory of the middle cerebral artery (MCA). Neuronal activity corresponding to the peaks of the N20m, P35m and P60m SEF deflections from the contralateral primary somatosensory cortex (SI) was modelled with equivalent current dipoles (ECDs), the locations and strengths of which were compared with those of an age-matched normal population. Four patients with pure motor stroke had symmetric SEFs. In one of the 4 patients with pure sensory stroke, and in 5 of the 7 patients with sensorimotor paresis, the SEFs were markedly attenuated or missing. All except one patient with abnormal SEFs had deficient two-point discrimination ability; especially the attenuation of N20m was more clearly correlated with two-point discrimination than with joint-position or vibration senses. Of the different SEF deflections, P35m and P60m were slightly more sensitive indicators of abnormality than N20m, the former being affected in two patients with symmetric N20m. Three patients with pure sensory stroke and lesions in the opercular cortex had normal SEFs from SI. We conclude that the SEF deflections N20m, P35m and P60m from SI are related to cutaneous sensation, in particular discriminative to touch. The results also demonstrate that basic somatosensory perception can be affected by lesions in the opercular cortex in patients with functionally intact SI.

Contextual clustering for analysis of functional MRI data.

We present a contextual clustering procedure for statistical parametric maps (SPM) calculated from time varying three-dimensional images. The algorithm can be used for the detection of neural activations from functional magnetic resonance images (fMRI). An important characteristic of SPM is that the intensity distribution of background (nonactive area) is known whereas the distributions of activation areas are not. The developed contextual clustering algorithm divides an SPM into background and activation areas so that the probability of detecting false activations by chance is controlled, i.e., hypothesis testing is performed. Unlike the much used voxel-by-voxel testing, neighborhood information is utilized, an important difference. This is achieved by using a Markov random field prior and iterated conditional modes (ICM) algorithm. However, unlike in the conventional use of ICM algorithm, the classification is based only on the distribution of background. The results from our simulations and human fMRI experiments using visual stimulation demonstrate that a better sensitivity is achieved with a given specificity in comparison to the voxel-by-voxel thresholding technique. The algorithm is computationally efficient and can be used to detect and delineate objects from a noisy background in other applications.

A cluster mass permutation test with contextual enhancement for fMRI activation detection.

Gaussian random field (GRF)-based methods are commonly used for statistical inference and to control the family-wise error rate (FWE) in neuroimaging. They require that the error fields are reasonable lattice approximations to an underlying continuous multivariate Gaussian random field and have differentiable and invertible spatial autocorrelation function. Permutation test estimates the distribution of the test statistic from the data and adjusts automatically for the FWE. Here we present a new analysis procedure, the cluster mass permutation test with contextual enhancement (CMPCE), and compare it to GRF. In CMPCE, the data are first pre-whitened to remove temporal autocorrelations. The FWE rates, the cluster detection probability and delineation accuracy of CMPCE and GRF were compared using measured null data and null data containing simulated activations. We also applied both methods to an fMRI experiment where tactile somatosensory stimulation into the right hand was used. When analyzing the FWE using null data, both CMPCE and GRF gave significantly higher FWEs (CMPCE up to 0.12, GRF up to 0.18) than the nominal significance level 0.05, indicating that the pre-whitening, motion correction or high-pass filtering partially failed. In the simulated activation data, CMPCE gave less falsely classified voxels for the same cluster detection probability level than GRF. The maximal cluster detection probability was on the other hand higher in the GRF-based method. Both methods gave qualitatively similar results in the tactile fMRI data. CMPCE seems to be a promising fMRI analysis method, especially if high delineation accuracy is required.

Reproducibility of fMRI: effect of the use of contextual information.

We studied the effect of use of contextual information on the reproducibility of the results in analysis of fMRI data. We used data from a repeated simple motor fMRI experiment. In the first approach, statistical parametric maps were computed from a spatially unsmoothed data and thresholded using a Bonferroni corrected threshold. In the second approach, the maps were computed from a spatially unsmoothed data but were segmented into nonactive and active regions using a spatial contextual clustering method. In the third approach, the statistical parametric maps were computed from spatially smoothed data and thresholded, using, optionally, a spatial extent threshold. The variation in the classification was largest in the Bonferroni thresholded statistical parametric maps. There were no significant differences in variation between statistical parametric maps generated with all the other methods. In addition to reproducibility, the detection rates of weak simulated activations in the presence of measured scanner and physiological noise were investigated. Contextual clustering method was the most sensitive method, while the least sensitive method was the Bonferroni corrected thresholding. Using simulated data, we demonstrated that the contextual clustering method preserves the shapes of activation regions better than the method using spatial smoothing of the data.

Distribution of cortical activation during visuospatial n-back tasks as revealed by functional magnetic resonance imaging.

Human neuroimaging studies conducted during visuospatial working memory tasks have inconsistently detected activation in the prefrontal cortical areas depending presumably on the type of memory and control tasks employed. We used functional magnetic resonance imaging to study brain activation related to the performance of a visuospatial n-back task with different memory loads (0-back, 1-back and 2-back tasks). Comparison of the 2-back versus 0-back tasks revealed consistent, bilateral activation in the medial frontal gyrus (MFG), superior frontal sulcus and adjacent cortical tissue (SFS/SFG) in all subjects and in six out of seven subjects in the intraparietal sulcus (IPS). Activation was also detected in the inferior frontal gyrus, medially in the superior frontal gyrus, precentral gyrus, superior and inferior parietal lobuli, occipital visual association areas, anterior and posterior cingulate areas and in the insula. Comparison between the 1-back versus 0-back tasks revealed activation only in a few brain areas. Activation in the MFG, SFS/SFG and IPS appeared dependent on memory load. The results suggest that the performance of a visuospatial working memory task engages a network of distributed brain areas and that areas in the dorsal visual pathway are engaged in mnemonic processing of visuospatial information.

Registration and display of brain SPECT and MRI using external markers.

Accurate anatomical localisation of abnormalities observed in brain perfusion single-photon emission computed tomography (SPECT) is difficult, but can be improved by correlating data from SPECT and other tomographic imaging modalities. For this purpose we have developed software to register, analyse and display 99mTc-hexamethylpropyleneamine oxime SPECT and 1.0 T MRI of the brain. For registration of SPECT and MRI data external skin markers containing 99mTc (220 kBq) in 50 microliters of coconut butter were used. The software is coded in the C programming language, and the X Window system and the OSF/Motif standards are used for graphics and definition of the user interface. The registration algorithm follows a noniterative least-squares method using singular value decomposition of a 3 x 3 covariance matrix. After registration, the image slices of both data sets are shown at identical tomographic levels. The registration error in phantom studies was on average 4 mm. In the two-dimensional display mode the orthogonal cross-sections of the data sets are displayed side by side. In the three-dimensional mode MRI data are displayed as a surface-shaded 3 D reconstruction and SPECT data as cut planes. The usefulness of this method is demonstrated in patients with cerebral infarcts, brain tumour, herpes simplex encephalitis and epilepsy.

Activation of multiple cortical areas in response to somatosensory stimulation: combined magnetoencephalographic and functional magnetic resonance imaging.

We combined information from functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG) to assess which cortical areas and in which temporal order show macroscopic activation after right median nerve stimulation. Five healthy subjects were studied with the two imaging modalities, which both revealed significant activation in the contra- and ipsilateral primary somatosensory cortex (SI), the contra- and ipsilateral opercular areas, the walls of the contralateral postcentral sulcus (PoCS), and the contralateral supplementary motor area (SMA). In fMRI, two separate foci of activation in the opercular cortex were discerned, one posteriorly in the parietal operculum (PO), and one anteriorly near the insula or frontal operculum (anterior operculum, AO). The activation sites from fMRI were used to constrain the solution of the inverse problem of MEG, which allowed us to construct a model of the temporal sequence of activation of the different sites. According to this model, the mean onset latency for significant activation at the contralateral SI was 20 msec (range, 17-22 msec), followed by activation of PoCS at 23 msec (range, 21-25 msec). The contralateral PO was activated at 26 msec (range, 19-32 msec) and AO at 33 msec (range, 22-51 msec). The contralateral SMA became active at 36 msec (range, 24-48 msec). The ipsilateral SI, PO, and AO became activated at 54-67 msec. We conclude that fMRI provides a useful means to constrain the inverse problem of MEG, allowing the construction of spatiotemporal models of cortical activation, which may have significant implications for the understanding of cortical network functioning.