Registration Of Magnetic Resonance Imaging Images Research Articles

A multi-view assisted registration network for MRI registration pre- and post-therapy.

Image registration of magnetic resonance imaging (MRI) pre- and post-therapy is an important part of evaluating the effect of therapy in tumor patients. The accuracy of evaluation results heavily relies on the alignment of the MRI image after registration. Although recent advancements have been made in medical image registration, applying these methods to MRI registration pre- and post-therapy remains challenging. Existing methods typically utilize single-view data for registration. However, when applied to MRI data where some slices are clear while others are blurred, these methods can be misled by erroneous spatial information in the blurred regions, leading to poor registration outcomes. To mitigate the interference caused by erroneous spatial information in single-view data, this paper proposes a multi-stream fusion-assisted registration network that incorporates different-view MRIs of the same patient at the same site. Additionally, a cross-attention guided fusion module is designed within the network to effectively utilize accurate spatial information from multi-view data. The proposed approach was evaluated on clinical data, and the experimental results demonstrated that incorporating multiple view data as auxiliary information significantly enhances the accuracy of MRI image registration before and after radiotherapy.

Multiscale feature fusion network for 3D head MRI image registration.

Image registration technology has become an important medical image preprocessing step with the wide application of computer-aided diagnosis technology in various medical image analysis tasks. We propose a multiscale feature fusion registration based on deep learning to achieve the accurate registration and fusion of head magnetic resonance imaging (MRI) and solve the problem that general registration methods cannot handle the complex spatial information and position information of head MRI. Our proposed multiscale feature fusion registration network consists of three sequentially trained modules. The first is an affine registration module that implements affine transformation; the second is to realize non-rigid transformation, a deformable registration module composed of top-down and bottom-up feature fusion subnetworks in parallel; and the third is a deformable registration module that also realizes non-rigid transformation and is composed of two feature fusion subnetworks in series. The network decomposes the deformation field of large displacement into multiple deformation fields of small displacement by multiscale registration and registration, which reduces the difficulty of registration. Moreover, multiscale information in head MRI is learned in a targeted manner, which improves the registration accuracy, by connecting the two feature fusion subnetworks. We used 29 3D head MRIs for training and seven volumes for testing and calculated the values of the registration evaluation metrics for the new algorithm to register anterior and posterior lateral pterygoid muscles. The Dice similarity coefficient was 0.745±0.021, the Hausdorff distance was 3.441±0.935mm, the Average surface distance was 0.738±0.098mm, and the Standard deviation of the Jacobian matrix was 0.425±0.043. Our new algorithm achieved a higher registration accuracy compared with state-of-the-art registration methods. Our proposed multiscale feature fusion registration network can realize end-to-end deformable registration of 3D head MRI, which can effectively cope with the characteristics of large deformation displacement and the rich details of head images and provide reliable technical support for the diagnosis and analysis of head diseases.

Clustered Redundant Keypoint Elimination SURF Method in MRI Image Registration Based on Alpha-Trimmed Relationship

Purpose: The process of Magnetic Resonance Imaging (MRI) image registration is one of the important branches in MRI image analysis, which is a necessary pre-processing to use the information in these images. The purpose of this paper is to present a new approach for MRI image registration that can maintain the total number of initial matches and have the highest precision.
 Materials and Methods: The Clustered Redundant Keypoint Elimination Method-Scale Invariant Feature Transform (CRKEM-SIFT) algorithm has recently been introduced to eliminate redundancies and upgrade the correspondence precision. The disadvantages of this algorithm include the high execution time and the number of incorrect correspondences. In this paper, to increase the accuracy and speed of MRI image registration, the CRKEM method is first used over the Speeded Up Robust Features (SURF) algorithm. Then, Spatial Relations Correspondence (SRC) and Alpha-Trimmed Spatial Relations Correspondence (ATSRC) methods are suggested to improve correspondences. These suggested methods, unlike conventional methods such as Random Sample Consensus (RANSAC(, which only eliminates incorrect correspondences, detect incorrect correspondences based on spatial relationships and turn them into correct correspondences. Converting incorrect correspondences to correct ones can increase the number of correct correspondences and ultimately increase the precision of correspondences.
 Results: The simulation results show that the suggested CRKEMSURF-ATSRC approach improves the mean by 28.92% in terms of precision and 37.58% in SITMMC compared to those of the SIFT-ARANSAC method.
 Conclusion: The suggested SRC and ATSRC methods use the spatial relations of the initial correspondences to convert the incorrect correspondences into correct ones. The number of initial correspondences is maintained in these suggested approaches. These methods are better than other methods of improving correspondences such as RANSAC, and Graph Transformation Matching (GTM). These suggested methods can be used as a new and efficient approach to improve the correspondence of medical images.

Construction of 3D model of knee joint motion based on MRI image registration

Abstract There is a growing demand for information and computational technology for surgeons help with surgical planning as well as prosthetics design. The two-dimensional images are registered to the three-dimensional (3D) model for high efficiency. To reconstruct the 3D model of knee joint including bone structure and main soft tissue structure, the evaluation and analysis of sports injury and rehabilitation treatment are detailed in this study. Mimics 10.0 was used to reconstruct the bone structure, ligament, and meniscus according to the pulse diffusion-weighted imaging sequence (PDWI) and stir sequences of magnetic resonance imaging (MRI). Excluding congenital malformations and diseases of the skeletal muscle system, MRI scanning was performed on bilateral knee joints. Proton weighted sequence (PDWI sequence) and stir pulse sequence were selected for MRI. The models were imported into Geomagic Studio 11 software for refinement and modification, and 3D registration of bone structure and main soft tissue structure was performed to construct a digital model of knee joint bone structure and accessory cartilage and ligament structure. The 3D knee joint model including bone, meniscus, and collateral ligament was established. Reconstruction and image registration based on mimics and Geomagic Studio can build a 3D model of knee joint with satisfactory morphology, which can meet the requirements of teaching, motion simulation, and biomechanical analysis.

Analysis on Brain Image Characteristics of Patients with Parkinson's Disease Under Multimodal Magnetic Resonance Technology

In order to improve the early diagnosis rate of Parkinson's disease (PD), reduce the complications of PD in the later stage, and make the clinical intervention to alleviate the pain of Parkinson's patients early, Magnetic resonance imaging (MRI), positron emission tomography (PET), and computerized tomography (CT) were used to evaluate the characteristics of PD. A total of 34 patients diagnosed with PD admitted to Qilu Hospital of Shandong University from January 2017 to December 2018 were included in the research. According to the severity of the disease, the patients were divided into the initial group (IG) (22 cases) and the latter group (LG) (12 cases). MRI, PET, and CT were used to scan the patient's brain to obtain different scan images. The obtained image was processed to improve the image resolution. Brain MRI and CT images were registered. The processed MRI and PET images were segmented to obtain the corresponding caudate nucleus (CN) segmented images. The complete CN volume of patients was obtained from the pixel points and compared with the calculated value of Pen G's Multifunctional Operative Dissector (PMOD). The accuracy of the established multimodal magnetic resonance (MMR) technique in the diagnosis of PD was determined by manual cutting (MC). The rate of CN lesion was calculated according to the formula to determine the correlation between PD and CN lesion. The results showed that MRI, PET and CT could successfully obtain brain images of patients. After processing, the sharpness of the picture increased, which was easy to observe and provided guarantee for follow-up experiments. Brain MRI and CT image registration can register brain images in different modes to the same space. MRI image can segment the complete CN, while PET image can segment the intact CN. Compared with MC, the probability of CN calculated by the formula had a small error, which could be ignored. The error value was less than the calculated value of PMOD. The rate of CN lesion in the IG was greatly lower than that in the LG (P < 0.05), which showed that the MRI, PET and CT combined multimodal MRI can be used to diagnose PD with a high rate of quasi-discontinuity, which provides a new technique for clinical diagnosis.

ProsRegNet: A deep learning framework for registration of MRI and histopathology images of the prostate.

Magnetic resonance imaging (MRI) is an increasingly important tool for the diagnosis and treatment of prostate cancer. However, interpretation of MRI suffers from high inter-observer variability across radiologists, thereby contributing to missed clinically significant cancers, overdiagnosed low-risk cancers, and frequent false positives. Interpretation of MRI could be greatly improved by providing radiologists with an answer key that clearly shows cancer locations on MRI. Registration of histopathology images from patients who had radical prostatectomy to pre-operative MRI allows such mapping of ground truth cancer labels onto MRI. However, traditional MRI-histopathology registration approaches are computationally expensive and require careful choices of the cost function and registration hyperparameters. This paper presents ProsRegNet, a deep learning-based pipeline to accelerate and simplify MRI-histopathology image registration in prostate cancer. Our pipeline consists of image preprocessing, estimation of affine and deformable transformations by deep neural networks, and mapping cancer labels from histopathology images onto MRI using estimated transformations. We trained our neural network using MR and histopathology images of 99 patients from our internal cohort (Cohort 1) and evaluated its performance using 53 patients from three different cohorts (an additional 12 from Cohort 1 and 41 from two public cohorts). Results show that our deep learning pipeline has achieved more accurate registration results and is at least 20 times faster than a state-of-the-art registration algorithm. This important advance will provide radiologists with highly accurate prostate MRI answer keys, thereby facilitating improvements in the detection of prostate cancer on MRI. Our code is freely available at https://github.com/pimed//ProsRegNet.

Registration of presurgical MRI and histopathology images from radical prostatectomy via RAPSODI.

PurposeMagnetic resonance imaging (MRI) has great potential to improve prostate cancer diagnosis; however, subtle differences between cancer and confounding conditions render prostate MRI interpretation challenging. The tissue collected from patients who undergo radical prostatectomy provides a unique opportunity to correlate histopathology images of the prostate with preoperative MRI to accurately map the extent of cancer from histopathology images onto MRI. We seek to develop an open‐source, easy‐to‐use platform to align presurgical MRI and histopathology images of resected prostates in patients who underwent radical prostatectomy to create accurate cancer labels on MRI.MethodsHere, we introduce RAdiology Pathology Spatial Open‐Source multi‐Dimensional Integration (RAPSODI), the first open‐source framework for the registration of radiology and pathology images. RAPSODI relies on three steps. First, it creates a three‐dimensional (3D) reconstruction of the histopathology specimen as a digital representation of the tissue before gross sectioning. Second, RAPSODI registers corresponding histopathology and MRI slices. Third, the optimized transforms are applied to the cancer regions outlined on the histopathology images to project those labels onto the preoperative MRI.ResultsWe tested RAPSODI in a phantom study where we simulated various conditions, for example, tissue shrinkage during fixation. Our experiments showed that RAPSODI can reliably correct multiple artifacts. We also evaluated RAPSODI in 157 patients from three institutions that underwent radical prostatectomy and have very different pathology processing and scanning. RAPSODI was evaluated in 907 corresponding histpathology‐MRI slices and achieved a Dice coefficient of 0.97 ± 0.01 for the prostate, a Hausdorff distance of 1.99 ± 0.70 mm for the prostate boundary, a urethra deviation of 3.09 ± 1.45 mm, and a landmark deviation of 2.80 ± 0.59 mm between registered histopathology images and MRI.ConclusionOur robust framework successfully mapped the extent of cancer from histopathology slices onto MRI providing labels from training machine learning methods to detect cancer on MRI.

Clinical implementation, logistics and workflow guide for MRI image based interstitial HDR brachytherapy for gynecological cancers.

Interstitial brachytherapy (IBT) is often utilized to treat women with bulky endometrial or cervical cancers not amendable to intracavitary treatments. A modern trend in IBT is the utilization of magnetic resonance imaging (MRI) with a high dose rate (HDR) afterloader for conformal 3D image‐based treatments. The challenging part of this procedure is to properly complete many sequenced and co‐related physics preparations. We presented the physics preparations and clinical workflow required for implementing MRI‐based HDR IBT (MRI‐HDR‐IBT) of gynecologic cancer patients in a high‐volume brachytherapy center. The present document is designed to focus on the clinical steps required from a physicist’s standpoint. Those steps include: (a) testing IBT equipment with MRI scanner, (b) preparation of templates and catheters, (c) preparation of MRI line markers, (d) acquisition, importation and registration of MRI images, (e) development of treatment plans and (f) treatment evaluation and documentation. The checklists of imaging acquisition, registration and plan development are also presented. Based on the TG‐100 recommendations, a workflow chart, a fault tree analysis and an error‐solution table listing the speculated errors and solutions of each step are provided. Our workflow and practice indicated the MRI‐HDR‐IBT is achievable in most radiation oncology clinics if the following equipment is available: MRI scanner, CT (computed tomography) scanner, MRI/CT compatible templates and applicators, MRI line markers, HDR afterloader and a brachytherapy treatment planning system capable of utilizing MRI images. The OR/procedure room availability and anesthesiology support are also important. The techniques and approaches adopted from the GEC‐ESTRO (Groupe Européen de Curiethérapie ‐ European Society for Therapeutic Radiology and Oncology) recommendations and other publications are proven to be feasible. The MRI‐HDR‐IBT program can be developed over time and progressively validated through clinical experience, this document is expected to serve as a reference workflow guideline for implementing and performing the procedure.

Pseudo-CT Generation for External Radiotherapy Planning from Magnetic Resonance Imaging Data on Brain Regions

Background: Nowadays, medical imaging has an important role in radiotherapy and treatment planning process. Despite the increasing usage of the magnetic resonance imaging (MRI) in the external radiotherapy (RT) design process, computed tomography (CT) remains a basic imaging modality in radiotherapy because of its relation with electron density value. In conventional external radiotherapy, MRI is used in functional tissue structures with registration on the CT image, which causes systematic errors during the registration of MRI and CT images. Objectives: The main purpose of this paper was to investigate the possibility of removing the CT simulator and replacing it with pseudo-CT for the electron density calculation in radiotherapy treatment planning. Materials and Methods: The pseudo-CT images were generated for 10 randomly chosen patients with brain disease. The data consisted of image voxels chosen within the segmented area of the brain in both MRI and CT images. The relationship between electron density information in CT images and MRI intensity value was derived from simple polynomial models. Results: It was found that the MRI intensity value is related to the Hounsfield unit (HU) value within different parts including the skull bone, sinus, and brain in CT images. The mean errors of the conversion model were 0.7479, 0.334 and -0.043 HU in the brain, skull, and air regions respectively, with the simple polynomial model in the upper slice. Conclusion: The proposed method generated pseudo-CT images from different segmented parts of the brain of MRI series. The application requires segmentation of the cortical bones in MR images. The result indicated that finding a complex model does not necessarily lead to achieving a better result.

Pseudo-CT Generation from Magnetic Resonance Imaging by fuzzy look up table algorithm

Introduction: Despite growing interest in the use of magnetic resonance imaging (MRI) in the external radiotherapy design process (RT), Computer Tomography (CT) remains a gold standard and is regarded as a basic imaging modality in radiotherapy. MRI shows the high contrast in soft tissues without any radiation exposure to patients. As a result, MRI is used in functional tissue structures with registration on the CT images. Unfortunately, this causes systematic errors during the registration of MRI and CT images. The purpose of this study is to investigate the possibility of removing the CT simulator images and replacing it with pseudo-CT images (which created by MRI images) for the electron density calculation in radiotherapy treatment planning. Materials and Methods: The pseudo-CT images were generated for 10 randomly chosen patients with brain disease. Data consisted of image voxels chosen within the segmented area of the brain in both MRI and CT images. The relation between MRI intensity and electron density was derived from HU converted model by fuzzy look up table algorithm. Results: It was found that the MRI intensity value is related to the HU value within different parts such as skull bone, sinus, and brain. The mean prediction errors of the conversion model are -0.53, -0.082, 0.0145HU in brain, skull and air regions respectively. The mean absolute errors are 19.40 , 50.53,1.22 Conclusion: The proposed method enables generation of pseudo-CT data for the different segmented part of the brain from MRI series with appropriate mean prediction errors.

Development of a dedicated phantom for multi-target single-isocentre stereotactic radiosurgery end to end testing.

PurposeThe aim of this project was to design and manufacture a cost‐effective end‐to‐end (E2E) phantom for quantifying the geometric and dosimetric accuracy of a linear accelerator based, multi‐target single‐isocenter (MTSI) frameless stereotactic radiosurgery (SRS) technique.MethodA perspex Multi‐Plug device from a Sun Nuclear ArcCheck phantom (Sun Nuclear, Melbourne, FL) was enhanced to make it more applicable for MTSI SRS E2E testing. The following steps in the SRS chain were then analysed using the phantom: magnetic resonance imaging (MRI) distortion, planning computed tomography (CT) scan and MRI image registration accuracy, phantom setup accuracy using CBCT, dosimetric accuracy using ion chamber, planar film dose measurements and coincidence of linear accelerator mega‐voltage (MV), and kilo‐voltage (kV) isocenters using Winston‐Lutz testing (WLT).ResultsThe dedicated E2E phantom was able to successfully quantify the geometric and dosimetric accuracy of the MTSI SRS technique. MRI distortions were less than 0.5 mm, or half a voxel size. The average MRI‐CT registration accuracy was 0.15 mm (±0.31 mm), 0.20 mm (±0.16 mm), and 0.39 mm (±0.11 mm) in the superior/inferior, left/right and, anterior/posterior directions, respectively. The phantom setup accuracy using CBCT was better than 0.2 mm and 0.1°. Point dose measurements were within 5% of the treatment planning system predicted dose. The comparison of planar film doses to the planning system dose distributions, performed using gamma analysis, resulted in pass rates greater than 97% for 3%/1 mm gamma criteria. Finally, off‐axis WLT showed MV/kV coincidence to be within 1 mm for off‐axis distances up to 60 mm.ConclusionA novel, versatile and cost‐effective phantom for comprehensive E2E testing of MTSI SRS treatments was developed, incorporating multiple detector types and fiducial markers. The phantom is capable of quantifying the accuracy of each step in the MTSI SRS planning and treatment process.

MR Safe Robotic Manipulator for MRI-Guided Intracardiac Catheterization

This paper introduces a robotic manipulator to realize robot-assisted intracardiac catheterization in magnetic resonance imaging (MRI) environment. MRI can offer high-resolution images to visualize soft tissue features such as scars or edema. We hypothesize that robotic catheterization, combined with the enhanced monitoring of lesions creation using MRI intraoperatively, will significantly improve the procedural safety, accuracy, and effectiveness. This is designed particularly for cardiac electrophysiological (EP) intervention, which is an effective treatment of arrhythmia. We present the first MR Safe robot for intracardiac EP intervention. The robot actuation features small hysteresis, effective force transmission, and quick response, which has been experimentally verified for its capability to precisely telemanipulate a standard clinically used EP catheter. We also present timely techniques for real-time positional tracking in MRI and intraoperative image registration, which can be integrated with the presented manipulator to improve the performance of teleoperated robotic catheterization.

Personalized biomechanical modeling of prostate deformation based on elastography for MRI three-dimensional transrectal ultrasound(TRUS) image registration

Objective To evaluate the clinical significance of personalized biomechanical modeling of prostate deformation based on ultrasound elastography for magnetic resonance imaging (MRI)-transrectal ultrasound (TRUS) image registration. Methods A total number of 5 patients and 1 commercial prostate phantom were imaged via transrectal ultrasound elastography, 3D-TRUS and MRI from June 2016 to December 2016. A personalized biomechanical model via the patient-specific ultrasound elastography was made for the deformable registration of prostate MRI and 3D-TRUS images. The registration accuracy was evaluated by the target registration error (TRE) and also the t-test was conducted to validate the statistical significance of our results. Results All the 5 sets of patient data as well as the phantom data were successfully registered. The TRE value of the phantom data was 1.65 mm. The mean TRE value of 5 patients was 1.31 mm, compared with the 2.52 mm TRE value of the registration method without patient-specific biomechanical properties via elastography, was approximately 48% lower(P<0.05). Conclusions Personalized biomechanical modeling of prostate deformation based on ultrasound elastography for MRI-TRUS image registration possesses important clinical significance and is a promising way to provide more quality guidance and improve the accuracy of prostate biopsy. Key words: Elasticity imaging techniques; Three-dimensional transrectal ultrasound; Medical image registration; Biomechanical model

Fast and robust multimodal image registration using a local derivative pattern.

Deformable multimodal image registration, which can benefit radiotherapy and image guided surgery by providing complementary information, remains a challenging task in the medical image analysis field due to the difficulty of defining a proper similarity measure. This article presents a novel, robust and fast binary descriptor, the discriminative local derivative pattern (dLDP), which is able to encode images of different modalities into similar image representations. dLDP calculates a binary string for each voxel according to the pattern of intensity derivatives in its neighborhood. The descriptor similarity is evaluated using the Hamming distance, which can be efficiently computed, instead of conventional L1 or L2 norms. For the first time, we validated the effectiveness and feasibility of the local derivative pattern for multimodal deformable image registration with several multi-modal registration applications. dLDP was compared with three state-of-the-art methods in artificial image and clinical settings. In the experiments of deformable registration between different magnetic resonance imaging (MRI) modalities from BrainWeb, between computed tomography and MRI images from patient data, and between MRI and ultrasound images from BITE database, we show our method outperforms localized mutual information and entropy images in terms of both accuracy and time efficiency. We have further validated dLDP for the deformable registration of preoperative MRI and three-dimensional intraoperative ultrasound images. Our results indicate that dLDP reduces the average mean target registration error from 4.12mm to 2.30mm. This accuracy is statistically equivalent to the accuracy of the state-of-the-art methods in the study; however, in terms of computational complexity, our method significantly outperforms other methods and is even comparable to the sum of the absolute difference. The results reveal that dLDP can achieve superior performance regarding both accuracy and time efficiency in general multimodal image registration. In addition, dLDP also indicates the potential for clinical ultrasound guided intervention.

Pulmonary Microvascular Blood Flow in Mild Chronic Obstructive Pulmonary Disease and Emphysema. The MESA COPD Study.

Smoking-related microvascular loss causes end-organ damage in the kidneys, heart, and brain. Basic research suggests a similar process in the lungs, but no large studies have assessed pulmonary microvascular blood flow (PMBF) in early chronic lung disease. To investigate whether PMBF is reduced in mild as well as more severe chronic obstructive pulmonary disease (COPD) and emphysema. PMBF was measured using gadolinium-enhanced magnetic resonance imaging (MRI) among smokers with COPD and control subjects age 50 to 79 years without clinical cardiovascular disease. COPD severity was defined by standard criteria. Emphysema on computed tomography (CT) was defined by the percentage of lung regions below -950 Hounsfield units (-950 HU) and by radiologists using a standard protocol. We adjusted for potential confounders, including smoking, oxygenation, and left ventricular cardiac output. Among 144 participants, PMBF was reduced by 30% in mild COPD, by 29% in moderate COPD, and by 52% in severe COPD (all P < 0.01 vs. control subjects). PMBF was reduced with greater percentage emphysema-950HU and radiologist-defined emphysema, particularly panlobular and centrilobular emphysema (all P ≤ 0.01). Registration of MRI and CT images revealed that PMBF was reduced in mild COPD in both nonemphysematous and emphysematous lung regions. Associations for PMBF were independent of measures of small airways disease on CT and gas trapping largely because emphysema and small airways disease occurred in different smokers. PMBF was reduced in mild COPD, including in regions of lung without frank emphysema, and may represent a distinct pathological process from small airways disease. PMBF may provide an imaging biomarker for therapeutic strategies targeting the pulmonary microvasculature.

In Vivo Detection of Perinatal Brain Metabolite Changes in a Rabbit Model of Intrauterine Growth Restriction (IUGR).

BackgroundIntrauterine growth restriction (IUGR) is a risk factor for abnormal neurodevelopment. We studied a rabbit model of IUGR by magnetic resonance imaging (MRI) and spectroscopy (MRS), to assess in vivo brain structural and metabolic consequences, and identify potential metabolic biomarkers for clinical translation.MethodsIUGR was induced in 3 pregnant rabbits at gestational day 25, by 40–50% uteroplacental vessel ligation in one horn; the contralateral horn was used as control. Fetuses were delivered at day 30 and weighted. A total of 6 controls and 5 IUGR pups underwent T2-w MRI and localized proton MRS within the first 8 hours of life, at 7T. Changes in brain tissue volumes and respective contributions to each MRS voxel were estimated by semi-automated registration of MRI images with a digital atlas of the rabbit brain. MRS data were used for: (i) absolute metabolite quantifications, using linear fitting; (ii) local temperature estimations, based on the water chemical shift; and (iii) classification, using spectral pattern analysis.ResultsLower birth weight was associated with (i) smaller brain sizes, (ii) slightly lower brain temperatures, and (iii) differential metabolite profile changes in specific regions of the brain parenchyma. Specifically, we found estimated lower levels of aspartate and N-acetylaspartate (NAA) in the cerebral cortex and hippocampus (suggesting neuronal impairment), and higher glycine levels in the striatum (possible marker of brain injury). Our results also suggest that the metabolic changes in cortical regions are more prevalent than those detected in hippocampus and striatum.ConclusionsIUGR was associated with brain metabolic changes in vivo, which correlate well with the neurostructural changes and neurodevelopment problems described in IUGR. Metabolic parameters could constitute non invasive biomarkers for the diagnosis and abnormal neurodevelopment of perinatal origin.

Value of Multiparametric MRI in the Work-up of Prostate Cancer

The role of magnetic resonance imaging (MRI) in prostate cancer evaluation is controversial and likely underestimated. Technological advances over the past 5years have demonstrated that multiparametric MRI, including diffusion-weighted imaging (DWI) and dynamic contrast-enhanced MRI, can evaluate the actual tumor burden of a newly diagnosed prostate cancer more accurately than sextant biopsy protocols. Tumor risk, defined by the D'Amico criteria, hence can be re-evaluated by multiparametric MRI. As a result, there is increasing evidence that MRI before repeat or even initial biopsy can accurately select patients who require immediate biopsies and those in whom biopsy could be deferred. Also, a relationship between apparent diffusion coefficient (ADC), calculated from DWI, and Gleason score was found. Thus, MRI before biopsy helps to detect high-grade tumors to target biopsies within areas of low ADC values. To achieve good targeting accuracy, transrectal ultrasound (TRUS)-MRI image registration is necessary. Three-dimensional deformable registration is sufficiently accurate to match TRUS and MRI volumes with a topographic precision of 1mm. Real-time MRI-guided biopsy is another technique under evaluation. Both approaches will allow for increasing acceptance of focal therapies, should these techniques be validated in the future.

Preliminary Study Evaluating the Accuracy of MRI Images on CBCT Images in the Field of Orthodontics

The purpose of this study was to explore the 3-dimensional (3D) accuracy of magnetic resonance imaging (MRI) on cone-beam computed tomography (CBCT) images after the registration of MRI images on CBCT images. Three Japanese adult females volunteered for this study. To transform digital imaging and communication in medicine (DICOM) data derived from MRI and CBCT images into polygon data, five software programs were used. CBCT and MRI images were obtained within one week, and both were registered by the iterative closest point (ICP) method. To assess the accuracy of the composite MRI-CBCT, the measurement errors of the MRI-CBCT were verified Measurement values were compared using frontal and cephalometric soft-tissue landmarks. Differences were analyzed using the non-parametric Mann-Whitney U test. There were no significant linear measurement errors (P > 0.05) when the images were measured from the superimposed MRI-CBCT images. The MRI images attained from MRI - CBCT registration showed accurate 3D linear measurements.

WE‐E‐220‐04: Focused Ultrasound Ablation of Tumour Hypoxic Tissue of Small Animals under PET and MRI Guidance

Purpose: Motivated by the need of alleviating tumor hypoxia‐related resistance to radiation therapy, we propose to develop a magnetic resonance imaging (MRI) guided focused ultrasound system (MRgFUS) incorporating positron emission tomography (PET) hypoxia imaging to detect and coagulate the hypoxic areas in mouse tumors before radiation treatment. We plan to use this system to test the hypothesis that thermal coagulation of evidently hypoxic regions of a tumor can improve radiotherapy outcome. Methods: A prototype system was established to seamlessly integrate the processes of PET imaging and MRgFUS ablation. It includes (1) A small animal specialized MR‐compatible FUS applicator using a spherically‐focused ultrasound transducer. (2) 18F‐ fluoromisonidazole microPET imaging (Siemens) to acquire and correlate the uptake of 18F‐miso tracers to hypoxic tissue in tumors. (3) A small animal positioning and immobilization device that is compatible both to PET and MRI, and can facilitate anesthesia supply, breathing sensor and FUS applicator. (4) An effective method to register PET images with MRI images so that the regions of hypoxic, necrotic, and normoxic tissue can be delineated within a tumor. (5) MRgFUS ablation of the segmented tumor hypoxic areas with the guidance of MRI‐acquired (7.0 T Bruker) temperature maps. To test the robustness of this system, we conducted in vivo experiments on six A/J mice bearing 4T1 tumors. Results: The tumor hypoxic regions can be clearly distinguished by 18F‐miso PET scans of mouse tumors with i.v. injection of 1 mCi/mouse and 1.5–2 hours of uptake. The positioning and immobilization device allowed accurate registration of PET and MRI images. Accurate targeting and heating (error &lt;2 mm) was achieved with MRI‐temperature guidance (resolution: 0.2×0.5mm, per 4 s). Conclusions: The overall system/approach has proven the feasibility of PET+MRgFUS and should be useful in the study of combined treatment strategies incorporating radiation and thermal therapy using small animals.

Computed Tomography–Magnetic Resonance Image Registration in Radiotherapy Treatment Planning

Magnetic resonance imaging (MRI) is being increasingly used in radiotherapy treatment planning (RTP). MRI has the potential to provide improved localisation of target volumes, leading to better tumour control rates and reduced normal tissue complications, due to capabilities including excellent soft-tissue discrimination and the ability to provide scans in which the image contrast is weighted according to different tissue properties. When computed tomography (CT)–MRI image registration is deployed, MR’s advantages are combined with CT’s geometrical security and its ability to provide electron density information. The quality of CT–MRI image registration can be favourably influenced by aspects of scan acquisition, including patient positioning/immobilisation and scan protocols. Appropriate protocols can ameliorate the possible presence of MR spatial distortions and other artefacts, but quality assurance of scanning remains essential. Here, the methods and quality assurance of CT–MR image registration are discussed. Developments in MRI scanner technology are progressively offering advantages for RTP, in terms of the possibility of better matching of patient positioning versus CT in a greater range of anatomical regions, while allowing thinner slices for better image quality in reformatted orthogonal planes.