The three-prong method: a novel assessment of residual stress in laser powder bed fusion

<p><b>Boxplots of quantitative parameters</b> included <b>a)</b> ratio of N-acetylaspartate and N-acetylaspartylglutamate (NAA) to creatine and phosphocreatine (Cr) both for chemical shift imaging (CSI) and single voxel (SV) measurements, <b>b)</b> ratio of choline containing compounds (Cho) to Cr both for CSI and SV, <b>c)</b> myelin water fraction (MWF), <b>d)</b> magnetization transfer ratio (MTR), <b>e)</b> quantitative susceptibility mapping (QSM), and <b>f)</b> R2*. Parameters were measured in frontal white matter (WM) and two parameters within the cortico-spinal tract (CST): at the level of the posterior limb of internal capsule (PLIC) and at the level of the centrum semiovale (CS), see also <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0167274#pone.0167274.g001" target="_blank">Fig 1</a>.</p

Exemplary lesion detection results in an MRI-negative patient.

(A) Overlays of the prediction statistic on the inflated template surface for different selected univariate and multivariate approaches. The colorscale is aligned according to the false-positive fraction in each case. The green outline depicts the border of the lobar hypothesis label. (B, C) Original image data with overlaid tracings of the reconstructed pial surface, colored according to the cortical thickness GLM z-score, showing the maximum value in the temporal hypothesis label (B) and frontopolar (C). Overall, the results agree well with the clinical constellation in this patient: Whereas the hypothesis was formally defined as right insular, upon intracranial EEG also seizures originating from right frontal were recorded; in the end, the multifocal localization contraindicated surgery.</p

Results of the automated detection performance evaluation for univariate surface measure analysis, summarized by the area under the alternative free-response ROC curve (AFROC AUC).

(A) MRI-positive patients, (B) MRI-negative cohort with electroclinical lobar hypotheses, compared between MPRAGE- (blue) and MP2RAGE-based (red) data. The color scale depicts the uncorrected p-value for each AUC under the simulated null distribution. The dashed line reflects the p-value/AUC threshold for a family-wise error rate (FWER) of 0.05 with Bonferroni correction for 38 comparisons in each cohort.</p

Results of the automated detection performance evaluation for unsupervised (IF: Isolation forest, MAH: Mahalanobis distance) and supervised (RFC: Random forest) classifiers, based on different sets of surface measures each with (w/) or without (w/o) FLAIR intensity data.

Performance is summarized by the area under the alternative free-response ROC curve (AFROC AUC, y-axis). (A) MRI-positive patients, (B) MRI-negative cohort with electroclinical lobar hypotheses, compared between MPRAGE- (blue) and MP2RAGE-based (red) data. The color scale depicts the uncorrected p-value for each AUC under the simulated null distribution. The dashed line reflects the p-value/AUC threshold for a family-wise error rate (FWER) of 0.05 with Bonferroni correction for 36 comparisons in each cohort.</p

Fig 1 -

(A) Modeled alternative free-response receiver operating characteristic (AFROC) curves of a random ‘guessing’ process for different relative ground truth lesion label sizes φ. It can be appreciated that for φ = 1, TPF = FPF and AUC = 0.5 (the ground truth labels cover the entirety of every ‘lesional’ subject, the task effectively becomes a binary classification as in classical ROC analysis). Contrarily, for φ → 0, guessing AUC → 0. (B-D) Simulated random process AFROC curves for φ = 0.2 and different numbers of subjects n, overlaid onto the model (3 curves each). It is visible that for lower n, the distribution broadens.</p

Exemplary lesion detection results in a patient with histologically proven FCD IIb.

Left: Coronal views of the original image data showing the lesion. Right: Overlays of the prediction statistic on the inflated template surface for different selected univariate and multivariate approaches. The colorscale is aligned according to the false-positive fraction in each case. The green outline depicts the border of the manually defined ground truth label. It can be appreciated that the MP2RAGE outperforms MPRAGE, and that whereas different approaches all allow detection of the lesion, simple univariate analysis of cortical thickness performs best in this case.</p

Fig 4 -

(A, B) Overlay of simulated ‘guessing’ AFROC (alternative free-response receiver operating characteristic) curves based on the ground truth lesion label characteristics in our cohorts. Dashed lines show a least-mean-squares fit of the exponential model function TPF = 1-(1-FPF)φ to all data. (C, D) Histograms of AUCS (areas under the AFROC curves) for 105 simulations. (A, C) refer to our MRI-negative and (B, C) to the MRI-positive subjects.</p

S1 File -

In drug-resistant focal epilepsy, detecting epileptogenic lesions using MRI poses a critical diagnostic challenge. Here, we assessed the utility of MP2RAGE–a T1-weighted sequence with self-bias correcting properties commonly utilized in ultra-high field MRI–for the detection of epileptogenic lesions using a surface-based morphometry pipeline based on FreeSurfer, and compared it to the common approach using T1w MPRAGE, both at 3T. We included data from 32 patients with focal epilepsy (5 MRI-positive, 27 MRI-negative with lobar seizure onset hypotheses) and 94 healthy controls from two epilepsy centres. Surface-based morphological measures and intensities were extracted and evaluated in univariate GLM analyses as well as multivariate unsupervised ‘novelty detection’ machine learning procedures. The resulting prediction maps were analyzed over a range of possible thresholds using alternative free-response receiver operating characteristic (AFROC) methodology with respect to the concordance with predefined lesion labels or hypotheses on epileptogenic zone location. We found that MP2RAGE performs at least comparable to MPRAGE and that especially analysis of MP2RAGE image intensities may provide additional diagnostic information. Secondly, we demonstrate that unsupervised novelty-detection machine learning approaches may be useful for the detection of epileptogenic lesions (maximum AFROC AUC 0.58) when there is only a limited lesional training set available. Third, we propose a statistical method of assessing lesion localization performance in MRI-negative patients with lobar hypotheses of the epileptogenic zone based on simulation of a random guessing process as null hypothesis. Based on our findings, it appears worthwhile to study similar surface-based morphometry approaches in ultra-high field MRI (≥ 7 T).</div

Comparison of per-subject volumetric measures (total cortical gray matter volume, total cerebral white matter volume, brain segmentation volume) between MPRAGE and MP2RAGE scans.

It becomes apparent that MP2RAGE results in slightly smaller cortex and brain segmentations. Boxplots show absolute distributions, histograms paired differences (MP2RAGE-MPRAGE). All units are milliliters. In all three cases, group differences are statistically significant (Wilcoxon signed rank paired test p < 0.01).</p

Fig 3 -

(A) Mean vertex-wise paired differences of cortical thickness between MPRAGE and MP2RAGE reconstructions–in control subjects–rendered on the template inflated cortical surface. Overall, the cortex was estimated thinner based on MP2RAGE. Large deviations appear in the basal frontal lobe, which may be due to misclassification of dura due to extracerebral noise in MP2RAGE. The cortex is reconstructed thicker in the central and medio-occipital regions, corresponding to the ‘unimodal’ primary sensory, motor and visual areas–a pattern that resembles and may be explained by different cortical myelination (see discussion). (B) Histograms of normalized T1-image intensity in all control vertices, sampled at 2 mm subcortically (left) and mid-cortically (right), for MPRAGE and MP2RAGE. MP2RAGE intensity distributions are narrower, likely a result of advantageous bias-field correction characteristics of MP2RAGE.</p