DCAF15 control of cohesin dynamics sustains acute myeloid leukemia.

The CRL4-DCAF15 E3 ubiquitin ligase complex is targeted by the aryl-sulfonamide molecular glues, leading to neo-substrate recruitment, ubiquitination, and proteasomal degradation. However, the physiological function of DCAF15 remains unknown. Using a domain-focused genetic screening approach, we reveal DCAF15 as an acute myeloid leukemia (AML)-biased dependency. Loss of DCAF15 results in suppression of AML through compromised replication fork integrity and consequent accumulation of DNA damage. Accordingly, DCAF15 loss sensitizes AML to replication stress-inducing therapeutics. Mechanistically, we discover that DCAF15 directly interacts with the SMC1A protein of the cohesin complex and destabilizes the cohesin regulatory factors PDS5A and CDCA5. Loss of PDS5A and CDCA5 removal precludes cohesin acetylation on chromatin, resulting in uncontrolled chromatin loop extrusion, defective DNA replication, and apoptosis. Collectively, our findings uncover an endogenous, cell autonomous function of DCAF15 in sustaining AML proliferation through post-translational control of cohesin dynamics.

Identifying novel data-driven subgroups in congenital heart disease using multi-modal measures of brain structure.

Individuals with congenital heart disease (CHD) have an increased risk of neurodevelopmental impairments. Given the hypothesized complexity linking genomics, atypical brain structure, cardiac diagnoses and their management, and neurodevelopmental outcomes, unsupervised methods may provide unique insight into neurodevelopmental variability in CHD. Using data from the Pediatric Cardiac Genomics Consortium Brain and Genes study, we identified data-driven subgroups of individuals with CHD from measures of brain structure. Using structural magnetic resonance imaging (MRI; N = 93; cortical thickness, cortical volume, and subcortical volume), we identified subgroups that differed primarily on cardiac anatomic lesion and language ability. In contrast, using diffusion MRI (N = 88; white matter connectivity strength), we identified subgroups that were characterized by differences in associations with rare genetic variants and visual-motor function. This work provides insight into the differential impacts of cardiac lesions and genomic variation on brain growth and architecture in patients with CHD, with potentially distinct effects on neurodevelopmental outcomes.

Meta-regression of sulcal patterns, clinical and environmental factors on neurodevelopmental outcomes in participants with multiple CHD types.

Congenital heart disease affects 1% of infants and is associated with impaired neurodevelopment. Right- or left-sided sulcal features correlate with executive function among people with Tetralogy of Fallot or single ventricle congenital heart disease. Studies of multiple congenital heart disease types are needed to understand regional differences. Further, sulcal pattern has not been studied in people with d-transposition of the great arteries. Therefore, we assessed the relationship between sulcal pattern and executive function, general memory, and processing speed in a meta-regression of 247 participants with three congenital heart disease types (114 single ventricle, 92 d-transposition of the great arteries, and 41 Tetralogy of Fallot) and 94 participants without congenital heart disease. Higher right hemisphere sulcal pattern similarity was associated with improved executive function (Pearson r = 0.19, false discovery rate-adjusted P = 0.005), general memory (r = 0.15, false discovery rate P = 0.02), and processing speed (r = 0.17, false discovery rate P = 0.01) scores. These positive associations remained significant in for the d-transposition of the great arteries and Tetralogy of Fallot cohorts only in multivariable linear regression (estimated change ß = 0.7, false discovery rate P = 0.004; ß = 4.1, false discovery rate P = 0.03; and ß = 5.4, false discovery rate P = 0.003, respectively). Duration of deep hypothermic circulatory arrest was also associated with outcomes in the multivariate model and regression tree analysis. This suggests that sulcal pattern may provide an early biomarker for prediction of later neurocognitive challenges among people with congenital heart disease.

The role of cortical structural variance in deep learning-based prediction of fetal brain age.

Background: Deep-learning-based brain age estimation using magnetic resonance imaging data has been proposed to identify abnormalities in brain development and the risk of adverse developmental outcomes in the fetal brain. Although saliency and attention activation maps have been used to understand the contribution of different brain regions in determining brain age, there has been no attempt to explain the influence of shape-related cortical structural features on the variance of predicted fetal brain age. Methods: We examined the association between the predicted brain age difference (PAD: predicted brain age-chronological age) from our convolution neural networks-based model and global and regional cortical structural measures, such as cortical volume, surface area, curvature, gyrification index, and folding depth, using regression analysis. Results: Our results showed that global brain volume and surface area were positively correlated with PAD. Additionally, higher cortical surface curvature and folding depth led to a significant increase in PAD in specific regions, including the perisylvian areas, where dramatic agerelated changes in folding structures were observed in the late second trimester. Furthermore, PAD decreased with disorganized sulcal area patterns, suggesting that the interrelated arrangement and areal patterning of the sulcal folds also significantly affected the prediction of fetal brain age. Conclusion: These results allow us to better understand the variance in deep learning-based fetal brain age and provide insight into the mechanism of the fetal brain age prediction model.

Magnetoencephalography for the pediatric population, indications, acquisition and interpretation for the clinician.

Magnetoencephalography (MEG) is an imaging technique that enables the assessment of cortical activity via direct measures of neurophysiology. It is a non-invasive and passive technique that is completely painless. MEG has gained increasing prominence in the field of pediatric neuroimaging. This dedicated review article for the pediatric population summarizes the fundamental technical and clinical aspects of MEG for the clinician. We discuss methods tailored for children to improve data quality, including child-friendly MEG facility environments and strategies to mitigate motion artifacts. We provide an in-depth overview on accurate localization of neural sources and different analysis methods, as well as data interpretation. The contemporary platforms and approaches of two quaternary pediatric referral centers are illustrated, shedding light on practical implementations in clinical settings. Finally, we describe the expanding clinical applications of MEG, including its pivotal role in presurgical evaluation of epilepsy patients, presurgical mapping of eloquent cortices (somatosensory and motor cortices, visual and auditory cortices, lateralization of language), its emerging relevance in autism spectrum disorder research and potential future clinical applications, and its utility in assessing mild traumatic brain injury. In conclusion, this review serves as a comprehensive resource of clinicians as well as researchers, offering insights into the evolving landscape of pediatric MEG. It discusses the importance of technical advancements, data acquisition strategies, and expanding clinical applications in harnessing the full potential of MEG to study neurological conditions in the pediatric population.

Real-time imaging of axonal membrane protein life cycles.

The construction of neuronal membranes is a dynamic process involving the biogenesis, vesicular packaging, transport, insertion and recycling of membrane proteins. Optical imaging is well suited for the study of protein spatial organization and transport. However, various shortcomings of existing imaging techniques have prevented the study of specific types of proteins and cellular processes. Here we describe strategies for protein tagging and labeling, cell culture and microscopy that enable the real-time imaging of axonal membrane protein trafficking and subcellular distribution as they progress through some stages of their life cycle. First, we describe a process for engineering membrane proteins with extracellular self-labeling tags (either HaloTag or SNAPTag), which can be labeled with fluorescent ligands of various colors and cell permeability, providing flexibility for investigating the trafficking and spatiotemporal regulation of multiple membrane proteins in neuronal compartments. Next, we detail the dissection, transfection and culture of dorsal root ganglion sensory neurons in microfluidic chambers, which physically compartmentalizes cell bodies and distal axons. Finally, we describe four labeling and imaging procedures that utilize these enzymatically tagged proteins, flexible fluorescent labels and compartmentalized neuronal cultures to study axonal membrane protein anterograde and retrograde transport, the cotransport of multiple proteins, protein subcellular localization, exocytosis and endocytosis. Additionally, we generated open-source software for analyzing the imaging data in a high throughput manner. The experimental and analysis workflows provide an approach for studying the dynamics of neuronal membrane protein homeostasis, addressing longstanding challenges in this area. The protocol requires 5-7 days and expertise in cell culture and microscopy.

SE(3)-Equivariant and Noise-Invariant 3D Rigid Motion Tracking in Brain MRI.

Rigid motion tracking is paramount in many medical imaging applications where movements need to be detected, corrected, or accounted for. Modern strategies rely on convolutional neural networks (CNN) and pose this problem as rigid registration. Yet, CNNs do not exploit natural symmetries in this task, as they are equivariant to translations (their outputs shift with their inputs) but not to rotations. Here we propose EquiTrack, the first method that uses recent steerable SE(3)-equivariant CNNs (E-CNN) for motion tracking. While steerable E-CNNs can extract corresponding features across different poses, testing them on noisy medical images reveals that they do not have enough learning capacity to learn noise invariance. Thus, we introduce a hybrid architecture that pairs a denoiser with an E-CNN to decouple the processing of anatomically irrelevant intensity features from the extraction of equivariant spatial features. Rigid transforms are then estimated in closed-form. EquiTrack outperforms state-of-the-art learning and optimisation methods for motion tracking in adult brain MRI and fetal MRI time series. Our code is available at https://github.com/BBillot/EquiTrack.

Automatic cortical surface parcellation in the fetal brain using attention-gated spherical U-net.

Cortical surface parcellation for fetal brains is essential for the understanding of neurodevelopmental trajectories during gestations with regional analyses of brain structures and functions. This study proposes the attention-gated spherical U-net, a novel deep-learning model designed for automatic cortical surface parcellation of the fetal brain. We trained and validated the model using MRIs from 55 typically developing fetuses [gestational weeks: 32.9 ± 3.3 (mean ± SD), 27.4-38.7]. The proposed model was compared with the surface registration-based method, SPHARM-net, and the original spherical U-net. Our model demonstrated significantly higher accuracy in parcellation performance compared to previous methods, achieving an overall Dice coefficient of 0.899 ± 0.020. It also showed the lowest error in terms of the median boundary distance, 2.47 ± 1.322 (mm), and mean absolute percent error in surface area measurement, 10.40 ± 2.64 (%). In this study, we showed the efficacy of the attention gates in capturing the subtle but important information in fetal cortical surface parcellation. Our precise automatic parcellation model could increase sensitivity in detecting regional cortical anomalies and lead to the potential for early detection of neurodevelopmental disorders in fetuses.

Insights from serial magnetic resonance imaging in neonatal encephalopathy in term infants.

BACKGROUND: Limited serial neuroimaging studies use magnetic resonance imaging (MRI) to define the evolution of hypoxic-ischemic insults to the brain of term infants and encompass both the primary injury and its secondary impact on cerebral development. The optimal timing of MRI to fully evaluate the impact of hypoxic-ischemic encephalopathy on brain development and associated neurodevelopmental sequelae remains unknown. METHODS: Goals: (a) review literature related to serial neuroimaging in term infants with HIE; (b) describe pilot data in two infants with HIE treated with therapeutic hypothermia who had a brain injury at day 3-5 and underwent four additional MRIs over the next 12 weeks of life and developmental evaluation at 24 months of age. RESULTS: Early MRI defines primary injury on diffusion-weighted imaging, yet the full impact may not be fully apparent until after 1 month of life. CONCLUSION: The full impact of an ischemic injury on the neonatal brain may not be fully visible until several weeks after the initial insult. This suggests the benefit of obtaining later time points for MRI to fully define the extent of injury and its neurodevelopmental impact. IMPACT: Few studies inform the nature of the evolution of brain injury with hypothermia in HIE, limiting understanding of potential neuroprotection. MRI is the standard of care for prognosis in infants with HIE, however timing for optimal prognostic prediction remains unclear. Insights from MRI after the first week of life may assist in defining the full extent of brain injury and prognostic significance. A pilot study using five MRI timepoints up to 3 months of age, is presented. More data is required with a systematic evaluation of the impact of early brain injury on brain development in term infants with HIE following TH.

Zero-DeepSub: Zero-shot deep subspace reconstruction for rapid multiparametric quantitative MRI using 3D-QALAS.

PURPOSE: To develop and evaluate methods for (1) reconstructing 3D-quantification using an interleaved Look-Locker acquisition sequence with T2 preparation pulse (3D-QALAS) time-series images using a low-rank subspace method, which enables accurate and rapid T1 and T2 mapping, and (2) improving the fidelity of subspace QALAS by combining scan-specific deep-learning-based reconstruction and subspace modeling. THEORY AND METHODS: A low-rank subspace method for 3D-QALAS (i.e., subspace QALAS) and zero-shot deep-learning subspace method (i.e., Zero-DeepSub) were proposed for rapid and high fidelity T1 and T2 mapping and time-resolved imaging using 3D-QALAS. Using an ISMRM/NIST system phantom, the accuracy and reproducibility of the T1 and T2 maps estimated using the proposed methods were evaluated by comparing them with reference techniques. The reconstruction performance of the proposed subspace QALAS using Zero-DeepSub was evaluated in vivo and compared with conventional QALAS at high reduction factors of up to nine-fold. RESULTS: Phantom experiments showed that subspace QALAS had good linearity with respect to the reference methods while reducing biases and improving precision compared to conventional QALAS, especially for T2 maps. Moreover, in vivo results demonstrated that subspace QALAS had better g-factor maps and could reduce voxel blurring, noise, and artifacts compared to conventional QALAS and showed robust performance at up to nine-fold acceleration with Zero-DeepSub, which enabled whole-brain T1, T2, and PD mapping at 1 mm isotropic resolution within 2 min of scan time. CONCLUSION: The proposed subspace QALAS along with Zero-DeepSub enabled high fidelity and rapid whole-brain multiparametric quantification and time-resolved imaging.

Compartment-specific regulation of NaV1.7 in sensory neurons after acute exposure to TNF-α.

Tumor necrosis factor α (TNF-α) is a major pro-inflammatory cytokine, important in many diseases, that sensitizes nociceptors through its action on a variety of ion channels, including voltage-gated sodium (NaV) channels. We show here that TNF-α acutely upregulates sensory neuron excitability and current density of threshold channel NaV1.7. Using electrophysiological recordings and live imaging, we demonstrate that this effect on NaV1.7 is mediated by p38 MAPK and identify serine 110 in the channel's N terminus as the phospho-acceptor site, which triggers NaV1.7 channel insertion into the somatic membrane. We also show that the N terminus of NaV1.7 is sufficient to mediate this effect. Although acute TNF-α treatment increases NaV1.7-carrying vesicle accumulation at axonal endings, we did not observe increased channel insertion into the axonal membrane. These results identify molecular determinants of TNF-α-mediated regulation of NaV1.7 in sensory neurons and demonstrate compartment-specific effects of TNF-α on channel insertion in the neuronal plasma membrane.

A Prospective Multi-Institutional Study Comparing the Brain Development in the Third Trimester between Opioid-Exposed and Nonexposed Fetuses Using Advanced Fetal MR Imaging Techniques.

BACKGROUND AND PURPOSE: While the adverse neurodevelopmental effects of prenatal opioid exposure on infants and children in the United States are well described, the underlying causative mechanisms have yet to be fully understood. This study aims to compare quantitative volumetric and surface-based features of the fetal brain between opioid-exposed fetuses and unexposed controls by using advanced MR imaging processing techniques. MATERIALS AND METHODS: This is a multi-institutional IRB-approved study in which pregnant women with and without opioid use during the current pregnancy were prospectively recruited to undergo fetal MR imaging. A total of 14 opioid-exposed (31.4 ± 2.3 weeks of gestation) and 15 unexposed (31.4 ± 2.4 weeks) fetuses were included. Whole brain volume, cortical plate volume, surface area, sulcal depth, mean curvature, and gyrification index were computed as quantitative features by using our fetal brain MR imaging processing pipeline. RESULTS: After correcting for gestational age, fetal sex, maternal education, polysubstance use, high blood pressure, and MR imaging acquisition site, all of the global morphologic features were significantly lower in the opioid-exposed fetuses compared with the unexposed fetuses, including brain volume, cortical volume, cortical surface area, sulcal depth, cortical mean curvature, and gyrification index. In regional analysis, the opioid-exposed fetuses showed significantly decreased surface area and sulcal depth in the bilateral Sylvian fissures, central sulci, parieto-occipital fissures, temporal cortices, and frontal cortices. CONCLUSIONS: In this small cohort, prenatal opioid exposure was associated with altered fetal brain development in the third trimester. This adds to the growing body of literature demonstrating that prenatal opioid exposure affects the developing brain.

Sleep Spindle Generation Before and After Epilepsy Surgery: A Source Imaging Study in Children with Drug-Resistant Epilepsy.

INTRODUCTION: Literature lacks studies investigating the cortical generation of sleep spindles in drug-resistant epilepsy (DRE) and how they evolve after resection of the epileptogenic zone (EZ). Here, we examined sleep EEGs of children with focal DRE who became seizure-free after focal epilepsy surgery, and aimed to investigate the changes in the spindle generation before and after the surgery using low-density scalp EEG and electrical source imaging (ESI). METHODS: We analyzed N2-sleep EEGs from 19 children with DRE before and after surgery. We identified slow (8-12 Hz) and fast spindles (13-16 Hz), computed their spectral features and cortical generators through ESI and computed their distance from the EZ and irritative zone (IZ). We performed two-way ANOVA testing the effect of spindle type (slow vs. fast) and surgical phase (pre-surgery vs. post-surgery) on each feature. RESULTS: Power, frequency and cortical activation of slow spindles increased after surgery (p < 0.005), while this was not seen for fast spindles. Before surgery, the cortical generators of slow spindles were closer to the EZ (57.3 vs. 66.2 mm, p = 0.007) and IZ (41.3 vs. 55.5 mm, p = 0.02) than fast spindle generators. CONCLUSIONS: Our data indicate alterations in the EEG slow spindles after resective epilepsy surgery. Fast spindle generation on the contrary did not change after surgery. Although the study is limited by its retrospective nature, lack of healthy controls, and reduced cortical spatial sampling, our findings suggest a spatial relationship between the slow spindles and the epileptogenic generators.

Association of genetic and sulcal traits with executive function in congenital heart disease.

OBJECTIVE: Persons with congenital heart disease (CHD) are at increased risk of neurodevelopmental disabilities, including impairments to executive function. Sulcal pattern features correlate with executive function in adolescents with single-ventricle heart disease and tetralogy of Fallot. However, the interaction of sulcal pattern features with genetic and participant factors in predicting executive dysfunction is unknown. METHODS: We studied sulcal pattern features, participant factors, and genetic risk for executive function impairment in a cohort with multiple CHD types using stepwise linear regression and machine learning. RESULTS: Genetic factors, including predicted damaging de novo or rare inherited variants in neurodevelopmental disabilities risk genes, apolipoprotein E genotype, and principal components of sulcal pattern features were associated with executive function measures after adjusting for age at testing, sex, mother's education, and biventricular versus single-ventricle CHD in a linear regression model. Using regression trees and bootstrap validation, younger participant age and larger alterations in sulcal pattern features were consistently identified as important predictors of decreased cognitive flexibility with left hemisphere graph topology often selected as the most important predictor. Inclusion of both sulcal pattern and genetic factors improved model fit compared to either alone. INTERPRETATION: We conclude that sulcal measures remain important predictors of cognitive flexibility, and the model predicting executive outcomes is improved by inclusion of potential genetic sources of neurodevelopmental risk. If confirmed, measures of sulcal patterning may serve as early imaging biomarkers to identify those at heightened risk for future neurodevelopmental disabilities.

Outcomes of Consolidative Nephrectomy following Primary Immunotherapy in Advanced Renal Cell Carcinoma: A Multicenter Analysis.

BACKGROUND: To evaluate effect and outcomes of combination primary immunotherapy (IO) and nephrectomy for advanced renal cell carcinoma (RCC). METHODS: We conducted a multicenter, retrospective analysis of patients with advanced/metastatic RCC who received IO followed by nephrectomy. Primary outcome was Bifecta (negative surgical margins and no 30-day surgical complications). Secondary outcomes included progression-free survival (PFS) following surgery, reduction in tumor/thrombus size, RENAL score, and clinical/pathologic downstaging. Cox regression multivariable analysis was conducted for predictors of Bifecta and PFS. Kaplan-Meier analysis assessed PFS, comparing Bifecta and non-Bifecta groups. RESULTS: A total of 56 patients were analyzed (median age 63 years; median follow-up 22.5 months). A total of 40 (71.4%) patients were intermediate IMDC risk. Patients were treated with immunotherapy for median duration of 8.1 months. Immunotherapy resulted in reductions in tumor size (P < .001), thrombus size (P = .02), and RENAL score (P < .001); 38 (67.9%) patients were clinically downstaged on imaging (P < .001) and 25 (44.6%) patients were pathologically downstaged following surgery (P < .001). Bifecta was achieved in 38 (67.9%) patients. Predictors for bifecta achievement included decreasing tumor size (HR 1.08, P = .043) and pathological downstaging (HR 2.13, P = .047). Bifecta (HR 5.65, P = .009), pathologic downstaging (HR 5.15, P = .02), and increasing reduction in tumor size (HR 1.2, P = .007) were associated with improved PFS. Bifecta patients demonstrated improved 2-year PFS (84% vs. 71%, P = .019). CONCLUSIONS: Primary immunotherapy reduced tumor/thrombus size and complexity. Pathologically downstaged patients were more likely to achieve bifecta, and these patients displayed improved 2-year PFS. Our study supports further inquiry in the use of CRN following primary immunotherapy for advanced renal cancer.

BOston Neonatal Brain Injury Dataset for Hypoxic Ischemic Encephalopathy (BONBID-HIE): Part I. MRI and Manual Lesion Annotation.

Hypoxic ischemic encephalopathy (HIE) is a brain injury that occurs in 1 ~ 5/1000 term neonates. Accurate identification and segmentation of HIE-related lesions in neonatal brain magnetic resonance images (MRIs) is the first step toward predicting prognosis, identifying high-risk patients, and evaluating treatment effects. It will lead to a more accurate estimation of prognosis, a better understanding of neurological symptoms, and a timely prediction of response to therapy. We release the first public dataset containing neonatal brain diffusion MRI and expert annotation of lesions from 133 patients diagnosed with HIE. HIE-related lesions in brain MRI are often diffuse (i.e., multi-focal), and small (over half the patients in our data having lesions occupying <1% of brain volume). Segmentation for HIE MRI data is remarkably different from, and arguably more challenging than, other segmentation tasks such as brain tumors with focal and relatively large lesions. We hope that this dataset can help fuel the development of MRI lesion segmentation methods for HIE and small diffuse lesions in general.

Acute Diffusion-Weighted Imaging Signaling Severe Periventricular Leukomalacia in Preterm Infants: Case Report and Review of Literature.

Introduction: Periventricular leukomalacia occurs in up to 25% of very preterm infants resulting in adverse neurodevelopmental outcomes. In its acute phase, periventricular leukomalacia is clinically silent. Although ultrasonography is widely available, its sensitivity in the early detection of periventricular leukomalacia is low. Case Report and Published Literature: We identified a preterm infant with early diffusion-weighted imaging changes that later evolved to periventricular leukomalacia. Thirty-two cases of abnormal diffusion-weighted imaging reliably heralding severe periventricular leukomalacia in the preterm infant have been published in the literature. Notable features include the following: (1) infants were more mature preterm infants (29-36 weeks' gestation); (2) findings were often serendipitous with benign clinical courses; (3) diffusion-weighted imaging changes only were evident in the first weeks of life with later evolution to more classical abnormalities on conventional magnetic resonance imaging (MRI) or ultrasonography. Conclusion: Diffusion-weighted imaging in the first week of life may be a reliable early marker of severe periventricular leukomalacia injury in more mature preterm infants.

SSL-QALAS: Self-Supervised Learning for rapid multiparameter estimation in quantitative MRI using 3D-QALAS.

PURPOSE: To develop and evaluate a method for rapid estimation of multiparametric T1 , T2 , proton density, and inversion efficiency maps from 3D-quantification using an interleaved Look-Locker acquisition sequence with T2 preparation pulse (3D-QALAS) measurements using self-supervised learning (SSL) without the need for an external dictionary. METHODS: An SSL-based QALAS mapping method (SSL-QALAS) was developed for rapid and dictionary-free estimation of multiparametric maps from 3D-QALAS measurements. The accuracy of the reconstructed quantitative maps using dictionary matching and SSL-QALAS was evaluated by comparing the estimated T1 and T2 values with those obtained from the reference methods on an International Society for Magnetic Resonance in Medicine/National Institute of Standards and Technology phantom. The SSL-QALAS and the dictionary-matching methods were also compared in vivo, and generalizability was evaluated by comparing the scan-specific, pre-trained, and transfer learning models. RESULTS: Phantom experiments showed that both the dictionary-matching and SSL-QALAS methods produced T1 and T2 estimates that had a strong linear agreement with the reference values in the International Society for Magnetic Resonance in Medicine/National Institute of Standards and Technology phantom. Further, SSL-QALAS showed similar performance with dictionary matching in reconstructing the T1 , T2 , proton density, and inversion efficiency maps on in vivo data. Rapid reconstruction of multiparametric maps was enabled by inferring the data using a pre-trained SSL-QALAS model within 10 s. Fast scan-specific tuning was also demonstrated by fine-tuning the pre-trained model with the target subject's data within 15 min. CONCLUSION: The proposed SSL-QALAS method enabled rapid reconstruction of multiparametric maps from 3D-QALAS measurements without an external dictionary or labeled ground-truth training data.