Variability and magnitude of brain glutamate levels in schizophrenia: a meta and mega-analysis.

Glutamatergic dysfunction is implicated in schizophrenia pathoaetiology, but this may vary in extent between patients. It is unclear whether inter-individual variability in glutamate is greater in schizophrenia than the general population. We conducted meta-analyses to assess (1) variability of glutamate measures in patients relative to controls (log coefficient of variation ratio: CVR); (2) standardised mean differences (SMD) using Hedges g; (3) modal distribution of individual-level glutamate data (Hartigan's unimodality dip test). MEDLINE and EMBASE databases were searched from inception to September 2022 for proton magnetic resonance spectroscopy (1H-MRS) studies reporting glutamate, glutamine or Glx in schizophrenia. 123 studies reporting on 8256 patients and 7532 controls were included. Compared with controls, patients demonstrated greater variability in glutamatergic metabolites in the medial frontal cortex (MFC, glutamate: CVR = 0.15, p < 0.001; glutamine: CVR = 0.15, p = 0.003; Glx: CVR = 0.11, p = 0.002), dorsolateral prefrontal cortex (glutamine: CVR = 0.14, p = 0.05; Glx: CVR = 0.25, p < 0.001) and thalamus (glutamate: CVR = 0.16, p = 0.008; Glx: CVR = 0.19, p = 0.008). Studies in younger, more symptomatic patients were associated with greater variability in the basal ganglia (BG glutamate with age: z = -0.03, p = 0.003, symptoms: z = 0.007, p = 0.02) and temporal lobe (glutamate with age: z = -0.03, p = 0.02), while studies with older, more symptomatic patients associated with greater variability in MFC (glutamate with age: z = 0.01, p = 0.02, glutamine with symptoms: z = 0.01, p = 0.02). For individual patient data, most studies showed a unimodal distribution of glutamatergic metabolites. Meta-analysis of mean differences found lower MFC glutamate (g = -0.15, p = 0.03), higher thalamic glutamine (g = 0.53, p < 0.001) and higher BG Glx in patients relative to controls (g = 0.28, p < 0.001). Proportion of males was negatively associated with MFC glutamate (z = -0.02, p < 0.001) and frontal white matter Glx (z = -0.03, p = 0.02) in patients relative to controls. Patient PANSS total score was positively associated with glutamate SMD in BG (z = 0.01, p = 0.01) and temporal lobe (z = 0.05, p = 0.008). Further research into the mechanisms underlying greater glutamatergic metabolite variability in schizophrenia and their clinical consequences may inform the identification of patient subgroups for future treatment strategies.

Development of image-based decision support systems utilizing information extracted from radiological free-text report databases with text-based transformers.

OBJECTIVES: To investigate the potential and limitations of utilizing transformer-based report annotation for on-site development of image-based diagnostic decision support systems (DDSS). METHODS: The study included 88,353 chest X-rays from 19,581 intensive care unit (ICU) patients. To label the presence of six typical findings in 17,041 images, the corresponding free-text reports of the attending radiologists were assessed by medical research assistants ("gold labels"). Automatically generated "silver" labels were extracted for all reports by transformer models trained on gold labels. To investigate the benefit of such silver labels, the image-based models were trained using three approaches: with gold labels only (MG), with silver labels first, then with gold labels (MS/G), and with silver and gold labels together (MS+G). To investigate the influence of invested annotation effort, the experiments were repeated with different numbers (N) of gold-annotated reports for training the transformer and image-based models and tested on 2099 gold-annotated images. Significant differences in macro-averaged area under the receiver operating characteristic curve (AUC) were assessed by non-overlapping 95% confidence intervals. RESULTS: Utilizing transformer-based silver labels showed significantly higher macro-averaged AUC than training solely with gold labels (N = 1000: MG 67.8 [66.0-69.6], MS/G 77.9 [76.2-79.6]; N = 14,580: MG 74.5 [72.8-76.2], MS/G 80.9 [79.4-82.4]). Training with silver and gold labels together was beneficial using only 500 gold labels (MS+G 76.4 [74.7-78.0], MS/G 75.3 [73.5-77.0]). CONCLUSIONS: Transformer-based annotation has potential for unlocking free-text report databases for the development of image-based DDSS. However, on-site development of image-based DDSS could benefit from more sophisticated annotation pipelines including further information than a single radiological report. CLINICAL RELEVANCE STATEMENT: Leveraging clinical databases for on-site development of artificial intelligence (AI)-based diagnostic decision support systems by text-based transformers could promote the application of AI in clinical practice by circumventing highly regulated data exchanges with third parties. KEY POINTS: • The amount of data from a database that can be used to develop AI-assisted diagnostic decision systems is often limited by the need for time-consuming identification of pathologies by radiologists. • The transformer-based structuring of free-text radiological reports shows potential to unlock corresponding image databases for on-site development of image-based diagnostic decision support systems. • However, the quality of image annotations generated solely on the content of a single radiology report may be limited by potential inaccuracies and incompleteness of this report.

End-to-end automated body composition analyses with integrated quality control for opportunistic assessment of sarcopenia in CT.

OBJECTIVES: To develop a pipeline for automated body composition analysis and skeletal muscle assessment with integrated quality control for large-scale application in opportunistic imaging. METHODS: First, a convolutional neural network for extraction of a single slice at the L3/L4 lumbar level was developed on CT scans of 240 patients applying the nnU-Net framework. Second, a 2D competitive dense fully convolutional U-Net for segmentation of visceral and subcutaneous adipose tissue (VAT, SAT), skeletal muscle (SM), and subsequent determination of fatty muscle fraction (FMF) was developed on single CT slices of 1143 patients. For both steps, automated quality control was integrated by a logistic regression model classifying the presence of L3/L4 and a linear regression model predicting the segmentation quality in terms of Dice score. To evaluate the performance of the entire pipeline end-to-end, body composition metrics, and FMF were compared to manual analyses including 364 patients from two centers. RESULTS: Excellent results were observed for slice extraction (z-deviation = 2.46 ± 6.20 mm) and segmentation (Dice score for SM = 0.95 ± 0.04, VAT = 0.98 ± 0.02, SAT = 0.97 ± 0.04) on the dual-center test set excluding cases with artifacts due to metallic implants. No data were excluded for end-to-end performance analyses. With a restrictive setting of the integrated segmentation quality control, 39 of 364 patients were excluded containing 8 cases with metallic implants. This setting ensured a high agreement between manual and fully automated analyses with mean relative area deviations of ΔSM = 3.3 ± 4.1%, ΔVAT = 3.0 ± 4.7%, ΔSAT = 2.7 ± 4.3%, and ΔFMF = 4.3 ± 4.4%. CONCLUSIONS: This study presents an end-to-end automated deep learning pipeline for large-scale opportunistic assessment of body composition metrics and sarcopenia biomarkers in clinical routine. KEY POINTS: • Body composition metrics and skeletal muscle quality can be opportunistically determined from routine abdominal CT scans. • A pipeline consisting of two convolutional neural networks allows an end-to-end automated analysis. • Machine-learning-based quality control ensures high agreement between manual and automatic analysis.

Detection of liver cirrhosis in standard T2-weighted MRI using deep transfer learning.

OBJECTIVES: To investigate the diagnostic performance of deep transfer learning (DTL) to detect liver cirrhosis from clinical MRI. METHODS: The dataset for this retrospective analysis consisted of 713 (343 female) patients who underwent liver MRI between 2017 and 2019. In total, 553 of these subjects had a confirmed diagnosis of liver cirrhosis, while the remainder had no history of liver disease. T2-weighted MRI slices at the level of the caudate lobe were manually exported for DTL analysis. Data were randomly split into training, validation, and test sets (70%/15%/15%). A ResNet50 convolutional neural network (CNN) pre-trained on the ImageNet archive was used for cirrhosis detection with and without upstream liver segmentation. Classification performance for detection of liver cirrhosis was compared to two radiologists with different levels of experience (4th-year resident, board-certified radiologist). Segmentation was performed using a U-Net architecture built on a pre-trained ResNet34 encoder. Differences in classification accuracy were assessed by the χ2-test. RESULTS: Dice coefficients for automatic segmentation were above 0.98 for both validation and test data. The classification accuracy of liver cirrhosis on validation (vACC) and test (tACC) data for the DTL pipeline with upstream liver segmentation (vACC = 0.99, tACC = 0.96) was significantly higher compared to the resident (vACC = 0.88, p < 0.01; tACC = 0.91, p = 0.01) and to the board-certified radiologist (vACC = 0.96, p < 0.01; tACC = 0.90, p < 0.01). CONCLUSION: This proof-of-principle study demonstrates the potential of DTL for detecting cirrhosis based on standard T2-weighted MRI. The presented method for image-based diagnosis of liver cirrhosis demonstrated expert-level classification accuracy. KEY POINTS: • A pipeline consisting of two convolutional neural networks (CNNs) pre-trained on an extensive natural image database (ImageNet archive) enables detection of liver cirrhosis on standard T2-weighted MRI. • High classification accuracy can be achieved even without altering the pre-trained parameters of the convolutional neural networks. • Other abdominal structures apart from the liver were relevant for detection when the network was trained on unsegmented images.

A pilot study of magnetic resonance fingerprinting in Parkinson's disease.

Parkinson's disease (PD) affects more than six million people, but reliable MRI biomarkers with which to diagnose patients have not been established. Magnetic resonance fingerprinting (MRF) is a recent quantitative technique that can provide relaxometric maps from a single sequence. The purpose of this study is to assess the potential of MRF to identify PD in patients and their disease severity, as well as to evaluate comfort during MRF. Twenty-five PD patients and 25 matching controls underwent 3 T MRI, including an axial 2D spoiled gradient echo MRF sequence. T1 and T2 maps were generated by voxel-wise matching the measured MRF signal to a precomputed dictionary. All participants also received standard inversion recovery T1 and multi-echo T2 mapping. An ROI-based analysis of relaxation times was performed. Differences between patients and controls as well as techniques were determined by logistic regression, Spearman correlation and t-test. Patients were asked to estimate the subjective comfort of the MRF sequence. Both MRF-based T1 and T2 mapping discriminated patients from controls: T1 relaxation times differed most in cortical grey matter (PD 1337 ± 38 vs. control 1386 ± 37 ms; mean ± SD; P = .0001) and, in combination with normal-appearing white matter, enabled correct discrimination in 85.7% of cases (sensitivity 83.3%; specificity 88.0%; receiver-operating characteristic [ROC]) area under the curve [AUC] 0.87), while for T2 mapping the left putamen was the strongest classifier (40.54 ± 6.28 vs. 34.17 ± 4.96 ms; P = .0001), enabling differentiation of groups in 84.0% of all cases (sensitivity 80.0%; specificity 88.0%; ROC AUC 0.87). Relaxation time differences were not associated with disease severity. Standard mapping techniques generated significantly different relaxation time values and identified other structures as different between groups other than MRF. Twenty-three out of 25 PD patients preferred the MRF examination instead of a standard MRI. MRF-based mapping can identify PD patients with good comfort but needs further assessment regarding disease severity identification and its potential for comparability with standard mapping technique results.

MR fingerprinting as a diagnostic tool in patients with frontotemporal lobe degeneration: A pilot study.

Several very rare forms of dementia are associated with characteristic focal atrophy predominantly of the frontal and/or temporal lobes and currently lack imaging solutions to monitor disease. Magnetic resonance fingerprinting (MRF) is a recently developed technique providing quantitative relaxivity maps and images with various tissue contrasts out of a single sequence acquisition. This pilot study explores the utility of MRF-based T1 and T2 mapping to discover focal differences in relaxation times between patients with frontotemporal lobe degenerative dementia and healthy controls. 8 patients and 30 healthy controls underwent a 3 T MRI including an axial 2D spoiled gradient echo MRF sequence. T1 and T2 relaxation maps were generated based on an extended phase graphs algorithm-founded dictionary involving inner product pattern matching. A region of interest (ROI)-based analysis of T1 and T2 relaxation times was performed with FSL and ITK-SNAP. Depending on the brain region analyzed, T1 relaxation times were up to 10.28% longer in patients than in controls reaching significant differences in cortical gray matter (P = .047) and global white matter (P = .023) as well as in both hippocampi (P = .001 left; P = .027 right). T2 relaxation times were similarly longer in the hippocampus by up to 19.18% in patients compared with controls. The clinically most affected patient had the most control-deviant relaxation times. There was a strong correlation of T1 relaxation time in the amygdala with duration of the clinically manifest disease (Spearman Rho = .94; P = .001) and of T1 relaxation times in the left hippocampus with disease severity (Rho = .90, P = .002). In conclusion, MRF-based relaxometry is a promising and time-saving new MRI tool to study focal cerebral alterations and identify patients with frontotemporal lobe degeneration. To validate the results of this pilot study, MRF is worth further exploration as a diagnostic tool in neurodegenerative diseases.

Quantification of Liver Fibrosis at T1 and T2 Mapping with Extracellular Volume Fraction MRI: Preclinical Results.

Purpose To evaluate MRI T1 and T2 mapping with calculation of extracellular volume (ECV) for diagnosis and grading of liver fibrosis. Materials and Methods Different grades of fibrosis were induced in 60 male Sprague-Dawley rats by bile duct ligation (BDL) and carbon-tetrachloride (CCl4) intoxication. Portal pressure was measured invasively, whereas hepatic fibrosis was quantified by hydroxyproline content, Sirius red staining, and α smooth muscle actin staining. T1 values, T2 values, and ECV were assessed by using quantitative MRI mapping techniques. Results T1 values in animals 4 weeks after BDL were greater than in control animals (718 msec ± 74 vs 578 msec ± 33, respectively; P < .001). T2 values at 4 weeks were also greater in animals that underwent BDL than in control animals (46 msec ± 6 vs 29 msec ± 2, respectively; P < .001). Similar T1 and T2 findings were observed after CCl4 intoxication. ECV was greater in animals 4 weeks after BDL compared with control animals (31.3% ± 1.3 vs 18.2% ± 3.5, respectively; P < .001), with similar results after CCl4 intoxication. High correlations were found between ECV and hepatic hydroxyproline content (BDL: r = 0.68, P < .001; CCl4: r = 0.65, P < .001), Sirius red staining (BDL: r = 0.88, P < .001; CCl4: r = 0.82, P < .001), α smooth muscle actin staining (BDL: r = 0.70, P < .001; CCl4: r = 0.73, P < .001), and portal pressure (BDL: r = 0.54, P = .003; CCl4: r = 0.39, P = .043). Conclusion Elevation of T1 and T2 values and ECV was associated with severity of liver fibrosis and portal hypertension in an experimental animal model.

Revised PROPELLER for T2-weighted imaging of the prostate at 3 Tesla: impact on lesion detection and PI-RADS classification.

PURPOSE: To evaluate revised PROPELLER (RevPROP) for T2-weighted imaging (T2WI) of the prostate as a substitute for turbo spin echo (TSE). MATERIALS AND METHODS: Three-Tesla MR images of 50 patients with 55 cancer-suspicious lesions were prospectively evaluated. Findings were correlated with histopathology after MRI-guided biopsy. T2 RevPROP, T2 TSE, diffusion-weighted imaging, dynamic contrast enhancement, and MR-spectroscopy were acquired. RevPROP was compared to TSE concerning PI-RADS scores, lesion size, lesion signal-intensity, lesion contrast, artefacts, and image quality. RESULTS: There were 41 carcinomas in 55 cancer-suspicious lesions. RevPROP detected 41 of 41 carcinomas (100%) and 54 of 55 lesions (98.2%). TSE detected 39 of 41 carcinomas (95.1%) and 51 of 55 lesions (92.7%). RevPROP showed fewer artefacts and higher image quality (each p < 0.001). No differences were observed between single and overall PI-RADS scores based on RevPROP or TSE (p = 0.106 and p = 0.107). Lesion size was not different (p = 0.105). T2-signal intensity of lesions was higher and T2-contrast of lesions was lower on RevPROP (each p < 0.001). CONCLUSION: For prostate cancer detection RevPROP is superior to TSE with respect to motion robustness, image quality and detection rates of lesions. Therefore, RevPROP might be used as a substitute for T2WI. KEY POINTS: • Revised PROPELLER can be used as a substitute for T2-weighted prostate imaging. • Revised PROPELLER detected more carcinomas and more suspicious lesions than TSE. • Revised PROPELLER showed fewer artefacts and better image quality compared to TSE. • There were no significant differences in PI-RADS scores between revised PROPELLER and TSE. • The lower T2-contrast of revised PROPELLER did not impair its diagnostic quality.

Yttrium-90 Radioembolization of Advanced, Unresectable Breast Cancer Liver Metastases-A Single-Center Experience.

PURPOSE: To determine value of transarterial radioembolization (TARE) for palliative treatment of unresectable liver-dominant breast metastases (LdBM) and to determine prognostic parameters. MATERIALS AND METHODS: Records of patients undergoing TARE for progressing LdBM between June 2006 and March 2015 were retrospectively reviewed; 44 female patients (mean age 56.1 y; range, 34.9-85.3 y) underwent 69 TAREs (56 resin-based, 13 glass-based). Of 44 patients, 42 had bilobar disease. Mean administered activity was 1.35 GBq ± 0.71. Median clinical and imaging follow-up times were 121 days (range, 26-870 d; n = 42 patients) and 93 days (range, 26-2,037 d; n = 38 patients). Clinical and biochemical toxicities, imaging response (according to Response Evaluation Criteria In Solid Tumors), time to progression, and overall survival (OS) were evaluated. Data were analyzed with stratification according to clinical and procedural parameters. RESULTS: Toxicities included 1 cholecystitis (grade 2) and 1 duodenal ulceration (grade 3); no grade ≥ 4 clinical toxicities were noted. Objective response rate (complete + partial response) was 28.9% (11/38); disease control rate (response + stable disease) was 71.1% (27/38). Median time to progression of treated liver lobe was 101 days (range, 30-2,037 d). During follow-up, 34/42 patients died (median OS after first TARE: 184 d [range 29-2,331 d]). On multivariate analysis, baseline Eastern Cooperative Oncology Group (ECOG) status of 0 (P < .0001, hazard ratio [HR] = 0.146) and low baseline γ-glutamyltransferase (GGT) levels (P = .0146, HR = 0.999) were predictors of longer OS. CONCLUSIONS: TARE can successfully delay progression of therapy-refractory LdBM with low complication rate. Nonelevated baseline ECOG status and low GGT levels were identified as prognostic factors.

Intravoxel Incoherent Motion Diffusion-Weighted MR Imaging for Prediction of Early Arterial Blood Flow Stasis in Radioembolization of Breast Cancer Liver Metastases.

PURPOSE: To retrospectively evaluate predictive value of intravoxel incoherent motion (IVIM) diffusion-weighted imaging (DWI) for early arterial blood flow stasis during transarterial radioembolization (TARE) of liver dominant breast metastases (LdBM). MATERIALS AND METHODS: Preinterventional 1.5T DWI (b0, b1, b2 = 0, 50, 800 s/mm(2)) data for 28 liver lobes of 18 female patients treated by resin-based radioembolization (10 bilobar and 8 unilobar treatments) were analyzed. Apparent diffusion coefficient (ADC) (0, 800) and an estimation of the true diffusion coefficient D' and of the perfusion fraction f' were calculated for the 2 largest metastases. Response rate at 3 months and survival were analyzed. Procedures without full dose application because of early stasis were assigned to group A (n = 15), and procedures with full dose application were assigned to group B (n = 13). RESULTS: Metastases in group A showed significantly lower f' (0.035 ± 0.018 vs 0.076 ± 0.015, P < .0001) and a trend toward lower ADC(0, 800) with values given in 10(-6) mm(2)/s (1,066 ± 141 vs 1,189 ± 176, P = .051); no group difference was shown for D'. Groups were best discriminated by weighted mean f' values of the 2 largest metastases with accuracy of 100%. Mean tumor diameter before and after TARE was 51 mm ± 18 and 50 mm ± 24 in group A and 47 mm ± 27 and 48 mm ± 32 for group B. Imaging response did not differ between groups (P = .545). Overall survival did not differ significantly between group A (230 d) and B (155 d) (P = .124). CONCLUSIONS: Perfusion-sensitive IVIM parameter f' may predict early blood flow stasis in patients undergoing TARE for LdBM. Determination of this parameter before intervention may increase awareness of the interventionalist and increase safety of microsphere administration.

Comparison between modified Dixon MRI techniques, MR spectroscopic relaxometry, and different histologic quantification methods in the assessment of hepatic steatosis.

OBJECTIVES: To compare systematically quantitative MRI, MR spectroscopy (MRS), and different histological methods for liver fat quantification in order to identify possible incongruities. METHODS: Fifty-nine consecutive patients with liver disorders were examined on a 3 T MRI system. Quantitative MRI was performed using a dual- and a six-echo variant of the modified Dixon (mDixon) sequence, calculating proton density fat fraction (PDFF) maps, in addition to single-voxel MRS. Histological fat quantification included estimation of the percentage of hepatocytes containing fat vesicles as well as semi-automatic quantification (qHisto) using tissue quantification software. RESULTS: In 33 of 59 patients, the hepatic fat fraction was >5% as determined by MRS (maximum 45%, mean 17%). Dual-echo mDixon yielded systematically lower PDFF values than six-echo mDixon (mean difference 1.0%; P < 0.001). Six-echo mDixon correlated excellently with MRS, qHisto, and the estimated percentage of hepatocytes containing fat vesicles (R = 0.984, 0.967, 0.941, respectively, all P < 0.001). Mean values obtained by the estimated percentage of hepatocytes containing fat were higher by a factor of 2.5 in comparison to qHisto. Six-echo mDixon and MRS showed the best agreement with values obtained by qHisto. CONCLUSIONS: Six-echo mDixon, MRS, and qHisto provide the most robust and congruent results and are therefore most appropriate for reliable quantification of liver fat. KEY POINTS: • Six-echo mDixon correlates excellently with MRS, qHisto, and the estimated percentage of fat-containing hepatocytes. • Six-echo mDixon, MRS, and qHisto provide the most robust and congruent results. • Dual-echo mDixon yields systematically lower PDFF values than six-echo mDixon. • The percentage of fat-containing hepatocytes is 2.5-fold higher than fat fraction determined by qHisto. • Performance characteristics and systematic differences of the various methods should be considered.

Gradient Spin Echo (GraSE) imaging for fast myocardial T2 mapping.

BACKGROUND: Quantitative Cardiovascular Magnetic Resonance (CMR) techniques have gained high interest in CMR research. Myocardial T2 mapping is thought to be helpful in diagnosis of acute myocardial conditions associated with myocardial edema. In this study we aimed to establish a technique for myocardial T2 mapping based on gradient-spin-echo (GraSE) imaging. METHODS: The local ethics committee approved this prospective study. Written informed consent was obtained from all subjects prior to CMR. A modified GraSE sequence allowing for myocardial T2 mapping in a single breath-hold per slice using ECG-triggered acquisition of a black blood multi-echo series was developed at 1.5 Tesla. Myocardial T2 relaxation time (T2-RT) was determined by maximum likelihood estimation from magnitude phased-array multi-echo data. Four GraSE sequence variants with varying number of acquired echoes and resolution were evaluated in-vitro and in 20 healthy volunteers. Inter-study reproducibility was assessed in a subset of five volunteers. The sequence with the best overall performance was further evaluated by assessment of intra- and inter-observer agreement in all volunteers, and then implemented into the clinical CMR protocol of five patients with acute myocardial injury (myocarditis, takotsubo cardiomyopathy and myocardial infarction). RESULTS: In-vitro studies revealed the need for well defined sequence settings to obtain accurate T2-RT measurements with GraSE. An optimized 6-echo GraSE sequence yielded an excellent agreement with the gold standard Carr-Purcell-Meiboom-Gill sequence. Global myocardial T2 relaxation times in healthy volunteers was 52.2 ± 2.0 ms (mean ± standard deviation). Mean difference between repeated examinations (n = 5) was -0.02 ms with 95% limits of agreement (LoA) of [-4.7; 4.7] ms. Intra-reader and inter-reader agreement was excellent with mean differences of -0.1 ms, 95% LoA = [-1.3; 1.2] ms and 0.1 ms, 95% LoA = [-1.5; 1.6] ms, respectively (n = 20). In patients with acute myocardial injury global myocardial T2-RTs were prolonged (mean: 61.3 ± 6.7 ms). CONCLUSION: Using an optimized GraSE sequence CMR allows for robust, reliable, fast myocardial T2 mapping and quantitative tissue characterization. Clinically, the GraSE-based T2-mapping has the potential to complement qualitative CMR in patients with acute myocardial injuries.

Ultrafast volumetric B1 (+) mapping for improved radiofrequency shimming in 3 tesla body MRI.

PURPOSE: To evaluate the use of the recently proposed ultrafast B1 (+) mapping approach DREAM (Dual Refocusing Echo Acquisition Mode) for a refinement of patient adaptive radiofrequency (RF) shimming. MATERIALS AND METHODS: Volumetric DREAM B1 (+) calibration scans centered in the upper abdomen were acquired in 20 patients and three volunteers with written informed consent at a clinical dual source 3 Tesla (T) MR system. Based on these data, RF transmit settings were optimized by central-slice based RF-shimming (CS-RF shim) and by a refined, multi-slice adaptive approach (MS-RF shim). Simulations were performed to compare flip angle accuracy and B1 (+) homogeneity (cv = stddev/mean) achieved by CS-RF shim versus MS-RF shim for transversal and coronal slices, and for volume shimming on the spine. RESULTS: By MS-RF shim, mean deviation from nominal flip angle was reduced to less than 11% in all slices, all targets, and all subjects. Relative improvements in B1 (+) cv (MS-RF shim versus CS-RF) were up to 14%/39%/47% in transversal slices/coronal slices/ spine area. CONCLUSION: Volumetric information about B1 (+) can be used to further improve the accuracy and homogeneity of the B1 (+) field yielding higher diagnostic confidence, and will also be of value for various quantitative methods which are sensitive to flip angle imperfections.

Optical Flow-Guided Cine MRI Segmentation With Learned Corrections.

In cardiac cine magnetic resonance imaging (MRI), the heart is repeatedly imaged at numerous time points during the cardiac cycle. Frequently, the temporal evolution of a certain region of interest such as the ventricles or the atria is highly relevant for clinical diagnosis. In this paper, we devise a novel approach that allows for an automatized propagation of an arbitrary region of interest (ROI) along the cardiac cycle from respective annotated ROIs provided by medical experts at two different points in time, most frequently at the end-systolic (ES) and the end-diastolic (ED) cardiac phases. At its core, a 3D TV- L1 -based optical flow algorithm computes the apparent motion of consecutive MRI images in forward and backward directions. Subsequently, the given terminal annotated masks are propagated by this bidirectional optical flow in 3D, which results, however, in improper initial estimates of the segmentation masks due to numerical inaccuracies. These initially propagated segmentation masks are then refined by a 3D U-Net-based convolutional neural network (CNN), which was trained to enforce consistency with the forward and backward warped masks using a novel loss function. Moreover, a penalization term in the loss function controls large deviations from the initial segmentation masks. This method is benchmarked both on a new dataset with annotated single ventricles containing patients with severe heart diseases and on a publicly available dataset with different annotated ROIs. We emphasize that our novel loss function enables fine-tuning the CNN on a single patient, thereby yielding state-of-the-art results along the complete cardiac cycle.

Direct deep learning-based survival prediction from pre-interventional CT prior to transcatheter aortic valve replacement.

PURPOSE: To investigate survival prediction in patients undergoing transcatheter aortic valve replacement (TAVR) using deep learning (DL) methods applied directly to pre-interventional CT images and to compare performance with survival models based on scalar markers of body composition. METHOD: This retrospective single-center study included 760 patients undergoing TAVR (mean age 81 ± 6 years; 389 female). As a baseline, a Cox proportional hazards model (CPHM) was trained to predict survival on sex, age, and the CT body composition markers fatty muscle fraction (FMF), skeletal muscle radiodensity (SMRD), and skeletal muscle area (SMA) derived from paraspinal muscle segmentation of a single slice at L3/L4 level. The convolutional neural network (CNN) encoder of the DL model for survival prediction was pre-trained in an autoencoder setting with and without a focus on paraspinal muscles. Finally, a combination of DL and CPHM was evaluated. Performance was assessed by C-index and area under the receiver operating curve (AUC) for 1-year and 2-year survival. All methods were trained with five-fold cross-validation and were evaluated on 152 hold-out test cases. RESULTS: The CNN for direct image-based survival prediction, pre-trained in a focussed autoencoder scenario, outperformed the baseline CPHM (CPHM: C-index = 0.608, 1Y-AUC = 0.606, 2Y-AUC = 0.594 vs. DL: C-index = 0.645, 1Y-AUC = 0.687, 2Y-AUC = 0.692). Combining DL and CPHM led to further improvement (C-index = 0.668, 1Y-AUC = 0.713, 2Y-AUC = 0.696). CONCLUSIONS: Direct DL-based survival prediction shows potential to improve image feature extraction compared to segmentation-based scalar markers of body composition for risk assessment in TAVR patients.

Reduction of Gadolinium-Based Contrast Agents in MRI Using Convolutional Neural Networks and Different Input Protocols: Limited Interchangeability of Synthesized Sequences With Original Full-Dose Images Despite Excellent Quantitative Performance.

OBJECTIVES: The purpose of this study was to implement a state-of-the-art convolutional neural network used to synthesize artificial T1-weighted (T1w) full-dose images from corresponding noncontrast and low-dose images (using various settings of input sequences) and test its performance on a patient population acquired prospectively. MATERIALS AND METHODS: In this monocentric, institutional review board-approved study, a total of 138 participants were included who received an adapted imaging protocol with acquisition of a T1w low dose after administration of 10% of the standard dose and acquisition of a T1w full dose after administration of the remaining 90% of the standard dose of a gadolinium-containing contrast agent. A total of 83 participants formed the training sample (51.7 ± 16.5 years, 36 women), 25 the validation sample (55.3 ± 16.4 years, 11 women), and 30 the test sample (55.0 ± 15.0 years, 9 women). Four input settings were differentiated: only the T1w noncontrast and T1w low-dose images (standard setting), only the T1w noncontrast and T1w low-dose images with a prolonged postinjection time of 5 minutes (5-minute setting), multiple noncontrast sequences (T1w, T2w, diffusion) and the T1w low-dose images (extended setting), and only noncontrast sequences (T1w, T2w, diffusion) were used (zero-dose setting). For each setting, a deep neural network was trained to synthesize artificial T1w full-dose images, which were assessed on the test sample using an objective evaluation based on quantitative metrics and a subjective evaluation through a reader-based study. Three readers scored the overall image quality, the interchangeability in regard to the clinical conclusion compared with the true T1w full-dose sequence, the contrast enhancement of lesions, and their conformity to the respective references in the true T1w full dose. RESULTS: Quantitative analysis of the artificial T1w full-dose images of the standard setting provided a peak signal-to-noise ratio of 33.39 ± 0.62 (corresponding to an average improvement of the low-dose sequences of 5.2 dB) and a structural similarity index measure of 0.938 ± 0.005. In the 4-fold cross-validation, the extended setting yielded similar performance to the standard setting in terms of peak signal-to-noise ratio ( P = 0.20), but a slight improvement in structural similarity index measure ( P < 0.0001). For all settings, the reader study found comparable overall image quality between the original and artificial T1w full-dose images. The proportion of scans scored as fully or mostly interchangeable was 55%, 58%, 43%, and 3% and the average counts of false positives per case were 0.42 ± 0.83, 0.34 ± 0.71, 0.82 ± 1.15, and 2.00 ± 1.07 for the standard, 5-minute, extended, and zero-dose setting, respectively. Using a 5-point Likert scale (0 to 4, 0 being the worst), all settings of synthesized full-dose images showed significantly poorer contrast enhancement of lesions compared with the original full-dose sequence (difference of average degree of contrast enhancement-standard: -0.97 ± 0.83, P = <0.001; 5-minute: -0.93 ± 0.91, P = <0.001; extended: -0.96 ± 0.97, P = <0.001; zero-dose: -2.39 ± 1.14, P = <0.001). The average scores of conformity of the lesions compared with the original full-dose sequence were 2.25 ± 1.21, 2.22 ± 1.27, 2.24 ± 1.25, and 0.73 ± 0.93 for the standard, 5-minute, extended, and zero-dose setting, respectively. CONCLUSIONS: The tested deep learning algorithm for synthesis of artificial T1w full-dose sequences based on images after administration of only 10% of the standard dose of a gadolinium-based contrast agent showed very good quantitative performance. Despite good image quality in all settings, both false-negative and false-positive signals resulted in significantly limited interchangeability of the synthesized sequences with the original full-dose sequences.

Deep learning enables automated MRI-based estimation of uterine volume also in patients with uterine fibroids undergoing high-intensity focused ultrasound therapy.

BACKGROUND: High-intensity focused ultrasound (HIFU) is used for the treatment of symptomatic leiomyomas. We aim to automate uterine volumetry for tracking changes after therapy with a 3D deep learning approach. METHODS: A 3D nnU-Net model in the default setting and in a modified version including convolutional block attention modules (CBAMs) was developed on 3D T2-weighted MRI scans. Uterine segmentation was performed in 44 patients with routine pelvic MRI (standard group) and 56 patients with uterine fibroids undergoing ultrasound-guided HIFU therapy (HIFU group). Here, preHIFU scans (n = 56), postHIFU imaging maximum one day after HIFU (n = 54), and the last available follow-up examination (n = 53, days after HIFU: 420 ± 377) were included. The training was performed on 80% of the data with fivefold cross-validation. The remaining data were used as a hold-out test set. Ground truth was generated by a board-certified radiologist and a radiology resident. For the assessment of inter-reader agreement, all preHIFU examinations were segmented independently by both. RESULTS: High segmentation performance was already observed for the default 3D nnU-Net (mean Dice score = 0.95 ± 0.05) on the validation sets. Since the CBAM nnU-Net showed no significant benefit, the less complex default model was applied to the hold-out test set, which resulted in accurate uterus segmentation (Dice scores: standard group 0.92 ± 0.07; HIFU group 0.96 ± 0.02), which was comparable to the agreement between the two readers. CONCLUSIONS: This study presents a method for automatic uterus segmentation which allows a fast and consistent assessment of uterine volume. Therefore, this method could be used in the clinical setting for objective assessment of therapeutic response to HIFU therapy.

Association of amygdala volumes with cortisol secretion in unipolar depressed patients.

Major depressive disorder (MDD) is accompanied by morphological changes of brain structures which are of great importance in the neural circuitry mediating depression like the hippocampus and the amygdala. Hyperactivity of the hypothalamic-pituitary-adrenocortical (HPA) system resulting in enhanced glucocorticoid secretion can often be observed during depression and has been thought to play an important role in inducing these morphological changes. We used magnetic resonance imaging to investigate alterations of amygdala and hippocampal volumes in 86 in-patients with unipolar depression and 87 healthy controls, and we then correlated amygdala and hippocampal volumes of 76 in-patients with the area under the curve of cortisol secretion in the dexamethasone/corticotropin releasing hormone (Dex/CRH) test at baseline and during short-term antidepressant therapy. In line with recently published studies both left and right amygdala volumes of patients in a first depressive episode were smaller than those of healthy controls. Patients with recurrent depressive episodes showed a reduction of hippocampal volumes, while amygdala volumes were normal. Larger left and right amygdala volumes correlated with a more pronounced reduction of HPA activity, measured by the cortisol secretion in the combined DEX/CRH test, during antidepressant therapy in patients with recurrent depressive episodes.

Simplified intravoxel incoherent motion DWI for differentiating malignant from benign breast lesions.

BACKGROUND: To evaluate simplified intravoxel incoherent motion (IVIM) diffusion-weighted imaging (DWI) for differentiating malignant versus benign breast lesions as (i) stand-alone tool and (ii) add-on to dynamic contrast-enhanced magnetic resonance imaging. METHODS: 1.5-T DWI data (b = 0, 50, 250, 800 s/mm2) were retrospectively analysed for 126 patients with malignant or benign breast lesions. Apparent diffusion coefficient (ADC) ADC (0, 800) and IVIM-based parameters D1' = ADC (50, 800), D2' = ADC (250, 800), f1' = f (0, 50, 800), f2' = f (0, 250, 800) and D*' = D* (0, 50, 250, 800) were voxel-wise calculated without fitting procedures. Regions of interest were analysed in vital tumour and perfusion hot spots. Beside the single parameters, the combined use of D1' with f1' and D2' with f2' was evaluated. Lesion differentiation was investigated for lesions (i) with hyperintensity on DWI with b = 800 s/mm2 (n = 191) and (ii) with suspicious contrast-enhancement (n = 135). RESULTS: All lesions with suspicious contrast-enhancement appeared also hyperintense on DWI with b = 800 s/mm2. For task (i), best discrimination was reached for the combination of D1' and f1' using perfusion hot spot regions-of-interest (accuracy 93.7%), which was higher than that of ADC (86.9%, p = 0.003) and single IVIM parameters D1' (88.0%) and f1' (87.4%). For task (ii), best discrimination was reached for single parameter D1' using perfusion hot spot regions-of-interest (92.6%), which were slightly but not significantly better than that of ADC (91.1%) and D2' (88.1%). Adding f1' to D1' did not improve discrimination. CONCLUSIONS: IVIM analysis yielded a higher accuracy than ADC. If stand-alone DWI is used, perfusion analysis is of special relevance.