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Background: Giant cell arteritis (GCA) is the most common systemic vasculitis in persons aged 50 and above (incidence, 3.5 per 100 000 per year). It affects cranial arteries, the aorta, and arteries elsewhere in the body, e.g., in the limbs.
Methods: We selectively review the pertinent literature, including guidelines and recommendations from Germany and abroad.
Results: The typical symptoms of new-onset GCA are bi-temporal headaches, jaw claudiacation, scalp tenderness, visual disturbances, systemic symptoms such as fever and weight loss, and polymyalgia. The diagnostic assessment comprises laboratory testing (erythrocyte sedimentation rate, C-reactive protein), imaging studies (duplex sonography, high-resolution magnetic resonance imaging, positron-emission tomography), and temporal artery biopsy. The standard treatment is with corticosteroids (adverse effects: diabetes mellitus, osteoporosis, cataract, arterial hypertension). A meta-analysis of three randomized controlled trials led to a recommendation for treatment with methotrexate to lower the recurrence rate and spare steroids. Patients for whom methotrexate is contraindicated or who cannot tolerate the drug can be treated with azathioprine instead.
Conclusion: Giant cell arteritis, if untreated, progresses to involve the aorta and its collateral branches, leading to various complications. Late diagnosis and treatment can have serious consequences, including irreversible loss of visual function.
Background
Partial segmental thrombosis of the corpus cavernosum (PSTCC) is a rare disease predominantly occurring in young men. Cardinal symptoms are pain and perineal swelling. Although several risk factors are described in the literature, the exact etiology of penile thrombosis remains unclear in most cases. MRI or ultrasound (US) is usually used for diagnosing this condition.
Case presentation
We report a case of penile thrombosis after left-sided varicocele ligature in a young patient. The diagnosis was established using contrast-enhanced ultrasound (CEUS) and was confirmed by contrast-enhanced magnetic resonance imaging (ceMRI). Successful conservative treatment consisted of systemic anticoagulation using low molecular weight heparin and acetylsalicylic acid.
Conclusion
PSTCC is a rare condition in young men and appears with massive pain and perineal swelling. In case of suspected PSTCC utilization of CEUS may be of diagnostic benefit.
Objective:
Over the past decade, myocardial triglyceride content has become an accepted biomarker for chronic metabolic and cardiac disease. The purpose of this study was to use proton (hydrogen 1)-magnetic resonance spectroscopy (\(^{1}\)H-MRS) at 3Tesla (3 T) field strength to assess potential gender-related differences in myocardial triglyceride content in healthy individuals.
Methods:
Cardiac MR imaging was performed to enable accurate voxel placement and obtain functional and morphological information. Double triggered (i.e., ECG and respiratory motion gating) \(^{1}\)H-MRS was used to quantify myocardial triglyceride levels for each gender. Two-sample t-test and Mann-Whitney U-test were used for statistical analyses.
Results:
In total, 40 healthy volunteers (22 male, 18 female; aged >18 years and age matched) were included in the study. Median myocardial triglyceride content was 0.28% (interquartile range [IQR] 0.17–0.42%) in male and 0.24% (IQR 0.14–0.45%) in female participants, and no statistically significant difference was observed between the genders. Furthermore, no gender-specific difference in ejection fraction was observed, although on average, male participants presented with a higher mean ± SD left ventricular mass (136.3 ± 25.2 g) than female participants (103.9 ± 16.1 g).
Conclusions:
The study showed that \(^{1}\)H-MRS is a capable, noninvasive tool for acquisition of myocardial triglyceride metabolites. Myocardial triglyceride concentration was shown to be unrelated to gender in this group of healthy volunteers.
Background
Salvage radiotherapy (SRT) is clinically established in prostate cancer (PC) patients with PSA persistence or biochemical relapse (BCR) after prior radical surgery. PET/CT imaging prior to SRT may be performed to localize disease recurrence. The recently introduced \(^{68}\)Ga-PSMA outperforms other PET tracers for detection of recurrence and is therefore expected also to impact radiation planning.
Forty-five patients with PSA persistence (16 pts) or BCR (29 pts) after prior prostatectomy, scheduled to undergo SRT of the prostate bed, underwent \(^{68}\)Ga-PSMA PET/CT. The median PSA level was 0.67 ng/ml. The impact of \(^{68}\)Ga-PSMA PET/CT on the treatment decision was assessed. Patients with oligometastatic (≤5 lesions) PC underwent radiotherapy (RT), with the extent of the RT area and dose escalation being based on PET positivity.
Results
Suspicious lesions were detected in 24/45 (53.3 %) patients. In 62.5 % of patients, lesions were only detected by 68Ga-PSMA PET. Treatment was changed in 19/45 (42.2 %) patients, e.g., extending SRT to metastases (9/19), administering dose escalation in patients with morphological local recurrence (6/19), or replacing SRT by systemic therapy (2/19). 38/45 (84.4 %) followed the treatment recommendation, with data on clinical follow-up being available in 21 patients treated with SRT. All but one showed biochemical response (mean PSA decline 78 ± 19 %) within a mean follow-up of 8.12 ± 5.23 months.
Conclusions
\(^{68}\)Ga-PSMA PET/CT impacts treatment planning in more than 40 % of patients scheduled to undergo SRT. Future prospective studies are needed to confirm this significant therapeutic impact on patients prior to SRT.
Background
T1 mapping sequences such as MOLLI, ShMOLLI and SASHA make use of different technical approaches, bearing strengths and weaknesses. It is well known that obtained T1 relaxation times differ between the sequence techniques as well as between different hardware. Yet, T1 quantification is a promising tool for myocardial tissue characterization, disregarding the absence of established reference values. The purpose of this study was to evaluate the feasibility of native and post-contrast T1 mapping methods as well as ECV maps and its diagnostic benefits in a clinical environment when scanning patients with various cardiac diseases at 3 T.
Methods
Native and post-contrast T1 mapping data acquired on a 3 T full-body scanner using the three pulse sequences 5(3)3 MOLLI, ShMOLLI and SASHA in 19 patients with clinical indication for contrast enhanced MRI were compared. We analyzed global and segmental T1 relaxation times as well as respective extracellular volumes and compared the emerged differences between the used pulse sequences.
Results
T1 times acquired with MOLLI and ShMOLLI exhibited systematic T1 deviation compared to SASHA. Myocardial MOLLI T1 times were 19% lower and ShMOLLI T1 times 25% lower compared to SASHA. Native blood T1 times from MOLLI were 13% lower than SASHA, while post-contrast MOLLI T1-times were only 5% lower. ECV values exhibited comparably biased estimation with MOLLI and ShMOLLI compared to SASHA in good agreement with results reported in literature. Pathology-suspect segments were clearly differentiated from remote myocardium with all three sequences.
Conclusion
Myocardial T1 mapping yields systematically biased pre- and post-contrast T1 times depending on the applied pulse sequence. Additionally calculating ECV attenuates this bias, making MOLLI, ShMOLLI and SASHA better comparable. Therefore, myocardial T1 mapping is a powerful clinical tool for classification of soft tissue abnormalities in spite of the absence of established reference values.
Magnetic Particle Imaging (MPI) is a promising new tomographic modality for fast as well as three-dimensional visualization of magnetic material. For anatomical or structural information an additional imaging modality such as computed tomography (CT) is required. In this paper, the first hybrid MPI-CT scanner for multimodal imaging providing simultaneous data acquisition is presented.
Purpose
The aim of this study was to compare the wave‐CAIPI (controlled aliasing in parallel imaging) trajectory to the Cartesian sampling for accelerated free‐breathing 4D lung MRI.
Methods
The wave‐CAIPI k‐space trajectory was implemented in a respiratory self‐gated 3D spoiled gradient echo pulse sequence. Trajectory correction applying the gradient system transfer function was used, and images were reconstructed using an iterative conjugate gradient SENSE (CG SENSE) algorithm. Five healthy volunteers and one patient with squamous cell carcinoma in the lung were examined on a clinical 3T scanner, using both sampling schemes. For quantitative comparison of wave‐CAIPI and standard Cartesian imaging, the normalized mutual information and the RMS error between retrospectively accelerated acquisitions and their respective references were calculated. The SNR ratios were investigated in a phantom study.
Results
The obtained normalized mutual information values indicate a lower information loss due to acceleration for the wave‐CAIPI approach. Average normalized mutual information values of the wave‐CAIPI acquisitions were 10% higher, compared with Cartesian sampling. Furthermore, the RMS error of the wave‐CAIPI technique was lower by 19% and the SNR was higher by 14%. Especially for short acquisition times (down to 1 minute), the undersampled Cartesian images showed an increased artifact level, compared with wave‐CAIPI.
Conclusion
The application of the wave‐CAIPI technique to 4D lung MRI reduces undersampling artifacts, in comparison to a Cartesian acquisition of the same scan time. The benefit of wave‐CAIPI sampling can therefore be traded for shorter examinations, or enhancing image quality of undersampled 4D lung acquisitions, keeping the scan time constant.
Purpose
To fully automatically derive quantitative parameters from late gadolinium enhancement (LGE) cardiac MR (CMR) in patients with myocardial infarction and to investigate if phase sensitive or magnitude reconstructions or a combination of both results in best segmentation accuracy.
Methods
In this retrospective single center study, a convolutional neural network with a U-Net architecture with a self-configuring framework (“nnU-net”) was trained for segmentation of left ventricular myocardium and infarct zone in LGE-CMR. A database of 170 examinations from 78 patients with history of myocardial infarction was assembled. Separate fitting of the model was performed, using phase sensitive inversion recovery, the magnitude reconstruction or both contrasts as input channels.
Manual labelling served as ground truth. In a subset of 10 patients, the performance of the trained models was evaluated and quantitatively compared by determination of the Sørensen-Dice similarity coefficient (DSC) and volumes of the infarct zone compared with the manual ground truth using Pearson’s r correlation and Bland-Altman analysis.
Results
The model achieved high similarity coefficients for myocardium and scar tissue. No significant difference was observed between using PSIR, magnitude reconstruction or both contrasts as input (PSIR and MAG; mean DSC: 0.83 ± 0.03 for myocardium and 0.72 ± 0.08 for scars). A strong correlation for volumes of infarct zone was observed between manual and model-based approach (r = 0.96), with a significant underestimation of the volumes obtained from the neural network.
Conclusion
The self-configuring nnU-net achieves predictions with strong agreement compared to manual segmentation, proving the potential as a promising tool to provide fully automatic quantitative evaluation of LGE-CMR.
Background
Functional lung MRI techniques are usually associated with time-consuming post-processing, where manual lung segmentation represents the most cumbersome part. The aim of this study was to investigate whether deep learning-based segmentation of lung images which were scanned by a fast UTE sequence exploiting the stack-of-spirals trajectory can provide sufficiently good accuracy for the calculation of functional parameters.
Methods
In this study, lung images were acquired in 20 patients suffering from cystic fibrosis (CF) and 33 healthy volunteers, by a fast UTE sequence with a stack-of-spirals trajectory and a minimum echo-time of 0.05 ms. A convolutional neural network was then trained for semantic lung segmentation using 17,713 2D coronal slices, each paired with a label obtained from manual segmentation. Subsequently, the network was applied to 4920 independent 2D test images and results were compared to a manual segmentation using the Sørensen–Dice similarity coefficient (DSC) and the Hausdorff distance (HD). Obtained lung volumes and fractional ventilation values calculated from both segmentations were compared using Pearson’s correlation coefficient and Bland Altman analysis.
To investigate generalizability to patients outside the CF collective, in particular to those exhibiting larger consolidations inside the lung, the network was additionally applied to UTE images from four patients with pneumonia and one with lung cancer.
Results
The overall DSC for lung tissue was 0.967 ± 0.076 (mean ± standard deviation) and HD was 4.1 ± 4.4 mm. Lung volumes derived from manual and deep learning based segmentations as well as values for fractional ventilation exhibited a high overall correlation (Pearson’s correlation coefficent = 0.99 and 1.00). For the additional cohort with unseen pathologies / consolidations, mean DSC was 0.930 ± 0.083, HD = 12.9 ± 16.2 mm and the mean difference in lung volume was 0.032 ± 0.048 L.
Conclusions
Deep learning-based image segmentation in stack-of-spirals based lung MRI allows for accurate estimation of lung volumes and fractional ventilation values and promises to replace the time-consuming step of manual image segmentation in the future.
Background: Computed tomography (CT) pulmonary angiography is the diagnostic reference standard in suspected pulmonary embolism (PE). Favorable results for dual-energy CT (DECT) images have been reported for this condition. Nowadays, dual-energy data acquisition is feasible with different technical options, including a single-source split-filter approach. Therefore, the aim of this retrospective study was to investigate image quality and radiation dose of thoracic split-filter DECT in comparison to conventional single-energy CT in patients with suspected PE.
Methods: A total of 110 CT pulmonary angiographies were accomplished either as standard single-energy CT with automatic tube voltage selection (ATVS) (n=58), or as split-filter DECT (n=52). Objective [pulmonary artery CT attenuation, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR)] and subjective image quality [four-point Likert scale; three readers (R)] were compared among the two study groups. Size-specific dose estimates (SSDE), dose-length-product (DLP) and volume CT dose index (CTDIvol) were assessed for radiation dose analysis.
Results: Split-filter DECT images yielded 67.7% higher SNR (27.0 vs. 16.1; P<0.001) and 61.9% higher CNR (22.5 vs. 13.9; P<0.001) over conventional single-energy images, whereas CT attenuation was significantly lower (344.5 vs. 428.2 HU; P=0.013). Subjective image quality was rated good or excellent in 93.0%/98.3%/77.6% (R1/R2/R3) of the single-energy CT scans, and 84.6%/82.7%/80.8% (R1/R2/R3) of the split-filter DECT scans. SSDE, DLP and CTDIvol were significantly lower for conventional single-energy CT compared to split-filter DECT (all P<0.05), which was associated with 26.7% higher SSDE.
Conclusions: In the diagnostic workup of acute PE, the split-filter allows for dual-energy data acquisition from single-source single-layer CT scanners. The existing opportunity to assess pulmonary “perfusion” based on analysis of iodine distribution maps is associated with higher radiation dose in terms of increased SSDE than conventional single-energy CT with ATVS. Moreover, a proportion of up to 3.8% non-diagnostic examinations in the current reference standard test for PE is not negligible.