Localization of contrast-enhanced breast lesions in ultrafast screening MRI using deep convolutional neural networks.

OBJECTIVES: To develop a deep learning-based method for contrast-enhanced breast lesion detection in ultrafast screening MRI. MATERIALS AND METHODS: A total of 837 breast MRI exams of 488 consecutive patients were included. Lesion's location was independently annotated in the maximum intensity projection (MIP) image of the last time-resolved angiography with stochastic trajectories (TWIST) sequence for each individual breast, resulting in 265 lesions (190 benign, 75 malignant) in 163 breasts (133 women). YOLOv5 models were fine-tuned using training sets containing the same number of MIP images with and without lesions. A long short-term memory (LSTM) network was employed to help reduce false positive predictions. The integrated system was then evaluated on test sets containing enriched uninvolved breasts during cross-validation to mimic the performance in a screening scenario. RESULTS: In five-fold cross-validation, the YOLOv5x model showed a sensitivity of 0.95, 0.97, 0.98, and 0.99, with 0.125, 0.25, 0.5, and 1 false positive per breast, respectively. The LSTM network reduced 15.5% of the false positive prediction from the YOLO model, and the positive predictive value was increased from 0.22 to 0.25. CONCLUSIONS: A fine-tuned YOLOv5x model can detect breast lesions on ultrafast MRI with high sensitivity in a screening population, and the output of the model could be further refined by an LSTM network to reduce the amount of false positive predictions. CLINICAL RELEVANCE STATEMENT: The proposed integrated system would make the ultrafast MRI screening process more effective by assisting radiologists in prioritizing suspicious examinations and supporting the diagnostic workup. KEY POINTS: • Deep convolutional neural networks could be utilized to automatically pinpoint breast lesions in screening MRI with high sensitivity. • False positive predictions significantly increased when the detection models were tested on highly unbalanced test sets with more normal scans. • Dynamic enhancement patterns of breast lesions during contrast inflow learned by the long short-term memory networks helped to reduce false positive predictions.

Image quality of DWI at breast MRI depends on the amount of fibroglandular tissue: implications for unenhanced screening.

OBJECTIVES: To compare image quality of diffusion-weighted imaging (DWI) and contrast-enhanced breast MRI (DCE-T1) stratified by the amount of fibroglandular tissue (FGT) as a measure of breast density. METHODS: Retrospective, multi-reader, bicentric visual grading analysis study on breast density (A-D) and overall image and fat suppression quality of DWI and DCE-T1, scored on a standard 5-point Likert scale. Cross tabulations and visual grading characteristic (VGC) curves were calculated for fatty breasts (A/B) versus dense breasts (C/D). RESULTS: Image quality of DWI was higher in the case of increased breast density, with good scores (score 3-5) in 85.9% (D) and 88.4% (C), compared to 61.6% (B) and 53.5% (A). Overall image quality of DWI was in favor of dense breasts (C/D), with an area under the VGC curve of 0.659 (p < 0.001). Quality of DWI and DCE-T1 fat suppression increased with higher breast density, with good scores (score 3-5) for 86.9% and 45.7% of density D, and 90.2% and 42.9% of density C cases, compared to 76.0% and 33.6% for density B and 54.7% and 29.6% for density A (DWI and DCE-T1 respectively). CONCLUSIONS: Dense breasts show excellent fat suppression and substantially higher image quality in DWI images compared with non-dense breasts. These results support the setup of studies exploring DWI-based MR imaging without IV contrast for additional screening of women with dense breasts. CLINICAL RELEVANCE STATEMENT: Our findings demonstrate that image quality of DWI is robust in women with an increased amount of fibroglandular tissue, technically supporting the feasibility of exploring applications such as screening of women with mammographically dense breasts. KEY POINTS: • Image and fat suppression quality of diffusion-weighted imaging are dependent on the amount of fibroglandular tissue (FGT) which is closely connected to breast density. • Fat suppression quality in diffusion-weighted imaging of the breast is best in women with a high amount of fibroglandular tissue. • High image quality of diffusion-weighted imaging in women with a high amount of FGT in MRI supports that the technical feasibility of DWI can be explored in the additional screening of women with mammographically dense breasts.

Automated Breast Density Assessment in MRI Using Deep Learning and Radiomics: Strategies for Reducing Inter-Observer Variability.

BACKGROUND: Accurate breast density evaluation allows for more precise risk estimation but suffers from high inter-observer variability. PURPOSE: To evaluate the feasibility of reducing inter-observer variability of breast density assessment through artificial intelligence (AI) assisted interpretation. STUDY TYPE: Retrospective. POPULATION: Six hundred and twenty-one patients without breast prosthesis or reconstructions were randomly divided into training (N = 377), validation (N = 98), and independent test (N = 146) datasets. FIELD STRENGTH/SEQUENCE: 1.5 T and 3.0 T; T1-weighted spectral attenuated inversion recovery. ASSESSMENT: Five radiologists independently assessed each scan in the independent test set to establish the inter-observer variability baseline and to reach a reference standard. Deep learning and three radiomics models were developed for three classification tasks: (i) four Breast Imaging-Reporting and Data System (BI-RADS) breast composition categories (A-D), (ii) dense (categories C, D) vs. non-dense (categories A, B), and (iii) extremely dense (category D) vs. moderately dense (categories A-C). The models were tested against the reference standard on the independent test set. AI-assisted interpretation was performed by majority voting between the models and each radiologist's assessment. STATISTICAL TESTS: Inter-observer variability was assessed using linear-weighted kappa (κ) statistics. Kappa statistics, accuracy, and area under the receiver operating characteristic curve (AUC) were used to assess models against reference standard. RESULTS: In the independent test set, five readers showed an overall substantial agreement on tasks (i) and (ii), but moderate agreement for task (iii). The best-performing model showed substantial agreement with reference standard for tasks (i) and (ii), but moderate agreement for task (iii). With the assistance of the AI models, almost perfect inter-observer variability was obtained for tasks (i) (mean κ = 0.86), (ii) (mean κ = 0.94), and (iii) (mean κ = 0.94). DATA CONCLUSION: Deep learning and radiomics models have the potential to help reduce inter-observer variability of breast density assessment. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 1.

The Correlation of In Vivo MR Spectroscopy and Ex Vivo 2-Hydroxyglutarate Concentration for the Prediction of Isocitrate Dehydrogenase Mutation Status in Diffuse Glioma.

Isocitrate dehydrogenase (IDH) mutation status is an important biomarker in the glioma-defining subtype and corresponding prognosis. This study proposes a straightforward method for 2-hydroxyglutarate (2-HG) quantification by MR spectroscopy for IDH mutation status detection and directly compares in vivo 2-HG MR spectroscopy with ex vivo 2-HG concentration measured in resected tumor tissue. Eleven patients with suspected lower-grade glioma (ten IDH1; one IDHwt) were prospectively included. Preoperatively, 3T point-resolved spectroscopy (PRESS) was acquired; 2-HG was measured as the percentage elevation of Glx3 (the sum of 2-HG and Glx) compared to Glx4. IDH mutation status was assessed by immunochemistry or direct sequencing. The ex vivo 2-HG concentration was determined in surgically obtained tissue specimens using gas chromatography-mass spectrometry. Pearson correlation was used for assessing the correlation between in vivo MR spectroscopy and ex vivo 2-HG concentration. MR spectroscopy was positive for 2-HG in eight patients, all of whom had IDH1 tumors. A strong correlation (r = 0.80, p = 0.003) between 2-HG MR spectroscopy and the ex vivo 2-HG concentration was found. This study shows in vivo 2-HG MR spectroscopy can non-invasively determine IDH status in glioma and demonstrates a strong correlation with ex vivo 2-HG concentration in patients with lower-grade glioma.

Influence of reference tube location on the measured sodium concentrations in calf muscles using a birdcage coil at 3T.

PURPOSE: To investigate the influence of the sodium (Na) reference tube location in a birdcage coil on the quantification of Na in the calf muscle. Two correction methods were also evaluated. METHOD: Eight (4 × 20 mM, 4 × 30 mM Na) reference tubes were placed along the inner surface of the coil and one (30 mM Na) tube more centrally near the tibia. In two volunteers, four repeated UTE scans were acquired. In six calf muscles, the Na concentration was calculated based on each reference tube. Flip angle mapping of a homogenous Na phantom was used for correcting intensity values. Alternatively, a normalized intensity map was used for correcting the in vivo signal intensities. Results were given as range or SD of Na concentration measurements over the reference tubes. RESULTS: For calf Na measurements, there was limited space for positioning reference tubes away from coil B1 inhomogeneity. In both volunteers, the Na quantification depended greatly on the reference tube used with a range of up to 10 mM. The central tube location gave a Na quantification close to the mean of the other tubes. The flip angle and normalized signal intensity phantom-based correction methods decreased the quantification variation from 14.9% to 5.0% and 10.4% to 2.7%, respectively. Both correction methods had little influence (< 2.3%) on quantification based on the central tube. CONCLUSION: Despite use of a birdcage coil, location of the reference tube had a great impact on Na quantification in the calf muscles. Although both correction methods did reduce this variation, placing the reference tube more centrally was found to give the most reliable results.

Comparison of conventional and higher-resolution reduced-FOV diffusion-weighted imaging of breast tissue.

OBJECTIVE: Reduced FOV-diffusion-weighted imaging (rFOV-DWI) allows for acquisition of a tissue region without back-folding, and may have better fat suppression than conventional DWI imaging (c-DWI). The aim was to compare the ADCs obtained with c-DWI bilateral-breast imaging with single-breast rFOV-DWI. MATERIALS AND METHODS: Breasts of 38 patients were scanned at 3 T. The mean ADC values obtained for 38 lesions, and fibro-glandular (N = 35) and adipose (N = 38) tissue ROIs were compared between c-DWI and higher-resolution rFOV-DWI (Wilcoxon rank test). Also, the ADCs were compared between the two acquisitions for an oil-only phantom and a combined water/oil phantom. Furthermore, ghost artifacts were assessed. RESULTS: No significant difference in mean ADC was found between the acquisitions for lesions (c-DWI: 1.08 × 10-3 mm2/s, rFOV-DWI: 1.13 × 10-3 mm2/s) and fibro-glandular tissue. For adipose tissue, the ADC using rFOV-DWI (0.31 × 10-3 mm2/s) was significantly higher than c-DWI (0.16 × 10-3 mm2/s). For the oil-only phantom, no difference in ADC was found. However, for the water/oil phantom, the ADC of oil was significantly higher with rFOV-DWI compared to c-DWI. DISCUSSION: Although ghost artifacts were observed for both acquisitions, they appeared to have a greater impact for rFOV-DWI. However, no differences in mean lesions' ADC values were found, and therefore this study suggests that rFOV can be used diagnostically for single-breast DWI imaging.

Using deep learning to safely exclude lesions with only ultrafast breast MRI to shorten acquisition and reading time.

OBJECTIVES: To investigate the feasibility of automatically identifying normal scans in ultrafast breast MRI with artificial intelligence (AI) to increase efficiency and reduce workload. METHODS: In this retrospective analysis, 837 breast MRI examinations performed on 438 women from April 2016 to October 2019 were included. The left and right breasts in each examination were labelled normal (without suspicious lesions) or abnormal (with suspicious lesions) based on final interpretation. Maximum intensity projection (MIP) images of each breast were then used to train a deep learning model. A high sensitivity threshold was calculated based on the detection trade - off (DET) curve on the validation set. The performance of the model was evaluated by receiver operating characteristic analysis of the independent test set. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) with the high sensitivity threshold were calculated. RESULTS: The independent test set consisted of 178 examinations of 149 patients (mean age, 44 years ± 14 [standard deviation]). The trained model achieved an AUC of 0.81 (95% CI: 0.75-0.88) on the independent test set. Applying a threshold of 0.25 yielded a sensitivity of 98% (95% CI: 90%; 100%), an NPV of 98% (95% CI: 89%; 100%), a workload reduction of 15.7%, and a scan time reduction of 16.6%. CONCLUSION: This deep learning model has a high potential to help identify normal scans in ultrafast breast MRI and thereby reduce radiologists' workload and scan time. KEY POINTS: • Deep learning in TWIST may eliminate the necessity of additional sequences for identifying normal breasts during MRI screening. • Workload and scanning time reductions of 15.7% and 16.6%, respectively, could be achieved with the cost of 1 (1 of 55) false negative prediction.

Breast Tumor Identification in Ultrafast MRI Using Temporal and Spatial Information.

PURPOSE: To investigate the feasibility of using deep learning methods to differentiate benign from malignant breast lesions in ultrafast MRI with both temporal and spatial information. METHODS: A total of 173 single breasts of 122 women (151 examinations) with lesions above 5 mm were retrospectively included. A total of 109 out of 173 lesions were benign. Maximum intensity projection (MIP) images were generated from each of the 14 contrast-enhanced T1-weighted acquisitions in the ultrafast MRI scan. A 2D convolutional neural network (CNN) and a long short-term memory (LSTM) network were employed to extract morphological and temporal features, respectively. The 2D CNN model was trained with the MIPs from the last four acquisitions to ensure the visibility of the lesions, while the LSTM model took MIPs of an entire scan as input. The performance of each model and their combination were evaluated with 100-times repeated stratified four-fold cross-validation. Those models were then compared with models developed with standard DCE-MRI which followed the same data split. RESULTS: In the differentiation between benign and malignant lesions, the ultrafast MRI-based 2D CNN achieved a mean AUC of 0.81 ± 0.06, and the LSTM network achieved a mean AUC of 0.78 ± 0.07; their combination showed a mean AUC of 0.83 ± 0.06 in the cross-validation. The mean AUC values were significantly higher for ultrafast MRI-based models than standard DCE-MRI-based models. CONCLUSION: Deep learning models developed with ultrafast breast MRI achieved higher performances than standard DCE-MRI for malignancy discrimination. The improved AUC values of the combined models indicate an added value of temporal information extracted by the LSTM model in breast lesion characterization.

Early detection of heart function abnormality by native T1: a comparison of two T1 quantification methods.

OBJECTIVE: To compare the robustness of native T1 mapping using mean and median pixel-wise quantification methods. METHODS: Fifty-seven consecutive patients without overt signs of heart failure were examined in clinical routine for suspicion of cardiomyopathy. MRI included the acquisition of native T1 maps by a motion-corrected modified Look-Locker inversion recovery sequence at 1.5 T. Heart function status according to four established volumetric left ventricular (LV) cardio MRI parameter thresholds was used for retrospective separation into subgroups of normal (n = 26) or abnormal heart function (n = 31). Statistical normality of pixel-wise T1 was tested on each myocardial segment and mean and median segmental T1 values were assessed. RESULTS: Segments with normally distributed pixel-wise T1 (57/58%) showed no difference between mean and median quantification in either patient group, while differences were highly significant (p < 0.001) for the respective 43/42% non-normally distributed segments. Heart function differentiation between two patient groups was significant in 14 myocardial segments (p < 0.001-0.040) by median quantification compared with six (p < 0.001-0.042) by using the mean. The differences by median quantification were observed between the native T1 values of the three coronary artery territories of normal heart function patients (p = 0.023) and insignificantly in the abnormal patients (p = 0.053). CONCLUSION: Median quantification increases the robustness of myocardial native T1 definition, regardless of statistical normality of the data. Compared with the currently prevailing method of mean quantification, differentiation between LV segments and coronary artery territories is better and allows for earlier detection of heart function impairment. KEY POINTS: • Median pixel-wise quantification of native T1 maps is robust and can be applied regardless of the statistical distribution of data points. • Median quantification is more sensitive to early heart function abnormality compared with mean quantification. • The new method yields significant native T1 value differentiation between the three coronary artery territories.

Cardiac T2 * mapping: Techniques and clinical applications.

Cardiac T2 * mapping is a noninvasive MRI method that is used to identify myocardial iron accumulation in several iron storage diseases such as hereditary hemochromatosis, sickle cell disease, and ß-thalassemia major. The method has improved over the years in terms of MR acquisition, focus on relative artifact-free myocardium regions, and T2 * quantification. Several improvement factors involved include blood pool signal suppression, the reproducibility of T2 * measurement as affected by scanner hardware, and acquisition software. Regarding the T2 * quantification, improvement factors include the applied curve-fitting method with or without truncation of the signals acquired at longer echo times and whether or not T2 * measurement focuses on multiple segmental regions or the midventricular septum only. Although already widely applied in clinical practice, data processing still differs between centers, contributing to measurement outcome variations. State of the art T2 * measurement involves pixelwise quantification providing better spatial iron loading information than region of interest-based quantification. Improvements have been proposed, such as on MR acquisition for free-breathing mapping, the generation of fast mapping, noise reduction, automatic myocardial contour delineation, and different T2 * quantification methods. This review deals with the pro and cons of different methods used to quantify T2 * and generate T2 * maps. The purpose is to recommend a combination of MR acquisition and T2 * mapping quantification techniques for reliable outcomes in measuring and follow-up of myocardial iron overload. The clinical application of cardiac T2 * mapping for iron overload's early detection, monitoring, and treatment is addressed. The prospects of T2 * mapping combined with different MR acquisition methods, such as cardiac T1 mapping, are also described. Level of Evidence: 4 Technical Efficacy Stage: 5 J. Magn. Reson. Imaging 2019.

The prognostic value of proton magnetic resonance spectroscopy in term newborns treated with therapeutic hypothermia following asphyxia.

OBJECTIVE: The purpose of this study was to correlate brain metabolism assessed shortly after therapeutic hyperthermia by 1H magnetic resonance spectroscopy (MRS), with neurodevelopmental outcome. METHODS: At the age of 6.0±1.8days, brain metabolites of 35 term asphyxiated newborns, treated with therapeutic hypothermia, were quantified by multivoxel proton MRS of a volume cranial to the corpus callosum, containing both gray and white matter. At the age of 30months the Bayley Scale of Infant Development-III was performed. RESULTS: Infants that died had lower gray matter NAA levels than infants that survived (P=0.005). In surviving infants (28 of 35) there was a trend of negative correlation between gray matter choline levels and gross motor outcome (r=-0.45). In the white matter, choline correlated negatively with fine motor skills (r=-0.40), and creatine positively with gross motor skills (r=0.58, P=0.02). There was no relationship between lactate levels and outcome. CONCLUSION: MRS of asphyxiated neonates treated by therapeutic hypothermia can serve as predictor of outcome. Unlike previously reported associations in untreated asphyxiates, lactate levels had no relationship with outcome, which indicates that one of the working mechanisms of therapeutic hypothermia is reduction of the metabolic rate.

Diminished liver microperfusion in Fontan patients: A biexponential DWI study.

It has been demonstrated that hepatic apparent diffusion coefficients (ADC) are decreasing in patients with a Fontan circulation. It remains however unclear whether this is a true decrease of molecular diffusion, or rather reflects decreased microperfusion due to decreased portal blood flow. The purpose of this study was therefore to differentiate diffusion and microperfusion using intravoxel incoherent motion (IVIM) modeled diffusion-weighted imaging (DWI) for different liver segments in patients with a Fontan circulation, compare to a control group, and relate with liver function, chronic hepatic congestion and hepatic disease. For that purpose, livers of 59 consecutively included patients with Fontan circulation (29 men; mean-age, 19.1 years) were examined (Oct 2012─Dec 2013) with 1.5T MRI and DWI (b = 0,50,100,250,500,750,1500,1750 s/mm2). IVIM (Dslow, Dfast, ffast) and ADC were calculated for eight liver segments, compared to a control group (19 volunteers; 10 men; mean-age, 32.9 years), and correlated to follow-up duration, clinical variables, and laboratory measurements associated with liver function. The results demonstrated that microperfusion was reduced (p<0.001) in Fontan livers compared to controls with ─38.1% for Dfast and ─32.6% for ffast. Molecular diffusion (Dslow) was similar between patients and controls, while ADC was significantly lower (─14.3%) in patients (p<0.001). ADC decreased significantly with follow-up duration after Fontan operation (r = ─0.657). Dslow showed significant inverse correlations (r = ─0.591) with follow-up duration whereas Dfast and ffast did not. From these results it was concluded that the decreasing ADC values in Fontan livers compared with controls reflect decreases in hepatic microperfusion rather than any change in molecular diffusion. However, with the time elapsed since the Fontan operation molecular diffusion and ADC decreased while microperfusion remained stable. This indicates that after Fontan operation initial blood flow effects on the liver are followed by intracellular changes preceding the formation of fibrosis and cirrhosis.

Semi-automated myocardial segmentation of bright blood multi-gradient echo images improves reproducibility of myocardial contours and T2* determination.

OBJECTIVES: Early detection of iron loading is affected by the reproducibility of myocardial contour assessment. A novel semi-automatic myocardial segmentation method is presented on contrast-optimized composite images and compared to the results of manual drawing. MATERIALS AND METHODS: Fifty-one short-axis slices at basal, mid-ventricular and apical locations from 17 patients were acquired by bright blood multi-gradient echo MRI. Four observers produced semi-automatic and manual myocardial contours on contrast-optimized composite images. The semi-automatic segmentation method relies on vector field convolution active contours to generate the endocardial contour. After creating radial pixel clusters on the myocardial wall, a combination of pixel-wise coefficient of variance (CoV) assessment and k-means clustering establishes the epicardial contour for each segment. RESULTS: Compared to manual drawing, semi-automatic myocardial segmentation lowers the variability of T2* quantification within and between observers (CoV of 12.05 vs. 13.86% and 14.43 vs. 16.01%) by improving contour reproducibility (P < 0.001). In the presence of iron loading, semi-automatic segmentation also lowers the T2* variability within and between observers (CoV of 13.14 vs. 15.19% and 15.91 vs. 17.28%). CONCLUSION: Application of semi-automatic myocardial segmentation on contrast-optimized composite images improves the reproducibility of T2* quantification.

Assessment of the link between quantitative biexponential diffusion-weighted imaging and contrast-enhanced MRI in the liver.

PURPOSE: To investigate if intravoxel incoherent motion (IVIM) modeled diffusion-weighted imaging (DWI) can be linked to contrast-enhanced (CE-)MRI in liver parenchyma and liver lesions. METHODS: Twenty-five patients underwent IVIM-DWI followed by multiphase CE-MRI using Gd-EOB-DTPA (n=20) or Gd-DOTA (n=5) concluded with IVIM-DWI. Diffusion (Dslow), microperfusion (Dfast), its fraction (ffast), wash-in-rate (Rearly) and late-enhancement-rate (Rlate) of Gd-EOB-DTPA were calculated voxel-wise for the liver. Parenchyma and lesions were segmented. Pre-contrast IVIM was compared 1) between low, medium and high Rearly for parenchyma 2) to post-contrast IVIM substantiated with simulations 3) between low and high Rlate per lesion type. RESULTS: Dfast and ffast increased (P<0.001) with 25.6% and 33.8% between low and high Rearly of Gd-EOB-DTPA. Dslow decreased (-15.0%; P<0.001) with increasing Rearly. Gd-DOTA demonstrated similar observations. ffast (+10%; P<0.001) and Dfast (+6.6%; P<0.001) increased after Gd-EOB-DTPA, while decreasing after Gd-DOTA (-4.2% and -5.7%, P<0.001) and were confirmed by simulations. For focal nodular hyperplasia lesions (n=5) Dfast and ffast increased (P<0.001) with increasing Rlate, whereas for hepatocellular carcinoma (n=4) and adenoma (n=7) no differences were found. CONCLUSION: Microperfusion measured by IVIM reflects perfusion in a way resembling CE-MRI. Also IVIM separated intra- and extracellular MR contrast media. This underlines the potential of IVIM in quantitative liver imaging.

Diagnostic value of MRS-quantified brain tissue lactate level in identifying children with mitochondrial disorders.

OBJECTIVES: Magnetic resonance spectroscopy (MRS) of children with or without neurometabolic disease is used for the first time for quantitative assessment of brain tissue lactate signals, to elaborate on previous suggestions of MRS-detected lactate as a marker of mitochondrial disease. METHODS: Multivoxel MRS of a transverse plane of brain tissue cranial to the ventricles was performed in 88 children suspected of having neurometabolic disease, divided into 'definite' (n = 17, ≥1 major criteria), 'probable' (n = 10, ≥2 minor criteria), 'possible' (n = 17, 1 minor criterion) and 'unlikely' mitochondrial disease (n = 44, none of the criteria). Lactate levels, expressed in standardized arbitrary units or relative to creatine, were derived from summed signals from all voxels. Ten 'unlikely' children with a normal neurological exam served as the MRS reference subgroup. For 61 of 88 children, CSF lactate values were obtained. RESULTS: MRS lactate level (>12 arbitrary units) and the lactate-to-creatine ratio (L/Cr >0.22) differed significantly between the definite and the unlikely group (p = 0.015 and p = 0.001, respectively). MRS L/Cr also differentiated between the probable and the MRS reference subgroup (p = 0.03). No significant group differences were found for CSF lactate. CONCLUSION: MRS-quantified brain tissue lactate levels can serve as diagnostic marker for identifying mitochondrial disease in children. KEY POINTS: • MRS-detected brain tissue lactate levels can be quantified. • MRS lactate and lactate/Cr are increased in children with mitochondrial disease. • CSF lactate is less suitable as marker of mitochondrial disease.

Quantitative DWI implemented after DCE-MRI yields increased specificity for BI-RADS 3 and 4 breast lesions.

PURPOSE: To assess if specificity can be increased when semiautomated breast lesion analysis of quantitative diffusion-weighted imaging (DWI) is implemented after dynamic contrast-enhanced (DCE-) magnetic resonance imaging (MRI) in the workup of BI-RADS 3 and 4 breast lesions larger than 1 cm. MATERIALS AND METHODS: In all, 120 consecutive patients (mean-age, 48 years; age range, 23-75 years) with 139 breast lesions (≥1 cm) were examined (2010-2014) with 1.5T DCE-MRI and DWI (b = 0, 50, 200, 500, 800, 1000 s/mm2 ) and the BI-RADS classification and histopathology were obtained. For each lesion malignancy was excluded using voxelwise semiautomated breast lesion analysis based on previously defined thresholds for the apparent diffusion coefficient (ADC) and the three intravoxel incoherent motion (IVIM) parameters: molecular diffusion (Dslow ), microperfusion (Dfast ), and the fraction of Dfast (ffast ). The sensitivity (Se), specificity (Sp), and negative predictive value (NPV) based on only IVIM parameters combined in parallel (Dslow , Dfast , and ffast ), or the ADC or the BI-RADS classification by DCE-MRI were compared. Subsequently, the Se, Sp, and NPV of the combination of the BI-RADS classification by DCE-MRI followed by the IVIM parameters in parallel (or the ADC) were compared. RESULTS: In all, 23 of 139 breast lesions were benign. Se and Sp of DCE-MRI was 100% and 30.4% (NPV = 100%). Se and Sp of IVIM parameters in parallel were 92.2% and 52.2% (NPV = 57.1%) and for the ADC 95.7% and 17.4%, respectively (NPV = 44.4%). In all, 26 of 139 lesions were classified as BI-RADS 3 (n = 7) or BI-RADS 4 (n = 19). DCE-MRI combined with ADC (Se = 99.1%, Sp = 34.8%) or IVIM (Se = 99.1%, Sp = 56.5%) did significantly improve (P = 0.016) Sp of DCE-MRI alone for workup of BI-RADS 3 and 4 lesions (NPV = 92.9%). CONCLUSION: Quantitative DWI has a lower NPV compared to DCE-MRI for evaluation of breast lesions and may therefore not be able to replace DCE-MRI; when implemented after DCE-MRI as problem solver for BI-RADS 3 and 4 lesions, the combined specificity improves significantly. J. Magn. Reson. Imaging 2016;44:1642-1649.

Quantitative STIR of muscle for monitoring nerve regeneration.

PURPOSE: To assess whether short tau inversion recovery (STIR) MRI sequences can provide a tool for monitoring peripheral nerve regeneration, by comparing signal intensity changes in reinnervated muscle over time, and to determine potential clinical time points for monitoring. MATERIALS AND METHODS: For this prospective study, 29 patients with complete traumatic transection of the ulnar or median nerves in the forearm were followed up to 45 months postsurgery. Standardized 1.5 Tesla STIR-MRI scans of hand muscles were obtained at fixed time intervals. Muscle signal intensities were measured semi-quantitatively and correlated to functional outcome. RESULTS: For the patients with good function recovery, mean signal intensity ratios of 1.179 ± 0.039, 1.304 ± 0.180, 1.154 ± 0.121, 1.105 ± 0.046 and 1.038 ± 0.047 were found at 1-, 3-, 6-, 9-, and 12-month follow-up, respectively. In the group with poor function recovery, ratios of 1.240 ± 0.069, 1.374 ± 0.144, 1.407 ± 0.127, 1.386 ± 0.128 and 1.316 ± 0.116 were found. Comparing the groups showed significant differences from 6 months onward (P < 0.001), with normalizing signal intensities in the group with good function recovery and sustained elevated signal intensity in the group with poor function recovery. CONCLUSION: MRI of muscle can be used as a tool for monitoring motor nerve regeneration, by comparing STIR muscle signal intensities over time. A decrease in signal intensity ratio of 50% (as compared to the initial increase) seems to predict good function recovery. Long-term follow-up shows that STIR MRI can be used for at least 15 months after nerve transection to differentiate between denervated and (re)innervated muscles. J. Magn. Reson. Imaging 2016;44:401-410.