Norberto Malpica

Single-image super-resolution of brain MR images using overcomplete dictionaries

Resolution in Magnetic Resonance (MR) is limited by diverse physical, technological and economical considerations. In conventional medical practice, resolution enhancement is usually performed with bicubic or B-spline interpolations, strongly affecting the accuracy of subsequent processing steps such as segmentation or registration. This paper presents a sparse-based super-resolution method, adapted for easily including prior knowledge, which couples up high and low frequency information so that a high-resolution version of a low-resolution brain MR image is generated. The proposed approach includes a whole-image multi-scale edge analysis and a dimensionality reduction scheme, which results in a remarkable improvement of the computational speed and accuracy, taking nearly 26 min to generate a complete 3D high-resolution reconstruction. The method was validated by comparing interpolated and reconstructed versions of 29 MR brain volumes with the original images, acquired in a 3T scanner, obtaining a reduction of 70% in the root mean squared error, an increment of 10.3 dB in the peak signal-to-noise ratio, and an agreement of 85% in the binary gray matter segmentations. The proposed method is shown to outperform a recent state-of-the-art algorithm, suggesting a substantial impact in voxel-based morphometry studies.

Web-PACS for Multicenter Clinical Trials

Medical information systems are not designed for clinical trials using clinical imaging. This paper presents a conceptual model for clinical trials based on medical imaging from two complementary points of view: a technical model and a business model. A Web information system (WIS) for supporting multicenter clinical trials has been designed to implement the proposed model. We show that our approach overcomes the actual limitations by facilitating medical image management in the context of clinical trials or cooperative research

Parallel transmit pulse design for patients with deep brain stimulation implants

Specific absorption rate (SAR) amplification around active implantable medical devices during diagnostic MRI procedures poses a potential risk for patient safety. In this study, we present a parallel transmit (pTx) strategy that can be used to safely scan patients with deep brain stimulation (DBS) implants.

Fast Patch-Based Pseudo-CT Synthesis from T1-Weighted MR Images for PET/MR Attenuation Correction in Brain Studies

Attenuation correction in hybrid PET/MR scanners is still a challenging task. This paper describes a methodology for synthesizing a pseudo-CT volume from a single T1-weighted volume, thus allowing us to create accurate attenuation correction maps. Methods: We propose a fast pseudo-CT volume generation from a patient-specific MR T1-weighted image using a groupwise patch-based approach and an MRI–CT atlas dictionary. For every voxel in the input MR image, we compute the similarity of the patch containing that voxel to the patches of all MR images in the database that lie in a certain anatomic neighborhood. The pseudo-CT volume is obtained as a local weighted linear combination of the CT values of the corresponding patches. The algorithm was implemented in a graphical processing unit (GPU). Results: We evaluated our method both qualitatively and quantitatively for PET/MR correction. The approach performed successfully in all cases considered. We compared the SUVs of the PET image obtained after attenuation correction using the patient-specific CT volume and using the corresponding computed pseudo-CT volume. The patient-specific correlation between SUV obtained with both methods was high (R2 = 0.9980, P < 0.0001), and the Bland–Altman test showed that the average of the differences was low (0.0006 ± 0.0594). A region-of-interest analysis was also performed. The correlation between SUVmean and SUVmax for every region was high (R2 = 0.9989, P < 0.0001, and R2 = 0.9904, P < 0.0001, respectively). Conclusion: The results indicate that our method can accurately approximate the patient-specific CT volume and serves as a potential solution for accurate attenuation correction in hybrid PET/MR systems. The quality of the corrected PET scan using our pseudo-CT volume is comparable to having acquired a patient-specific CT scan, thus improving the results obtained with the ultrashort-echo-time–based attenuation correction maps currently used in the scanner. The GPU implementation substantially decreases computational time, making the approach suitable for real applications.

SAR reduction in 7T C-spine imaging using a "dark modes" transmit array strategy.

Local specific absorption rate (SAR) limits many applications of parallel transmit (pTx) in ultra high‐field imaging. In this Note, we introduce the use of an array element, which is intentionally inefficient at generating spin excitation (a “dark mode”) to attempt a partial cancellation of the electric field from those elements that do generate excitation. We show that adding dipole elements oriented orthogonal to their conventional orientation to a linear array of conventional loop elements can lower the local SAR hotspot in a C‐spine array at 7 T.

Project based learning experience in VHDL digital electronic circuit design

In this paper we present our experience in teaching digital electronic circuit and system design with FPGAs using VHDL. The course follows a Project Based Learning methodology, in which the students learn how to design digital circuits and systems in a practical way. During the course, students design electronic circuits of incremental complexity. At the end of the course they are capable of implementing relatively complex projects, such as image processing systems and videogames.

Automatic Segmentation of the Liver in CT Using Level Sets Without Edges

Liver volumetry is a required step for the planning of liver surgery and resection. It is generally based on Computerized tomography images, and segmentation of the liver is the most important step of the process. We propose an automatic segmentation algorithm based on a geometric level set method which provides an accurate segmentation of the liver, and requires no a priori information. We show results on different datasets, with and without a contrast agent. The segmentation is compared to manual delineation by a radiologist with good results.

Objective Assessment of Olfactory Function Using Functional Magnetic Resonance Imaging (fMRI)

Impairment of the olfactory sensibility can be an indicator of neurode generative disorders, such as Alzheimers disease and Parkinsons disease. The problem lies in obtaining an objective quantitative analysis of olfactory response. For this task, we will use functional magnetic resonance imaging (fMRI) and a device that will provide a selective and controlled stimulation of the olfactory system. The novel issues of our design are synchronization between the acquisition and the olfactory task, and automated control of experimental parameters, odorants sequences, and frequency. We present a review of the basic fMRI experimental design of event-related stimulus paradigms versus block design experiments, and their use in olfactory experiments. Finally, we present the preliminary results obtained on a real 3-T magnetic resonance imaging (MRI) scanner.

Multi-atlas and label fusion approach for patient-specific MRI based skull estimation.

MRI‐based skull segmentation is a useful procedure for many imaging applications. This study describes a methodology for automatic segmentation of the complete skull from a single T1‐weighted volume.

A Multi-Atlas and Label Fusion Approach for Patient-Specific MRI Based Skull Segmentation

MRI‐based skull segmentation is a useful procedure for many imaging applications. This study describes a methodology for automatic segmentation of the complete skull from a single T1‐weighted volume.