Large-scale 3D non-Cartesian coronary MRI reconstruction using distributed memory-efficient physics-guided deep learning with limited training data.

OBJECT: To enable high-quality physics-guided deep learning (PG-DL) reconstruction of large-scale 3D non-Cartesian coronary MRI by overcoming challenges of hardware limitations and limited training data availability. MATERIALS AND METHODS: While PG-DL has emerged as a powerful image reconstruction method, its application to large-scale 3D non-Cartesian MRI is hindered by hardware limitations and limited availability of training data. We combine several recent advances in deep learning and MRI reconstruction to tackle the former challenge, and we further propose a 2.5D reconstruction using 2D convolutional neural networks, which treat 3D volumes as batches of 2D images to train the network with a limited amount of training data. Both 3D and 2.5D variants of the PG-DL networks were compared to conventional methods for high-resolution 3D kooshball coronary MRI. RESULTS: Proposed PG-DL reconstructions of 3D non-Cartesian coronary MRI with 3D and 2.5D processing outperformed all conventional methods both quantitatively and qualitatively in terms of image assessment by an experienced cardiologist. The 2.5D variant further improved vessel sharpness compared to 3D processing, and scored higher in terms of qualitative image quality. DISCUSSION: PG-DL reconstruction of large-scale 3D non-Cartesian MRI without compromising image size or network complexity is achieved, and the proposed 2.5D processing enables high-quality reconstruction with limited training data.

The Relationship Between Depressive Symptoms and Psychological Variables in Patients With Schizophrenia.

INTRODUCTION: Depressive symptoms are common in schizophrenia and can be seen at any stage of the disease. Although various models have been proposed to explain the development of depression in schizophrenia, studies investigating related psychological factors are scarce and the studies that have been done usually focus on only a small number of possible factors. OBJECTIVE: The goal of this study was to investigate the predictability of some psychological factors on depression in patients with schizophrenia. For this purpose, patients with high and low depression scores were compared. METHODS: Two groups of individuals with schizophrenia, with (n=29) and without (n=31) depression, as determined by scores on the Calgary Depression Scale in Schizophrenia, were compared using a sociodemographic data form, the Positive and Negative Syndrome Scale (PANSS), the Beck Anxiety Inventory, the Rotter Internal-External Locus 2024 of Control Scale, the Multidimensional Scale of Perceived Social Support, and the Stress Coping Styles Scale. RESULTS: No differences were found between the 2 groups in terms of sociodemographic and clinical characteristics, social support scores, and coping styles. Statistically significant differences were found between the groups on the PANSS positive, negative, and general psychopathology subscales, in PANSS total scores, in anxiety scores, and in locus of control scores. CONCLUSIONS: This study showed that high levels of negative, positive, and general psychopathological symptoms, external locus of control, and high anxiety scores may be predictive of depression in individuals with schizophrenia. Studies that examine psychological factors in larger patient groups may provide the opportunity to detect and target these factors earlier in the course of schizophrenia, thereby reducing morbidity and mortality.

Unsupervised deep learning with convolutional neural networks for static parallel transmit design: A retrospective study.

PURPOSE: To mitigate B 1 + $$ {B}_1^{+} $$ inhomogeneity at 7T for multi-channel transmit arrays using unsupervised deep learning with convolutional neural networks (CNNs). METHODS: Deep learning parallel transmit (pTx) pulse design has received attention, but such methods have relied on supervised training and did not use CNNs for multi-channel B 1 + $$ {B}_1^{+} $$ maps. In this work, we introduce an alternative approach that facilitates the use of CNNs with multi-channel B 1 + $$ {B}_1^{+} $$ maps while performing unsupervised training. The multi-channel B 1 + $$ {B}_1^{+} $$ maps are concatenated along the spatial dimension to enable shift-equivariant processing amenable to CNNs. Training is performed in an unsupervised manner using a physics-driven loss function that minimizes the discrepancy of the Bloch simulation with the target magnetization, which eliminates the calculation of reference transmit RF weights. The training database comprises 3824 2D sagittal, multi-channel B 1 + $$ {B}_1^{+} $$ maps of the healthy human brain from 143 subjects. B 1 + $$ {B}_1^{+} $$ data were acquired at 7T using an 8Tx/32Rx head coil. The proposed method is compared to the unregularized magnitude least-squares (MLS) solution for the target magnetization in static pTx design. RESULTS: The proposed method outperformed the unregularized MLS solution for RMS error and coefficient-of-variation and had comparable energy consumption. Additionally, the proposed method did not show local phase singularities leading to distinct holes in the resulting magnetization unlike the unregularized MLS solution. CONCLUSION: Proposed unsupervised deep learning with CNNs performs better than unregularized MLS in static pTx for speed and robustness.

Anxiety and depression among patients with familial Mediterranean fever.

BACKGROUND: Familial Mediterranean fever (FMF) is a systemic autoinflammatory disease that requires lifelong treatment and is associated with several comorbidities, including mental health disorders such as anxiety and depression. FMF and mental health necessitate further research; hence, this study aims to observe anxiety and depression and their relationship with several variables in patients with FMF. METHODS: As the study population, 360 FMF patients were surveyed between June and October 2022. Surveys included inventories assessing anxiety and depression, i.e., the Beck's Depression Inventory (BDI), the Beck's Anxiety Inventory (BAI), and the State-Trait Anxiety Inventory (STAI). RESULTS: Mean scores for STAI-Y1 (state), STAI-Y2 (trait), BAI, and BDI were 42.2 ± 12.0, 45.9 ± 10.6, 24.0 ± 13.9, and 13.1 ± 8.99, respectively. Medication-adherent patients had significantly lower scores on STAI-Y1 (41.5 ± 11.4 vs. 45.2 ± 14.0; p-value: 0.04). M694V homozygous patients exhibited significantly lower scores in the BDI (12.4 ± 9.37 vs. 13.2 ± 8.93; p-value: < 0.001) and BAI (17.0 ± 12.1 vs. 25.1 ± 13.9; p-value: 0.001). The patients with an exon-10 mutation demonstrated significantly lower scores compared to patients with an exon­2 mutation (17.9 ± 12.3, 29.6 ± 13.3; p-value: < 0.001). CONCLUSION: The patients with FMF had mild depression and moderate anxiety scores. A higher level of education and medication adherence were associated with lower levels of anxiety. Likewise, the patients with genotypes associated with severe disease courses had lower levels of anxiety. We suggest that physicians should be more attentive to patients with a milder disease course and ensure that these patients are provided with sufficient treatment and knowledge about their disease.

High-fidelity Database-free Deep Learning Reconstruction for Real-time Cine Cardiac MRI.

Real-time cine cardiac MRI provides an ECG-free free-breathing alternative to clinical gold-standard ECG-gated breath-hold segmented cine MRI for evaluation of heart function. Real-time cine MRI data acquisition during free breathing snapshot imaging enables imaging of patient cohorts that cannot be imaged with segmented or breath-hold acquisitions, but requires rapid imaging to achieve sufficient spatial-temporal resolutions. However, at high acceleration rates, conventional reconstruction techniques suffer from residual aliasing and temporal blurring, including advanced methods such as compressed sensing with radial trajectories. Recently, deep learning (DL) reconstruction has emerged as a powerful tool in MRI. However, its utility for free-breathing real-time cine MRI has been limited, as database-learning of spatio-temporal correlations with varying breathing and cardiac motion patterns across subjects has been challenging. Zero-shot self-supervised physics-guided deep learning (PG-DL) reconstruction has been proposed to overcome such challenges of database training by enabling subject-specific training. In this work, we adapt zero-shot PG-DL for real-time cine MRI with a spatio-temporal regularization. We compare our method to TGRAPPA, locally low-rank (LLR) regularized reconstruction and database-trained PG-DL reconstruction, both for retrospectively and prospectively accelerated datasets. Results on highly accelerated real-time Cartesian cine MRI show that the proposed method outperforms other reconstruction methods, both visibly in terms of noise and aliasing, and quantitatively.

Improved Simultaneous Multi-slice imaging with Composition of k-space Interpolations (SMS-COOKIE) for myocardial T1 mapping.

The aim of this study is to develop and evaluate a regularized Simultaneous Multi-Slice (SMS) reconstruction method for improved Cardiac Magnetic Resonance Imaging (CMR). The proposed reconstruction method, SMS with COmpOsition of k-space IntErpolations (SMS-COOKIE) combines the advantages of Iterative Self-consistent Parallel Imaging Reconstruction (SPIRiT) and split slice-Generalized Autocalibrating Partially Parallel Acquisitions (GRAPPA), while allowing regularization for further noise reduction. The proposed SMS-COOKIE was implemented with and without regularization, and validated using a Saturation Pulse-Prepared Heart rate Independent inversion REcovery (SAPPHIRE) myocardial T1 mapping sequence. The performance of the proposed reconstruction method was compared to ReadOut (RO)-SENSE-GRAPPA and split slice-GRAPPA, on both retrospectively and prospectively three-fold SMS-accelerated data with an additional two-fold in-plane acceleration. All SMS reconstruction methods yielded similar T1 values compared to single band imaging. SMS-COOKIE showed lower spatial variability in myocardial T1 with significant improvement over RO-SENSE-GRAPPA and split slice-GRAPPA (P < 10-4). The proposed method with additional locally low rank (LLR) regularization reduced the spatial variability, again with significant improvement over RO-SENSE-GRAPPA and split slice-GRAPPA (P < 10-4). In conclusion, improved reconstruction quality was achieved with the proposed SMS-COOKIE, which also provided lower spatial variability with significant improvement over split slice-GRAPPA.

Evaluation of Depression, Anxiety, and Sexual Function in Rectal Cancer Patients Before and After Neoadjuvant Chemoradiotherapy.

OBJECTIVE: The association of treatment-related side effects and tumor characteristics with sexual function, depression, and anxiety were investigated in patients with locally advanced rectum cancer (LARC) receiving neoadjuvant chemoradiotherapy (CRT). MATERIAL AND METHODS: Thirty-two patients who received neoadjuvant CRT with LARC were included. The Arizona Sexual Experiences (ASEX) Scale was used to determine the sexual function status whereas the Beck Depression Inventory (BDI) and the Beck Anxiety Inventory (BAI) were used to evaluate the depression and anxiety status of the patient, respectively. The patients were asked to fill these scales before and at least 4 weeks after the neoadjuvant CRT. T-test and Mann-Whitney U test were used for the comparison of values. RESULTS: Median age was 52.5 years (range: 33-76). Twenty-six patients were male, and 6 patients were female. At presentation, the tumor was located mostly in lower third rectum (72%), and 69% of the patients had T3 tumors. There was a statistically significant deterioration in the sexual functions of the patients (p < 0.001), a statistically significant decrease in their anxiety level after CRT (p: 0.037). Depression level was changed from mild to minimal during this process (p: 0.17). A significant deterioration in the ASEX scale was observed especially in patients with grade 2 and above gastrointestinal side effects (p: 0.01). CONCLUSION: This prospective study showed that the patient's anxiety and depression levels had decreased during the treatment process probably due to the decrease in the patient's symptoms. However, deterioration of the sexual function status which might be correlated to increased gastrointestinal side effects during CRT has been observed. So, clinical and psychiatric support including therapies for sexual dysfunctions is needed for LARC patients during and after the neoadjuvant CRT.

Are bipolar disorder, major depression, and suicidality linked with Toxoplasma gondii? A seromolecular case-control study.

OBJECTIVE: The existence of predisposing effects of latent Toxoplasma gondii (T. gondii) infection in bipolar disorder (BD), major depression (MD), and even suicide attempt (SA) has long been debatable. This conjecture remains unclear because there is a lack of evidence regarding how T. gondii manipulates the brain and behavior. METHODS: We investigated the influence of T. gondii infection on BD and MD patients with or without SA compared to age-, sex-, and province-matched healthy controls (HCs) concurrently with serology and molecular-based evaluations. We prospectively assessed 147 volunteers with BD, 161 with MD, and 310 HCs. RESULTS: T. gondii seropositivity rates were 57.1% for BD, 29.2% for MD, 64.8% for SA, and 21.3% for HC. Binary logistic regression analyses revealed that T. gondii positive Immunoglobulin G (IgG) status may be a prominent tendentious agent for BD (OR = 3.52; 95% CI [2.19-5.80]; p < 0.001) and SA (OR = 17.17; 95% CI [8.12-36.28]; p < 0.001), but not for MD (OR = 1.21; 95% CI [0.74-1.99]; p = 0.45). Nevertheless, the T. gondii DNA ratios determined by polymerase chain reaction (PCR) were linked to BD and MD. CONCLUSION: Our findings strongly support the burgeoning interest in the possibility that latent T. gondii infection may be relevant to the etiology of BD and SA, although this connection remains ambiguous.

High-fidelity Database-free Deep Learning Reconstruction for Real-time Cine Cardiac MRI.

Real-time cine cardiac MRI provides an ECG-free free-breathing alternative to clinical gold-standard ECG-gated breath-hold segmented cine MRI for evaluation of heart function. Real-time cine MRI data acquisition during free breathing snapshot imaging enables imaging of patient cohorts that cannot be imaged with segmented or breath-hold acquisitions, but requires rapid imaging to achieve sufficient spatial-temporal resolutions. However, at high acceleration rates, conventional reconstruction techniques suffer from residual aliasing and temporal blurring, including advanced methods such as compressed sensing with radial trajectories. Recently, deep learning (DL) reconstruction has emerged as a powerful tool in MRI. However, its utility for free-breathing real-time cine MRI has been limited, as database-learning of spatio-temporal correlations with varying breathing and cardiac motion patterns across subjects has been challenging. Zero-shot self-supervised physics-guided deep learning (PG-DL) reconstruction has been proposed to overcome such challenges of database training by enabling subject-specific training. In this work, we adapt zero-shot PG-DL for real-time cine MRI with a spatio-temporal regularization. We compare our method to TGRAPPA, locally low-rank (LLR) regularized reconstruction and database-trained PG-DL reconstruction, both for retrospectively and prospectively accelerated datasets. Results on highly accelerated real-time Cartesian cine MRI show that the proposed method outperforms other reconstruction methods, both visibly in terms of noise and aliasing, and quantitatively.

Signal intensity informed multi-coil encoding operator for physics-guided deep learning reconstruction of highly accelerated myocardial perfusion CMR.

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.

Cardiac phase-resolved late gadolinium enhancement imaging.

Late gadolinium enhancement (LGE) with cardiac magnetic resonance (CMR) imaging is the clinical reference for assessment of myocardial scar and focal fibrosis. However, current LGE techniques are confined to imaging of a single cardiac phase, which hampers assessment of scar motility and does not allow cross-comparison between multiple phases. In this work, we investigate a three step approach to obtain cardiac phase-resolved LGE images: (1) Acquisition of cardiac phase-resolved imaging data with varying T 1 weighting. (2) Generation of semi-quantitative T 1 * maps for each cardiac phase. (3) Synthetization of LGE contrast to obtain functional LGE images. The proposed method is evaluated in phantom imaging, six healthy subjects at 3T and 20 patients at 1.5T. Phantom imaging at 3T demonstrates consistent contrast throughout the cardiac cycle with a coefficient of variation of 2.55 ± 0.42%. In-vivo results show reliable LGE contrast with thorough suppression of the myocardial tissue is healthy subjects. The contrast between blood and myocardium showed moderate variation throughout the cardiac cycle in healthy subjects (coefficient of variation 18.2 ± 3.51%). Images were acquired at 40-60 ms and 80 ms temporal resolution, at 3T and 1.5, respectively. Functional LGE images acquired in patients with myocardial scar visualized scar tissue throughout the cardiac cycle, albeit at noticeably lower imaging resolution and noise resilience than the reference technique. The proposed technique bears the promise of integrating the advantages of phase-resolved CMR with LGE imaging, but further improvements in the acquisition quality are warranted for clinical use.

Signal-Intensity Informed Multi-Coil MRI Encoding Operator for Improved Physics-Guided Deep Learning Reconstruction of Dynamic Contrast-Enhanced MRI.

Dynamic contrast enhanced (DCE) MRI acquires a series of images following the administration of a contrast agent, and plays an important clinical role in diagnosing various diseases. DCE MRI typically necessitates rapid imaging to provide sufficient spatio-temporal resolution and coverage. Conventional MRI acceleration techniques exhibit limited image quality at such high acceleration rates. Recently, deep learning (DL) methods have gained interest for improving highly-accelerated MRI. However, DCE MRI series show substantial variations in SNR and contrast across images. This hinders the quality and generalizability of DL methods, when applied across time frames. In this study, we propose signal intensity informed multi-coil MRI encoding operator for improved DL reconstruction of DCE MRI. The output of the corresponding inverse problem for this forward operator leads to more uniform contrast across time frames, since the proposed operator captures signal intensity variations across time frames while not altering the coil sensitivities. Our results in perfusion cardiac MRI show that high-quality images are reconstructed at very high acceleration rates, with substantial improvement over existing methods.

Myocardial Approximate Spin-lock Dispersion Mapping using a Simultaneous T2 and TRAFF2 Mapping at 3T MRI.

Ischemic heart disease (IHD) is one of the leading causes of death worldwide. Myocardial infarction (MI) represents a third of all IHD cases, and cardiac magnetic resonance imaging (MRI) is often used to assess its damage to myocardial viability. Late gadolinium enhancement (LGE) is the current gold standard, but the use of gadolinium-based agents limits the clinical applicability in some patients. Spin-lock (SL) dispersion has recently been proposed as a promising non-contrast biomarker for the assessment of MI. However, at 3T, the required range of SL preparations acquired at different amplitudes suffers from specific absorption rate (SAR) limitations and off-resonance artifacts. Relaxation Along a Fictitious Field (RAFF) is an alternative to SL preparations with lower SAR requirements, while still sampling relaxation in the rotating frame. In this study, a single breath-hold simultaneous TRAFF2 and T2 mapping sequence is proposed for SL dispersion mapping at 3T. Excellent reproducibility (coefficient of variations lower than 10%) was achieved in phantom experiments, indicating good intrascan repeatability. The average myocardial TRAFF2, T2, and SL dispersion obtained with the proposed sequence (68.0±10.7 ms, 44.0±4.0 ms, and 0.4±0.2 ×10-4 s2, respectively) were comparable to the reference methods (62.7±11.7 ms, 41.2±2.4 ms, and 0.3±0.2x 10-4s2, respectively). High visual map quality, free of B0 and B1+ related artifacts, for T2, TRAFF2, and SL dispersion maps were obtained in phantoms and in vivo, suggesting promise in clinical use at 3T. Clinical relevance - and imaging promises non-contrast assessment of scar and focal fibrosis in a single breath-hold using approximate spin-lock dispersion mapping.

Multi-mask self-supervised learning for physics-guided neural networks in highly accelerated magnetic resonance imaging.

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.

Residual RAKI: A hybrid linear and non-linear approach for scan-specific k-space deep learning.

Parallel imaging is the most clinically used acceleration technique for magnetic resonance imaging (MRI) in part due to its easy inclusion into routine acquisitions. In k-space based parallel imaging reconstruction, sub-sampled k-space data are interpolated using linear convolutions. At high acceleration rates these methods have inherent noise amplification and reduced image quality. On the other hand, non-linear deep learning methods provide improved image quality at high acceleration, but the availability of training databases for different scans, as well as their interpretability hinder their adaptation. In this work, we present an extension of Robust Artificial-neural-networks for k-space Interpolation (RAKI), called residual-RAKI (rRAKI), which achieves scan-specific machine learning reconstruction using a hybrid linear and non-linear methodology. In rRAKI, non-linear CNNs are trained jointly with a linear convolution implemented via a skip connection. In effect, the linear part provides a baseline reconstruction, while the non-linear CNN that runs in parallel provides further reduction of artifacts and noise arising from the linear part. The explicit split between the linear and non-linear aspects of the reconstruction also help improve interpretability compared to purely non-linear methods. Experiments were conducted on the publicly available fastMRI datasets, as well as high-resolution anatomical imaging, comparing GRAPPA and its variants, compressed sensing, RAKI, Scan Specific Artifact Reduction in K-space (SPARK) and the proposed rRAKI. Additionally, highly-accelerated simultaneous multi-slice (SMS) functional MRI reconstructions were also performed, where the proposed rRAKI was compred to Read-out SENSE-GRAPPA and RAKI. Our results show that the proposed rRAKI method substantially improves the image quality compared to conventional parallel imaging, and offers sharper images compared to SPARK and ℓ1-SPIRiT. Furthermore, rRAKI shows improved preservation of time-varying dynamics compared to both parallel imaging and RAKI in highly-accelerated SMS fMRI.

Co-existing bipolar disease and 17q12 deletion: a rare case report.

BACKGROUND: 17q12 microdeletion syndrome is a rare autosomal dominant chromosomal anomaly, caused by the deletion of a 1.4 Mb-spanning DNA sequence on the long arm of chromosome 17. Herein, we report the first bipolar disease (BPD) case with a 1.6-Mb deletion in the 17q11.2-17q12 chromosome region. MATERIALS AND METHODS: Physical examination of the case was performed. Karyotype and microarray analyses were performed for the case and the parents. RESULTS: Physical examination revealed mild dysmorphic features such as high and forehead, full cheeks, slightly depressed nasal bridge and arched eyebrow. Chromosomal analysis of the patient revealed 46, XX, del(17)(q12) karyotype, and parents' karyotype were normal. In the microarray analysis of patient, 1.6 megabases deletion was detected in the 17q12 region [arr(hg19) 17q12 (34,611,352-36,248,918) ×1]. The microarray analysis of the mother was normal. The father's microarray showed 473 kilobases duplication in the 11p11.12 region. CONCLUSION: Although 17q12 deletion syndrome has been associated with bipolar disorder, very few such cases have been described in the literature. Genetic counseling should be considered in patients with remarkable phenotype, complex symptomatology, neurodevelopmental disorder and additional conspicuous medical conditions.

20-fold Accelerated 7T fMRI Using Referenceless Self-Supervised Deep Learning Reconstruction.

High spatial and temporal resolution across the whole brain is essential to accurately resolve neural activities in fMRI. Therefore, accelerated imaging techniques target improved coverage with high spatio-temporal resolution. Simultaneous multi-slice (SMS) imaging combined with in-plane acceleration are used in large studies that involve ultrahigh field fMRI, such as the Human Connectome Project. However, for even higher acceleration rates, these methods cannot be reliably utilized due to aliasing and noise artifacts. Deep learning (DL) reconstruction techniques have recently gained substantial interest for improving highly-accelerated MRI. Supervised learning of DL reconstructions generally requires fully-sampled training datasets, which is not available for high-resolution fMRI studies. To tackle this challenge, self-supervised learning has been proposed for training of DL reconstruction with only undersampled datasets, showing similar performance to supervised learning. In this study, we utilize a self-supervised physics-guided DL reconstruction on a 5-fold SMS and 4-fold in-plane accelerated 7T fMRI data. Our results show that our self-supervised DL reconstruction produce high-quality images at this 20-fold acceleration, substantially improving on existing methods, while showing similar functional precision and temporal effects in the subsequent analysis compared to a standard 10-fold accelerated acquisition.

Toxoplasma gondii infection by serological and molecular methods in schizophrenia patients with and without suicide attempts: An age-sex-matched case-control study.

INTRODUCTION: The opinion that latent Toxoplasma gondii infection is having a broadly asymptomatic projection has now been interrogated, in specific due to the echoed association between the latent infection and an elevated incidence of schizophrenia or even suicide attempts. Notwithstanding conducted studies aimed to understand this feasible link are restricted. METHODS: In the present case-control study, we focused to illuminate the relationship between the serological and molecular presence of T gondii and schizophrenia with or without the suicide attempts by comparing it with healthy individuals. A total of 237 participants (117 in schizophrenia and 120 in healthy control) were included in this study. RESULTS: Overall, latent T gondii infections were found statistically higher in 63 (53.8%) of the 117 patients with schizophrenia and in 33 (27.5%) of the 120 controls (P < .001). In schizophrenia patients, seroprevalence T gondii was again found to be statistically higher in suicide attempters (59.6%), compared with no history of suicide attempts (48.3%; P < .05). The molecular positivity rate of T gondii DNA was higher in the schizophrenia group, compared with the healthy control group (P < .05), whereas the history of suicide attempts was not statistically associated (P = .831) with T gondii DNA positivity by polymerase chain reaction. CONCLUSION: This case-control study enlightens additional demonstration to the belief that T gondii infection would be an underlying component for the pathophysiology of schizophrenia. Regardless of the clarity results of this study, this supposition warrants further endorsement.

Free-breathing simultaneous T1 , T2 , and T2∗ quantification in the myocardium.

PURPOSE: To implement a free-breathing sequence for simultaneous quantification of T1 , T2 , and T2∗ for comprehensive tissue characterization of the myocardium in a single scan using a multi-gradient-echo readout with saturation and T2 preparation pulses. METHODS: In the proposed Saturation And T2 -prepared Relaxometry with Navigator-gating (SATURN) technique, a series of multi-gradient-echo (GRE) images with different magnetization preparations was acquired during free breathing. A total of 35 images were acquired in 26.5 ± 14.9 seconds using multiple saturation times and T2 preparation durations and with imaging at 5 echo times. Bloch simulations and phantom experiments were used to validate a 5-parameter fit model for accurate relaxometry. Free-breathing simultaneous T1 , T2 , and T2∗ measurements were performed in 10 healthy volunteers and 2 patients using SATURN at 3T and quantitatively compared to conventional single-parameter methods such as SASHA for T1 , T2 -prepared bSSFP, and multi-GRE for T2∗ . RESULTS: Simulations confirmed accurate fitting with the 5-parameter model. Phantom measurements showed good agreement with the reference methods in the relevant range for in vivo measurements. Compared to single-parameter methods comparable accuracy was achieved. SATURN produced in vivo parameter maps that were visually comparable to single-parameter methods. No significant difference between T1 , T2 , and T2∗ times acquired with SATURN and single-parameter methods was shown in quantitative measurements (SATURN T1=1573±86ms , T2=33.2±3.6ms , T2∗=25.3±6.1ms ; conventional methods: T1=1544±107ms , T2=33.2±3.6ms , T2∗=23.8±5.5ms ; P>.2 ) CONCLUSION: SATURN enables simultaneous quantification of T1 , T2 , and T2∗ in the myocardium for comprehensive tissue characterization with co-registered maps, in a single scan with good agreement to single-parameter methods.