Revealing Hidden Potentials of the q-Space Signal in Breast Cancer

Mammography screening for early detection of breast lesions currently suffers from high amounts of false positive findings, which result in unnecessary invasive biopsies. Diffusion-weighted MR images (DWI) can help to reduce many of these false-positive findings prior to biopsy. Current approaches estimate tissue properties by means of quantitative parameters taken from generative, biophysical models fit to the q-space encoded signal under certain assumptions regarding noise and spatial homogeneity. This process is prone to fitting instability and partial information loss due to model simplicity. We reveal unexplored potentials of the signal by integrating all data processing components into a convolutional neural network (CNN) architecture that is designed to propagate clinical target information down to the raw input images. This approach enables simultaneous and target-specific optimization of image normalization, signal exploitation, global representation learning and classification. Using a multicentric data set of 222 patients, we demonstrate that our approach significantly improves clinical decision making with respect to the current state of the art.Comment: Accepted conference paper at MICCAI 201

Impact of velocity- and acceleration-compensated encodings on signal dropout and black-blood state in diffusion-weighted magnetic resonance liver imaging at clinical TEs.

PurposeThe study aims to develop easy-to-implement concomitant field-compensated gradient waveforms with varying velocity-weighting (M1) and acceleration-weighting (M2) levels and to evaluate their efficacy in correcting signal dropouts and preserving the black-blood state in liver diffusion-weighted imaging. Additionally, we seek to determine an optimal degree of compensation that minimizes signal dropouts while maintaining blood signal suppression.MethodsNumerically optimized gradient waveforms were adapted using a novel method that allows for the simultaneous tuning of M1- and M2-weighting by changing only one timing variable. Seven healthy volunteers underwent diffusion-weighted magnetic resonance imaging (DWI) with five diffusion encoding schemes (monopolar, velocity-compensated (M1 = 0), acceleration-compensated (M1 = M2 = 0), 84%-M1-M2-compensated, 67%-M1-M2-compensated) at b-values of 50 and 800 s/mm2 at a constant echo time of 70 ms. Signal dropout correction and apparent diffusion coefficients (ADCs) were quantified using regions of interest in the left and right liver lobe. The blood appearance was evaluated using two five-point Likert scales.ResultsSignal dropout was more pronounced in the left lobe (19%-42% less signal than in the right lobe with monopolar scheme) and best corrected by acceleration-compensation (8%-10% less signal than in the right lobe). The black-blood state was best with monopolar encodings and decreased significantly (p ConclusionAll of the diffusion encodings used in this study demonstrated suitability for routine DWI application. The results indicate that a perfect value for the level of M1-M2-compensation does not exist. However, among the examined encodings, the 84%-M1-M2-compensated encodings provided a suitable tradeoff

Stability of Radiomic Features against Variations in Lesion Segmentations Computed on Apparent Diffusion Coefficient Maps of Breast Lesions

Diffusion-weighted imaging (DWI) combined with radiomics can aid in the differentiation of breast lesions. Segmentation characteristics, however, might influence radiomic features. To evaluate feature stability, we implemented a standardized pipeline featuring shifts and shape variations of the underlying segmentations. A total of 103 patients were retrospectively included in this IRB-approved study after multiparametric diagnostic breast 3T MRI with a spin-echo diffusion-weighted sequence with echoplanar readout (b-values: 50, 750 and 1500 s/mm 2 ). Lesion segmentations underwent shifts and shape variations, with >100 radiomic features extracted from apparent diffusion coefficient (ADC) maps for each variation. These features were then compared and ranked based on their stability, measured by the Overall Concordance Correlation Coefficient (OCCC) and Dynamic Range (DR). Results showed variation in feature robustness to segmentation changes. The most stable features, excluding shape-related features, were FO (Mean, Median, RootMeanSquared), GLDM (DependenceNonUniformity), GLRLM (RunLengthNonUniformity), and GLSZM (SizeZoneNonUniformity), which all had OCCC and DR > 0.95 for both shifting and resizing the segmentation. Perimeter, MajorAxisLength, MaximumDiameter, PixelSurface, MeshSurface, and MinorAxisLength were the most stable features in the Shape category with OCCC and DR > 0.95 for resizing. Considering the variability in radiomic feature stability against segmentation variations is relevant when interpreting radiomic analysis of breast DWI data.This research received no external funding

Influence of residual fat signal on diffusion kurtosis MRI of suspicious mammography findings

Abstract Recent studies showed the potential of diffusion kurtosis imaging (DKI) as a tool for improved classification of suspicious breast lesions. However, in diffusion-weighted imaging of the female breast, sufficient fat suppression is one of the main factors determining the success. In this study, the data of 198 patients examined in two study centres was analysed using standard diffusion and kurtosis evaluation methods and three DKI fitting approaches accounting phenomenologically for fat-related signal contamination of the lesions. Receiver operating characteristic curve analysis showed the highest area under the curve (AUC) for the method including fat correction terms (AUC = 0.85, p < 0.015) in comparison to the values obtained with the standard diffusion (AUC = 0.77) and kurtosis approach (AUC = 0.79). Comparing the two study centres, the AUC value improved from 0.77 to 0.86 (p = 0.036) using a fat correction term for the first centre, while no significant difference with no adverse effects was observed for the second centre (AUC 0.89 vs. 0.90, p = 0.95). Contamination of the signal in breast lesions with unsuppressed fat causing a reduction of diagnostic performance of diffusion kurtosis imaging may potentially be counteracted by proposed adapted evaluation methods

Image quality assessment using deep learning in high b-value diffusion-weighted breast MRI

AbstractThe objective of this IRB approved retrospective study was to apply deep learning to identify magnetic resonance imaging (MRI) artifacts on maximum intensity projections (MIP) of the breast, which were derived from diffusion weighted imaging (DWI) protocols. The dataset consisted of 1309 clinically indicated breast MRI examinations of 1158 individuals (median age [IQR]: 50 years [16.75 years]) acquired between March 2017 and June 2020, in which a DWI sequence with a high b-value equal to 1500 s/mm2 was acquired. From these, 2D MIP images were computed and the left and right breast were cropped out as regions of interest (ROI). The presence of MRI image artifacts on the ROIs was rated by three independent observers. Artifact prevalence in the dataset was 37% (961 out of 2618 images). A DenseNet was trained with a fivefold cross-validation to identify artifacts on these images. In an independent holdout test dataset (n = 350 images) artifacts were detected by the neural network with an area under the precision-recall curve of 0.921 and a positive predictive value of 0.981. Our results show that a deep learning algorithm is capable to identify MRI artifacts in breast DWI-derived MIPs, which could help to improve quality assurance approaches for DWI sequences of breast examinations in the future.</jats:p

Tumor infiltration in enhancing and non-enhancing parts of glioblastoma: A correlation with histopathology

To correlate histopathologic findings from biopsy specimens with their corresponding location within enhancing areas, non-enhancing areas and necrotic areas on contrast enhanced T1-weighted MRI scans (cT1).In 37 patients with newly diagnosed glioblastoma who underwent stereotactic biopsy, we obtained a correlation of 561 1mm3 biopsy specimens with their corresponding position on the intraoperative cT1 image at 1.5 Tesla. Biopsy points were categorized as enhancing (CE), non-enhancing (NE) or necrotic (NEC) on cT1 and tissue samples were categorized as "viable tumor cells", "blood" or "necrotic tissue (with or without cellular component)". Cell counting was done semi-automatically.NE had the highest content of tissue categorized as viable tumor cells (89% vs. 60% in CE and 30% NEC, respectively). Besides, the average cell density for NE (3764 ± 2893 cells/mm2) was comparable to CE (3506 ± 3116 cells/mm2), while NEC had a lower cell density with 2713 ± 3239 cells/mm2. If necrotic parts and bleeds were excluded, cell density in biopsies categorized as "viable tumor tissue" decreased from the center of the tumor (NEC, 5804 ± 3480 cells/mm2) to CE (4495 ± 3209 cells/mm2) and NE (4130 ± 2817 cells/mm2).The appearance of a glioblastoma on a cT1 image (circular enhancement, central necrosis, peritumoral edema) does not correspond to its diffuse histopathological composition. Cell density is elevated in both CE and NE parts. Hence, our study suggests that NE contains considerable amounts of infiltrative tumor with a high cellularity which might be considered in resection planning

Correlation between morphological expansion and impairment of intra- and prelesionary motility in inflammatory small bowel lesions in patients with Crohn's disease - Preliminary data

INTRODUCTION: The aim of this study is to investigate if alterations of intra- and prelesionary motility in inflamed small-bowel segments correlate with length, wall-thickness and prelesionary dilatation of inflammatory small bowel lesions in patients suffering from Crohn's disease assessed with MRI. METHODS AND MATERIALS: This retrospective IRB approved study included 25 patients (12 males, 18-77y) with inflammatory lesions examined using (MRE) magnetic resonance imaging enterography. Cine MRE was performed using a coronal 2D steady-state free precession sequence (TR 2.9, TE 1.25) on a 1.5T MRI scanner. Small bowel motility was examined using a dedicated MR-motility assessment software (Motasso, Vers. 1.0, Sohard AG, Bern, Switzerland). Motility patterns (contraction frequency, relative occlusion rate and mean diameter) were assessed in correlation to wall thickness, length and prelesionary dilatation of the lesions. Statistical analysis was performed by calculation of the Pearson's-Correlation coefficient. RESULTS: The length of the inflammatory segments, the wall thickening and prelesionary dilatation did not correlate with the frequency of the contractions (r=0.17, p=0.477; r=0.316, p=0.123; r=0.161, p=0.441) or the impairment of luminal occlusion (r=0.274, p=0.184; r=0.199, p=.0339; r=0.015, p=0.945) and only the prelesionary dilatation (r=0.410, p=0.042) correlated to the mean luminal diameter of the segment. CONCLUSION: The degree of motility impairment within inflammatory small bowel lesions does not significantly correlate with the extent of the lesion but with the motility measured in prelesionary, non-affected segments, suggesting an interdependent functional aspect of inflammation even in morphologically non-affected small bowel segments

Integrated circuit detector technology in abdominal CT: Added value in obese patients

OBJECTIVE. The purpose of this article was to assess the effect of an integrated circuit (IC) detector for abdominal CT on image quality. MATERIALS AND METHODS. In the first study part, an abdominal phantom was scanned with various extension rings using a CT scanner equipped with a conventional discrete circuit (DC) detector and on the same scanner with an IC detector (120 kVp, 150 effective mAs, and 75 effective mAs). In the second study part, 20 patients were included who underwent abdominal CT both with the IC detector and previously at similar protocol parameters (120 kVp tube current-time product and 150 reference mAs using automated tube current modulation) with the DC detector. Images were reconstructed with filtered back projection. RESULTS. Image quality in the phantom was higher for images acquired with the IC compared with the DC detector. There was a gradually increasing noise reduction with increasing phantom sizes, with the highest (37% in the largest phantom) at 75 effective mAs (p < 0.001). In patients, noise was overall significantly (p = 0.025) reduced by 6.4% using the IC detector. Similar to the phantom, there was a gradual increase in noise reduction to 7.9% in patients with a body mass index of 25 kg/m(2) or lower (p = 0.008). Significant correlation was found in patients between noise and abdominal diameter in DC detector images (r = 0.604, p = 0.005), whereas no such correlation was found for the IC detector (r = 0.427, p = 0.060). CONCLUSION. Use of an IC detector in abdominal CT improves image quality and reduces image noise, particularly in overweight and obese patients. This noise reduction has the potential for dose reduction in abdominal CT

Reduction of metal artifacts from hip prostheses on CT images of the pelvis: Value of iterative reconstructions

Purpose:To assess the value of iterative frequency split-normalized (IFS) metal artifact reduction (MAR) for computed tomography (CT) of hip prostheses.Materials and Methods:This study had institutional review board and local ethics committee approval. First, a hip phantom with steel and titanium prostheses that had inlays of water, fat, and contrast media in the pelvis was used to optimize the IFS algorithm. Second, 41 consecutive patients with hip prostheses who were undergoing CT were included. Data sets were reconstructed with filtered back projection, the IFS algorithm, and a linear interpolation MAR algorithm. Two blinded, independent readers evaluated axial, coronal, and sagittal CT reformations for overall image quality, image quality of pelvic organs, and assessment of pelvic abnormalities. CT attenuation and image noise were measured. Statistical analysis included the Friedman test, Wilcoxon signed-rank test, and Levene test.Results:Ex vivo experiments demonstrated an optimized IFS algorithm by using a threshold of 2200 HU with four iterations for both steel and titanium prostheses. Measurements of CT attenuation of the inlays were significantly (P < .001) more accurate for IFS when compared with filtered back projection. In patients, best overall and pelvic organ image quality was found in all reformations with IFS (P < .001). Pelvic abnormalities in 11 of 41 patients (27%) were diagnosed with significantly (P = .002) higher confidence on the basis of IFS images. CT attenuation of bladder (P < .001) and muscle (P = .043) was significantly less variable with IFS compared with filtered back projection and linear interpolation MAR. In comparison with that of FBP and linear interpolation MAR, noise with IFS was similar close to and far from the prosthesis (P = .295).Conclusion:The IFS algorithm for CT image reconstruction significantly reduces metal artifacts from hip prostheses, improves the reliability of CT number measurements, and improves the confidence for depicting pelvic abnormalities.© RSNA, 2013