Brain tumor quantification equation: modeled on complete step response algorithm

In Image Guided neuro-Surgery (IGnS) protocol relating to tumor, the planning stage is the bottleneck where most times are spent reconstructing the slices in order to; quantify the tumor, get the tumor shape and location relative to adjacent cells, and determine best incursion route among others. This time consuming assignment is handled by a surgeon using any of the standardized IGnS software. It has been observed that the approach taken to quantify tumor in those software are simply replicating the surgeons’ experience-based brain tumor quantification technique fashionable in the pre-imaging era. The result is a quantification method that is time consuming, and at bests an approximation. What is presented here is a novel brain tumor quantification method based on step response algorithm utilizing a model which itself was based on step response model resulting in smart and rapid quantification of brain tumor volume

Prikaz slučaja pogrešno dijagnosticirane subkortikalne vaskularne demencije - važnost dobrog poznavanja psihijatrije i pravilnog uzimanja povijesti bolesti

Psychiatric symptoms at presentation may often be missed, if not suspected or specifically explored. A missed psychiatric diagnosis may lead to dire consequences in terms of poor quality of life and function for the patient, affecting overall quality of healthcare provided. This lady presented with depressive symptoms after multiple strokes and was initially diagnosed as post stroke depression. However, after it was observed that she did not show any improvement in symptoms despite being on antidepressants, subsequent further investigations revealed a history more suggestive of subcortical vascular dementia. Consequently, detailed neuropsychological and neuropsychiatric assessments, including NUCOG, and relevant investigations including MRI brain scans were performed suggesting a diagnosis of vascular dementia. This case illustrates that an insufficiently thorough assessment and treatment process results in unnecessary morbidity, prolongs duration of illness, and increases social and occupational dysfunction to the patient. Hence, it further underscores the need to perform a thorough history, physical examination and relevant investigations to ensure organic etiologies are ruled out in clients with relevant sociodemographic and clinical risk factors.Psihijatrijski simptomi često se mogu previdjeti ako se na njih ne posumnja ili ako se posebno ne istraže. Propuštena psihijatrijska dijagnoza može dovesti do dalekosežnih posljedica u smislu loše kvalitete života i funkcionalnosti pacijenta, što u konačnici utječe na ukupnu kvalitetu pružene zdravstvene zaštite. Prikazana je pacijentica koja se prezentirala simptomima depresije nakon višestrukih moždanih udara te joj je početno dijagnosticiran organski afektivni poremećaj (nakon moždanog udara). Međutim, nakon što je primijećeno da nije došlo do regresije simptoma unatoč uzimanju antidepresiva, daljnjom dijagnostičkom obradom otkrivena je podloga koja više sugerira na subkortikalnu vaskularnu demenciju. Slijedom toga, provedene su detaljne neuropsihološke i neuropsihijatrijske procjene, uključujući NUCOG i daljnja ispitivanja, uključujući MRI snimke mozga prema kojima je sugerirana dijagnoza vaskularne demencije. Ovaj slučaj ilustrira da nedovoljno temeljita procjena i postupak liječenja rezultiraju nepotrebnim morbiditetom, produljuju trajanje bolesti i povećavaju socijalnu i profesionalnu disfunkciju pacijenta. Stoga, nadalje naglašava potrebu uzimanja temeljite povijesti bolesti, provođenja fizikalnog pregleda i relevantne dijagnostičke obrade kako bi se osiguralo isključivanje organske etiologije kod pacijenata s određenim sociodemografskim i kliničkim čimbenicima rizika

A severe anti-NMDA-receptor encephalitis case with extensive cortical and white matter changes, cerebral atrophy and communicating hydrocephalus

A 21-year-old woman presented with a viral prodrome, abnormal behaviours, confusion and short-term memory loss, followed by status epilepticus that later evolved to orofacial dyskinesias, autonomic dysfunctions and hypoventilation requiring prolonged ventilator support and ICU admission. Cerebrospinal fluid (CSF) and serum analysis confirmed the presence of anti-NMDAR autoantibodies. A left salpingoopherectomy was performed on day 35 of admission revealing an immature ovarian teratoma. Following surgical and two courses of intravenous immunoglobulin therapy, her response remained poor. Initial brain magnetic resonance imaging (MRI) during the acute stage showed enlarged left hippocampus. Further MRI follow-up 13 weeks after admission showed unusual findings of extensive cortical and white matter changes, generalised cerebral atrophy, dilated ventricles and possible transependymal CSF seepage of communicating hydrocephalus. A ventriculo-peritoneal shunt was performed subsequently and she was discharged 6 months after admission without significant change in her clinical status. Follow-up 4 months later showed some improvement but patient remained severely disabled

The spectrum of vessel wall imaging (VWI) findings in COVID-19-associated neurological syndromes: a review

Since the start of the pandemic, there have been extensive studies from all over the world reporting on coronavirus disease 2019 (COVID-19)-associated neurological syndromes. Although initially thought of as primarily a respiratory pathogen, it became increasingly clear that the virus does have other systemic manifestations, including on the neurological system. Since then, the discovery of the many neuroimaging features of COVID-19-associated neurological syndromes have puzzled researchers and physicians in terms of interpretation, and how best to manage these findings to benefit patients. We sought to review the neuroimaging findings of COVID-19-associated neurological syndromes, particularly the vessel wall imaging (VWI) features, in the hope of finding a common feature that would better guide physicians in terms of further management of this group of patients. We will also look into the potential pitfalls of interpreting the VWI findings in these patients

Automatic white matter lesion detection and segmentation on Magnetic Resonance Imaging: A review of past and current state-of-the-art

White matter lesion (WML) is an abnormal tissue occurring in white matter. It indicated the damage of the myelin sheath that used to surround the axon of a neurone. This resulting neurological and vascular disorder occur in the patient, also commonly developed in the healthy brain of elderly. Magnetic Resonance Imaging is a non-invasive medical equipment preferred choice by the clinician to diagnose and observed the injury of brain tissue. However, WML quantitative assessment and analyse on the large volume of MR imaging is a challenge. In this paper, we provide an intensive review of the past and recent WML delineation and detection methods. This review included visual scoring assessment, a common preprocessing step for WML segmentation, false positive elimination, and the latest automatic WML segmentation approaches will be presented

Pictorial essay: Neuroimaging of stroke

Evaluation of early stroke is one of the most common radiological procedures requested. This pictorial essay illustrates on the basic neuroimaging findings of ischemic stroke. The early signs of ischemia including loss of the insular ribbon sign, hyperdense middle cerebral artery and focal loss of grey white matter differentiation on computed tomography (CT) and localization of stroke using diffusion- weighted MR sequences will be presented. It will expose the clinician through the spectrum of imaging findings of ischemic stroke. Integration of MRI, vascular and perfusion imaging is given in different clinical scenario

Evolution of brain tumor growth model: a back-in-time approach

One of the crucial stages in Image Guided Surgery (IGS) protocol is the IGS planning stage, the major requirements at that stage are to determine tumor extent and size by reconstructing tumor slices, and to determine the best way to approach affected site. The surgeon is clinically responsible to do these with the aid of IGS software that is fashioned in line with the surgeon training. It has been observed that lots of time used at that stage could be saved if a good model that will accurately estimate tumor growth from few slices could be developed to assist at that stage. This paper presents a tumor growth modeling approach based on step response of electrical energy devices, and its application to tumor estimation using non-IGS protocol image slices to produce tumor area estimation that is comparable for image reconstruction purposes with tumor area found on the thin slice IGS protocol images, thereby eliminating the need for time consuming IGS protocol imaging

Tumor volume determination technique using pixel filling and discrete integral function

The subject of tumor has generated lots of interest from the medical point of view and recently from engineering/research perspective owing to advancement in medical equipment and increased computerization in many areas including medical field. This paper discusses a post segmentation tumor volume determination technique that employs mainly pixel filling, and the application of discrete integral function to determine tumor volume. The prior steps taken involve finding pixel area resolution, image field of view (FOV), then FFT-Based interpolation to achieve smaller decimation of tumor area, and finally the use of discrete integral function (DIF) to accurately determine tumor volume. The result of the work will enhance the field of oncology in general, and image guided surgery (IGS) in particular

Detection of subtle white matter lesions in MRI through texture feature extraction and boundary delineation using an embedded clustering strategy.

White matter lesions (WML) underlie multiple brain disorders, and automatic WML segmentation is crucial to evaluate the natural disease course and effectiveness of clinical interventions, including drug discovery. Although recent research has achieved tremendous progress in WML segmentation, accurate detection of subtle WML present early in the disease course remains particularly challenging. Here we propose an approach to automatic WML segmentation of mild WML loads using an intensity standardisation technique, gray level co-occurrence matrix (GLCM) embedded clustering technique, and random forest (RF) classifier to extract texture features and identify morphology specific to true WML. We precisely define their boundaries through a local outlier factor (LOF) algorithm that identifies edge pixels by local density deviation relative to its neighbors. The automated approach was validated on 32 human subjects, demonstrating strong agreement and correlation (excluding one outlier) with manual delineation by a neuroradiologist through Intra-Class Correlation (ICC = 0.881, 95% CI 0.769, 0.941) and Pearson correlation (r = 0.895, p-value < 0.001), respectively, and outperforming three leading algorithms (Trimmed Mean Outlier Detection, Lesion Prediction Algorithm, and SALEM-LS) in five of the six established key metrics defined in the MICCAI Grand Challenge. By facilitating more accurate segmentation of subtle WML, this approach may enable earlier diagnosis and intervention