Assessment of Subject Head Motion in Diffusion MRI.

Subject head motion during the acquisition of diffusion-weighted imaging (DWI) of the brain induces artifacts and affects image quality. Information about the frequency and extent of motion could reveal which aspects of motion correction are most necessary. Therefore, we investigate the extent of translation and rotation among participants, and how the motion changes during the scan acquisition. We analyze 5,380 DWI scans from 1,034 participants. We measure the rotations and translations in the sagittal, coronal and transverse planes needed to align the volumes to the first and previous volumes, as well as the displacement. The different types of motion are compared with each other and compared over time. The largest rotation (per minute) is around the right - left axis (median 0.378 °/min, range 0.000 - 11.466°) and the largest translation (per minute) is along the anterior - posterior axis (median 1.867 mm/min, range 0.000 - 10.944 mm). We additionally observe that spikes in movement occur at the beginning of the scan, particularly in anterior - posterior translation. The results show that all scans are affected by subtle head motion, which may impact subsequent image analysis.

Learning site-invariant features of connectomes to harmonize complex network measures.

Multi-site diffusion MRI data is often acquired on different scanners and with distinct protocols. Differences in hardware and acquisition result in data that contains site dependent information, which confounds connectome analyses aiming to combine such multi-site data. We propose a data-driven solution that isolates site-invariant information whilst maintaining relevant features of the connectome. We construct a latent space that is uncorrelated with the imaging site and highly correlated with patient age and a connectome summary measure. Here, we focus on network modularity. The proposed model is a conditional, variational autoencoder with three additional prediction tasks: one for patient age, and two for modularity trained exclusively on data from each site. This model enables us to 1) isolate site-invariant biological features, 2) learn site context, and 3) re-inject site context and project biological features to desired site domains. We tested these hypotheses by projecting 77 connectomes from two studies and protocols (Vanderbilt Memory and Aging Project (VMAP) and Biomarkers of Cognitive Decline Among Normal Individuals (BIOCARD) to a common site. We find that the resulting dataset of modularity has statistically similar means (p-value <0.05) across sites. In addition, we fit a linear model to the joint dataset and find that positive correlations between age and modularity were preserved.

Comparing flow and pulmonary artery growth post-patent ductus arteriosus stenting in patients with ductal-dependent pulmonary flow using 4D magnetic resonance imaging.

Aims: The 4D magnetic resonance imaging (4D-flow MRI) provides a qualitative and quantitative assessment of cardiovascular structures and processes. 4D-flow MRI was used to study pulmonary flow in post-patent ductus arteriosus (PDA) stent insertion in duct-dependent pulmonary flow neonates at baseline (PDA stent insertion) and after 6 months, and also, to evaluate the effect of flow dynamics on the growth of pulmonary arteries (PAs). Methods and results: This prospective observational study included neonates with ductus arteriosus-dependent pulmonary circulation who underwent ductal stenting between June 2021 and November 2022. Cardiac 4D-flow MRI and magnetic resonance angiography were conducted in two phases; after the deployment of the PDA stent during the neonatal period and after 6 months from stent deployment. Eight neonates were recruited, but only five completed both scans. A total of 10 PAs were evaluated during each phase. The median left PA (LPA) and right PA (RPA) diameters and indexed flow for LPA and RPA were evaluated. The growth rate of LPA was observed to be lower than that of RPA (percentage diameter increase: 74 vs. 153%). LPA Z-score was lower than RPA. Indexed flow in both LPA and RPA showed a reduction in the 6-month scan, which was consistent with reduced stent patency. Conclusion: 4D-flow cardiac MRI showed different growth rates and reduced flow between LPA and RPA post-PDA stent. These insights can aid in future management decisions.

A Case of Neck Pain in a Patient with Spontaneous Cerebrospinal Fluid Leak Who Showed Characteristic Findings on Cervical Spine MRI.

We report a case of a patient with neck pain only who was diagnosed with spontaneous cerebrospinal fluid leak (SCFL) based on characteristic findings on cervical spine magnetic resonance imaging (MRI). The patient was a 47-year-old man who had been experiencing neck pain for 3 days. He experienced neck pain when he got up in the morning and could not keep standing. Cervical spine MRI showed significant dilatation of the anterolateral dural canal veins at the C2 level. Under the suspicion of SCFL, additional thoracic spine MRI was performed, which revealed epidural fluid collection in the lower thoracic spine. He underwent bed rest, but his symptoms returned. Epidural blood patch (EBP) was performed. The symptoms improved after EBP, and the venous dilatation disappeared on MRI. Venous dilatation in SCFL is considered an effect of epidural space enlargement due to dural sac shrinkage caused by cerebrospinal fluid loss. Therefore, venous dilatation in the cervical spine is an indirect finding of SCFL. It has been reported that images of epidural fluid collection and dural canal collapse on spinal MRI are useful as direct findings in the diagnosis of SCFL, and these findings are most noticeable at the thoracic level. Because SCFL with neck pain only also exists, dilated images of the epidural vein are valuable for diagnosing SCFL, and neurospine surgeons should be aware of this finding when encountering patients complaining of neck pain.

Comparison of left ventricular mass and wall thickness between cardiac computed tomography angiography and cardiac magnetic resonance imaging using machine learning algorithms.

Aims: Cardiac magnetic resonance imaging (MRI) is the gold standard in the assessment of left ventricle (LV) mass and wall thickness. In recent years, cardiac computed tomography angiography (CCTA) has gained widespread usage as an imaging modality. Despite this, limited previous investigations have specifically addressed the potential of CCTA as an alternative modality for quantitative LV assessment. The aim of this study was to compare CCTA derived LV mass and wall thickness with cardiac MRI utilizing machine learning algorithms. Methods and results: Fifty-seven participants who underwent both CCTA and cardiac MRI were identified. LV mass and wall thickness was calculated using LV contours which were automatically placed using in-house developed machine learning models. Pearson's correlation coefficients were calculated along with Bland-Altman plots to assess the agreement between the LV mass and wall thickness per region on CCTA and cardiac MRI. Inter-observer correlations were tested using Pearson's correlation coefficient. Average LV mass and wall thickness for CCTA and cardiac MRI were 127 g, 128 g, 7, and 8 mm, respectively. Bland-Altman plots demonstrated mean differences and corresponding 95% limits of agreement of -1.26 (25.06; -27.58) and -0.57 (1.78; -2.92), for LV mass and average LV wall thickness, respectively. Mean differences and corresponding 95% limits of agreement for wall thickness per region were -0.75 (1.34; -2.83), -0.58 (2.14; -3.30), and -0.29 (3.21; -3.79) for the basal, mid, and apical regions, respectively. Inter-observer correlations were excellent. Conclusion: Quantitative assessment of LV mass and wall thickness on CCTA using machine learning algorithms seems feasible and shows good agreement with cardiac MRI.

A Rare Case of a Heterotopic Sacrococcygeal Glial Nodule.

Heterotopic glial nodule is a rare congenital non-neoplastic lesion that is characterized by ectopic brain tissue. It has occasionally been reported to affect areas such as the nose and face. The report presents a rare case of sacrococcygeal heterotopic glial nodule. Although teratomas are the most common neoplasms in this region, clinicians and radiologists should consider heterotopic glial nodule as a differential diagnosis, despite rarity and nonspecific imaging findings. Histopathology plays a crucial role in diagnosis, which intensely stains with glial fibrillary acidic protein and S-100.

Adaptive brachytherapy for cervical cancer in combined 1.5 T MR/HDR suite: Impact of repeated imaging.

Introduction: At our department we have a dedicated 1.5 Tesla MRI/HDR brachytherapy suite, which provides the possibility of repeated MRI scanning before, during and after applicator insertion and before and/or after irradiation for patients with advanced cervical cancer. In this study we analysed the effect of this adaptive workflow. We investigated the number of interventions, their impact on organ doses (OAR) and the respective dose differences between total prescribed and total delivered doses. Materials and methods: Seventy patients with locally advanced cervical cancer FIGO2009 stages IB-IVA, treated from June 2016 till August 2020, were retrospectively analysed. The standard brachytherapy schedule consisted of two applicator insertions and delivery of three or four HDR fractions.OARs were recontoured on the repeated MRI scans. The D2cm3 dose difference between total prescribed and total delivered dose for bladder, rectum, sigmoid and bowel were calculated. Results: In total 153 interventions were performed, 3 replacements of the applicator, 23 adaptations of needle positions, bladder filling was changed 74 times and repeated rectal degassing 53 times. The impact of the rectal interventions was on average -1.2 Gy EQD23. Dose differences between total delivered and total prescribed D2cm3 for bladder, rectum, sigmoid and bowel were -0.6, 0.3, 2.2 and -0.6 Gy EQD23, respectively. Conclusions: An MRI scanner integrated into the brachytherapy suite enables multiple interventions based on the scans before treatment planning and dose delivery. This allows for customized treatment according to the changing anatomy of the individual patient and a better estimation of the delivered dose.

Imaging Features and World Health Organization Classification of Rare Breast Tumors.

Breast radiologists encounter unusual lesions, which may not be well described in the literature. Previously based on histologic and molecular classifications, the World Health Organization (WHO) classification of tumors has become increasingly multidisciplinary. Familiarity with imaging features and basic pathology of infrequent breast lesions, as well as their current classification according to the WHO, may help the radiologist evaluate biopsy results for concordance and help direct the management of uncommon breast lesions. This review article provides a case-based review of imaging features and WHO histologic classification of rare breast tumors.

[Clinical Features of 17 Patients With Primary Angiitis of the Central Nervous System Confirmed by Brain Biopsy].

Objective To analyze the clinical features of 17 patients with primary angiitis of the central nervous system (PACNS) and thus facilitate the early diagnosis and treatment,reduce the recurrence and mortality,and improve the prognoses of this disease. Methods We collected the data of patients with PACNS diagnosed by brain biopsy from January 2009 to June 2023 and analyzed their clinical presentations,laboratory and imaging manifestations,electrophysiological and pathological changes,and treatment regimens and prognosis. Results The 17 patients diagnosed with PACNS via brain biopsy included one child and 16 adults.The subtyping results showed that 10,2,3,2,1,and 1 patients had tumorous,spinal cord-involved,angiography-positive,rapidly progressive,hemorrhagic,and amyloid ß-related PACNS,respectively.Eleven (64.7%) of the patients were complicated with secondary epilepsy.All the patients exhibited abnormal manifestations in head MRI,with 94.1% showing lesions with uneven enhancement around the lesions or in the leptomeninges. Magnetic resonance angiography revealed large vessel abnormalities in 3 patients,and spinal cord involvement was observed in 2 patients.Histopathological typing revealed 7 (43.7%) patients with lymphocytic vasculitis and 5 (31.2%) patients with necrotizing vasculitis.Eleven patients were treated with glucocorticoids and cyclophosphamide,which resulted in partial lesion disappearance and symptom amelioration in 6 patients upon reevaluation with head MRI after 3 months of maintenance therapy.Two,1,and 3 patients experienced rapid disease progression,death,and recurrence within 1 year,respectively.Three patients showed insensitivity to hormonotherapy and residual disabilities.Two patients received rituximab after relapse and remained clinically stable during a follow-up period of 0.5-1 year. Conclusion Tumorous PACNS was more prone to epilepsy,mainly occurring in males.The most common histopathological type was necrotizing vasculitis,which responded to hormonotherapy and had favorable outcomes.Therefore,for the young patients with epilepsy and intracranial tumorous lesions,the possibility of PACNS should be considered.Spinal cord involvement in PACNS was often located in the thoracic and cervical cords,suggesting a poorer prognosis.Electromyography commonly revealed neural conduction abnormalities in the anterior horn or roots,providing clues for differential diagnosis.For suspected spinal cord involvement,comprehensive electromyography is recommended.Rapidly progressive PACNS often presented infratentorial lesions,such as lesions in the pons and medulla,with a higher mortality rate.Hemorrhagic PACNS was rare,and a multifocal hemorrhagic lesion with enhancement in the intracranial region,particularly in young patients,should raise suspicion.For the patients with recurrent or progressive disease,rituximab is a recommended therapeutic option.

Super-Resolving and Denoising 4D flow MRI of Neurofluids Using Physics-Guided Neural Networks.

PURPOSE: To obtain high-resolution velocity fields of cerebrospinal fluid (CSF) and cerebral blood flow by applying a physics-guided neural network (div-mDCSRN-Flow) to 4D flow MRI. METHODS: The div-mDCSRN-Flow network was developed to improve spatial resolution and denoise 4D flow MRI. The network was trained with patches of paired high-resolution and low-resolution synthetic 4D flow MRI data derived from computational fluid dynamic simulations of CSF flow within the cerebral ventricles of five healthy cases and five Alzheimer's disease cases. The loss function combined mean squared error with a binary cross-entropy term for segmentation and a divergence-based regularization term for the conservation of mass. Performance was assessed using synthetic 4D flow MRI in one healthy and one Alzheimer' disease cases, an in vitro study of healthy cerebral ventricles, and in vivo 4D flow imaging of CSF as well as flow in arterial and venous blood vessels. Comparison was performed to trilinear interpolation, divergence-free radial basis functions, divergence-free wavelets, 4DFlowNet, and our network without divergence constraints. RESULTS: The proposed network div-mDCSRN-Flow outperformed other methods in reconstructing high-resolution velocity fields from synthetic 4D flow MRI in healthy and AD cases. The div-mDCSRN-Flow network reduced error by 22.5% relative to linear interpolation for in vitro core voxels and by 49.5% in edge voxels. CONCLUSION: The results demonstrate generalizability of our 4D flow MRI super-resolution and denoising approach due to network training using flow patches and physics-based constraints. The mDCSRN-Flow network can facilitate MRI studies involving CSF flow measurements in cerebral ventricles and association of MRI-based flow metrics with cerebrovascular health.

Diffusion tensor imaging and gray matter volumetry to evaluate cerebral remodeling processes after a pure motor stroke: a longitudinal study.

BACKGROUND AND OBJECTIVES: Clinical factors are not sufficient to fix a prognosis of recovery after stroke. Pyramidal tract or alternate motor fiber (aMF: reticulo-, rubrospinal pathways and transcallosal fibers) integrity and remodeling processes assessable by diffusion tensor MRI (DTI) and voxel-based morphometry (VBM) may be of interest. The primary objective was to study longitudinal cortical brain changes using VBM and longitudinal corticospinal tract changes using DTI during the first 4 months after lacunar cerebral infarction. The second objective was to determine which changes were correlated to clinical improvement. METHODS: Twenty-one patients with deep brain ischemic infarct with pure motor deficit (NIHSS score ≥ 2) were recruited at Purpan Hospital and included. Motor deficit was measured [Nine peg hole test (NPHT), dynamometer (DYN), Hand-Tapping Test (HTT)], and a 3T MRI scan (VBM and DTI) was performed during the acute and subacute phases. RESULTS: White matter changes: corticospinal fractional anisotropy (FACST) was significantly reduced at follow-up (approximately 4 months) on the lesion side. FAr (FA ratio in affected/unaffected hemispheres) in the corona radiata was correlated to the motor performance at the NPHT, DYN, and HTT at follow-up. The presence of aMFs was not associated with the extent of recovery. Grey matter changes: VBM showed significant increased cortical thickness in the ipsilesional premotor cortex at follow-up. VBM changes in the anterior cingulum positively correlated with improvement in motor measures between baseline and follow-up. DISCUSSION: To our knowledge, this study is original because is a longitudinal study combining VBM and DTI during the first 4 months after stroke in a series of patients selected on pure motor deficit. Our data would suggest that good recovery relies on spared CST fibers, probably from the premotor cortex, rather than on the aMF in this group with mild motor deficit. The present study suggests that VBM and FACST could provide reliable biomarkers of post-stroke atrophy, reorganization, plasticity and recovery. GOV IDENTIFIER: NCT01862172, registered May 24, 2013.

Differentiation of bacterial orbital cellulitis and diffuse non-specific orbital inflammation on magnetic resonance imaging.

PURPOSE: To examine the radiological differences between bacterial orbital cellulitis (OC) and diffuse non-specific orbital inflammation (DNSOI) on magnetic resonance imaging (MRI). METHODS: Retrospective study of patients with OC and DNSOI with an MRI orbital scan. Localised orbital inflammation (e.g., idiopathic dacryoadenitis and myositis), quiescent orbital inflammation and pre-septal cellulitis were excluded. RESULTS: Thirty-two patients presenting between 2008 and 2023, including twenty-one OC patients (mean age: 42.5 ± 24.9 years old, male: 6), and eleven DNSOI patients (mean age: 52.3 ± 17.8 years old, male: 16). Both OC and DNSOI demonstrate orbital fat contrast-enhancement. However, whilst OC demonstrated a hyperintense T2 signal (P < 0.001), variable signal was observed in DNSOI, with a hypointense T2 signal more suggestive of DNSOI (P = 0.012). When the lacrimal glands were involved, indistinct margins were more likely in OC (P < 0.001), whilst gross enlargement and contrast-enhancement was observed in DNSOI (P = 0.032 and 0.017, respectively). Peripheral contrast-enhancement of the extraocular muscle (EOM) (P = 0.002) was more common in OC, whilst DNSOI demonstrated variable contrast-enhancement throughout the affected EOM (P < 0.001). The presence of contralateral abnormalities, such as lacrimal gland enlargement and EOM involvement, are more suggestive of DNSOI. CONCLUSION: Several MRI features, beyond overt sinogenic disease, may help to differentiate OC from DNSOI, including the orbital fat signal intensity, EOM and/or lacrimal gland involvement, and contralateral orbital abnormalities. However, these features may not be specific, and thus highlights the ongoing radiological dilemma clinicians are faced when tasked with differentiating between infectious and non-infectious orbital inflammatory disease.

Bioimaging of sense organs and the central nervous system in extant fishes and reptiles in situ: A review.

Bioimaging is changing the field of sensory biology, especially for taxa that are lesser-known, rare, and logistically difficult to source. When integrated with traditional neurobiological approaches, developing an archival, digital repository of morphological images can offer the opportunity to improve our understanding of whole neural systems without the issues of surgical intervention and negate the risk of damage and artefactual interpretation. This review focuses on current approaches to bioimaging the peripheral (sense organs) and central (brain) nervous systems in extant fishes (cartilaginous and bony) and non-avian reptiles in situ. Magnetic resonance imaging (MRI), micro-computed tomography (µCT), both super-resolution track density imaging and diffusion tensor-based imaging, and a range of other new technological advances are presented, together with novel approaches in optimizing both contrast and resolution, for developing detailed neuroanatomical atlases and enhancing comparative analyses of museum specimens. For MRI, tissue preparation, including choice of fixative, impacts tissue MR responses, where both resolving power and signal-to-noise ratio improve as field strength increases. Time in fixative, concentration of contrast agent, and duration of immersion in the contrast agent can also significantly affect relaxation times, and thus image quality. For µCT, the use of contrast-enhancing stains (iodine-, non-iodine-, or nanoparticle-based) is critical, where the type of fixative used, and the concentration of stain and duration of staining time often require species-specific optimization. Advanced reconstruction algorithms to reduce noise and artifacts and post-processing techniques, such as deconvolution and filtering, are now being used to improve image quality and resolution.

A Case of Non-calcified Intrahepatic Primary Osteosarcoma: A Case Report and a Literature Review.

INTRODUCTION: Intrahepatic primary osteosarcoma is a rare disease with a very low incidence but a very poor prognosis. A total of 12 cases have been previously reported, and in most of these cases, intra-focal calcification was observed. This paper aims to report a case of non-calcified intrahepatic primary osteosarcoma. CASE DESCRIPTION: We hereby report a female patient with hepatitis B for 20 years, identified during a routine examination due to a mass in the right lobe of the liver. The patient experienced no significant discomfort, and the serological tumor markers were not elevated. Surgical resection was performed after comprehensive examinations, and postoperative pathology showed primary osteosarcoma of the liver. The patient experienced recurrence and metastasis seven months postoperatively and died eight and a half months postoperatively. CONCLUSION: Intrahepatic primary osteosarcoma is an extremely rare disease, and it currently requires a combination of clinical, radiological, and pathological findings to make a diagnosis of exclusion. Further, patients may benefit from early diagnosis, aggressive surgery, and post-operative chemotherapy.

Multiparametric MRI Scoring System of the Pancreas for the Diagnosis of Chronic Pancreatitis.

BACKGROUND: Ductal features alone may not offer high diagnostic sensitivity or most accurate disease severity of chronic pancreatitis (CP). PURPOSE: Diagnose CP based on multiparametric MRI and MRCP features. STUDY TYPE: Prospective. POPULATION: Between February 2019 and May 2021, 46 control (23 males, 49.3 ± 14.1 years), 45 suspected (20 males, 48.7 ± 12.5 years), and 46 definite (20 males, 53.7 ± 14.6 years) CP patients were enrolled at seven hospitals enrolled in the MINIMAP study. CP classification was based on imaging findings and clinical presentation. FIELD STRENGTH AND SEQUENCES: 1.5 T. T1-weighted (T1W) spoiled gradient echo, T1 map with variable flip angle, dual-echo Dixon, secretin-enhanced MRCP before and after secretin infusion. ASSESSMENT: Dual-echo fat fraction (FF), T1 relaxation time, extracellular volume (ECV), T1 signal intensity ratio of the pancreas to the spleen (T1 score), arterial-to-venous enhancement ratio (AVR), pancreatic tail diameter (PTD), pancreas volume, late gadolinium enhancement, pancreatic ductal elasticity (PDE), and duodenal filling grade of secretin-enhanced MRCP were measured. STATISTICAL TESTS: Logistic regression analysis generated CP-MRI and secretin-enhanced CP-SMRI scores. Receiver operating characteristics analysis was used to differentiate definite CP from control. Interobserver agreement was assessed using Lin's concordance correlation coefficient. RESULTS: Compared to control, definite CP cohort showed significantly higher dual-echo FF (7% vs. 11%), lower AVR (1.35 vs. 0.85), smaller PTD (2.5 cm vs. 1.95 cm), higher ECV (28% vs. 38%), and higher incidence of PDE loss (6.5% vs. 50%). With the cut-off of >2.5 CP-MRI score (dual-echo FF, AVR, and PTD) and CP-SMRI score (dual-echo FF, AVR, PTD, and PDE) had cross-validated area under the curves of 0.84 (sensitivity 87%, specificity 68%) and 0.86 (sensitivity 89%, specificity 67%), respectively. Interobserver agreement for both CP-MRI and CP-SMRI scores was 0.74. CONCLUSION: The CP-MRI and CP-SMRI scores yielded acceptable performance and interobserver agreement for the diagnosis of CP. EVIDENCE LEVEL: 1 TECHNICAL EFFICACY: Stage 2.

The application value of Rs-fMRI-based machine learning models for differentiating mild cognitive impairment from Alzheimer's disease: a systematic review and meta-analysis.

BACKGROUND: Various machine learning (ML) models based on resting-state functional MRI (Rs-fMRI) have been developed to facilitate differential diagnosis of mild cognitive impairment (MCI) and Alzheimer's disease (AD). However, the diagnostic accuracy of such models remains understudied. Therefore, we conducted this systematic review and meta-analysis to explore the diagnostic accuracy of Rs-fMRI-based radiomics in differentiating MCI from AD. METHODS: PubMed, Embase, Cochrane, and Web of Science were searched from inception up to February 8, 2024, to identify relevant studies. Meta-analysis was conducted using a bivariate mixed-effects model, and sub-group analyses were carried out by the types of ML tasks (binary classification and multi-class classification tasks). FINDINGS: In total, 23 studies, comprising 5,554 participants were enrolled in the study. In the binary classification tasks (twenty studies), the diagnostic accuracy of the ML model for AD was 0.99 (95%CI: 0.34 ~ 1.00), with a sensitivity of 0.94 (95%CI: 0.89 ~ 0.97) and a specificity of 0.98 (95%CI: 0.95 ~ 1.00). In the multi-class classification tasks (six studies), the diagnostic accuracy of the ML model was 0.98 (95%CI: 0.98 ~ 0.99) for NC, 0.96 (95%CI: 0.96 ~ 0.96) for early mild cognitive impairment (EMCI), 0.97 (95%CI: 0.96 ~ 0.97) for late mild cognitive impairment (LMCI), and 0.95 (95%CI: 0.95 ~ 0.95) for AD. CONCLUSIONS: The Rs-fMRI-based ML model can be adapted to multi-class classification tasks. Therefore, multi-center studies with large samples are needed to develop intelligent application tools to promote the development of intelligent ML models for disease diagnosis.

Assessment of neurofluid dynamics in relation to clinical improvement after tap-test: pilot study.

PURPOSE: Idiopathic Normal pressure hydrocephalus (iNPH) is an under-diagnosed in elderly patients but none of the diagnostic tests are currently sufficiently sensitive or specific. The objective of this study was to analyze the dynamics of neurofluids by PC-MRI in relation to clinical evolution as measured using the iNPH grading scale after tap-test. METHOD: We prospectively included patients with suspected iNPH. All these patients underwent PCMRI to assess craniospinal hemohydrodynamics with analysis of the stroke volume of the cephalospinal fluid (CSF) within the Sylvius' aqueduct, within the high cervical subarachnoid spaces and the arteriovenous stroke volume. By this means, we calculated a compliance index. Morphological analysis was carried out using the DESH score. The infusion test was measuring the resistance to CSF flow. We analysed all these parameters according to the clinical improvement of the patients. RESULTS: 23 patients were included. Compliance index assessed by PC-MRI was significantly higher in the group of patients with improvement > 10% (p = 0.015). CONCLUSIONS: Our study highlights the importance of investigating arteriovenous and CSF interactions in iNPH. This involves understanding the physiological and pathophysiological mechanisms related to the circulation of neurofluids. The analysis of the interactions of these neurofluids allows for a comprehensive understanding of the system.

Development and Assessment of a Predictive Model for Ki-67 Expression Using Ultrasound Indicators and Non-Morphological Magnetic Resonance Imaging Parameters Before Breast Cancer Therapy.

To formulate a predictive model for assessing Ki-67 expression in breast cancer by integrating pre-treatment ultrasound features with non-morphological magnetic resonance imaging (MRI) parameters, encompassing functional and hemodynamic indicators. A retrospective study was conducted on 167 patients. All patients underwent a breast mass biopsy for histopathological and Ki-67 analysis prior to neoadjuvant chemotherapy (NAC) treatment. Additionally, all patients underwent ultrasonography and MRI examinations prior to the biopsy. The recorded variables were Ki-67, apparent diffusion coefficient (ADC) values, Max Slope, time to peak (TTP), signal enhancement ratio (SER), early enhancement rate (EER), time-signal intensity curve (TIC), tumor maximum diameter, tumor margins and boundaries, aspect ratio, microcalcification, color Doppler flow imaging grading, resistance index (RI), and axillary lymph node metastasis. Statistical analysis was performed using the R software package. Normally distributed continuous data are presented as mean ± standard deviation (SD), skewed continuous data as median, and categorical variables as frequency or percentage. The dataset was randomly divided into a modeling group and a validation group following a 7:3 ratio, employing a predetermined random seed. The selection of variables was conducted using the random forest algorithm. Specifically, in the initial analysis, we trained a random forest model using all available variables. By evaluating the Gini importance scores of each variable, we identified those that contributed the most to predicting Ki-67 expression. The predictive model for Ki-67 expression was constructed using selected variables: Maximum Diameter, ADC value, SER value, Max Slope value, TTP value, and EER value. Within the validation group, the evaluation metrics demonstrated an Area under the curve of 0.961 with a 95% confidence interval ranging from 0.865 to 0.995. The model achieved a kappa score of 1.00, precision of 0.949, recall of 1, an F1 score of 0.974, sensitivity of 100%, specificity of 85.71%, a positive predictive value of 94.87%, and a negative predictive value of 100%. The combination of non-morphological MRI parameters and pre-treatment ultrasound features in a breast cancer prediction model powered by RF machine learning demonstrated favorable clinical outcomes and improved diagnostic performance.