MyoFold: Joint myocardial tissue composition and wall motion quantification via a highly folded sequence.

PURPOSE: This study aims to develop and evaluate a novel cardiovascular MR sequence, MyoFold, designed for the simultaneous quantifications of myocardial tissue composition and wall motion. METHODS: MyoFold is designed as a 2D single breathing-holding sequence, integrating joint T1/T2 mapping and cine imaging. The sequence uses a 2-fold accelerated balanced SSFP (bSSFP) for data readout and incorporates electrocardiogram synchronization to align with the cardiac cycle. MyoFold initially acquires six single-shot inversion-recovery images, completed during the diastole of six successive heartbeats. T2 preparation (T2-prep) is applied to introduce T2 weightings for the last three images. Subsequently, over the following six heartbeats, segmented bSSFP is performed for the movie of the entire cardiac cycle, synchronized with an electrocardiogram. A neural network trained using numerical simulations of MyoFold is used for T1 and T2 calculations. MyoFold was validated through phantom and in vivo experiments, with comparisons made against MOLLI, SASHA, T2-prep bSSFP, and the conventional cine. RESULTS: In phantom studies, MyoFold exhibited a 10% overestimation in T1 measurements, whereas T2 measurements demonstrated high accuracy. In vivo experiments revealed that MyoFold T1 had comparable accuracy to SASHA and precision similar to MOLLI. MyoFold demonstrated good agreement with T2-prep bSSFP in myocardial T2 measurements. No significant differences were observed in the quantification of left-ventricle wall thickness and function between MyoFold and the conventional cine. CONCLUSION: MyoFold presents as a rapid, simple, and multitasking approach for quantitative cardiovascular MR examinations, offering simultaneous assessment of tissue composition and wall motion. The sequence's multitasking capabilities make it a promising tool for comprehensive cardiac evaluations in clinical settings.

Deciduous pulp tissue implantation into the root canal of mandibular incisor resulted in pulp revascularization: a case report with a 5-year follow-up.

To explore a new method to implant deciduous tooth pulp into the canal of young permanent teeth with necrotic pulps and apical periodontitis for the regenerative endodontic treatment of tooth no: 41 in a 7-year-old male. Briefly, 1.5% Sodium Hypochlorite (NaOCl) irrigation and calcium hydroxide-iodoform paste were used as root canal disinfectant at the first visit. After 2 weeks, the intracanal medication was removed, and the root canal was slowly rinsed with 17% Ethylene Diamine Tetraacetic Acid (EDTA), followed by flushing with 20 mL saline and then drying with paper points. Tooth no: 72 was extracted, and its pulp was extracted and subsequently implanted into the disinfected root canal along with induced apical bleeding. Calcium hydroxide iodoform paste was gently placed over the bleeding clot, and after forming a mineral trioxide aggregate (MTA) coronal barrier, the accessed cavities were restored using Z350 resin composite. The root developments were evaluated via radiographic imaging at 6 months, 1 year and 5 years after treatment. Imaging and clinical analysis showed closure of the apical foramen, thickening of the root canal wall, and satisfactory root length growth. Autologous transplantation might be useful to regenerate dental pulp in necrotic young permanent teeth.

JOINEDTrans: Prior guided multi-task transformer for joint optic disc/cup segmentation and fovea detection.

Deep learning-based image segmentation and detection models have largely improved the efficiency of analyzing retinal landmarks such as optic disc (OD), optic cup (OC), and fovea. However, factors including ophthalmic disease-related lesions and low image quality issues may severely complicate automatic OD/OC segmentation and fovea detection. Most existing works treat the identification of each landmark as a single task, and take into account no prior information. To address these issues, we propose a prior guided multi-task transformer framework for joint OD/OC segmentation and fovea detection, named JOINEDTrans. JOINEDTrans effectively combines various spatial features of the fundus images, relieving the structural distortions induced by lesions and other imaging issues. It contains a segmentation branch and a detection branch. To be noted, we employ an encoder with prior-learning in a vessel segmentation task to effectively exploit the positional relationship among vessel, OD/OC, and fovea, successfully incorporating spatial prior into the proposed JOINEDTrans framework. There are a coarse stage and a fine stage in JOINEDTrans. In the coarse stage, OD/OC coarse segmentation and fovea heatmap localization are obtained through a joint segmentation and detection module. In the fine stage, we crop regions of interest for subsequent refinement and use predictions obtained in the coarse stage to provide additional information for better performance and faster convergence. Experimental results demonstrate that JOINEDTrans outperforms existing state-of-the-art methods on the publicly available GAMMA, REFUGE, and PALM fundus image datasets. We make our code available at https://github.com/HuaqingHe/JOINEDTrans.

Carbon-based Nanomaterials in Photothermal Therapy Guided by Photoacoustic Imaging: State of Knowledge and Recent Advances.

Carbon-based nanomaterials (CBNM)have been widely used in various fields due to their excellent physicochemical properties. In particular, in the area of tumor diagnosis and treatment, researchers have frequently reported them for their potential fluorescence, photoacoustic (PA), and ultrasound imaging performance, as well as their photothermal, photodynamic, sonodynamic, and other therapeutic properties. As the functions of CBNM are increasingly developed, their excellent imaging properties and superior tumor treatment effects make them extremely promising theranostic agents. This review aims to integrate the considered and researched information in a specific field of this research topic and systematically present, summarize, and comment on the efforts made by authoritative scholars. In this review, we summarized the work exploring carbon-based materials in the field of tumor imaging and therapy, focusing on PA imaging-guided photothermal therapy (PTT) and discussing their imaging and therapeutic mechanisms and developments. Finally, the current challenges and potential opportunities of carbon-based materials for PA imaging-guided PTT are presented, and issues that researchers should be aware of when studying CBNM are provided.

SSiT: Saliency-Guided Self-Supervised Image Transformer for Diabetic Retinopathy Grading.

Self-supervised Learning (SSL) has been widely applied to learn image representations through exploiting unlabeled images. However, it has not been fully explored in the medical image analysis field. In this work, Saliency-guided Self-Supervised image Transformer (SSiT) is proposed for Diabetic Retinopathy (DR) grading from fundus images. We novelly introduce saliency maps into SSL, with a goal of guiding self-supervised pre-training with domain-specific prior knowledge. Specifically, two saliency-guided learning tasks are employed in SSiT: 1) Saliency-guided contrastive learning is conducted based on the momentum contrast, wherein fundus images' saliency maps are utilized to remove trivial patches from the input sequences of the momentum-updated key encoder. Thus, the key encoder is constrained to provide target representations focusing on salient regions, guiding the query encoder to capture salient features. 2) The query encoder is trained to predict the saliency segmentation, encouraging the preservation of fine-grained information in the learned representations. To assess our proposed method, four publicly-accessible fundus image datasets are adopted. One dataset is employed for pre-training, while the three others are used to evaluate the pre-trained models' performance on downstream DR grading. The proposed SSiT significantly outperforms other representative state-of-the-art SSL methods on all downstream datasets and under various evaluation settings. For example, SSiT achieves a Kappa score of 81.88% on the DDR dataset under fine-tuning evaluation, outperforming all other ViT-based SSL methods by at least 9.48%.

The State-of-the-Art and Perspectives of Laser Ablation for Tumor Treatment.

Tumors significantly impact individuals' physical well-being and quality of life. With the ongoing advancements in optical technology, information technology, robotic technology, etc., laser technology is being increasingly utilized in the field of tumor treatment, and laser ablation (LA) of tumors remains a prominent area of research interest. This paper presents an overview of the recent progress in tumor LA therapy, with a focus on the mechanisms and biological effects of LA, commonly used ablation lasers, image-guided LA, and robotic-assisted LA. Further insights and future prospects are discussed in relation to these aspects, and the paper proposed potential future directions for the development of tumor LA techniques.

The LMIT: Light-mediated minimally-invasive theranostics in oncology.

Minimally-invasive diagnosis and therapy have gradually become the trend and research hotspot of current medical applications. The integration of intraoperative diagnosis and treatment is a development important direction for real-time detection, minimally-invasive diagnosis and therapy to reduce mortality and improve the quality of life of patients, so called minimally-invasive theranostics (MIT). Light is an important theranostic tool for the treatment of cancerous tissues. Light-mediated minimally-invasive theranostics (LMIT) is a novel evolutionary technology that integrates diagnosis and therapeutics for the less invasive treatment of diseased tissues. Intelligent theranostics would promote precision surgery based on the optical characterization of cancerous tissues. Furthermore, MIT also requires the assistance of smart medical devices or robots. And, optical multimodality lay a solid foundation for intelligent MIT. In this review, we summarize the important state-of-the-arts of optical MIT or LMIT in oncology. Multimodal optical image-guided intelligent treatment is another focus. Intraoperative imaging and real-time analysis-guided optical treatment are also systemically discussed. Finally, the potential challenges and future perspectives of intelligent optical MIT are discussed.

Simultaneous alignment and surface regression using hybrid 2D-3D networks for 3D coherent layer segmentation of retinal OCT images with full and sparse annotations.

Layer segmentation is important to quantitative analysis of retinal optical coherence tomography (OCT). Recently, deep learning based methods have been developed to automate this task and yield remarkable performance. However, due to the large spatial gap and potential mismatch between the B-scans of an OCT volume, all of them were based on 2D segmentation of individual B-scans, which may lose the continuity and diagnostic information of the retinal layers in 3D space. Besides, most of these methods required dense annotation of the OCT volumes, which is labor-intensive and expertise-demanding. This work presents a novel framework based on hybrid 2D-3D convolutional neural networks (CNNs) to obtain continuous 3D retinal layer surfaces from OCT volumes, which works well with both full and sparse annotations. The 2D features of individual B-scans are extracted by an encoder consisting of 2D convolutions. These 2D features are then used to produce the alignment displacement vectors and layer segmentation by two 3D decoders coupled via a spatial transformer module. Two losses are proposed to utilize the retinal layers' natural property of being smooth for B-scan alignment and layer segmentation, respectively, and are the key to the semi-supervised learning with sparse annotation. The entire framework is trained end-to-end. To the best of our knowledge, this is the first work that attempts 3D retinal layer segmentation in volumetric OCT images based on CNNs. Experiments on a synthetic dataset and three public clinical datasets show that our framework can effectively align the B-scans for potential motion correction, and achieves superior performance to state-of-the-art 2D deep learning methods in terms of both layer segmentation accuracy and cross-B-scan 3D continuity in both fully and semi-supervised settings, thus offering more clinical values than previous works.

Toward hippocampal volume measures on ultra-high field magnetic resonance imaging: a comprehensive comparison study between deep learning and conventional approaches.

The hippocampus is a complex brain structure that plays an important role in various cognitive aspects such as memory, intelligence, executive function, and path integration. The volume of this highly plastic structure is identified as one of the most important biomarkers of specific neuropsychiatric and neurodegenerative diseases. It has also been extensively investigated in numerous aging studies. However, recent studies on aging show that the performance of conventional approaches in measuring the hippocampal volume is still far from satisfactory, especially in terms of delivering longitudinal measures from ultra-high field magnetic resonance images (MRIs), which can visualize more boundary details. The advancement of deep learning provides an alternative solution to measuring the hippocampal volume. In this work, we comprehensively compared a deep learning pipeline based on nnU-Net with several conventional approaches including Freesurfer, FSL and DARTEL, for automatically delivering hippocampal volumes: (1) Firstly, we evaluated the segmentation accuracy and precision on a public dataset through cross-validation. Results showed that the deep learning pipeline had the lowest mean (L = 1.5%, R = 1.7%) and the lowest standard deviation (L = 5.2%, R = 6.2%) in terms of volume percentage error. (2) Secondly, sub-millimeter MRIs of a group of healthy adults with test-retest 3T and 7T sessions were used to extensively assess the test-retest reliability. Results showed that the deep learning pipeline achieved very high intraclass correlation coefficients (L = 0.990, R = 0.986 for 7T; L = 0.985, R = 0.983 for 3T) and very small volume percentage differences (L = 1.2%, R = 0.9% for 7T; L = 1.3%, R = 1.3% for 3T). (3) Thirdly, a Bayesian linear mixed effect model was constructed with respect to the hippocampal volumes of two healthy adult datasets with longitudinal 7T scans and one disease-related longitudinal dataset. It was found that the deep learning pipeline detected both the subtle and disease-related changes over time with high sensitivity as well as the mild differences across subjects. Comparison results from the aforementioned three aspects showed that the deep learning pipeline significantly outperformed the conventional approaches by large margins. Results also showed that the deep learning pipeline can better accommodate longitudinal analysis purposes.

GAMMA challenge: Glaucoma grAding from Multi-Modality imAges.

Glaucoma is a chronic neuro-degenerative condition that is one of the world's leading causes of irreversible but preventable blindness. The blindness is generally caused by the lack of timely detection and treatment. Early screening is thus essential for early treatment to preserve vision and maintain life quality. Colour fundus photography and Optical Coherence Tomography (OCT) are the two most cost-effective tools for glaucoma screening. Both imaging modalities have prominent biomarkers to indicate glaucoma suspects, such as the vertical cup-to-disc ratio (vCDR) on fundus images and retinal nerve fiber layer (RNFL) thickness on OCT volume. In clinical practice, it is often recommended to take both of the screenings for a more accurate and reliable diagnosis. However, although numerous algorithms are proposed based on fundus images or OCT volumes for the automated glaucoma detection, there are few methods that leverage both of the modalities to achieve the target. To fulfil the research gap, we set up the Glaucoma grAding from Multi-Modality imAges (GAMMA) Challenge to encourage the development of fundus & OCT-based glaucoma grading. The primary task of the challenge is to grade glaucoma from both the 2D fundus images and 3D OCT scanning volumes. As part of GAMMA, we have publicly released a glaucoma annotated dataset with both 2D fundus colour photography and 3D OCT volumes, which is the first multi-modality dataset for machine learning based glaucoma grading. In addition, an evaluation framework is also established to evaluate the performance of the submitted methods. During the challenge, 1272 results were submitted, and finally, ten best performing teams were selected for the final stage. We analyse their results and summarize their methods in the paper. Since all the teams submitted their source code in the challenge, we conducted a detailed ablation study to verify the effectiveness of the particular modules proposed. Finally, we identify the proposed techniques and strategies that could be of practical value for the clinical diagnosis of glaucoma. As the first in-depth study of fundus & OCT multi-modality glaucoma grading, we believe the GAMMA Challenge will serve as an essential guideline and benchmark for future research.

The effects of cocaine use severity and abstinence on behavioral performance and neural processes of response inhibition.

Previous studies identified cerebral markers of response inhibition dysfunction in cocaine dependence. However, whether deficits in response inhibition vary with the severity of cocaine use or ameliorate during abstinence remain unclear. This study aimed to address these issues and the neural mechanisms supporting the individual variation. We examined the data of 67 individuals with cocaine dependence (CD) and 84 healthy controls (HC) who underwent functional magnetic resonance imaging during a stop-signal task (SST). The stop-signal reaction time (SSRT) was computed using the integration method, with a longer SSRT indicating poorer response inhibition. The results showed that, while CD and HC did not differ significantly in SSRT, years of cocaine use (YOC) and days of abstinence (DOA) were each positively and negatively correlated with the SSRT in CD. Whole-brain regressions of stop minus go success trials on SSRT revealed correlates in bilateral superior temporal gyrus (STG) in response inhibition across CD and HC. Further, mediation and path analyses revealed that YOC and DOA affected SSRT through the STG activities in CD. Together, the findings characterized the contrasting effects of cocaine use severity and abstinence on response inhibition as well as the neural processes that support these effects in cocaine dependence.

Stroke analysis and recognition in functional near-infrared spectroscopy signals using machine learning methods.

Stroke is a high-incidence disease with high disability and mortality rates. It is a serious public health problem worldwide. Shortened onset-to-image time is very important for the diagnosis and treatment of stroke. Functional near-infrared spectroscopy (fNIRS) is a noninvasive monitoring tool with real-time, noninvasive, and convenient features. In this study, we propose an automatic classification framework based on cerebral oxygen saturation signals to identify patients with hemorrhagic stroke, patients with ischemic stroke, and normal subjects. The reflected fNIRS signals were used to detect the cerebral oxygen saturation and the relative value of oxygen and deoxyhemoglobin concentrations of the left and right frontal lobes. The wavelet time-frequency analysis-based features from these signals were extracted. Such features were used to analyze the differences in cerebral oxygen saturation signals among different types of stroke patients and healthy humans and were selected to train the machine learning models. Furthermore, an important analysis of the features was performed. The accuracy of the models trained was greater than 85%, and the accuracy of the models after data augmentation was greater than 90%, which is of great significance in distinguishing patients with hemorrhagic stroke or ischemic stroke. This framework has the potential to shorten the onset-to-diagnosis time of stroke.

YoloCurvSeg: You only label one noisy skeleton for vessel-style curvilinear structure segmentation.

Weakly-supervised learning (WSL) has been proposed to alleviate the conflict between data annotation cost and model performance through employing sparsely-grained (i.e., point-, box-, scribble-wise) supervision and has shown promising performance, particularly in the image segmentation field. However, it is still a very challenging task due to the limited supervision, especially when only a small number of labeled samples are available. Additionally, almost all existing WSL segmentation methods are designed for star-convex structures which are very different from curvilinear structures such as vessels and nerves. In this paper, we propose a novel sparsely annotated segmentation framework for curvilinear structures, named YoloCurvSeg. A very essential component of YoloCurvSeg is image synthesis. Specifically, a background generator delivers image backgrounds that closely match the real distributions through inpainting dilated skeletons. The extracted backgrounds are then combined with randomly emulated curves generated by a Space Colonization Algorithm-based foreground generator and through a multilayer patch-wise contrastive learning synthesizer. In this way, a synthetic dataset with both images and curve segmentation labels is obtained, at the cost of only one or a few noisy skeleton annotations. Finally, a segmenter is trained with the generated dataset and possibly an unlabeled dataset. The proposed YoloCurvSeg is evaluated on four publicly available datasets (OCTA500, CORN, DRIVE and CHASEDB1) and the results show that YoloCurvSeg outperforms state-of-the-art WSL segmentation methods by large margins. With only one noisy skeleton annotation (respectively 0.14%, 0.03%, 1.40%, and 0.65% of the full annotation), YoloCurvSeg achieves more than 97% of the fully-supervised performance on each dataset. Code and datasets will be released at https://github.com/llmir/YoloCurvSeg.

CHSNet: Automatic lesion segmentation network guided by CT image features for acute cerebral hemorrhage.

Stroke is a cerebrovascular disease that can lead to severe sequelae such as hemiplegia and mental retardation with a mortality rate of up to 40%. In this paper, we proposed an automatic segmentation network (CHSNet) to segment the lesions in cranial CT images based on the characteristics of acute cerebral hemorrhage images, such as high density, multi-scale, and variable location, and realized the three-dimensional (3D) visualization and localization of the cranial lesions after the segmentation was completed. To enhance the feature representation of high-density regions, and capture multi-scale and up-down information on the target location, we constructed a convolutional neural network with encoding-decoding backbone, Res-RCL module, Atrous Spatial Pyramid Pooling, and Attention Gate. We collected images of 203 patients with acute cerebral hemorrhage, constructed a dataset containing 5998 cranial CT slices, and conducted comparative and ablation experiments on the dataset to verify the effectiveness of our model. Our model achieved the best results on both test sets with different segmentation difficulties, test1: Dice = 0.918, IoU = 0.853, ASD = 0.476, RVE = 0.113; test2: Dice = 0.716, IoU = 0.604, ASD = 5.402, RVE = 1.079. Based on the segmentation results, we achieved 3D visualization and localization of hemorrhage in CT images of stroke patients. The study has important implications for clinical adjuvant diagnosis.

Investigation of functional connectivity in Bell's palsy using functional magnetic resonance imaging: prospective cross-sectional study.

Background: The most common cause of lower motor neuron facial palsy is Bell's palsy (BP). BP results in partial or complete inability to automatically move the facial muscles on the affected side and, in some cases, to close the eyelids, which can cause permanent eye damage. This study investigated changes in brain function and connectivity abnormalities in patients with BP. Methods: This study included 46 patients with unilateral BP and 34 healthy controls (HCs). Resting-state brain functional magnetic resonance imaging (fMRI) images were acquired, and Toronto Facial Grading System (TFGS) scores were obtained for all participants. The fractional amplitude of low-frequency fluctuation (fALFF) was estimated, and the relationship between the TFGS and fALFF was determined using correlation analysis for brain regions with changes in fALFF in those with BP versus HCs. Brain regions associated with TFGS were used as seeds for further functional connectivity (FC) analysis; relationships between FC values of abnormal areas and TFGS scores were also analyzed. Results: Activation of the right precuneus, right angular gyrus, left supramarginal gyrus, and left middle occipital gyrus was significantly decreased in the BP group. fALFF was significantly higher in the right thalamus, vermis, and cerebellum of the BP group compared with that in the HC group (P<0.05). The FC between the left middle occipital gyrus and right angular gyrus, left precuneus, and right middle frontal gyrus increased sharply, but decreased in the left angular gyrus, left posterior cingulate gyrus, left middle frontal gyrus, inferior cerebellum, and left middle temporal gyrus. Furthermore, the fALFF in the left middle occipital gyrus was negatively correlated with TFGS score (R=0.144; P=0.008). Conclusions: The pathogenesis of BP is closely related to functional reorganization of the cerebral cortex. Patients with BP have altered fALFF activity in cortical regions associated with facial motion feedback monitoring.

Accurate measurement of magnetic resonance parkinsonism index by a fully automatic and deep learning quantification pipeline.

OBJECTIVES: This study aims at a fully automatic pipeline for measuring the magnetic resonance parkinsonism index (MRPI) using deep learning methods. METHODS: MRPI is defined as the product of the pons area to the midbrain area ratio and the middle cerebellar peduncle (MCP) width to the superior cerebellar peduncle (SCP) width ratio. In our proposed pipeline, we first used nnUNet to segment the brainstem and then employed HRNet to identify two key boundary points so as to sub-divide the whole brainstem into midbrain and pons. HRNet was also employed to predict the MCP endpoints for measuring the MCP width. Finally, we segmented the SCP on an oblique coronal plane and calculated its width. A total of 400 T1-weighted magnetic resonance images (MRIs) were used to train the nnUNet and HRNet models. Five-fold cross-validation was conducted to evaluate our proposed pipeline's performance on the training dataset. We also evaluated the performance of our proposed pipeline on three external datasets. Two of them had two raters manually measuring the MRPI values, providing insights into automatic accuracy versus inter-rater variability. RESULTS: We obtained average absolute percentage errors (APEs) of 17.21%, 18.17%, 20.83%, and 22.83% on the training dataset and the three external validation datasets, while the inter-rater average APE measured on the first two external validation datasets was 11.31%. Our proposed pipeline significantly improved the MRPI quantification accuracy over a representative state-of-the-art traditional approach (p < 0.001). CONCLUSION: The proposed automatic pipeline can accurately predict MRPI that is comparable with manual measurement. CLINICAL RELEVANCE STATEMENT: This study presents an automated magnetic resonance parkinsonism index measurement tool that can analyze large amounts of magnetic resonance images, enhance the efficiency of Parkinsonism-Plus syndrome diagnosis, reduce the workload of clinicians, and minimize the impact of human factors on diagnosis. KEY POINTS: • We propose an automatic pipeline for measuring the magnetic resonance parkinsonism index from magnetic resonance images. • The effectiveness of the proposed pipeline is successfully established on multiple datasets and comparisons with inter-rater measurements. • The proposed pipeline significantly outperforms a state-of-the-art quantification approach, being much closer to ground truth.

DeepFittingNet: A deep neural network-based approach for simplifying cardiac T1 and T2 estimation with improved robustness.

PURPOSE: To develop and evaluate a deep neural network (DeepFittingNet) for T1 /T2 estimation of the most commonly used cardiovascular MR mapping sequences to simplify data processing and improve robustness. THEORY AND METHODS: DeepFittingNet is a 1D neural network composed of a recurrent neural network (RNN) and a fully connected (FCNN) neural network, in which RNN adapts to the different number of input signals from various sequences and FCNN subsequently predicts A, B, and Tx of a three-parameter model. DeepFittingNet was trained using Bloch-equation simulations of MOLLI and saturation-recovery single-shot acquisition (SASHA) T1 mapping sequences, and T2 -prepared balanced SSFP (T2 -prep bSSFP) T2 mapping sequence, with reference values from the curve-fitting method. Several imaging confounders were simulated to improve robustness. The trained DeepFittingNet was tested using phantom and in-vivo signals, and compared to the curve-fitting algorithm. RESULTS: In testing, DeepFittingNet performed T1 /T2 estimation of four sequences with improved robustness in inversion-recovery T1 estimation. The mean bias in phantom T1 and T2 between the curve-fitting and DeepFittingNet was smaller than 30 and 1 ms, respectively. Excellent agreements between both methods was found in the left ventricle and septum T1 /T2 with a mean bias <6 ms. There was no significant difference in the SD of both the left ventricle and septum T1 /T2 between the two methods. CONCLUSION: DeepFittingNet trained with simulations of MOLLI, SASHA, and T2 -prep bSSFP performed T1 /T2 estimation tasks for all these most used sequences. Compared with the curve-fitting algorithm, DeepFittingNet improved the robustness for inversion-recovery T1 estimation and had comparable performance in terms of accuracy and precision.

Improved outcomes of UM171-expanded cord blood transplantation compared with other graft sources: real-world evidence.

Cord blood (CB) transplantation is hampered by low cell dose and high nonrelapse mortality (NRM). A phase 1-2 trial of UM171-expanded CB transplants demonstrated safety and favorable preliminary efficacy. The aim of the current analysis was to retrospectively compare results of the phase 1-2 trial with those after unmanipulated CB and matched-unrelated donor (MUD) transplants. Data from recipients of CB and MUD transplants were obtained from the Center for International Blood and Marrow Transplant Research (CIBMTR) database. Patients were directly matched for the number of previous allogeneic hematopoietic stem cell transplants (alloHCT), disease and refined Disease Risk Index. Patients were further matched by propensity score for age, comorbidity index, and performance status. Primary end points included NRM, progression-free survival (PFS), overall survival (OS), and graft-versus-host disease (GVHD)-free relapse-free survival (GRFS) at 1 and 2 years after alloHCT. Overall, 137 patients from CIBMTR (67 CB, 70 MUD) and 22 with UM171-expanded CB were included. NRM at 1 and 2 years was lower, PFS and GRFS at 2 years and OS at 1 year were improved for UM171-expanded CBs compared with CB controls. Compared with MUD controls, UM171 recipients had lower 1- and 2-year NRM, higher 2-year PFS, and higher 1- and 2-year GRFS. Furthermore, UM171-expanded CB recipients experienced less grades 3-4 acute GVHD and chronic GVHD compared with MUD graft recipients. Compared with real-world evidence with CB and MUD alloHCT, this study suggests that UM171-expanded CB recipients may benefit from lower NRM and higher GRFS. This trial was registered at www.clinicaltrials.gov as #NCT02668315.