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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.
Whole-Body [\(^{18}\)F]FDG PET/CT Can Alter Diagnosis in Patients with Suspected Rheumatic Disease
(2021)
The 2-deoxy-d-[\(^{18}\)F]fluoro-D-glucose (FDG) positron emission tomography/computed tomography (PET/CT) is widely utilized to assess the vascular and articular inflammatory burden of patients with a suspected diagnosis of rheumatic disease. We aimed to elucidate the impact of [\(^{18}\)F]FDG PET/CT on change in initially suspected diagnosis in patients at the time of the scan. Thirty-four patients, who had undergone [\(^{18}\)F]FDG PET/CT, were enrolled and the initially suspected diagnosis prior to [18F]FDG PET/CT was compared to the final diagnosis. In addition, a semi-quantitative analysis including vessel wall-to-liver (VLR) and joint-to-liver (JLR) ratios was also conducted. Prior to [\(^{18}\)F]FDG PET/CT, 22/34 (64.7%) of patients did not have an established diagnosis, whereas in 7/34 (20.6%), polymyalgia rheumatica (PMR) was suspected, and in 5/34 (14.7%), giant cell arteritis (GCA) was suspected by the referring rheumatologists. After [\(^{18}\)F]FDG PET/CT, the diagnosis was GCA in 19/34 (55.9%), combined GCA and PMR (GCA + PMR) in 9/34 (26.5%) and PMR in the remaining 6/34 (17.6%). As such, [\(^{18}\)F]FDG PET/CT altered suspected diagnosis in 28/34 (82.4%), including in all unclear cases. VLR of patients whose final diagnosis was GCA tended to be significantly higher when compared to VLR in PMR (GCA, 1.01 ± 0.08 (95%CI, 0.95–1.1) vs. PMR, 0.92 ± 0.1 (95%CI, 0.85–0.99), p = 0.07), but not when compared to PMR + GCA (1.04 ± 0.14 (95%CI, 0.95–1.13), p = 1). JLR of individuals finally diagnosed with PMR (0.94 ± 0.16, (95%CI, 0.83–1.06)), however, was significantly increased relative to JLR in GCA (0.58 ± 0.04 (95%CI, 0.55–0.61)) and GCA + PMR (0.64 ± 0.09 (95%CI, 0.57–0.71); p < 0.0001, respectively). In individuals with a suspected diagnosis of rheumatic disease, an inflammatory-directed [\(^{18}\)F]FDG PET/CT can alter diagnosis in the majority of the cases, particularly in subjects who were referred because of diagnostic uncertainty. Semi-quantitative assessment may be helpful in establishing a final diagnosis of PMR, supporting the notion that a quantitative whole-body read-out may be useful in unclear cases.
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.
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.
Prostate-specific membrane antigen (PSMA)-directed positron emission tomography/computed tomography (PET/CT) is increasingly utilized for staging of men with prostate cancer (PC). To increase interpretive certainty, the standardized PSMA reporting and data system (RADS) has been proposed. Using PSMA-RADS, we characterized lesions in 18 patients imaged with \(^{18}\)F-PSMA-1007 PET/CT for primary staging and determined the stability of semi-quantitative parameters. Six hundred twenty-three lesions were categorized according to PSMA-RADS and manually segmented. In this context, PSMA-RADS-3A (soft-tissue) or -3B (bone) lesions are defined as being indeterminate for the presence of PC. For PMSA-RADS-4 and -5 lesions; however, PC is highly likely or almost certainly present [with further distinction based on absence (PSMA-RADS-4) or presence (PSMA-RADS-5) of correlative findings on CT]. Standardized uptake values (SUV\(_{max}\), SUV\(_{peak}\), SUV\(_{mean}\)) were recorded, and volumetric parameters [PSMA-derived tumor volume (PSMA-TV); total lesion PSMA (TL-PSMA)] were determined using different maximum intensity thresholds (MIT) (40 vs. 45 vs. 50%). SUV\(_{max}\) was significantly higher in PSMA-RADS-5 lesions compared to all other PSMA-RADS categories (p ≤ 0.0322). In particular, the clinically challenging PSMA-RADS-3A lesions showed significantly lower SUV\(_{max}\) and SUV\(_{peak}\) compared to the entire PSMA-RADS-4 or -5 cohort (p < 0.0001), while for PSMA-RADS-3B this only applies when compared to the entire PSMA-RADS-5 cohort (p < 0.0001), but not to the PSMA-RADS-4 cohort (SUV\(_{max}\), p = 0.07; SUV\(_{peak}\), p = 0.08). SUV\(_{mean}\) (p = 0.30) and TL-PSMA (p = 0.16) in PSMA-RADS-5 lesions were not influenced by changing the MIT, while PSMA-TV showed significant differences when comparing 40 vs. 50% MIT (p = 0.0066), which was driven by lymph nodes (p = 0.0239), but not bone lesions (p = 0.15). SUV\(_{max}\) was significantly higher in PSMA-RADS-5 lesions compared to all other PSMA-RADS categories in \(^{18}\)F-PSMA-1007 PET/CT. As such, the latter parameter may assist the interpreting molecular imaging specialist in assigning the correct PSMA-RADS score to sites of disease, thereby increasing diagnostic certainty. In addition, changes of the MIT in PSMA-RADS-5 lesions had no significant impact on SUV\(_{mean}\) and TL-PSMA in contrast to PSMA-TV.
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
The effect of smoking on coronary vasomotion has been investigated in the past with various imaging techniques in both short- and long-term smokers. Additionally, coronary vasomotion has been shown to be normalized in long-term smokers by L-Arginine acting as a substrate for NO synthase, revealing the coronary endothelium as the major site of abnormal vasomotor response. Aim of the prospective cohort study was to investigate coronary vasomotion of young healthy short-term smokers via magnetic resonance cold pressor test with and without the administration of L-Arginine and compare obtained results with the ones from nonsmokers.
Methods
Myocardial blood flow (MBF) was quantified with first-pass perfusion MRI on a 1.5 T scanner in healthy short-term smokers (N = 10, age: 25.0 ± 2.8 years, 5.0 ± 2.9 pack years) and nonsmokers (N = 10, age: 34.3 ± 13.6) both at rest and during cold pressor test (CPT). Smokers underwent an additional examination after administration of L-Arginine within a median of 7 days of the naïve examination.
Results
MBF at rest turned out to be 0.77 ± 0.30 (smokers with no L-Arginine; mean ± standard deviation), 0.66 ± 0.21 (smokers L-Arginine) and 0.84 ± 0.08 (nonsmokers). Values under CPT were 1.21 ± 0.42 (smokers no L-Arginine), 1.09 ± 0.35 (smokers L-Arginine) and 1.63 ± 0.33 (nonsmokers). In all groups, MBF was significantly increased under CPT compared to the corresponding rest examination (p < 0.05 in all cases). Additionally, MBF under CPT was significantly different between the smokers and the nonsmokers (p = 0.002). MBF at rest was significantly different between the smokers when L-Arginine was given and the nonsmokers (p = 0.035).
Conclusion
Short-term smokers showed a reduced response to cold both with and without the administration of L-Arginine. However, absolute MBF values under CPT were lower compared to nonsmokers independently of L-Arginine administration.
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.
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.