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A 14-year-old girl presented with a facial-pigmented lesion suspicious of melanoma clinically and dermoscopically. In vivo, reflectance confocal microscopy (RCM) findings excluded melanoma by revealing typical epidermal honeycomb and cobblestone patterns. Well-defined follicular contours were seen at the dermal-epidermal junction; there were no elongated, "medusa head-like" follicular protrusions or folliculotropism, which are classical findings seen in lentigo maligna. With this report, we aim to demonstrate the significance of utilizing RCM technology in difficult to diagnose lentiginous pigmented lesions., (© 2024 Wiley Periodicals LLC.)

Purpose: To develop a physics-guided deep learning (PG-DL) reconstruction strategy based on a signal intensity informed multi-coil (SIIM) encoding operator for highly-accelerated simultaneous multislice (SMS) myocardial perfusion cardiac MRI (CMR)., Methods: First-pass perfusion CMR acquires highly-accelerated images with dynamically varying signal intensity/SNR following the administration of a gadolinium-based contrast agent. Thus, using PG-DL reconstruction with a conventional multi-coil encoding operator leads to analogous signal intensity variations across different time-frames at the network output, creating difficulties in generalization for varying SNR levels. We propose to use a SIIM encoding operator to capture the signal intensity/SNR variations across time-frames in a reformulated encoding operator. This leads to a more uniform/flat contrast at the output of the PG-DL network, facilitating generalizability across time-frames. PG-DL reconstruction with the proposed SIIM encoding operator is compared to PG-DL with conventional encoding operator, split slice-GRAPPA, locally low-rank (LLR) regularized reconstruction, low-rank plus sparse (L + S) reconstruction, and regularized ROCK-SPIRiT., Results: Results on highly accelerated free-breathing first pass myocardial perfusion CMR at three-fold SMS and four-fold in-plane acceleration show that the proposed method improves upon the reconstruction methods use for comparison. Substantial noise reduction is achieved compared to split slice-GRAPPA, and aliasing artifacts reduction compared to LLR regularized reconstruction, L + S reconstruction and PG-DL with conventional encoding. Furthermore, a qualitative reader study indicated that proposed method outperformed all methods., Conclusion: PG-DL reconstruction with the proposed SIIM encoding operator improves generalization across different time-frames /SNRs in highly accelerated perfusion CMR., (© 2022 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine.)

Self-supervised learning has shown great promise because of its ability to train deep learning (DL) magnetic resonance imaging (MRI) reconstruction methods without fully sampled data. Current self-supervised learning methods for physics-guided reconstruction networks split acquired undersampled data into two disjoint sets, where one is used for data consistency (DC) in the unrolled network, while the other is used to define the training loss. In this study, we propose an improved self-supervised learning strategy that more efficiently uses the acquired data to train a physics-guided reconstruction network without a database of fully sampled data. The proposed multi-mask self-supervised learning via data undersampling (SSDU) applies a holdout masking operation on the acquired measurements to split them into multiple pairs of disjoint sets for each training sample, while using one of these pairs for DC units and the other for defining loss, thereby more efficiently using the undersampled data. Multi-mask SSDU is applied on fully sampled 3D knee and prospectively undersampled 3D brain MRI datasets, for various acceleration rates and patterns, and compared with the parallel imaging method, CG-SENSE, and single-mask SSDU DL-MRI, as well as supervised DL-MRI when fully sampled data are available. The results on knee MRI show that the proposed multi-mask SSDU outperforms SSDU and performs as well as supervised DL-MRI. A clinical reader study further ranks the multi-mask SSDU higher than supervised DL-MRI in terms of signal-to-noise ratio and aliasing artifacts. Results on brain MRI show that multi-mask SSDU achieves better reconstruction quality compared with SSDU. The reader study demonstrates that multi-mask SSDU at R = 8 significantly improves reconstruction compared with single-mask SSDU at R = 8, as well as CG-SENSE at R = 2., (© 2022 The Authors. NMR in Biomedicine published by John Wiley & Sons Ltd.)

Purpose: To develop a strategy for training a physics-guided MRI reconstruction neural network without a database of fully sampled data sets., Methods: Self-supervised learning via data undersampling (SSDU) for physics-guided deep learning reconstruction partitions available measurements into two disjoint sets, one of which is used in the data consistency (DC) units in the unrolled network and the other is used to define the loss for training. The proposed training without fully sampled data is compared with fully supervised training with ground-truth data, as well as conventional compressed-sensing and parallel imaging methods using the publicly available fastMRI knee database. The same physics-guided neural network is used for both proposed SSDU and supervised training. The SSDU training is also applied to prospectively two-fold accelerated high-resolution brain data sets at different acceleration rates, and compared with parallel imaging., Results: Results on five different knee sequences at an acceleration rate of 4 shows that the proposed self-supervised approach performs closely with supervised learning, while significantly outperforming conventional compressed-sensing and parallel imaging, as characterized by quantitative metrics and a clinical reader study. The results on prospectively subsampled brain data sets, in which supervised learning cannot be used due to lack of ground-truth reference, show that the proposed self-supervised approach successfully performs reconstruction at high acceleration rates (4, 6, and 8). Image readings indicate improved visual reconstruction quality with the proposed approach compared with parallel imaging at acquisition acceleration., Conclusion: The proposed SSDU approach allows training of physics-guided deep learning MRI reconstruction without fully sampled data, while achieving comparable results with supervised deep learning MRI trained on fully sampled data., (© 2020 International Society for Magnetic Resonance in Medicine.)

Background: This study aimed to investigate serum 25[OH]D levels between patients with vasovagal syncope (VVS) diagnosed with head-up tilt table test (HUTT) and age-matched healthy people., Methods: The study included 75 consecutive patients (32.3 ± 10.7 years), who presented with syncope and underwent HUTT and 52 healthy controls (32.9 ± 14.1 years). HUTT patients were divided into two groups according to whether there was syncope response to the test. Patients underwent cardiac, psychiatric, and neurological investigation. Serum 25[OH]D levels were measured by chemiluminescent microparticle immunoassay method., Results: There was no difference between the two groups in terms of age, gender, body mass index (BMI), echocardiographic findings ( P  > .05). Mean serum 25[OH]D (24.5 ± 6.3 vs 20.1 ± 8.8 ng/mL, P  = .003) and vitamin B12 levels (436.4 ± 199.2 vs 363.1 ± 107.6 pg/mL, P  = .009) was lower in syncope patients when compared to the control group. In correlation analyses, syncope was shown as correlated with the vitamin D ( r  = -264, P  = .003) and vitamin B12 levels ( r  = -233, P  = .009). But, multivariate regression analyses showed that only vitamin D increased risk of syncope [OR: 0.946, 95% CI (0.901-0.994)]. There was no difference in terms of age, gender, BMI, echocardiographic findings between the in HUTT positive (n = 45) and negative groups (n = 29). Only vitamin D level was significantly lower in HUTT positive group (17.5 ± 7.7 vs 24.4 ± 9.1 ng/mL, P  = .002). There was no difference among in the vasovagal subgroups in terms of vitamin D level and other features., Conclusion: Vitamin D and B12 levels were reasonably low in syncope patients, but especially low Vitamin D levels were associated with VVS diagnosed in HUTT., Competing Interests: Authors declare no conflict of interests for this article. Written informed consent was obtained from all patients before participating in the study, (© 2020 The Authors. Journal of Arrhythmia published by John Wiley & Sons Australia, Ltd on behalf of the Japanese Heart Rhythm Society.)