Fusion Of MR Images Research Articles

Medical Image Fusion for Multiple Diseases Features Enhancement

ABSTRACTThroughout the past 20 years, medical imaging has found extensive application in clinical diagnosis. Doctors may find it difficult to diagnose diseases using only one imaging modality. The main objective of multimodal medical image fusion (MMIF) is to improve both the accuracy and quality of clinical assessments by extracting structural and spectral information from source images. This study proposes a novel MMIF method to assist doctors and postoperations such as image segmentation, classification, and further surgical procedures. Initially, the intensity‐hue‐saturation (IHS) model is utilized to decompose the positron emission tomography (PET)/single photon emission computed tomography (SPECT) image, followed by a hue‐angle mapping method to discriminate high‐ and low‐activity regions in the PET images. Then, a proposed structure feature adjustment (SFA) mechanism is used as a fusion strategy for high‐ and low‐activity regions to obtain structural and anatomical details with minimum color distortion. In the second step, a new multi‐discriminator generative adversarial network (MDcGAN) approach is proposed for obtaining the final fused image. The qualitative and quantitative results demonstrate that the proposed method is superior to existing MMIF methods in preserving the structural, anatomical, and functional details of the PET/SPECT images. Through our assessment, involving visual analysis and subsequent verification using statistical metrics, it becomes evident that color changes contribute substantial visual information to the fusion of PET and MR images. The quantitative outcomes demonstrate that, in the majority of cases, the proposed algorithm consistently outperformed other methods. Yet, in a few instances, it achieved the second‐highest results. The validity of the proposed method was confirmed using diverse modalities, encompassing a total of 1012 image pairs.

MR–CT image fusion method of intracranial tumors based on Res2Net

BackgroundInformation complementarity can be achieved by fusing MR and CT images, and fusion images have abundant soft tissue and bone information, facilitating accurate auxiliary diagnosis and tumor target delineation.PurposeThe purpose of this study was to construct high-quality fusion images based on the MR and CT images of intracranial tumors by using the Residual-Residual Network (Res2Net) method.MethodsThis paper proposes an MR and CT image fusion method based on Res2Net. The method comprises three components: feature extractor, fusion layer, and reconstructor. The feature extractor utilizes the Res2Net framework to extract multiscale features from source images. The fusion layer incorporates a fusion strategy based on spatial mean attention, adaptively adjusting fusion weights for feature maps at each position to preserve fine details from the source images. Finally, fused features are input into the feature reconstructor to reconstruct a fused image.ResultsQualitative results indicate that the proposed fusion method exhibits clear boundary contours and accurate localization of tumor regions. Quantitative results show that the method achieves average gradient, spatial frequency, entropy, and visual information fidelity for fusion metrics of 4.6771, 13.2055, 1.8663, and 0.5176, respectively. Comprehensive experimental results demonstrate that the proposed method preserves more texture details and structural information in fused images than advanced fusion algorithms, reducing spectral artifacts and information loss and performing better in terms of visual quality and objective metrics.ConclusionThe proposed method effectively combines MR and CT image information, allowing the precise localization of tumor region boundaries, assisting clinicians in clinical diagnosis.

The diagnostic value of multimodal imaging based on MR combined with ultrasound in benign and malignant breast diseases

We aimed to construct and validate a multimodality MRI combined with ultrasound based on radiomics for the evaluation of benign and malignant breast diseases. The preoperative enhanced MRI and ultrasound images of 131 patients with breast diseases confirmed by pathology in Aerospace Center Hospital from January 2021 to August 2023 were retrospectively analyzed, including 73 benign diseases and 58 malignant diseases. Ultrasound and 3.0 T multiparameter MRI scans were performed in all patients. Then, all the data were divided into training set and validation set in a 7:3 ratio. Regions of interest were drawn layer by layer based on ultrasound and MR enhanced sequences to extract radiomics features. The optimal radiomic features were selected by the best feature screening method. Logistic Regression classifier was used to establish models according to the best features, including ultrasound model, MRI model, ultrasound combined with MRI model. The model efficacy was evaluated by the area under the curve (AUC) of the receiver operating characteristic, sensitivity, specificity, and accuracy. The F-test based on ANOVA was used to screen out 20 best ultrasonic features, 11 best MR Features, and 14 best features from the combined model. Among them, texture features accounted for the largest proportion, accounting for 79%.The ultrasound combined with MR Image fusion model based on logistic regression classifier had the best diagnostic performance. The AUC of the training group and the validation group were 0.92 and 091, the sensitivity was 0.80 and 0.67, the specificity was 0.90 and 0.94, and the accuracy was 0.84 and 0.79, respectively. It was better than the simple ultrasound model (AUC of validation set was 0.82) or the simple MR model (AUC of validation set was 0.85). Compared with the traditional ultrasound or magnetic resonance diagnosis of breast diseases, the multimodal model of MRI combined with ultrasound based on radiomics can more accurately predict the benign and malignant breast diseases, thus providing a better basis for clinical diagnosis and treatment.

HyperMRI: hyperspectral and magnetic resonance fusion methodology for neurosurgery applications

PurposeMagnetic resonance imaging (MRI) is a common technique in image-guided neurosurgery (IGN). Recent research explores the integration of methods like ultrasound and tomography, among others, with hyperspectral (HS) imaging gaining attention due to its non-invasive real-time tissue classification capabilities. The main challenge is the registration process, often requiring manual intervention. This work introduces an automatic, markerless method for aligning HS images with MRI.MethodsThis work presents a multimodal system that combines RGB-Depth (RGBD) and HS cameras. The RGBD camera captures the patient’s facial geometry, which is used for registration with the preoperative MR through ICP. Once MR-depth registration is complete, the integration of HS data is achieved using a calibrated homography transformation. The incorporation of external tracking with a novel calibration method allows camera mobility from the registration position to the craniotomy area. This methodology streamlines the fusion of RGBD, HS and MR images within the craniotomy area.ResultsUsing the described system and an anthropomorphic phantom head, the system has been characterised by registering the patient’s face in 25 positions and 5 positions resulted in a fiducial registration error of 1.88 ± 0.19 mm and a target registration error of 4.07 ± 1.28 mm, respectively.ConclusionsThis work proposes a new methodology to automatically register MR and HS information with a sufficient accuracy. It can support the neurosurgeons to guide the diagnosis using multimodal data over an augmented reality representation. However, in its preliminary prototype stage, this system exhibits significant promise, driven by its cost-effectiveness and user-friendly design.

Segmentation-Based Fusion of CT and MR Images.

In this paper, a segmentation-based image fusion method is proposed for the fusion of MR and CT images to obtain a high contrast fused image that contains complementary information from both input images. The proposed method uses the fuzzy C-mean method to extract information about the skull from the CT image. This skull information is used to extract soft tissue information from the MR image. Both the skull information and the soft tissue information are then fused using the fusion rule. The efficiency of the proposed method over other state-of-the-art fusion methods is analyzed and compared using qualitative and quantitative analysis methods. Qualitative analysis shows the improvement in the contrast between the bone and the soft tissue using the proposed method over other state-of-the-art methods without introducing any artifacts or distortions. Classical and gradient-based quantitative analysis also show significant improvement in the fused image obtained using the proposed method over the five state-of-the-art methods. The percentage improvement in the standard deviation, average gradient, entropy, spatial frequency, QABF, and LABF of the proposed method over the best value obtained by the five state-of-the-art methods is 27.11%, 12.06%, 23.64%, 11.30%, 5.59%, and 13.70% respectively.

Multi-modality MRI fusion with patch complementary pre-training for internet of medical things-based smart healthcare

Magnetic Resonance Imaging (MRI) is a pivotal neuroimaging technique capable of generating images with various contrasts, known as multi-modal images. The integration of these diverse modalities is essential for improving model performance across various tasks. However, in real clinical scenarios, acquiring MR images for all modalities is frequently hindered by factors such as patient comfort and scanning costs. Therefore, effectively fusing different modalities to synthesize missing modalities has become a research hot-spot in the field of smart healthcare, particularly in the context of the Internet of Medical Things (IoMT). In this study, we introduce a multi-modal coordinated fusion network (MCF-Net) with Patch Complementarity Pre-training. This network leverages the complementarity and correlation between different modalities to make the fusion of multi-modal MR images, addressing challenges in the IoMT. Specifically, we first employ a Patch Complementarity Mask Autoencoder (PC-MAE) for self-supervised pre-training. The complementarity learning mechanism is introduced to align masks and visual annotations between two modalities. Subsequently, a dual-branch MAE architecture and a shared encoder–decoder are adopted to facilitate cross-modal interactions within mask tokens. Furthermore, during the fine-tuning phase, we incorporate an Attention-Driven Fusion (ADF) module into the MCF-Net. This module synthesizes missing modal images by fusion of multi-modal features from the pre-trained PC-MAE encoder. Additionally, we leverage the pre-trained encoder to extract high-level features from both synthetic and corresponding real images, ensuring consistency throughout the training process. Our experimental findings showcase a notable enhancement in performance across various modalities with our fusion method, outperforming state-of-the-art techniques.

Anisotropic diffusion filter based fusion of NSST transformed medical images

In this paper, an image fusion method is proposed for the fusion of MR and CT images to obtain high contrast fused image that contains complementary information from both the input images. Proposed method uses NSST based fusion method to fuse the information from the two input images, whereas to enhance the edge information and to increase the contrast in the fused image, anisotropic diffusion based fusion method is used and its result is added to the NSST based fusion method. Proposed method is analyzed and compared with five state-of-the-art fusion methods in terms of both visual perception and objective assessment. Minimum percentage improvement of the proposed method over the five state-of-the-art methods are 5.99%, 129.84%, 7.48%, 7.52%, and 5.25% respectively, whereas maximum percentage improvement of the proposed method over the five state-of-the-art methods are 18.51%, 196.52%, 29.64%, 26.35%, and 16.05% respectively.

Brain tumor segmentation in multimodal MRI via pixel-level and feature-level image fusion.

Brain tumor segmentation in multimodal MRI volumes is of great significance to disease diagnosis, treatment planning, survival prediction and other relevant tasks. However, most existing brain tumor segmentation methods fail to make sufficient use of multimodal information. The most common way is to simply stack the original multimodal images or their low-level features as the model input, and many methods treat each modality data with equal importance to a given segmentation target. In this paper, we introduce multimodal image fusion technique including both pixel-level fusion and feature-level fusion for brain tumor segmentation, aiming to achieve more sufficient and finer utilization of multimodal information. At the pixel level, we present a convolutional network named PIF-Net for 3D MR image fusion to enrich the input modalities of the segmentation model. The fused modalities can strengthen the association among different types of pathological information captured by multiple source modalities, leading to a modality enhancement effect. At the feature level, we design an attention-based modality selection feature fusion (MSFF) module for multimodal feature refinement to address the difference among multiple modalities for a given segmentation target. A two-stage brain tumor segmentation framework is accordingly proposed based on the above components and the popular V-Net model. Experiments are conducted on the BraTS 2019 and BraTS 2020 benchmarks. The results demonstrate that the proposed components on both pixel-level and feature-level fusion can effectively improve the segmentation accuracy of brain tumors.

CT-MR Image Fusion for Post-Implant Dosimetry Analysis in Brain Tumor Seed Implantation- a Preliminary Study.

Purpose To calculate and evaluate postimplant dosimetry (PID) with CT-MR fusion technique after brain tumor brachytherapy and compare the result with CT-based PID. Methods and Materials 16 brain tumor patients received MR-guided intervention with Iodine-125 (125I) seed implantation entered this preliminary study for PID evaluation. Registration and fusion of CT and MR images of the same patients were performed one day after operation. Seeds identification and targets delineation were carried out on CT, MR, and CT-MR fusion images, each. The number and location of seeds on MR or CT- MR fusion images were compared with those of actually implanted seeds. Clinical target volume (CTV) and dosimetric parameters such as %D90, %V100 and external V100 were measured and calculated. In addition, the correlation of the fusion to CT CTV ratio and other factors were analyzed. Results The numbers of fusion seeds were not significantly different compared with reference seeds (t =1.76, p >0.05). The difference between reference seeds numbers and truly extracted MR seeds numbers was statistically significant (t =3.91, p <0.05). All dosimetric parameters showed significant differences between the two techniques (p <0.05). The mean CTV delineated on fusion images was 34.3 ± 33.6, smaller than that on CT images. The mean values of external V100, %V100 and %D90 on fusion images were larger than those on CT images. Correlation analysis showed that the fusion-CT V100 ratio was positively and significantly correlated with the fusion-CT volume ratio. Conclusions This preliminary study indicated that CT-MR fusion-based PID exhibited good accuracy for 125I brain tumor brachytherapy dosimetry when compared to CT-based PID and merits further research to establish best-outcome protocols.

A novel variational optimization model for medical CT and MR image fusion

A novel variational optimization model for medical CT and MR image fusion

A Multiparametric Fusion Deep Learning Model Based on DCE-MRI for Preoperative Prediction of Microvascular Invasion in Intrahepatic Cholangiocarcinoma.

Assessment of microvascular invasion (MVI) in intrahepatic cholangiocarcinoma (ICC) by using a noninvasive method is an unresolved issue. Deep learning (DL) methods based on multiparametric fusion of MR images have the potential of preoperative assessment of MVI. To investigate whether a multiparametric fusion DL model based on MR images can be used for preoperative assessment of MVI in ICC. Retrospective. A total of 519 patients (200 females and 319 males) with a single ICC were categorized as a training (n=361), validation (n=90), and an external test cohort (n=68). A 1.5 T and 3.0 T; axial T2-weighted turbo spin-echo sequence, diffusion-weighted imaging with a single-shot spin-echo planar sequence, and dynamic contrast-enhanced (DCE) imaging with T1-weighted three-dimensional quick spoiled gradient echo sequence. DL models of multiparametric fusion convolutional neural network (CNN) and late fusion CNN were both constructed for evaluating MVI in ICC. Gradient-weighted class activation mapping was used for visual interpretation of MVI status in ICC. The DL model performance was assessed through the receiver operating characteristic curve (ROC) analysis, and the area under the ROC curve (AUC) with the accuracy, sensitivity, and specificity were measured. P value &lt; 0.05 was considered as statistical significance. In the external test cohort, the proposed multiparametric fusion DL model achieved an AUC of 0.888 with an accuracy of 86.8%, sensitivity of 85.7%, and specificity of 87.0% for evaluating MVI in ICC, and the positive predictive value and negative predictive value were 63.2% and 95.9%, respectively. The late fusion DL model achieved a lower AUC of 0.866, with an accuracy of 83.8%, sensitivity of 78.6%, specificity of 85.2% for evaluating MVI in ICC. Our DL model based on multiparametric fusion of MRI achieved a good diagnostic performance in the evaluation of MVI in ICC. 3 TECHNICAL EFFICACY: Stage 2.

Efficient Clinical Implementation of an MRI-Guided Adaptive Radiation Therapy Program

<h3>Purpose/Objective(s)</h3> Magnetic Resonance Image-guided Adaptive Radiation Therapy (MRgART) is ideal for intra-abdominal tumors as it accounts for daily anatomical changes in target volumes and nearby organs at risk (OAR) caused by soft-tissue deformation, respiration, organ filling/emptying and patient weight changes. The dosimetric gains associated with MRgART may be undermined by longer treatment times, resulting in unaccounted for intra-fractional OAR changes and patient motion. In this study, we aim to evaluate each step in our MRgART workflow for optimal resource utilization and potential treatment time savings. <h3>Materials/Methods</h3> This IRB approved retrospective review included pancreas cancer patients treated at our institution between 2018 and 2020 with MR guidance. We examined MRgART prescription details, and steps in the adaptive workflow, including MR simulation, image fusion, contouring, re-plan/QA and tumor tracking for resource allocation and clinical workflow efficiency. A generalized estimating equation (GEE) model with an exchangeable working correlation structure and Wilcoxon signed rank test were used for statistical time analysis. <h3>Results</h3> The study included 17 eligible pancreas cancer patients treated consecutively on our 0.35 T MR-Linac with MRgART. We examined 115 treated fractions (77 adapted). Two of the patients received conventional fractionation, and 15 were treated with stereotactic body radiation therapy (SBRT). SBRT prescription dose was 40 Gy to PTV in 5 fractions with 6MV FFF X-rays. Non-adaptive fractions averaged 38.3 ± 3.2 min (mean ± s.d.) compared to significantly longer adaptive treatments lasting 57.8 ± 4.3 min (an increase of 19.6 min or 51.3%, <b>P</b> < 0.01). Within each adapted treatment, the greatest time was spent on OAR contouring (13.6 min), followed by patient setup (10.6 min), imaging (9.1 min), re-plan (7.3 min), and QA (2.2 min). Beam-on time was 26% of overall treatment duration. Major contributors to increased treatment time with MRgART were contouring, physics review, replan and QA. Other sub-categories, such as, patient setup, imaging and beam-on times remained unchanged when compared to non-adaptive treatments. On average, treatment times decreased by 13.6% (61.4 min to 53 min, <b>P</b> = 0.06) as patients progressed through their treatment course reflecting improved patient confidence, compliance and comfort with their treatments and breathing instructions. Gains in treatment time also resulted from limited OAR contouring around the PTV and audio/visual coaching of patients leading to longer duty cycle. <h3>Conclusion</h3> Efficient resource utilization is important for safe and effective implementation of MRgART. Online OAR contouring, imaging and patient setup are potential targets for further time improvements and could be achieved <b>via</b> offline staff training, emphasis on robust AI based imaging, contouring and planning strategies, and in-room monitor device for improved active patient participation, comfort and compliance.

Review of Clinical and Technological Consideration for MRI-Guided Robotic Prostate Brachytherapy

Low Dose Rate Brachytherapy (LDR-BT) is a technique for treating localized prostate cancer by implanting radioactive seeds. In conventional practice, the delivery of seeds is performed using transrectal ultrasonography (TRUS) imaging for implant guidance and checked using computed-tomography for post-implant dosimetry. In the case of TRUS, accuracy can be compromised due to sub-optimal imaging. Magnetic Resonance Imaging (MRI), however, is known to provide better soft-tissue contrast, therefore, increasing the ability to detect small lesions; for that reason, the integration of intraoperative MRI in BT workflows has been investigated over the last two decades. The fusion of preoperative MR-images during TRUS-brachytherapy is possible. However, the image registration process introduces a source of uncertainty. Manual, real-time intra-operative LDR-BT is challenging under MRI due to confined space and procedural workflows. This motivates the development of MRI-compatible robots for prostate BT, with potential advantages of improved source placement accuracy and final dosimetry. In this paper, the state-of-art of technological components in MRI compatible robots, especially for LDR-BT, has been presented. This systematic review helps us to position an ongoing Cooperative Brachytherapy project, developing a real-time MRI-guided robot for adaptive LDR-BT. The design approach includes integrating separate modules: imaging, dose planning, needles, and robot.

Utilization of image interpolation and fusion in brain tumor segmentation.

Brain tumor is a mass of anomalous cells in the brain. Medical imagining techniques have a vital role in the diagnosis of brain tumors. Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) techniques are the most popular techniques to localize the tumor area. Brain tumor segmentation is very important for the diagnosis of tumors. In this paper, we introduce a framework to perform brain tumor segmentation, and then localize the region of the tumor, accurately. The proposed framework begins with the fusion of MR and CT images by the Non-Sub-Sampled Shearlet Transform (NSST) with the aid of the Modified Central Force Optimization (MCFO) to get the optimum fusion result from the quality metrics perspective. After that, image interpolation is applied to obtain a High-Resolution (HR) image from the Low-Resolution (LR) ones. The objective of the interpolation process is to enrich the details of the fusion result prior to segmentation. Finally, the threshold and the watershed segmentation are applied sequentially to localize the tumor region, clearly. The proposed framework enhances the efficiency of segmentation to help the specialists diagnose brain tumors.

Glioma classification via MR images radiomics analysis

Accurate glioma classification before surgery is of the utmost important in clinical decision making and prognosis prediction. In this paper, we investigate the impact of multi-modal MR image fusion for the differentiation of low-grade gliomas (LGG) versus high-grade gliomas (HGG) via integrative analyses of radiomic features and machine learning approaches. A set of 80 histologically confirmed gliomas patients (40 HGG and 40 LGG) obtained from the MICCAI BraTS 2019 data were involved in this study. To achieve this work, we propose to combine T1 with T2 or FLAIR modality in the non-subsampled shearlet domain. Firstly, the pre-processed source MR images are decomposed into low-frequency (LF) and several high-frequency (HF) sub-images. LF sub-images are fused using the proposed weight local features fusion rule while HF sub-images are combined based on the novel sum-modified-laplacian technique. Experimental results demonstrate that the proposed fusion approach outperformed the recent state-of-the-art approaches in terms of entropy and feature mutual information. Subsequently, a key radiomics signature was retrieved by the least absolute shrinkage and selection operator regression algorithm. Five machine learning classifiers were established and evaluated with the retrieved dataset, then with the fused dataset using tenfold cross-validation scheme. As a result, the random forest had the highest accuracy of 96.5% with 21 features selected from the raw data and 96.1% with 16 features selected from the fused data. Finally, the experimental findings confirm that the proposed aided diagnosis framework represents a promising tool to aid radiologists in differentiating HGG and LGG.

Fusion of registered medical images using deep learning convolutional neural network with statistics based steered image filter

Medical image fusion technique plays an increasingly critical role in many clinical applications by deriving the complementary information from medical images with different modalities. In this, a novel MR and CT image fusion approach is proposed which utilises the deep learning convolutional neural networks (CNNs) with statistics based steered image filter (SSIF). In our method, a deep learning convolutional network is adopted to generate a weight map which integrates the pixel activity information from MR and CT images. The fusion process is conducted via SSIF fusion rule which computes the weights of obtained detail layers using image statistics. In addition, weighted average method is utilised to obtain the fused image. Further, proposed fusion algorithm is extended to applicable for RGB image fusion. Experimental results demonstrate that the proposed method can achieve promising results in terms of both visual quality and objective assessment.

Fusion of registered medical images using deep learning convolutional neural network with statistics based steered image filter

Medical image fusion technique plays an increasingly critical role in many clinical applications by deriving the complementary information from medical images with different modalities. In this, a novel MR and CT image fusion approach is proposed which utilises the deep learning convolutional neural networks (CNNs) with statistics based steered image filter (SSIF). In our method, a deep learning convolutional network is adopted to generate a weight map which integrates the pixel activity information from MR and CT images. The fusion process is conducted via SSIF fusion rule which computes the weights of obtained detail layers using image statistics. In addition, weighted average method is utilised to obtain the fused image. Further, proposed fusion algorithm is extended to applicable for RGB image fusion. Experimental results demonstrate that the proposed method can achieve promising results in terms of both visual quality and objective assessment.

Fusion of high B-value diffusion-weighted and T2-weighted MR images increases sensitivity for identification of extraprostatic disease in prostate cancer

PurposeTo evaluate whether fusion of high b-value diffusion-weighted imaging (DWI) and T2-weighted imaging (T2WI) increases radiologists' ability to detect pathologic features responsible for upstaging in prostate cancer patients prior to radical prostatectomy (RP). Basic proceduresThis was a retrospective study including 103 patients who underwent RP and a prostate MRI performed at 3T. High b-value DWI and T2WI were fused and interpreted by three radiologists with different degrees of experience. Prior to and after fusion, readers answered questionnaires about cancer presence, extraprostatic extension (EPE), seminal vesicle (SV) invasion, lymph node (LN) involvement, and reader confidence. Pathology reports served as the reference standard. Main findingsHigh b-value DWI-T2WI fusion increased sensitivity for detection of EPE from 65.6% to 77.4% (p < 0.05), SV invasion from 40.5% to 48.8% (p < 0.05), and LN metastasis by 23.8% to 44.4% (p < 0.05). Readers' confidence significantly improved with the use of fusion imaging. Across all readers, confidence of cancer detection increased by 12.5% (p < 0.05), EPE by 14.7% (p < 0.05), SV invasion by 8.1% (p < 0.05), and LN metastasis by 2.5% (p < 0.05) using Wilcoxon signed rank test. Principal conclusionsFusion overlay of high b-value DWI and T2WI increases sensitivity for detection of extraprostatic disease resulting in upstaging at the time of RP.

Fusion of Multiple-angles Intraoperative US Images and Pretreatment MR Images for USgHIFU Treatment of Uterine Fibroid: Retrospective Evaluation Based on Clinical Dataset.

High-intensity focused ultrasound (HIFU) has been widely used for treatment of uterine fibroids. However, due to the limited resolution of ultrasound image in deep organs, the guidance of ultrasound-guided HIFU (USgHIFU) treatment greatly depends on clinicians' experience in US image. To address this issue, fusion of intraoperative US images and pretreatment MR images has been proposed. Contour segmentation and multiple-angles 2D US images combination are performed to obtain 3D points along the contour of uterus. Iterative closest point (ICP) algorithm based on prior knowledge is used to register these point sets. MR and US images of six treated patients are used for evaluation. The mean distance error (MDE) of our algorithm is 1.71±0.59 mm, and the average running time is 0.18 s. The results have verified the feasibility of fusion of MR images and US images for USgHIFU guidance. In addition, this method may be also potential for post-ablation evaluation with follow-up MR images.

Two-stage multi-modal MR images fusion method based on Parametric Logarithmic Image Processing (PLIP) Model

MRI is one of the most compliant technique that is used for the screening of Brain Tumor. MRI can be acquired in four available modalities which are MR-T1, MR-T2, MR-PD and MR-Gad; among these MR-T2 comprises of most of the detailed information of the tumorous tissues. However, the accuracy and reliability of the diagnosis may be affected due to lack of sufficient details in each modality (as different MRI modalities highlight different set of tissues). Therefore, MR Image(s) fusion is essential to obtain a more illustrative image containing the requisite complementary details of each modality. For this purpose, multi-modal fusion of MR-T2 with MR-T1, MR-PD and MR-Gad have been dealt in this work using the proposed fusion method. This paper presents a two-stage fusion method using Stationary Wavelet Transform (SWT) in combination with Parameterized Logarithmic Image Processing (PLIP) model. At Stage-I of sub-band decomposition: the first level SWT coefficients contain large amount of noise thus suppressing the necessary edge information. This aspect has been resolved at Stage-II by employing second level SWT decomposition along with Principal Component Analysis (PCA). The fusion coefficients from both the stages are finally fused using PLIP operators (prior to reconstruction). The obtained results are compared qualitatively as well as quantitatively using fusion metrics like Entropy, Fusion Factor, Standard Deviation and Edge Strength. Noteworthy visual response is obtained with PLIP fusion model in coherence with Human Visual System (HVS) characteristics.