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Management of brain tumors is based on clinical and radiological information with presumed grade dictating treatment. Hence, a non-invasive assessment of tumor grade is of paramount importance to choose the best treatment plan. Convolutional Neural Networks (CNNs) represent one of the effective Deep Learning (DL)-based techniques that have been used for brain tumor diagnosis. However, they are unable to handle input modifications effectively. Capsule neural networks (CapsNets) are a novel type of machine learning (ML) architecture that was recently developed to address the drawbacks of CNNs. CapsNets are resistant to rotations and affine translations, which is beneficial when processing medical imaging datasets. Moreover, Vision Transformers (ViT)-based solutions have been very recently proposed to address the issue of long-range dependency in CNNs. This survey provides a comprehensive overview of brain tumor classification and segmentation techniques, with a focus on ML-based, CNN-based, CapsNet-based, and ViT-based techniques. The survey highlights the fundamental contributions of recent studies and the performance of state-of-the-art techniques. Moreover, we present an in-depth discussion of crucial issues and open challenges. We also identify some key limitations and promising future research directions. We envisage that this survey shall serve as a good springboard for further study.

Diffusion-weighted imaging (DWI) is a key contrast mechanism in MRI which allows for the assessment of microstructural properties of brain tissues by measuring the displacement of water molecules. Several diffusion models, including the tensor (DTI), kurtosis (DKI), and neurite orientation dispersion and density imaging (NODDI), are commonly used in both research and clinical practice. However, there is currently no standardized method for validating the stability and repeatability of these models over time. This study evaluates the use of a DTI phantom as a standard reference for diffusion MRI model validation. The phantom, along with four healthy volunteers, was scanned repeatedly on different days to assess repeatability and stability. The acquired data were fitted to the diffusion models, with repeatability assessed in the phantom using the coefficient of variation (CoV), while stability in vivo was assessed using the repeatability coefficient (RC). The phantom was consecutively scanned eight times to investigate the impact of gradient coil heating on measurement consistency. Results showed that the phantom provided a highly reproducible reference, with CoVs below 5% across repeated and consecutive acquisitions, confirming the robustness of the diffusion models. In vivo, the low RCs indicated that the models remained stable over time, despite potential physiological variability. This study highlights the essential role of phantoms in diffusion MRI research, providing a reference framework for model validation. Future research will expand on this work to a multi-center study to assess inter-scanner variability, potentially incorporating the phantom into calibration protocols to standardize diffusion MRI measurements across different MRI systems.

Image reconstruction in Magnetic Resonance Imaging (MRI) is fundamentally a linear inverse problem, such that the image can be recovered via explicit pseudoinversion of the encoding matrix by solving —a method referred to here as Pinv-Recon. While the benefits of this approach were acknowledged in early studies, the field has historically favored fast Fourier transforms (FFT) and iterative techniques due to perceived computational limitations of the pseudoinversion approach. This work revisits Pinv-Recon in the context of modern hardware, software, and optimized linear algebra routines. We compare various matrix inversion strategies, assess regularization effects, and demonstrate incorporation of advanced encoding physics into a unified reconstruction framework. While hardware advances have already significantly reduced computation time compared to earlier studies, our work further demonstrates that leveraging Cholesky decomposition leads to a two-order-of-magnitude improvement in computational efficiency over previous Singular Value Decomposition-based implementations. Moreover, we demonstrate the versatility of Pinv-Recon on diverse in vivo datasets encompassing a range of encoding schemes, starting with low- to medium-resolution functional and metabolic imaging and extending to high-resolution cases. Our findings establish Pinv-Recon as a versatile and robust reconstruction framework that aligns with the increasing emphasis on open-source and reproducible MRI research.

Brain tumors represent the highest cause of mortality in the pediatric oncological population. Diagnosis is commonly performed with magnetic resonance imaging. Survival biomarkers are challenging to identify due to the relatively low numbers of individual tumor types. 69 children with biopsy-confirmed brain tumors were recruited into this study. All participants had perfusion and diffusion weighted imaging performed at diagnosis. Imaging data were processed using conventional methods, and a Bayesian survival analysis performed. Unsupervised and supervised machine learning were performed with the survival features, to determine novel sub-groups related to survival. Sub-group analysis was undertaken to understand differences in imaging features. Survival analysis showed that a combination of diffusion and perfusion imaging were able to determine two novel sub-groups of brain tumors with different survival characteristics (p < 0.01), which were subsequently classified with high accuracy (98%) by a neural network. Analysis of high-grade tumors showed a marked difference in survival (p = 0.029) between the two clusters with high risk and low risk imaging features. This study has developed a novel model of survival for pediatric brain tumors. Tumor perfusion plays a key role in determining survival and should be considered as a high priority for future imaging protocols.

As hyperpolarized (HP) carbon-13 ( 13 C) metabolic imaging is clinically translated, there is a need for easy-to-implement, fast, and robust imaging techniques. However, achieving high temporal resolution without decreasing spatial and/or spectral resolution, whilst maintaining the usability of the imaging sequence is challenging. Therefore, this study looked to accelerate HP 13 C MRI by combining a well-established and robust sequence called two-dimensional Chemical Shift Imaging (2D CSI) with prospective under sampling and SENSitivity Encoding (SENSE) reconstruction. Due to the low natural abundance of 13 C, the sensitivity maps cannot be pre-acquired for the reconstruction. As such, the implementation of sodium ( 23 Na) sensitivity maps for SENSE reconstructed 13 C CSI was demonstrated in a phantom and in vivo in the pig kidney. Results showed that SENSE reconstruction using 23 Na sensitivity maps corrected aliased images with a four-fold acceleration. With high temporal resolution, the kidney spectra produced a detailed metabolic arrival and decay curve, useful for further metabolite kinetic modelling or denoising. Metabolic ratio maps were produced in three pigs demonstrating the technique’s ability for repeat metabolic measurements. In cases with unknown metabolite spectra or limited HP MRI specialist knowledge, this robust acceleration method ensures comprehensive capture of metabolic signals, mitigating the risk of missing spectral data.

Hyperpolarized carbon-13 magnetic resonance imaging is a promising technique for in vivo metabolic interrogation of alterations between health and disease. This study introduces a formalism for quantifying the metabolic information in hyperpolarized imaging. This study investigated a novel perfusion formalism and metabolic clearance rate (MCR) model in pre-clinical stroke and in the healthy human brain. Simulations showed that the proposed model was robust to perturbations in T 1 , transmit B 1 , and k PL . A significant difference in ipsilateral vs contralateral pyruvate derived cerebral blood flow (CBF) was detected in rats (140 ± 2 vs 89 ± 6 mL/100 g/min, p < 0.01, respectively) and pigs (139 ± 12 vs 95 ± 5 mL/100 g/min, p = 0.04, respectively), along with an increase in fractional metabolism (26 ± 5 vs 4 ± 2%, p < 0.01, respectively) in the rodent brain. In addition, a significant increase in ipsilateral vs contralateral MCR (0.034 ± 0.007 vs 0.017 ± 0.02/s, p = 0.03, respectively) and a decrease in mean transit time (31 ± 8 vs 60 ± 2 s, p = 0.04, respectively) was observed in the porcine brain. In conclusion, MCR mapping is a simple and robust approach to the post-processing of hyperpolarized magnetic resonance imaging.

Given growing clinical needs, in recent years Artificial Intelligence (AI) techniques have increasingly been used to define the best approaches for survival assessment and prediction in patients with brain tumors. Advances in computational resources, and the collection of (mainly) public databases, have promoted this rapid development. This narrative review of the current state-of-the-art aimed to survey current applications of AI in predicting survival in patients with brain tumors, with a focus on Magnetic Resonance Imaging (MRI). An extensive search was performed on PubMed and Google Scholar using a Boolean research query based on MeSH terms and restricting the search to the period between 2012 and 2022. Fifty studies were selected, mainly based on Machine Learning (ML), Deep Learning (DL), radiomics-based methods, and methods that exploit traditional imaging techniques for survival assessment. In addition, we focused on two distinct tasks related to survival assessment: the first on the classification of subjects into survival classes (short and long-term or eventually short, mid and long-term) to stratify patients in distinct groups. The second focused on quantification, in days or months, of the individual survival interval. Our survey showed excellent state-of-the-art methods for the first, with accuracy up to ∼98%. The latter task appears to be the most challenging, but state-of-the-art techniques showed promising results, albeit with limitations, with C-Index up to ∼0.91. In conclusion, according to the specific task, the available computational methods perform differently, and the choice of the best one to use is non-univocal and dependent on many aspects. Unequivocally, the use of features derived from quantitative imaging has been shown to be advantageous for AI applications, including survival prediction. This evidence from the literature motivates further research in the field of AI-powered methods for survival prediction in patients with brain tumors, in particular, using the wealth of information provided by quantitative MRI techniques.

The adoptive transfer of regulatory T-cells (Tregs) is a promising therapeutic approach in transplantation and autoimmunity. However, because large cell numbers are needed to achieve a therapeutic effect, in vitro expansion is required. By comparing their function, phenotype and transcriptomic profile against ex vivo Tregs, we demonstrate that expanded human Tregs switch their metabolism to aerobic glycolysis and show enhanced suppressive function through hypoxia-inducible factor 1-alpha (HIF1A) driven acquisition of CD73 expression. In conjunction with CD39, CD73 expression enables expanded Tregs to convert ATP to immunosuppressive adenosine. We conclude that for maximum therapeutic benefit, Treg expansion protocols should be optimised for CD39/CD73 co-expression.Jarvis et al demonstrate that expanded human Tregs switch their metabolism to aerobic glycolysis and show enhanced suppressive function through hypoxia-inducible factor 1-alpha (HIF1A)-driven acquisition of CD73 expression, which along with CD39, enables expanded Tregs to convert ATP to immunosuppressive adenosine. Given this, the data suggests that Treg expansion protocols should be optimised for CD39/CD73 co-expression to enhance therapeutic potential.

Our purpose is to investigate the feasibility of imaging tumor metabolism in breast cancer patients using 13C magnetic resonance spectroscopic imaging (MRSI) of hyperpolarized 13C label exchange between injected [1-13C]pyruvate and the endogenous tumor lactate pool. Treatment-naïve breast cancer patients were recruited: four triple-negative grade 3 cancers; two invasive ductal carcinomas that were estrogen and progesterone receptor-positive (ER/PR+) and HER2/neu-negative (HER2−), one grade 2 and one grade 3; and one grade 2 ER/PR+ HER2− invasive lobular carcinoma (ILC). Dynamic 13C MRSI was performed following injection of hyperpolarized [1-13C]pyruvate. Expression of lactate dehydrogenase A (LDHA), which catalyzes 13C label exchange between pyruvate and lactate, hypoxia-inducible factor-1 (HIF1α), and the monocarboxylate transporters MCT1 and MCT4 were quantified using immunohistochemistry and RNA sequencing. We have demonstrated the feasibility and safety of hyperpolarized 13C MRI in early breast cancer. Both intertumoral and intratumoral heterogeneity of the hyperpolarized pyruvate and lactate signals were observed. The lactate-to-pyruvate signal ratio (LAC/PYR) ranged from 0.021 to 0.473 across the tumor subtypes (mean ± SD: 0.145 ± 0.164), and a lactate signal was observed in all of the grade 3 tumors. The LAC/PYR was significantly correlated with tumor volume (R = 0.903, P = 0.005) and MCT 1 (R = 0.85, P = 0.032) and HIF1α expression (R = 0.83, P = 0.043). Imaging of hyperpolarized [1-13C]pyruvate metabolism in breast cancer is feasible and demonstrated significant intertumoral and intratumoral metabolic heterogeneity, where lactate labeling correlated with MCT1 expression and hypoxia.

Hyperpolarized 13C Magnetic Resonance Imaging (13C-MRI) provides a highly sensitive tool to probe tissue metabolism in vivo and has recently been translated into clinical studies. We report the cerebral metabolism of intravenously injected hyperpolarized [1–13C]pyruvate in the brain of healthy human volunteers for the first time. Dynamic acquisition of 13C images demonstrated 13C-labeling of both lactate and bicarbonate, catalyzed by cytosolic lactate dehydrogenase and mitochondrial pyruvate dehydrogenase respectively. This demonstrates that both enzymes can be probed in vivo in the presence of an intact blood-brain barrier: the measured apparent exchange rate constant (kPL) for exchange of the hyperpolarized 13C label between [1–13C]pyruvate and the endogenous lactate pool was 0.012 ± 0.006 s−1 and the apparent rate constant (kPB) for the irreversible flux of [1–13C]pyruvate to [13C]bicarbonate was 0.002 ± 0.002 s−1. Imaging also revealed that [1–13C]pyruvate, [1–13C]lactate and [13C]bicarbonate were significantly higher in gray matter compared to white matter. Imaging normal brain metabolism with hyperpolarized [1–13C]pyruvate and subsequent quantification, have important implications for interpreting pathological cerebral metabolism in future studies. [Display omitted]