Neurological Images Research Articles

Application of the "Klotski Technique" in Cervical Ossification of the Posterior Longitudinal Ligament With En Bloc Type Dura Ossification.

The anterior controllable antedisplacement and fusion (ACAF) technique is a new procedure for the treatment of cervical ossification of the posterior longitudinal ligament (OPLL) that requires management of the disc adjacent to the ossification. This study describes a novel technique to reduce the number of fixed segments, namely, the "Klotski technique." The efficacy of ACAF using the Klotski technique was compared with that of anterior cervical corpectomy and fusion (ACCF) in the treatment of OPLL with en bloc type dural ossification (DO). The clinical data of 25 patients with severe OPLL and en bloc type DO who were treated by the ACAF Klotski technique or ACCF at our hospital from January 2020 to January 2022 were retrospectively analyzed. In the Klotski technique, the number of segments fused within the OPLL is limited. The antedisplacement space was designed according to the shape of the vertebrae-OPLL-DO complex (VODC). Then, the entire VODC was antedisplaced as in Klotski. Neurological function and image examination were assessed preoperatively and postoperatively. Complications associated with surgery were recorded. Patients were followed up for 24-36 months. There were 11 patients who were treated with ACAF and 14 patients who were treated with ACCF. At 2 weeks after surgery, the incidence of neurological deterioration was 21.4% (3 of 14) in the ACCF group and 9.1% (1 of 11) in the ACAF group. The incidence of intraoperative cerebrospinal fluid leakage (CFL) was 35.7% (5 of 14) in the ACCF group and 9.1% (1 of 11) in the ACAF group. The postoperative follow-up JOA scores of the patients in both groups were significantly better than their preoperative JOA scores (p<0.05). The Klotski technique for ACAF is a good option for the treatment of patients with en bloc type OPLL-DO, as it limits the number of fused segments, has a low incidence of CFL and neurologic deficits and is associated with good neurological recovery.

Endovascular treatment of acute arteriosclerotic vertebrobasilar occlusion: A single center experience.

Few experiences on vertebrobasilar occlusion over underlying intracranial atherosclerotic disease have been reported in literature and the optimal strategy on how to perform a mechanical thrombectomy is unclear. The aim of this paper is to bring our experience based on patients admitted with acute vertebrobasilar occlusion with underlying atheromatous lesions. Several data were collected from August 2009 to October 2022 including clinical history, pre- and post-treatment neurological objectivity, diagnostic images and angiographic procedural images, and clinical outcome at 6months. We selected 13 patients from August 2009 to October 2022, 12 men and 1 woman, aged 40 to 82years (mean age, 62.6years). Mechanical thrombectomy with a thromboaspiration was performed in all patients as beginning of the procedure. In three patients, the procedures resulted in excellent angiographic result and clinical outcome, while in three patients, we observed a failure of the procedural and clinical outcome. For residual intracranial stenosis in three patients, an angioplasty was performed obtaining an ischemic area related to the posterior circulation. In four patients, a stent was placed, in three patients, we obtained a good clinical outcome with a mRS between 0 and 2, while one treatment resulted in death, probably due to a late endovascular treatment. Endovascular treatment with stent deployment appears to result in an excellent outcome in patients with occlusion of the vertebrobasilar circulation in cases of occlusion on atheromatic plaque. The degree of residual stenosis after thrombospiration can significantly affect subsequent type of treatment.

An artificial intelligence algorithm for analyzing globus pallidus necrosis after carbon monoxide intoxication.

Globus pallidus necrosis (GPN) is one of typical neurological imaging features in patients with carbon monoxide (CO) poisoning. Current clinical guideline recommends neurological imaging examination for CO-intoxicated patients with conscious disturbance rather than routine screening, which may lead to undiagnosed GPN. We aimed to develop an artificial intelligence algorithm for predicting GPN in CO intoxication patients. We included CO intoxication patients with neurological images between 2000 and 2019 in Chang Gung Memorial Hospital. We collected 41 clinical and laboratory parameters on the first day of admission for algorithm development. We used fivefold cross validation and applied several machine learning algorithms. Random forest classifier (RFC) provided the best predictive performance in our cohort. Among the 261 patients with CO intoxication, 52 patients presented with GPN. The artificial intelligence algorithm using the RFC-based AI model achieved an accuracy = 79.2 ± 2.6%, sensitivity = 77.7%, precision score = 81.9 ± 3.4%, and F1 score = 73.2 ± 1.8%. The area under receiver operating characteristic was approximately 0.64. Top five weighted variables were Platelet count, carboxyhemoglobin, Glasgow Coma scale, creatinine, and hemoglobin. Our RFC-based algorithm is the first to predict GPN in patients with CO intoxication and provides fair predictive ability. Further studies are needed to validate our findings.

Protocolized Post-Cardiac Arrest Care with Targeted Temperature Management.

Improvements in teamwork and resuscitation science have considerably increased the success rate of cardiopulmonary resuscitation. Cerebral injury, myocardial dysfunction, systemic ischemia and reperfusion response, and precipitating pathology after the return of spontaneous circulation (ROSC) constitute post-cardiac arrest syndrome. Because the entire body is involved in cardiac arrest and the early post-arrest period, protocolized post-arrest care consisting of cardiovascular optimization, ventilation and oxygenation adjustment, coronary revascularization, targeted temperature management (TTM), and control of seizures and blood sugar would benefit survival and neurological outcomes. Emergent coronary angiography is suggested for cardiac arrest survivors suspected of having ST-elevation myocardial infarction, however the superiority of culprit or complete revascularization in patients with multivessel coronary lesions remains undetermined. High-quality TTM should be considered for comatose patients who are successfully resuscitated from cardiac arrest, however the optimal target temperature may depend on the severity of their condition. The optimal timing for making prognostication should be no earlier than 72 h after rewarming in TTM patients, and 72 h following ROSC in non-TTM patients. To predict neurological recovery correctly may need the use of several prognostic tools together, including clinical neurological examinations, brain images, neurological studies and biomarkers.

Optimized Multimodal Neurological Image Fusion Based on Low-Rank Texture Prior Decomposition and Super-Pixel Segmentation

Multimodal image fusion has become a crucial tool in the field of computer-assisted diagnosis due to its powerful ability to provide a concise and accurate visualization of important diagnostic information. This paper presents a multimodal neurological image fusion method using an optimized low-rank texture prior (OLTP) model and super-pixel segmentation. At first, the source images are decomposed into cartoon and texture components using the OLTP model. The texture components are fused using super-pixel segmentation based on a pixel-related Gaussian mixture model to preserve the local oscillating patterns without introducing edge distortion. To fuse cartoon components, a weighted energy fusion rule is used to preserve the global structural information. Grey wolf optimization is used to tune the regularization coefficients of OLTP model to balance the characterization of cartoon and texture components. The efficacy of the proposed method is demonstrated using substantial experiments conducted on various multimodal image pairs. The experimental results validate the superiority of the proposed method over the state-of-the-art fusion methods in both visual and objective performance.

Transpedicular Corpectomy and Anterior Column Reconstruction for the Treatment of Traumatic Thoracolumbar Fractures.

To retrospectively analyze the results obtained from the posterior-only approach in non-pathological traumatic thoracolumbar body fractures with spinal cord compression. A total of 17 patients with traumatic non-pathological thoracolumbar fractures were included in the study. Demographic details include preoperative data such as neurological status, deformity, pain scores, and radiology; intraoperative data such as blood loss, duration of surgery, and complications, and postoperative data including the neurologic status; the duration of hospital stay, pain scores, and deformity correction were analyzed. Among the 17 patients, 8 were in ASIA A, 9 had incomplete neurologic deficits (ASIA C - D), and none was neurologically intact (ASIA E) preoperatively. All patients had TLICS scores > 4 and were treated surgically. The mean TLICS score was 7.31. Although no worsening was detected in the neurological images of the patients during the postoperative period, neurological improvement of at least one ASIA grade was detected in 13 patients. However, it was found that the neurological functions remained the same in the 4 patients. With significant improvement, the mean preoperative VAS score was 8.2, while the mean postoperative VAS score was 3.3. In addition, satisfactory outcomes were obtained in radiological examinations, both in terms of kyphotic deformity and vertebral body collapse. Traumatic thoracolumbar fractures can be effectively fixed with the posterior-only approach and the transpedicular route. One of the most significant advantages of this procedure is that peripheral decompression, reduction, anterior column reconstruction, and instrumentation all can be performed simultaneously in the same session.

Facial Feature Removal for Anonymization of Neurological Image Data

Abstract Interdisciplinary exchange of medical datasets between clinicians and engineers is essential for clinical research. Due to the Data Protection Act, which preserves the rights of patients, full anonymization is necessary before any exchange can take place. Due to the continuous improvement of image quality of tomographic datasets, anonymization of patient-specific information is not sufficient. In this work, we present a prototype that allows to reliably obscure the facial features of patient data, thus enabling anonymization of neurological datasets in image space.

Optimized CT-MR neurological image fusion framework using biologically inspired spiking neural model in hybrid ℓ1 − ℓ0 layer decomposition domain

Medical image fusion plays an important role in the clinical diagnosis of several critical neurological diseases by merging complementary information available in multimodal images. In this paper, a novel CT-MR neurological image fusion framework is proposed using an optimized biologically inspired feedforward neural model in two-scale hybrid ℓ1 − ℓ0 decomposition domain using gray wolf optimization to preserve the structural as well as texture information present in source CT and MR images. Initially, the source images are subjected to two-scale ℓ1 − ℓ0 decomposition with optimized parameters, giving a scale-1 detail layer, a scale-2 detail layer and a scale-2 base layer. Two detail layers at scale-1 and 2 are fused using an optimized biologically inspired neural model and weighted average scheme based on local energy and modified spatial frequency to maximize the preservation of edges and local textures, respectively, while the scale-2 base layer gets fused using choose max rule to preserve the background information. To optimize the hyper-parameters of hybrid ℓ1 − ℓ0 decomposition and biologically inspired neural model, a fitness function is evaluated based on spatial frequency and edge index of the resultant fused image obtained by adding all the fused components. The fusion performance is analyzed by conducting extensive experiments on different CT-MR neurological images. Experimental results indicate that the proposed method provides better-fused images and outperforms the other state-of-the-art fusion methods in both visual and quantitative assessments.

Multi‐scale decomposition‐based CT‐MR neurological image fusion using optimized bio‐inspired spiking neural model with meta‐heuristic optimization

AbstractMulti‐modal medical image fusion plays an important role in clinical diagnosis and works as an assistance model for clinicians. In this paper, a computed tomography‐magnetic resonance (CT‐MR) image fusion model is proposed using an optimized bio‐inspired spiking feedforward neural network in different decomposition domains. First, source images are decomposed into base (low‐frequency) and detail (high‐frequency) layer components. Low‐frequency subbands are fused using texture energy measures to capture the local energy, contrast, and small edges in the fused image. High‐frequency coefficients are fused using firing maps obtained by pixel‐activated neural model with the optimized parameters using three different optimization techniques such as differential evolution, cuckoo search, and gray wolf optimization, individually. In the optimization model, a fitness function is computed based on the edge index of resultant fused images, which helps to extract and preserve sharp edges available in the source CT and MR images. To validate the fusion performance, a detailed comparative analysis is presented among the proposed and state‐of‐the‐art methods in terms of quantitative and qualitative measures along with computational complexity. Experimental results show that the proposed method produces a significantly better visual quality of fused images meanwhile outperforms the existing methods.

NSST domain CT–MR neurological image fusion using optimised biologically inspired neural network

Diagnostic medical imaging plays an imperative role in clinical assessment and treatment of medical abnormalities. The fusion of multimodal medical images merges complementary information present in the multi-source images and provides a better interpretation with improved diagnostic accuracy. This paper presents a CT–MR neurological image fusion method using an optimised biologically inspired neural network in nonsubsampled shearlet (NSST) domain. NSST decomposed coefficients are utilised to activate the optimised neural model using particle swarm optimisation method and to generate the firing maps. Low and high-frequency NSST subbands get fused using max-rule based on firing maps. In the optimisation process, a fitness function is evaluated based on spatial frequency and edge index of the resultant fused image. To analyse the fusion performance, extensive experiments are conducted on the different CT–MR neurological image dataset. Objective performance is evaluated based on different metrics to highlight the clarity, contrast, correlation, visual quality, complementary information, salient information, and edge information present in the fused images. Experimental results show that the proposed method is able to provide better-fused images and outperforms other existing methods in both visual and quantitative assessments.

Detail Enhanced Feature-Level Medical Image Fusion in Decorrelating Decomposition Domain

Medical image fusion improves clinical interpretation and analysis by combining the complementary information of multimodal images into one that leads to more accurate diagnosis and treatment planning. This article presents a novel feature-level medical image fusion (FMIF) method using a structural gradient-based decomposition, which provides uncorrelated structural and textural components. A feature codebook obtained from multiple low-scale features followed by clustering and choose-max with consistency verification rule is applied to fuse structural components. An optimized pulse-coupled neural network is utilized to fuse texture component using a differential evolution algorithm, which helps to improve the model efficiency and retain the natural response of pixel activity. The fusion performance of the proposed method is explored on a large data set of neurological images. The experimental result demonstrates that the proposed method provides better fusion results and outperforms the state-of-the-art fusion approaches with enhanced visual quality and computational parameters.

Multimodal Medical Image Fusion Using Hybrid Layer Decomposition With CNN-Based Feature Mapping and Structural Clustering

Multimodal medical image fusion aims to integrate multisensory medical data into a single image for better prediction of the diagnostic details. This article presents a feature-level multimodal medical image fusion with the use of a two-scale ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> - ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> hybrid layer decomposition scheme to maximize the structural details with the suppression of significant noise and artifacts. The proposed method uses a convolutional neural network (CNN) with consistency verification and structural patch clustering (fuzzy c-means-based) for the decomposed base and detail layer fusion, respectively. At first, a color space transform is used to separate the luminance and chrominance components extracted from both the source images and the luminance part of each image is decomposed. In the second step, a pretrained CNN model is used to extract the prominent features from each decomposed base layer component. For the output feature map, a regional energy-based activity measure is computed and generated a fusion score, which is refined in the consistency verification step to optimize the weight map for fusing the base layers. The two-scale detail layers are merged by the clustering-based prelearned dictionary for efficiently mapping of structural details of the layers. The color components associated with both the images are also combined using a color saliency measure. Finally, the fused base layer, detail layers, and color components are merged to get the resultant fused image. The experimental results justify the superiority of the proposed approach in terms of both subjective and objective assessments. The performance of the proposed scheme is tested on a large multimodal medical data set of MR-SPECT, MR-PET, and CT-MR neurological images. Furthermore, the proposed approach shows better results than the other state-of-the-art approaches with improved fusion quality and computational performances.

Multimodal Medical Image Sensor Fusion Model Using Sparse K-SVD Dictionary Learning in Nonsubsampled Shearlet Domain

Multimodal medical image sensor fusion (MMISF) has a significant role for better visualization of the diagnostic statistics computed by integrating the vital information taken from input source images acquired using multimodal imaging sensors. The MMISF also helps the medical professionals in precise diagnosis of several critical diseases and its treatment. Often, images taken from different imaging sensors are degraded by noise interferences during acquisition or data transmission that leads to the false perception of noise as a useful feature of the image. This paper presents a novel fusion framework for multimodal neurological images, which is able to capture small-scale details of input images with original structural details. In its procedural steps, at first, source images get decomposed by the nonsubsampled shearlet transform (NSST) into a low-frequency (lf) and several high-frequency (hf) components to separate out the two basic characteristics of source image, i.e., principal information and edge details. The lf layers get fused with a sparse representation-based model, and the hf components are merged by the guided filtering-based approach. Finally, fused images are reconstructed by employing the inverse NSST. The superiority of the proposed MMISF approach is confirmed by a large extent of analytical experimentations on the different real magnetic resonance single-photon emission computed tomography, magnetic resonance-positron emission tomography and computed tomography magnetic resonance neurological image data sets. Based on all these experimental results, it is stated that the proposed MMISF approach is superior to several other approaches as it produces better visually fused images with improved computational measures.

Clinical Features of Nephrotic Syndrome with Cerebral Hemorrhage.

BackgroundCerebral hemorrhage has been increasingly reported in patients with nephrotic syndrome (NS). However, the clinical features and pathogenesis of NS patients with cerebral hemorrhage remain unclear.Material/MethodsFrom January 2007 to August 2017, continuous NS patients with cerebral hemorrhage at the First Affiliated Hospital of Guangxi Medical University were selected. The clinical manifestations, laboratory measurements, and neurological images of these patients were collected and analyzed.ResultsAcute cerebral hemorrhage was recorded in 15 of 10 461 NS patients. The average age of these 15 patients (9 males and 6 females) was 50.87±23.27 years old. Among these 15 patients, conventional vascular risk factors were identified in 8 patients, hypoalbuminemia and proteinuria were recorded in all 15 patients, coagulopathy was observed in 9 patients, increased D-dimer level was recorded in 13 patients, hyperlipidemia was recorded in 11 patients, and impaired renal function was recorded in 9 patients. The hemorrhage developed in the lobe (n=9), basal ganglia (n=3), cerebellum (n=2), and cerebral hemisphere (n=1). Eight patients were in a coma on the day the cerebral hemorrhage occurred, while 12 patients had a poor prognosis after 30 days of hemorrhage onset.ConclusionsPoor prognosis was recorded in NS patients with cerebral hemorrhage. Although conventional vascular risk factors have only been identified in 8 patients, biochemical abnormalities (hypoalbuminemia, proteinuria, elevated D-dimer, and hyperlipidemia) were recorded in the majority of these 15 patients. Furthermore, most of the hemorrhages developed in the lobes. Coagulopathy might be the potential pathogenesis of cerebral hemorrhage in NS patients.

Multimodal neurological image fusion based on adaptive biological inspired neural model in nonsubsampled Shearlet domain

AbstractMultimodal medical image fusion (MMIF) has a significant role for a better visualization of the diagnostic statistics, those help the medical professionals in the precise diagnosis of several critical diseases. This paper presents an improved fusion framework that uses the entire features extracted by the nonsubsampled shearlet transform (NSST) and adaptive biologically inspired neural model. The proposed scheme retains the required information without losing the resolution of the disease morphology. In the proposed method, the adaptive neural model based on local visibility and log Gabor energy based rules are applied to low and high‐frequency components, respectively. The better fusion results obtained by the proposed approach are confirmed by a large extent of simulations on the different MR‐SPECT and CT‐MR neurological images. Based on all these simulated results, it states that the proposed approach is superior than the other approaches as it produces better visually fused images with improved computational measures.

Robust Ensemble Classification Methodology for I123-Ioflupane SPECT Images and Multiple Heterogeneous Biomarkers in the Diagnosis of Parkinson's Disease.

In last years, several approaches to develop an effective Computer-Aided-Diagnosis (CAD) system for Parkinson's Disease (PD) have been proposed. Most of these methods have focused almost exclusively on brain images through the use of Machine-Learning algorithms suitable to characterize structural or functional patterns. Those patterns provide enough information about the status and/or the progression at intermediate and advanced stages of Parkinson's Disease. Nevertheless this information could be insufficient at early stages of the pathology. The Parkinson's Progression Markers Initiative (PPMI) database includes neurological images along with multiple biomedical tests. This information opens up the possibility of comparing different biomarker classification results. As data come from heterogeneous sources, it is expected that we could include some of these biomarkers in order to obtain new information about the pathology. Based on that idea, this work presents an Ensemble Classification model with Performance Weighting. This proposal has been tested comparing Healthy Control subjects (HC) vs. patients with PD (considering both PD and SWEDD labeled subjects as the same class). This model combines several Support-Vector-Machine (SVM) with linear kernel classifiers for different biomedical group of tests—including CerebroSpinal Fluid (CSF), RNA, and Serum tests—and pre-processed neuroimages features (Voxels-As-Features and a list of defined Morphological Features) from PPMI database subjects. The proposed methodology makes use of all data sources and selects the most discriminant features (mainly from neuroimages). Using this performance-weighted ensemble classification model, classification results up to 96% were obtained.

Pregabalin‐induced parkinsonism 72 hours after iodinated contrast administration

AbstractBackgroundPregabalin is a drug which requires dose adjustment when renal clearance decreases below 60 mL/min.AimWe describe a case in which a 58‐year‐old woman, taking pregabalin for 3 years, developed symptoms of parkinsonism due to worsening of her kidney failure.Clinical detailsA 58‐year‐old woman developed symptoms of parkinsonism characterised by bradypsychia and drowsiness, inability to ambulate, stiffness in the extremities and loss of facial expression. Four days earlier the patient had undergone an abdominal computed axial tomography scan with iodinated contrast medium and her renal function had failed. Neurological exploration and image testing were performed with inconclusive results. Her treatment was revised and pregabalin was discontinued.OutcomesParkinsonian symptoms disappeared 10 days after the discontinuation of pregabalin.ConclusionsThis is the first reported case of pregabalin‐induced parkinsonism associated with acute renal failure or exacerbation of chronic renal failure. The monitoring of renal function in patients taking pregabalin is essential to prevent future similar adverse events.

Neurochemical changes in unilateral cerebral hemisphere during the subacute stage of focal cerebral ischemia-reperfusion in rats: An ex vivo 1H magnetic resonance spectroscopy study

Understanding the subacute may shed light on the mechanism of cerebral ischemia. The present study aimed to explore metabolic features underlying subacute stage of ischemia-reperfusion injury and developing effective treatments. Rats were divided into three groups: the permanent middle cerebral artery occlusion (pMCAO), transient cerebral focal ischemia (tMCAO) and sham group. Evaluation of animal models was performed by the neurological deficit, MR images and pathological morphological abnormality. To elucidate metabolic changes, we conducted a comparative analysis of metabolic composition of unilateral brain tissue using 1H nuclear magnetic resonance spectroscopy. The successful model was observed low signal on T1WI and high signal on T2WI lesions in the left cerebral. Histopathological results confirmed the formation of apparent lesions in the left striatum, hippocampus CA1 and cortex tissues of subacute cerebral ischemia rats and showed that rats with focal cerebral ischemia-reperfusion could alleviate the extent of pathological damage degree. In pMCAO rats 7 days after surgery, decreased levels of N-acetyl aspartate (NAA), γ-aminobutyric acid (GABA), glutamate (Glu) and succinate (Suc) concomitantly with increased levels of glutamine (Gln), myo-inositol (m-Ins) and lactate (Lac) were observed compared to the control. Whereas, increased level of Lac with decreased levels of NAA, GABA, Glu, Suc, creatine (Cre) were observed in the tMCAO rats. This demonstrated that experimental subacute ischemic stroke in rats caused extensive perturbation in energy metabolism, the tricarboxylic acid cycle and GABA shunt, which provided essential information for understanding the pathogenesis of subacute cerebral ischemia-reperfusion and provided guidance in choosing the suitable therapeutic schedule.

Intracisternal tuberculoma: a refractory type of tuberculoma indicating surgical intervention

BackgroundCentral nervous system (CNS) tuberculoma is a rare disease with severe neurological deficits. This retrospective research is to review the data of patients diagnosed as CNS tuberculoma. Surgeries were performed in all patients. The clinical features especially the neurological image and the anatomical characters of the tuberculomas were concerned.MethodsTotally 11 patients diagnosed as CNS tuberculoma were admitted in Guangzhou First People’s Hospital (7cases) and Changzheng Hospital (4 cases) during 2006–2015. The data including preoperative condition, neurological imaging, and surgical findings was collected and analyzed.ResultsThe lesions of nine patients (9/11) were totally or subtotally excised and two (2/11) were partially excised. Neurological functions of all patients were improved after surgery without secondary infection. Lesions of nine (9/11) patients preoperatively progressed as a result of paradoxical reaction. Of the 9 patients demonstrated paradoxical progression, all lesions were partially or totally located at the cisterns or the subarachnoid space. Preoperative ATTs lasted 2 to 12 months and tuberculomas were not eliminated. The arachnoid was found thickened and tightly adhered to the lesions during surgeries. Of the 2 cases that paradoxical reaction were excluded, both patients (case 6, intramedullary tuberculoma; case 11, intradural extramedullary tuberculoma) were admitted at onset of the disease. ATTs were preoperatively given for 1 week as neurological deficits aggravated. The tuberculous lesions of CNS or other system showed no obvious change and paradoxical reaction could not be established in both cases.ConclusionsExudates of tuberculosis is usually accumulated in the cisterns and frequently results in the paradoxical formation of tuberculoma. Intracisternal tuberculoma is closely related to paradoxical reaction and refractory to anti-tuberculosis therapy. Micro-surgical excision is safe and effective. Early surgical intervention may be considered in the diagnosis of intracisternal tuberculoma especially when paradoxical reaction participates in the development of tuberculoma.

Nonsubsampled shearlet domain fusion techniques for CT–MR neurological images using improved biological inspired neural model

The fusion of multimodality medical images performs a very crucial role in the clinical diagnosis, analysis and the treatment of especially in critical diseases. It is considered as an assisted approach for the radiologist by providing the composite images having significant diagnostic information acquired from the source images. The main purpose of this work is to develop an efficient framework for fusing the multimodal medical images. Three different fusion techniques are proposed in this paper that presents the CT and MR medical image fusion in nonsubsampled shearlet transform (NSST) domain using the adaptive spiking neural model. The NSST having different features and a competent depiction of the image coefficients provides several directional decomposition coefficients. Maximum selection approach and regional energy are utilized for low frequency coefficients fusion. Spatial frequency, novel modified spatial frequency and novel sum modified Laplacian motivated spiking model are used for every high frequency subimage component. Finally, fused images are reconstructed by applying inverse NSST. The performance of proposed fusion techniques is validated by extensive simulations performed on different CT-MR image datasets using proposed and other thirty seven existing fusion approaches in terms of both the subjective and objective manner. The results revealed that the proposed techniques provide better visualization of resultant images and higher quantitative measures compared to several existing fusion approaches.