Volume and shape in feature space on adaptive FCM in MRI segmentation.

Intensity non-uniformity (bias field) correction, contextual constraints over spatial intensity distribution and non-spherical cluster's shape in the feature space are incorporated into the fuzzy c-means (FCM) for segmentation of three-dimensional multi-spectral MR images. The bias field is modeled by a linear combination of smooth polynomial basis functions for fast computation in the clustering iterations. Regularization terms for the neighborhood continuity of either intensity or membership are added into the FCM cost functions. Since the feature space is not isotropic, distance measures, other than the Euclidean distance, are used to account for the shape and volumetric effects of clusters in the feature space. The performance of segmentation is improved by combining the adaptive FCM scheme with the criteria used in Gustafson-Kessel (G-K) and Gath-Geva (G-G) algorithms through the inclusion of the cluster scatter measure. The performance of this integrated approach is quantitatively evaluated on normal MR brain images using the similarity measures. The improvement in the quality of segmentation obtained with our method is also demonstrated by comparing our results with those produced by FSL (FMRIB Software Library), a software package that is commonly used for tissue classification.

Quantitative MRI of spinal cord injury in a rat model: Correlative studies

Serial quantitative and correlative studies of experimental spinal cord injury (SCI) in rats were conducted using three-dimensional magnetic resonance imaging (MRI). Correlative measures included morphological histopathology, neurobehavioral measures of functional deficit, and biochemical assays for N-acetyl-aspartate (NAA), lactate, pyruvate, and ATP. A spinal cord injury device was characterized and provided a reproducible injury severity. Injuries were moderate and consistent to within $\pm$20% (standard deviation). For MRI, a three-dimensional implementation of the single spin-echo FATE (Fast optimum angle, short TE) pulse sequence was used for rapid acquisition, with a 128 x 128 x 32 (x,y,z) matrix size and a 0.21 x 0.21 x 1.5 mm resolution. These serial studies revealed a bimodal characteristic in the evolution in MRI pathology with time. Early and late phases of SCI pathology were clearly visualized in $T\sb2$-weighted MRI, and these corresponded to specific histopathological changes in the spinal cord. Centralized hypointense MRI regions correlated with evidence of hemorrhagic and necrotic tissue, while surrounding hyperintense regions represented edema or myelomalacia. Unexpectedly, $T\sb2$-weighted MRI pathology contrast at 24 hours after injury appeared to subside before peaking at 72 hours after injury. This change is likely attributable to ongoing secondary injury processes, which may alter local $T\sb2$ values or reduce the natural anisotropy of the spinal cord. MRI, functional, and histological measures all indicated that 72 hours after injury was the temporal maximum for quantitative measures of spinal cord pathology. Thereafter, significant improvement was seen only in neurobehavioral scores. Significant correlations were found between quantitated MRI pathology and histopathology. Also, NAA and lactate levels correlated with behavioral measures of the level of function deficit. Asymmetric (rostral/caudal) changes in NAA and lactate due to injury indicate that rostral and caudal segments from the injury site are affected differently by the injury. These studies indicate that volumetric quantitation of MRI pathology from $T\sb2$-weighted images may play an important role in early prediction of neurologic deficit and spinal cord pathology. The loss of $T\sb2$ contrast at 24 hours suggests MR may be able to detect certain delayed mechanisms of secondary injury which are not resolved by histopathology or other radiological modalities. Furthermore, in vivo proton magnetic resonance spectroscopy (MRS) studies of SCI may provide a valuable addition source of information about changes in regional spinal cord lactate and NAA levels, which are indicative of local metabolic and pathological changes. ^

State-of-the-art aortic imaging: part I - fundamentals and perspectives of CT and MRI

Over the last two decades, imaging of the aorta has undergone a clinically relevant change. As part of the change non-invasive imaging techniques have replaced invasive intra-arterial digital subtraction angiography as the former imaging gold standard for aortic diseases. Computed tomography (CT) and magnetic resonance imaging (MRI) constitute the backbone of pre- and postoperative aortic imaging because they allow for imaging of the entire aorta and its branches. The first part of this review article describes the imaging principles of CT and MRI with regard to aortic disease, shows how both technologies can be applied in every day clinical practice, offering exciting perspectives. Recent CT scanner generations deliver excellent image quality with a high spatial and temporal resolution. Technical developments have resulted in CT scan performed within a few seconds for the entire aorta. Therefore, CT angiography (CTA) is the imaging technology of choice for evaluating acute aortic syndromes, for diagnosis of most aortic pathologies, preoperative planning and postoperative follow-up after endovascular aortic repair. However, radiation dose and the risk of contrast induced nephropathy are major downsides of CTA. Optimisation of scan protocols and contrast media administration can help to reduce the required radiation dose and contrast media. MR angiography (MRA) is an excellent alternative to CTA for both diagnosis of aortic pathologies and postoperative follow-up. The lack of radiation is particularly beneficial for younger patients. A potential side effect of gadolinium contrast agents is nephrogenic systemic fibrosis (NSF). In patients with high risk of NSF unenhanced MRA can be performed with both ECG- and breath-gating techniques. Additionally, MRI provides the possibility to visualise and measure both dynamic and flow information.

Multi-modal glioblastoma segmentation: man versus machine

BACKGROUND AND PURPOSE Reproducible segmentation of brain tumors on magnetic resonance images is an important clinical need. This study was designed to evaluate the reliability of a novel fully automated segmentation tool for brain tumor image analysis in comparison to manually defined tumor segmentations. METHODS We prospectively evaluated preoperative MR Images from 25 glioblastoma patients. Two independent expert raters performed manual segmentations. Automatic segmentations were performed using the Brain Tumor Image Analysis software (BraTumIA). In order to study the different tumor compartments, the complete tumor volume TV (enhancing part plus non-enhancing part plus necrotic core of the tumor), the TV+ (TV plus edema) and the contrast enhancing tumor volume CETV were identified. We quantified the overlap between manual and automated segmentation by calculation of diameter measurements as well as the Dice coefficients, the positive predictive values, sensitivity, relative volume error and absolute volume error. RESULTS Comparison of automated versus manual extraction of 2-dimensional diameter measurements showed no significant difference (p = 0.29). Comparison of automated versus manual segmentation of volumetric segmentations showed significant differences for TV+ and TV (p<0.05) but no significant differences for CETV (p>0.05) with regard to the Dice overlap coefficients. Spearman's rank correlation coefficients (ρ) of TV+, TV and CETV showed highly significant correlations between automatic and manual segmentations. Tumor localization did not influence the accuracy of segmentation. CONCLUSIONS In summary, we demonstrated that BraTumIA supports radiologists and clinicians by providing accurate measures of cross-sectional diameter-based tumor extensions. The automated volume measurements were comparable to manual tumor delineation for CETV tumor volumes, and outperformed inter-rater variability for overlap and sensitivity.

Patient-Specific Semi-Supervised Learning for Postoperative Brain Tumor Segmentation

In contrast to preoperative brain tumor segmentation, the problem of postoperative brain tumor segmentation has been rarely approached so far. We present a fully-automatic segmentation method using multimodal magnetic resonance image data and patient-specific semi-supervised learning. The idea behind our semi-supervised approach is to effectively fuse information from both pre- and postoperative image data of the same patient to improve segmentation of the postoperative image. We pose image segmentation as a classification problem and solve it by adopting a semi-supervised decision forest. The method is evaluated on a cohort of 10 high-grade glioma patients, with segmentation performance and computation time comparable or superior to a state-of-the-art brain tumor segmentation method. Moreover, our results confirm that the inclusion of preoperative MR images lead to a better performance regarding postoperative brain tumor segmentation.

Response to neoadjuvant treatment of invasive ductal breast carcinomas including outcome evaluation: MRI analysis by an automatic CAD system in comparison to visual evaluation

BACKGROUND The aim of this study was to evaluate imaging-based response to standardized neoadjuvant chemotherapy (NACT) regimen by dynamic contrast-enhanced magnetic resonance mammography (DCE-MRM), whereas MR images were analyzed by an automatic computer-assisted diagnosis (CAD) system in comparison to visual evaluation. MRI findings were correlated with histopathologic response to NACT and also with the occurrence of metastases in a follow-up analysis. PATIENTS AND METHODS Fifty-four patients with invasive ductal breast carcinomas received two identical MRI examinations (before and after NACT; 1.5T, contrast medium gadoteric acid). Pre-therapeutic images were compared with post-therapeutic examinations by CAD and two blinded human observers, considering morphologic and dynamic MRI parameters as well as tumor size measurements. Imaging-assessed response to NACT was compared with histopathologically verified response. All clinical, histopathologic, and DCE-MRM parameters were correlated with the occurrence of distant metastases. RESULTS Initial and post-initial dynamic parameters significantly changed between pre- and post-therapeutic DCE-MRM. Visually evaluated DCE-MRM revealed sensitivity of 85.7%, specificity of 91.7%, and diagnostic accuracy of 87.0% in evaluating the response to NACT compared to histopathology. CAD analysis led to more false-negative findings (37.0%) compared to visual evaluation (11.1%), resulting in sensitivity of 52.4%, specificity of 100.0%, and diagnostic accuracy of 63.0%. The following dynamic MRI parameters showed significant associations to occurring metastases: Post-initial curve type before NACT (entire lesions, calculated by CAD) and post-initial curve type of the most enhancing tumor parts after NACT (calculated by CAD and manually). CONCLUSIONS In the accurate evaluation of response to neoadjuvant treatment, CAD systems can provide useful additional information due to the high specificity; however, they cannot replace visual imaging evaluation. Besides traditional prognostic factors, contrast medium-induced dynamic MRI parameters reveal significant associations to patient outcome, i.e. occurrence of distant metastases.

The trouble with quality filtering based on relative Cramér-Rao lower bounds.

Cramér Rao Lower Bounds (CRLB) have become the standard for expression of uncertainties in quantitative MR spectroscopy. If properly interpreted as a lower threshold of the error associated with model fitting, and if the limits of its estimation are respected, CRLB are certainly a very valuable tool to give an idea of minimal uncertainties in magnetic resonance spectroscopy (MRS), although other sources of error may be larger. Unfortunately, it has also become standard practice to use relative CRLB expressed as a percentage of the presently estimated area or concentration value as unsupervised exclusion criterion for bad quality spectra. It is shown that such quality filtering with widely used threshold levels of 20% to 50% CRLB readily causes bias in the estimated mean concentrations of cohort data, leading to wrong or missed statistical findings-and if applied rigorously-to the failure of using MRS as a clinical instrument to diagnose disease characterized by low levels of metabolites. Instead, absolute CRLB in comparison to those of the normal group or CRLB in relation to normal metabolite levels may be more useful as quality criteria. Magn Reson Med, 2015. © 2015 Wiley Periodicals, Inc.

Noninvasive pressure difference mapping derived from 4D flow MRI in patients with unrepaired and repaired aortic coarctation.

PURPOSE To develop a method for computing and visualizing pressure differences derived from time-resolved velocity-encoded three-dimensional phase-contrast magnetic resonance imaging (4D flow MRI) and to compare pressure difference maps of patients with unrepaired and repaired aortic coarctation to young healthy volunteers. METHODS 4D flow MRI data of four patients with aortic coarctation either before or after repair (mean age 17 years, age range 3-28, one female, three males) and four young healthy volunteers without history of cardiovascular disease (mean age 24 years, age range 20-27, one female, three males) was acquired using a 1.5-T clinical MR scanner. Image analysis was performed with in-house developed image processing software. Relative pressures were computed based on the Navier-Stokes equation. RESULTS A standardized method for intuitive visualization of pressure difference maps was developed and successfully applied to all included patients and volunteers. Young healthy volunteers exhibited smooth and regular distribution of relative pressures in the thoracic aorta at mid systole with very similar distribution in all analyzed volunteers. Patients demonstrated disturbed pressures compared to volunteers. Changes included a pressure drop at the aortic isthmus in all patients, increased relative pressures in the aortic arch in patients with residual narrowing after repair, and increased relative pressures in the descending aorta in a patient after patch aortoplasty. CONCLUSIONS Pressure difference maps derived from 4D flow MRI can depict alterations of spatial pressure distribution in patients with repaired and unrepaired aortic coarctation. The technique might allow identifying pathophysiological conditions underlying complications after aortic coarctation repair.

In vitro study to simulate the intracardiac magnetohydrodynamic effect

PURPOSE Blood flow causes induced voltages via the magnetohydrodynamic (MHD) effect distorting electrograms (EGMs) made during magnetic resonance imaging. To investigate the MHD effect in this context MHD voltages occurring inside the human heart were simulated in an in vitro model system inside a 1.5 T MR system. METHODS The model was developed to produce MHD signals similar to those produced by intracardiac flow and to acquire them using standard clinical equipment. Additionally, a new approach to estimate MHD distortions on intracardiac electrograms is proposed based on the analytical calculation of the MHD signal from MR phase contrast data. RESULTS The recorded MHD signals were similar in magnitude to intracardiac signals that would be measured by an electrogram of the left ventricle. The dependency of MHD signals on magnetic field strength and electrode separation was well reflected by an analytical model. MHD signals reconstructed from MR flow data were in excellent agreement with the MHD signal measured by clinical equipment. CONCLUSION The in vitro model allows investigation of MHD effects on intracardiac electrograms. A phase contrast MR scan was successfully applied to characterize and estimate the MHD distortion on intracardiac signals allowing correction of these effects. Magn Reson Med, 2014. © 2014 Wiley Periodicals, Inc.

Influence of muscle fiber orientation on water and metabolite relaxation times, magnetization transfer, and visibility in human skeletal muscle

PURPOSE To gain a deeper understanding of the influence of skeletal muscle fiber orientation on metabolite visibility, magnetization transfer from water, and water proton relaxation rates in (1) H MR spectra. METHODS Non-water-suppressed MR spectroscopy was performed in tibialis anterior muscle (TA) of 10 healthy adults, with the TA oriented either parallel or at the magic angle to the 3T field. Spectra were acquired with metabolite-cycled PRESS, and water inversion from 50 to 2510 ms before excitation. Water proton T2 relaxation was sampled with STEAM with echo times from 12 to 272 ms. RESULTS Apparent concentrations of total creatine (tCr), taurine, and trimethylammonium compounds were reduced by 29% to 67% when TA was parallel to B0 . Both tCr peak areas were strongly correlated to the methylene peak splitting. Magnetization transfer rates from water to tCr CH3 were not significantly different between orientations. Water T1 s were similar between orientations, but T2 s were statistically significantly shorter by 1 ms in the parallel orientation (P = 0.002). CONCLUSION Muscle metabolite visibilities in MR spectroscopy and water T2 times depend substantially on muscle fiber orientation relative to B0 . In contrast, magnetization transfer rates appear to depend on muscle composition, rather than fiber orientation. Magn Reson Med, 2015. © 2015 Wiley Periodicals, Inc.

fMRI-Compatible Electromagnetic Haptic Interface

A new haptic interface device is suggested, which can be used for functional magnetic resonance imaging (fMRI) studies. The basic component of this 1 DOF haptic device are two coils that produce a Lorentz force induced by the large static magnetic field of the MR scanner. A MR-compatible optical angular encoder and a optical force sensor enable the implementation of different control architectures for haptic interactions. The challenge was to provide a large torque, and not to affect image quality by the currents applied in the device. The haptic device was tested in a 3T MR scanner. With a current of up to 1A and a distance of 1m to the focal point of the MR-scanner it was possible to generate torques of up to 4 Nm. Within these boundaries image quality was not affected.

NOVEL PHANTOMS AND POST-PROCESSING FOR DIFFUSION SPECTRUM IMAGING

High Angular Resolution Diffusion Imaging (HARDI) techniques, including Diffusion Spectrum Imaging (DSI), have been proposed to resolve crossing and other complex fiber architecture in the human brain white matter. In these methods, directional information of diffusion is inferred from the peaks in the orientation distribution function (ODF). Extensive studies using histology on macaque brain, cat cerebellum, rat hippocampus and optic tracts, and bovine tongue are qualitatively in agreement with the DSI-derived ODFs and tractography. However, there are only two studies in the literature which validated the DSI results using physical phantoms and both these studies were not performed on a clinical MRI scanner. Also, the limited studies which optimized DSI in a clinical setting, did not involve a comparison against physical phantoms. Finally, there is lack of consensus on the necessary pre- and post-processing steps in DSI; and ground truth diffusion fiber phantoms are not yet standardized. Therefore, the aims of this dissertation were to design and construct novel diffusion phantoms, employ post-processing techniques in order to systematically validate and optimize (DSI)-derived fiber ODFs in the crossing regions on a clinical 3T MR scanner, and develop user-friendly software for DSI data reconstruction and analysis. Phantoms with a fixed crossing fiber configuration of two crossing fibers at 90° and 45° respectively along with a phantom with three crossing fibers at 60°, using novel hollow plastic capillaries and novel placeholders, were constructed. T2-weighted MRI results on these phantoms demonstrated high SNR, homogeneous signal, and absence of air bubbles. Also, a technique to deconvolve the response function of an individual peak from the overall ODF was implemented, in addition to other DSI post-processing steps. This technique greatly improved the angular resolution of the otherwise unresolvable peaks in a crossing fiber ODF. The effects of DSI acquisition parameters and SNR on the resultant angular accuracy of DSI on the clinical scanner were studied and quantified using the developed phantoms. With a high angular direction sampling and reasonable levels of SNR, quantification of a crossing region in the 90°, 45° and 60° phantoms resulted in a successful detection of angular information with mean ± SD of 86.93°±2.65°, 44.61°±1.6° and 60.03°±2.21° respectively, while simultaneously enhancing the ODFs in regions containing single fibers. For the applicability of these validated methodologies in DSI, improvement in ODFs and fiber tracking from known crossing fiber regions in normal human subjects were demonstrated; and an in-house software package in MATLAB which streamlines the data reconstruction and post-processing for DSI, with easy to use graphical user interface was developed. In conclusion, the phantoms developed in this dissertation offer a means of providing ground truth for validation of reconstruction and tractography algorithms of various diffusion models (including DSI). Also, the deconvolution methodology (when applied as an additional DSI post-processing step) significantly improved the angular accuracy of the ODFs obtained from DSI, and should be applicable to ODFs obtained from the other high angular resolution diffusion imaging techniques.

Multimodal non-rigid image registration methods for therapy and surgery planning

La planificación pre-operatoria se ha convertido en una tarea esencial en cirugías y terapias de marcada complejidad, especialmente aquellas relacionadas con órgano blando. Un ejemplo donde la planificación preoperatoria tiene gran interés es la cirugía hepática. Dicha planificación comprende la detección e identificación precisa de las lesiones individuales y vasos así como la correcta segmentación y estimación volumétrica del hígado funcional. Este proceso es muy importante porque determina tanto si el paciente es un candidato adecuado para terapia quirúrgica como la definición del abordaje a seguir en el procedimiento. La radioterapia de órgano blando es un segundo ejemplo donde la planificación se requiere tanto para la radioterapia externa convencional como para la radioterapia intraoperatoria. La planificación comprende la segmentación de tumor y órganos vulnerables y la estimación de la dosimetría. La segmentación de hígado funcional y la estimación volumétrica para planificación de la cirugía se estiman habitualmente a partir de imágenes de tomografía computarizada (TC). De igual modo, en la planificación de radioterapia, los objetivos de la radiación se delinean normalmente sobre TC. Sin embargo, los avances en las tecnologías de imagen de resonancia magnética (RM) están ofreciendo progresivamente ventajas adicionales. Por ejemplo, se ha visto que el ratio de detección de metástasis hepáticas es significativamente superior en RM con contraste Gd–EOB–DTPA que en TC. Por tanto, recientes estudios han destacado la importancia de combinar la información de TC y RM para conseguir el mayor nivel posible de precisión en radioterapia y para facilitar una descripción precisa de las lesiones del hígado. Con el objetivo de mejorar la planificación preoperatoria en ambos escenarios se precisa claramente de un algoritmo de registro no rígido de imagen. Sin embargo, la gran mayoría de sistemas comerciales solo proporcionan métodos de registro rígido. Las medidas de intensidad de voxel han demostrado ser criterios de similitud de imágenes robustos, y, entre ellas, la Información Mutua (IM) es siempre la primera elegida en registros multimodales. Sin embargo, uno de los principales problemas de la IM es la ausencia de información espacial y la asunción de que las relaciones estadísticas entre las imágenes son homogéneas a lo largo de su domino completo. La hipótesis de esta tesis es que la incorporación de información espacial de órganos al proceso de registro puede mejorar la robustez y calidad del mismo, beneficiándose de la disponibilidad de las segmentaciones clínicas. En este trabajo, se propone y valida un esquema de registro multimodal no rígido 3D usando una nueva métrica llamada Información Mutua Centrada en el Órgano (Organ-Focused Mutual Information metric (OF-MI)) y se compara con la formulación clásica de la Información Mutua. Esto permite mejorar los resultados del registro en áreas problemáticas incorporando información regional al criterio de similitud, beneficiándose de la disponibilidad real de segmentaciones en protocolos estándares clínicos, y permitiendo que la dependencia estadística entre las dos modalidades de imagen difiera entre órganos o regiones. El método propuesto se ha aplicado al registro de TC y RM con contraste Gd–EOB–DTPA así como al registro de imágenes de TC y MR para planificación de radioterapia intraoperatoria rectal. Adicionalmente, se ha desarrollado un algoritmo de apoyo de segmentación 3D basado en Level-Sets para la incorporación de la información de órgano en el registro. El algoritmo de segmentación se ha diseñado específicamente para la estimación volumétrica de hígado sano funcional y ha demostrado un buen funcionamiento en un conjunto de imágenes de TC abdominales. Los resultados muestran una mejora estadísticamente significativa de OF-MI comparada con la Información Mutua clásica en las medidas de calidad de los registros; tanto con datos simulados (p<0.001) como con datos reales en registro hepático de TC y RM con contraste Gd– EOB–DTPA y en registro para planificación de radioterapia rectal usando OF-MI multi-órgano (p<0.05). Adicionalmente, OF-MI presenta resultados más estables con menor dispersión que la Información Mutua y un comportamiento más robusto con respecto a cambios en la relación señal-ruido y a la variación de parámetros. La métrica OF-MI propuesta en esta tesis presenta siempre igual o mayor precisión que la clásica Información Mutua y consecuentemente puede ser una muy buena alternativa en aplicaciones donde la robustez del método y la facilidad en la elección de parámetros sean particularmente importantes. Abstract Pre-operative planning has become an essential task in complex surgeries and therapies, especially for those affecting soft tissue. One example where soft tissue preoperative planning is of high interest is liver surgery. It involves the accurate detection and identification of individual liver lesions and vessels as well as the proper functional liver segmentation and volume estimation. This process is very important because it determines whether the patient is a suitable candidate for surgical therapy and the type of procedure. Soft tissue radiation therapy is a second example where planning is required for both conventional external and intraoperative radiotherapy. It involves the segmentation of the tumor target and vulnerable organs and the estimation of the planned dose. Functional liver segmentations and volume estimations for surgery planning are commonly estimated from computed tomography (CT) images. Similarly, in radiation therapy planning, targets to be irradiated and healthy and vulnerable tissues to be protected from irradiation are commonly delineated on CT scans. However, developments in magnetic resonance imaging (MRI) technology are progressively offering advantages. For instance, the hepatic metastasis detection rate has been found to be significantly higher in Gd–EOB–DTPAenhanced MRI than in CT. Therefore, recent studies highlight the importance of combining the information from CT and MRI to achieve the highest level of accuracy in radiotherapy and to facilitate accurate liver lesion description. In order to improve those two soft tissue pre operative planning scenarios, an accurate nonrigid image registration algorithm is clearly required. However, the vast majority of commercial systems only provide rigid registration. Voxel intensity measures have been shown to be robust measures of image similarity, and among them, Mutual Information (MI) is always the first candidate in multimodal registrations. However, one of the main drawbacks of Mutual Information is the absence of spatial information and the assumption that statistical relationships between images are the same over the whole domain of the image. The hypothesis of the present thesis is that incorporating spatial organ information into the registration process may improve the registration robustness and quality, taking advantage of the clinical segmentations availability. In this work, a multimodal nonrigid 3D registration framework using a new Organ- Focused Mutual Information metric (OF-MI) is proposed, validated and compared to the classical formulation of the Mutual Information (MI). It allows improving registration results in problematic areas by adding regional information into the similitude criterion taking advantage of actual segmentations availability in standard clinical protocols and allowing the statistical dependence between the two modalities differ among organs or regions. The proposed method is applied to CT and T1 weighted delayed Gd–EOB–DTPA-enhanced MRI registration as well as to register CT and MRI images in rectal intraoperative radiotherapy planning. Additionally, a 3D support segmentation algorithm based on Level-Sets has been developed for the incorporation of the organ information into the registration. The segmentation algorithm has been specifically designed for the healthy and functional liver volume estimation demonstrating good performance in a set of abdominal CT studies. Results show a statistical significant improvement of registration quality measures with OF-MI compared to MI with both simulated data (p<0.001) and real data in liver applications registering CT and Gd–EOB–DTPA-enhanced MRI and in registration for rectal radiotherapy planning using multi-organ OF-MI (p<0.05). Additionally, OF-MI presents more stable results with smaller dispersion than MI and a more robust behavior with respect to SNR changes and parameters variation. The proposed OF-MI always presents equal or better accuracy than the classical MI and consequently can be a very convenient alternative within applications where the robustness of the method and the facility to choose the parameters are particularly important.

Relationship between solar radiation on watercore on apple fruit assessed with MRI

This work is a preliminary studio of the possibility of assess a relationship between solar radiation and watercore development on apple fruit, during maturation, using a non destructive method such as Magnetic Resonance Imaging (MRI). For such purpose, several low cost solar radiation sensors were designed for the trial and placed at 2 different heights (1.5 and 2.5 m) on 6 adult ?Esperiega? apple trees, in a commercial orchard in Ademuz (Valencia). Sensors were connected along 27 days, during the end of the growth period and start of the fruit maturation process, and radiation measurements of the a-Si sensors were recorded every 1 minute. At the end of this period, fruits from the upper and the lower part of the canopy of each tree were harvested. In all, 152 apples were collected and images with MRI. A Principal Component Analysis, perfomed over the histograms of the images, as well as segmentation methods were performed on the MR images in order to find a pattern involving solar radiation and watercore incidence.