Background The most recent advances in Computed Tomography (CT) image reconstruction technology are Deep learning image reconstruction (DLIR) algorithms. Due to drawbacks in Iterative reconstruction (IR) techniques such as negative image texture and nonlinear spatial resolutions, DLIRs are gradually replacing them. However, the potential use of DLIR in Head and Chest CT has to be examined further. Hence, the purpose of the study is to review the influence of DLIR on Radiation dose (RD), Image noise (IN), and outcomes of the studies compared with IR and FBP in Head and Chest CT examinations. Methods We performed a detailed search in PubMed, Scopus, Web of Science, Cochrane Library, and Embase to find the articles reported using DLIR for Head and Chest CT examinations between 2017 to 2023. Data were retrieved from the short-listed studies using Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines. Results Out of 196 articles searched, 15 articles were included. A total of 1292 sample size was included. 14 articles were rated as high and 1 article as moderate quality. All studies compared DLIR to IR techniques. 5 studies compared DLIR with IR and FBP. The review showed that DLIR improved IQ, and reduced RD and IN for CT Head and Chest examinations. Conclusions DLIR algorithm have demonstrated a noted enhancement in IQ with reduced IN for CT Head and Chest examinations at lower dose compared with IR and FBP. DLIR showed potential for enhancing patient care by reducing radiation risks and increasing diagnostic accuracy.

Introduction Meningioma is the most common brain tumor in adults. Magnetic resonance imaging (MRI) is the preferred imaging modality for assessing tumor outcomes. Radiomics, an advanced imaging technique, assesses tumor heterogeneity and identifies predictive markers, offering a non-invasive alternative to biopsies. Machine learning (ML) based radiomics models enhances diagnostic and prognostic accuracy of tumors. Comprehensive review on ML-based radiomics models for predicting meningioma recurrence and survival are lacking. Hence, the aim of the study is to summarize the performance measures of ML based radiomics models in the prediction of outcomes such as progression/recurrence (P/R) and overall survival analysis of meningioma. Methods Data bases such as Scopus, Web of Science, PubMed, and Embase were used to conduct a literature search in order to find pertinent original articles that concentrated on meningioma outcome prediction. PRISMA (Preferred reporting items for systematic reviews and meta-analysis) recommendations were used to extract data from selected studies. Results Eight articles were included in the study. MRI Radiomics-based models combined with clinical and pathological data showed strong predictive performance for meningioma recurrence. A decision tree model achieved 90% accuracy, outperforming an apparent diffusion coefficient (ADC) based model (83%). A support vector machine (SVM) model reached an area under curve (AUC) of 0.80 with radiomic features, improving to 0.88 with ADC integration. A combined clinico-pathological radiomics model (CPRM) achieved an AUC of 0.88 in testing. Key predictors of recurrence include ADC values, radiomic scores, ki-67 index, and Simpson grading. For predicting overall survival analysis of meningioma, the combined clinicopathological and radiomic features achieved an AUC of 0.78. Conclusion Integrating radiomics with clinical and pathological data through ML models greatly improved the outcome prediction for meningioma. These ML models surpass conventional MRI in predicting meningioma recurrence and aggressiveness, providing crucial insights for personalized treatment and surgical planning.

Introduction Magnetic resonance imaging (MRI) is essential for brain imaging, but conventional methods rely on qualitative contrast, are time-intensive, and prone to variability. Magnetic resonance finger printing (MRF) addresses these limitations by enabling fast, simultaneous mapping of multiple tissue properties like T1, T2. Using dynamic acquisition parameters and a precomputed signal dictionary, MRF provides robust, qualitative maps, improving diagnostic precision and expanding clinical and research applications in brain imaging. Methods Database searches were performed through PubMed, Embase, Scopus, Web of science to identify relevant articles focusing on the application of MR finger printing in the adult brain. We utilized the preferred reporting items for systematic reviews and meta-analysis guidelines to extract data from the selected studies. Results Nine articles were included in the final review, with a total sample size of 332 participants. In healthy brains, notable regional, sex, age, and hemispheric variations were identified, particularly in the corpus callosum and thalamus. MRF effectively differentiated meningioma subtypes, glioma grades, and IDH mutation status, with T2 values providing particularly predictive for glioma classification. In brain metastases, significant relaxometry differences were noted between normal and lesional tissues. For multiple sclerosis, MRF values correlated with clinical and disability measures, distinguishing relapsing-remitting secondary progressive forms. In traumatic brain injury, longitudinal T1 changes strongly correlated with clinical recovery, surpassing T2 values. Conclusions The systematic review highlighted MRD as a groundbreaking technique that enhances neurological diagnosis by simultaneously quantifying T1 and T2 relaxation times. With reduced acquisition times, MRF outperforms conventional MRI in detecting subtle pathologies, distinguishing properties, and providing reliable biomarkers.

Parkinson's disease (PD) and progressive supranuclear palsy (PSP) are neurodegenerative conditions that have overlapping clinical and imaging features, thus making it difficult to distinguish and diagnose PSP from PD. Therefore, in this study, we aimed to investigate the optimal value of magnetic resonance planimetry and the parkinsonism index to differentiate between PSP and PD. In this retrospective study, we recruited a total of 84 patients (27 patients with PSP, 27 patients with PD and 27 normal controls) who underwent MRI brain examinations. For each subject, we calculated the corpus callosum area, midbrain area, pons area, middle cerebellar peduncle (MCP) width and superior cerebellar peduncle (SCP) width on MRI brain images. We also calculated the pons to midbrain area (P/M) ratio, MCP/SCP ratio and magnetic resonance parkinsonism index (MRPI). Receiver operating characteristic curve (ROC) analysis was used to identify the diagnostic value of each biomarker. MRPI had a sensitivity of 70.4%, a specificity of 88.9%, and a diagnostic accuracy of 79.6% with an optimum cut off of 24.3 for differentiating PSP from PD. P/M ratio had a sensitivity of 74.1%, a specificity of 77.8%, and a diagnostic accuracy of 75.9% with an optimal cutoff of 24.3 for differentiating PSP from PD. The MCP/SCP ratio had a sensitivity of 66.7%, a specificity of 77.8%, and an accuracy of 72.2% with an optimal cut off of 4.65 for differentiating PSP from PD. The study revealed that MRPI and P/M ratio are accurate markers for differentiating PSP from PD. The optimal cut-off values derived from our study can help in the early diagnosis of PD.

Background: Intravoxel incoherent motion (IVIM) is a potential diagnostic technique for acute ischemic stroke as it simultaneously quantifies the molecular diffusion and the minute capillary network microcirculation; however, the lack of comprehensive evidence of its utility in acute ischemic stroke necessitates a review to enable better quantitative estimation and understanding of IVIM parameters and their relation with parameters of conventional diffusion and perfusion techniques. Methods: An extensive search strategy yielded 2340 studies in six databases. After a two-step screening process, data from 10 studies were extracted and included in the meta-analysis. Heterogeneity, publication bias, and forest plot analysis were performed for each IVIM parameter: D, D*, f, and fD*. Results: The mean values for D ranged from 0.28 to 0.58 × 10 ^− 3 mm ² /s and from 0.56 to 0.93 × 10 ^− 3 mm ² /s in the lesion and contralateral normal region, respectively, with the standardised mean difference of 3.24 SD units and positive correlation with ADC. f-values were between 2.5 and 4.9% ipsilaterally and 4.3 to 7.9% contralaterally, with minimal heterogeneity and a statistically significant standardised mean difference of 2.65 SD units. D* showed a non-significant effect. fD* showed a significant standardised mean difference of 1.48 SD units. Conclusion: Quantitative differentiation between the core and normal regions was significantly seen in D, f, and fD* parametric maps. Although D* varied across studies, technical upgrading could improve its utility. Since IVIM parameters correlate well with the existing diffusion and perfusion parameters, its utility offers a robust, multifaceted approach to understanding acute ischemic stroke. Clinical trial number: Not applicable.

Introduction: Synthetic magnetic resonance imaging (SyMRI) is a non-invasive, robust MRI technique that generates multiple contrast-weighted images by acquiring a single MRI sequence within a few minutes, along with quantitative maps, automatic brain segmentation, and volumetry. Since its inception, it has undergone technical advancements and has also been tested for feasibility in various organs and pathological conditions. This scoping review comprehensively pinpoints the critical technical aspects and maps the wide range of clinical applications/benefits of SyMRI. Content: A comprehensive search was conducted across five databases, PubMed, Scopus, Web of Science, Embase, and CINAHL Ultimate, using appropriate keywords related to SyMRI. A total of 99 studies were included after a 2-step screening process. Data related to the technical factors and clinical application was charted. Summary: SyMRI provides quantitative maps and segmentation techniques comparable to conventional MRI and has demonstrated feasibility and applications across neuroimaging, musculoskeletal, abdominal and breast pathologies spanning the entire human lifespan, from prenatal development to advanced age. Certain drawbacks related to image quality have been encountered that can be overcome with technical advances, especially AI-based algorithms. Outlook: SyMRI has immense potential for being incorporated into routine imaging for various pathologies due to its added advantage of providing quantitative measurements for more robust diagnostic and prognostic work-up with faster acquisitions and greater post-processing options.