Moritz C. Wurnig

Mutations in the gene encoding PDGF-B cause brain calcifications in humans and mice

Calcifications in the basal ganglia are a common incidental finding and are sometimes inherited as an autosomal dominant trait (idiopathic basal ganglia calcification (IBGC)). Recently, mutations in the PDGFRB gene coding for the platelet-derived growth factor receptor β (PDGF-Rβ) were linked to IBGC. Here we identify six families of different ancestry with nonsense and missense mutations in the gene encoding PDGF-B, the main ligand for PDGF-Rβ. We also show that mice carrying hypomorphic Pdgfb alleles develop brain calcifications that show age-related expansion. The occurrence of these calcium depositions depends on the loss of endothelial PDGF-B and correlates with the degree of pericyte and blood-brain barrier deficiency. Thus, our data present a clear link between Pdgfb mutations and brain calcifications in mice, as well as between PDGFB mutations and IBGC in humans.

Systematic analysis of the intravoxel incoherent motion threshold separating perfusion and diffusion effects: Proposal of a standardized algorithm.

To systematically evaluate the dependence of intravoxel‐incoherent‐motion (IVIM) parameters on the b‐value threshold separating the perfusion and diffusion compartment, and to implement and test an algorithm for the standardized computation of this threshold.

Deep Learning in Mammography: Diagnostic Accuracy of a Multipurpose Image Analysis Software in the Detection of Breast Cancer

Objectives The aim of this study was to evaluate the diagnostic accuracy of a multipurpose image analysis software based on deep learning with artificial neural networks for the detection of breast cancer in an independent, dual-center mammography data set. Materials and Methods In this retrospective, Health Insurance Portability and Accountability Act-compliant study, all patients undergoing mammography in 2012 at our institution were reviewed (n = 3228). All of their prior and follow-up mammographies from a time span of 7 years (2008–2015) were considered as a reference for clinical diagnosis. After applying exclusion criteria (missing reference standard, prior procedures or therapies), patients with the first diagnosis of a malignoma or borderline lesion were selected (n = 143). Histology or clinical long-term follow-up served as reference standard. In a first step, a breast density-and age-matched control cohort was selected (n = 143) from the remaining patients with more than 2 years follow-up (n = 1003). The neural network was trained with this data set. From the publicly available Breast Cancer Digital Repository data set, patients with cancer and a matched control cohort were selected (n = 35 × 2). The performance of the trained neural network was also tested with this external data set. Three radiologists (3, 5, and 10 years of experience) evaluated the test data set. In a second step, the neural network was trained with all cases from January to September and tested with cases from October to December 2012 (screening-like cohort). The radiologists also evaluated this second test data set. The areas under the receiver operating characteristic curve between readers and the neural network were compared. A Bonferroni-corrected P value of less than 0.016 was considered statistically significant. Results Mean age of patients with lesion was 59.6 years (range, 35–88 years) and in controls, 59.1 years (35–83 years). Breast density distribution (A/B/C/D) was 21/59/42/21 and 22/60/41/20, respectively. Histologic diagnoses were invasive ductal carcinoma in 90, ductal in situ carcinoma in 13, invasive lobular carcinoma in 13, mucinous carcinoma in 3, and borderline lesion in 12 patients. In the first step, the area under the receiver operating characteristic curve of the trained neural network was 0.81 and comparable on the test cases 0.79 (P = 0.63). One of the radiologists showed almost equal performance (0.83, P = 0.17), whereas 2 were significantly better (0.91 and 0.94, P < 0.016). In the second step, performance of the neural network (0.82) was not significantly different from the human performance (0.77–0.87, P > 0.016); however, radiologists were consistently less sensitive and more specific than the neural network. Conclusions Current state-of-the-art artificial neural networks for general image analysis are able to detect cancer in mammographies with similar accuracy to radiologists, even in a screening-like cohort with low breast cancer prevalence.

Dynamic intravoxel incoherent motion imaging of skeletal muscle at rest and after exercise.

The purpose of this work was to demonstrate the feasibility of intravoxel incoherent motion imaging (IVIM) for non‐invasive quantification of perfusion and diffusion effects in skeletal muscle at rest and following exercise.

Rapid and robust pulmonary proton ZTE imaging in the mouse

Pulmonary MRI is challenging because of the low proton density and rapid transverse relaxation in the lung associated with microscopic magnetic field inhomogeneities caused by tissue–air interfaces. Therefore, low signal is obtained in gradient and spin echo proton images. Alternatively, non‐proton MRI using hyperpolarized gases or radial techniques with ultrashort or zero TE have been proposed to image the lung. Also with the latter approach, the general challenge remains to provide full coverage of the lung at sufficient spatial resolution, signal‐to‐noise ratio (SNR) and image quality within a reasonable scan time. This task is further aggravated by physiological motion and is particularly demanding in small animals, such as mice. In this work, three‐dimensional (3D) zero echo time (ZTE) imaging is employed for efficient pulmonary MRI. Four protocols with different averaging and respiratory triggering schemes are developed and compared with respect to image quality and SNR. To address the critical issue of background signal in ZTE images, a subtraction approach is proposed, providing images virtually free of disturbing signal from nearby hardware parts. The protocols are tested for pulmonary MRI in six mice at 4.7 T, consistently providing images of high quality with a 3D isotropic resolution of 313 µm and SNR values in the lung between 8.0 and 18.5 within scan times between 1 min 21 s and 4 min 44 s. A generally high robustness of the ZTE approach against motion is observed, whilst respiratory triggering further improves the SNR and visibility of image details. The developed techniques are expected to enable efficient preclinical animal studies in the lung and will also be of importance for human applications. Further improvements are expected from radiofrequency (RF) coils with increased SNR and reduced background signal. Copyright

Whole-Body Diffusion Kurtosis Imaging: Initial Experience on Non-Gaussian Diffusion in Various Organs

IntroductionDiffusion kurtosis imaging (DKI) is based on a non-Gaussian diffusion model that should inherently better account for restricted water diffusion within the complex microstructure of most tissues than the conventional diffusion-weighted imaging (DWI), which presumes Gaussian distributed water molecule displacement probability. The aim of this investigation was to test the technical feasibility of in vivo whole-body DKI, probe for organ-specific differences, and compare whole-body DKI and DWI results. Materials and MethodsEight healthy subjects underwent whole-body DWI on a clinical 3.0 T magnetic resonance imaging system. Echo-planar images in the axial orientation were acquired at b-values of 0, 150, 300, 500, and 800 mm2/s. Parametrical whole-body maps of the diffusion coefficient (D), the kurtosis (K), and the traditional apparent diffusion coefficient (ADC) were generated. Goodness of fit was compared between DKI and DWI fits using the sums of squared residuals. Data groups were tested for significant differences of the mean by paired Student t tests. ResultsGood-quality parametrical whole-body maps of D, K, and ADC could be computed. Compared with ADC values, D values were significantly higher in the cerebral gray matter (by 30%) and white matter (27%), renal cortex (23%) and medulla (21%), spleen (101%), as well as erector spinae muscle (34%) (each P value <0.001). No significant differences between D and ADC were found in the cerebrospinal fluid (P = 0.08) and in the liver (P = 0.13). Curves of DKI fitted the measurement points significantly better than DWI curves did in most organs. ConclusionsWhole-body DKI is technically feasible and may reflect tissue microstructure more meaningfully than whole-body DWI.

Feasibility of single-source dual-energy computed tomography for urinary stone characterization and value of iterative reconstructions

PurposeThe purposes of this study were to demonstrate the feasibility and accuracy of single-source dual-energy (DE) computed tomography (CT) with sequential data acquisition and a coregistration motion correction algorithm for urinary stone characterization and to evaluate the value of iterative reconstructions (IRs) in DE imaging. Materials and MethodsThirty-five urinary stones were placed in cylindrical phantoms with diameters of 30 and 40 cm. The phantoms were scanned on a 64-section CT machine with a single-source DE protocol consisting of 2 sequential acquisitions at 80 and 140 kilovolt (peak). The phantom was moved between the 80– and 140–kilovolt (peak) scans. Images were reconstructed with weighted filtered back projection (FBP) and with IR, and data were coregistered. Two independent and blinded readers assessed data sets for stone detection, overall image quality, and visibility of stones. Image noise and Hounsfield unit values of the stones were measured, and the DE index was calculated. In addition, the data sets were analyzed on color-coded images using the standard postprocessing software for differentiating uric acid- (UA) from non–UA-containing stones. ResultsThe motion correction algorithm achieved a good coregistration of the 2 scans with different energy levels. Both readers detected all stones on all data sets with both reconstruction types. The overall image quality was rated significantly higher in IR images in the 40-cm phantom as compared with that in FBP images (P < 0.05), whereas no significant difference was found for the 30-cm phantom. Visibility of stones was rated significantly higher for both the 30- and 40-cm phantoms on IR as compared with that on FBP images, an effect that was pronounced for UA stones (P < 0.05). Noise was significantly reduced by up to 31% in the 40-cm phantom when using IR as compared with FBP (P < 0.001). The DE index was similar in the FBP and IR data sets for the 30- (P = 0.116) and 40-cm phantoms (P = 0.544), being significantly different between UA-containing stones, cystine, and struvite stones as well as stones of other compositions (P < 0.001). The postprocessing software classified all stones correctly as UA- or non–UA-containing stones on color-coded images. In the 40-cm phantom, false-positively colored voxels were found in the FBP data sets, which were not seen when using IR instead. ConclusionsOur study indicates that single-source dual-energy CT with sequential acquisitions at different energy levels and a coregistration motion correction algorithm is feasible and accurate for characterizing urinary stone composition on the basis of phantom evaluation. As compared with reconstructions with FBP, the use of IR in dual-energy CT reduces noise, improves overall image quality and visibility of stones particularly in large phantoms, and helps to avoid false classifications of urinary stones.

Magnetic resonance imaging of the liver: apparent diffusion coefficients from multiexponential analysis of b values greater than 50 s/mm2 do not respond to caloric intake despite increased portal-venous blood flow.

PurposeThe purpose of this study was to measure potential changes of the apparent diffusion coefficient (ADC) in diffusion-weighted imaging of the liver before and after caloric challenge in correlation to the induced changes in portal vein flow. Materials and MethodsThe study was approved by the local ethics committee. Each of 10 healthy volunteers underwent 4 measurements in a 1.5-T whole-body magnetic resonance scanner on 2 different days: a first scan after fasting for at least 8 hours and a second scan 30 minutes after intake of a standardized caloric either a protein- or carbohydrate-rich meal. Diffusion-weighted spin-echo echo-planar magnetic resonance images were acquired at b values of 0, 50, 150, 250, 500, 750, and 1000 s/mm2. In addition, portal vein flow was quantified with 2-dimensional phase-contrast imaging (velocity encoding parallel to flow direction, 60 cm/s). Mean ADC values for regions of interest in 3 different slices were measured from b50 to b250 and from b500 to b1000 images. ResultsCarbohydrate- and protein-rich food intake both resulted in a substantial increase in the portal vein flow (fasting state, 638.6 ± 202.3 mL/min; after protein intake, 1322 ± 266.8; after carbohydrate intake, 1767 ± 421.6). The signal decay with increasingly strong diffusion weighting (b values from 0 to 1000 s/mm2) exhibited a triexponential characteristic, implying fast, intermediate, and slow-moving water-molecule proton-spin ensembles in the liver parenchyma. Mean ADC for high b values (b500-b1000) after fasting was 0.93 ± 0.09 × 10−3 mm2/s; that after protein intake, 0.93 ± 0.11 × 10−3; and that after carbohydrate intake, 0.93 ± 0.08 × 10−3. For intermediate b values (b50-b250), the signal-decay constants were 1.27 ± 0.14 × 10−3 mm2/s, 1.28 ± 0.15 × 10−3, and 1.31 ± 0.09 × 10−3, respectively. There was no statistically significant difference between fasting and caloric challenge. ConclusionsThe postprandial increase in portal vein flow is not accompanied by a change of liver parenchymal ADC values. In clinical diffusion imaging, patients may be scanned without prescan food-intake preparations. To minimize interference of perfusion effects, liver-tissue molecular water diffusion should be quantified using high b values (≥500 s/mm2) only.

ZTE imaging with long-T2 suppression

Three‐dimensional radial zero echo time (ZTE) imaging enables efficient direct MRI of tissues with rapid transverse relaxation. Yet, the feature of capturing signals with a wide range of T2 and T2* values is accompanied by a lack of contrast between the corresponding tissues. In particular, the targeted short‐T2 tissues may not be easily identified, and various approaches have been proposed to generate T2 contrast by reducing the long‐T2 signal of water and/or fat. The aim of this work was to provide efficient long‐T2 suppression for selective direct MRI of short‐T2 tissues using the ZTE technique. For magnetization preparation, suppression pulses for water and fat were designed to provide both good T2 selectivity and off‐resonance performance. To obtain high efficiency at short TRs, the pulses were applied in a segmented sequence scheme with minimized timing overhead, thus leading to a quasi‐steady state of magnetization. The sequence timing was adjusted for optimal tissue contrast in musculoskeletal applications by means of simulations and experiments, incorporating both T2 and T1 of the involved tissues. The developed technique was employed for imaging of a lamb joint sample at 4.7 T. ZTE images were obtained with effective suppression of signals from tissues with long‐T2 water, such as muscle or articular spaces, and fat. Hence, primarily short‐T2 tissues were visible, such as bone and tendon. The MR image intensity of bone showed strong similarity with bone density imaged with micro‐computed tomography. Copyright

Assessing lung transplantation ischemia-reperfusion injury by microcomputed tomography and ultrashort echo-time magnetic resonance imaging in a mouse model

PurposeIschemia-reperfusion injury (I/R) is a common early complication after lung transplantation. The purpose of this study was to compare ultrashort echo-time (UTE) sequences in magnetic resonance imaging (MRI) with a microcomputed tomography (micro-CT) reference standard for detection of I/R injury in a lung transplantation mouse model. Materials and MethodsSix mice (C57BL/6) underwent orthotopic lung transplantation using donor grafts that were exposed to 6-hour cold ischemia. Imaging was performed within 24 hours after the transplantation with high-resolution micro-CT (tube voltage, 50 kV; current, 500 mA; aluminum filter, 0.5 mm; voxel size, 35 × 35 × 35 &mgr;m3) and small-animal MRI at 4.7 T with a linearly polarized whole-body mouse coil. The imaging protocol comprised radial 3-dimensional UTE sequences with different echo times (repetition time, 8 milliseconds; echo time, 50/75/100/500/1500/3000/4000/5000 &mgr;s; voxel size, 350 × 350 × 350 &mgr;m3). Images were assessed visually and through calculation of contrast-to-noise ratio (CNR) values. Calculated S0 values and T2* transverse relaxation times (MRI) of lung parenchyma were compared with Hounsfield unit (HU) density in micro-CT images. Receiver operating characteristic curves and area under the curve values were calculated for comparison of diagnostic power. All samples underwent a histologic examination. ResultsThe results of both UTE MRI and micro-CT showed an excellent depiction of pulmonary infiltration due to I/R injury, with MRI exhibiting a significantly higher CNR (mean [SD] CNR MRI, 19.7 [8.0]; mean [SD] CNR micro-CT, 10.3 [2.5]; P < 0.001). Measured parametrical values were as follows: mean (SD) HU, −416 (120); mean (SD) S0 value, 1655 (440); mean (SD) T2*, 895 (870) &mgr;s for the non–transplanted right lung and mean (SD) HU, 29 (35); mean (SD) S0 value, 2310 (300); and mean (SD) T2*, 4550 (3230) &mgr;s for the transplanted left lung. Slight infiltration could be better discriminated with micro-CT, whereas, in strong infiltration, a better contrast was provided by UTE MRI. The area under the curve values resulting from the receiver operating characteristic curve analysis were 0.99 for HU density, 0.89 for S0, 0.96 for T2*, and 0.98 for the combination of S0 and T2*. ConclusionsResults show that MRI of the lung has a similar diagnostic power compared with that of micro-CT regarding the detection of I/R injury after experimental lung transplantation. Both modalities provide complementary information in the assessment of dense and slight infiltration in the early phase after lung transplantation. Therefore, UTE MRI seems to be a promising addition to computed tomographic imaging in the assessment of I/R injury after lung transplantation.