Congenital crico-thyroid dysplasia: a comprehensive description.

INTRODUCTION: We describe a first case of human congenital crico-thyroid dysplasia associated to a right sided aortic arch and an aberrant subclavian artery. CASE PRESENTATION: Our patient presented with a two-weeks history of acute dyspnea, and reported hoarseness since his childhood. An urgent tracheotomy was performed, followed by direct laryngoscopy. Endoscopic examination showed a deviation of the dorsoventral axis of the larynx, with an obstructive submucosal swelling the area of the right false cord and aryepiglottic fold. Computed tomography conducted the following day confirmed the crico-thyroid dysplasia, an infected laryngocele, and the presence of a right sided aortic arch and an aberrant subclavian artery. CONCLUSION: The embryological basis of these anomalies is attributed to congenital defects of the development of the fourth and sixth pharyngeal arches. To our knowledge, the congenital crico-thyroid dysplasia has not been previously reported in human. This case underscores the importance of recognizing anatomical variations in laryngeal cartilages, understanding their embryological origins, and potential associated malformations.

Intramedullary spinal cord metastasis from breast cancer: an ambiguous entity.

Intramedullary spinal cord metastasis (IMSC) from solid tumors is rare. In this report, we describe the case of a patient treated at our center for breast cancer with intramedullary spinal cord metastases without bone and brain metastases or meningitis. Management of the disease remains challenging even with recent advances in the treatment of metastatic breast cancer. Treatment options include surgery, radiotherapy and chemotherapy. The prognosis of these patients still very poor.

New Contrast Enhancement Method for Multiple Sclerosis Lesion Detection.

Multiple sclerosis (MS) is one of the most serious neurological diseases. It is the most frequent reason of non-traumatic disability among young adults. MS is an autoimmune disease wherein the central nervous system wrongly destructs the myelin sheath surrounding and protecting axons of nerve cells of the brain and the spinal cord which results in presence of lesions called plaques. The damage of myelin sheath alters the normal transmission of nerve flow at the plaques level, consequently, a loss of communication between the brain and other organs. The consequence of this poor transmission of nerve impulses is the occurrence of various neurological symptoms. MS lesions cause mobility, vision, cognitive, and memory disorders. Indeed, early detection of lesions provides an accurate MS diagnosis. Consequently, and with the adequate treatment, clinicians will be able to deal effectively with the disease and reduce the number of relapses. Therefore, the use of magnetic resonance imaging (MRI) is primordial which is proven as the relevant imaging tool for early diagnosis of MS patients. But, low contrast MRI images can hide important objects in the image such lesions. In this paper, we propose a new automated contrast enhancement (CE) method to ameliorate the low contrast of MRI images for a better enhancement of MS lesions. This step is very important as it helps radiologists in confirming their diagnosis. The developed algorithm called BDS is based on Brightness Preserving Dynamic Fuzzy Histogram Equalization (BPDFHE) and Singular Value Decomposition with Discrete Wavelet Transform (SVD-DWT) techniques. BDS is dedicated to improve the low quality of MRI images with preservation of the brightness level and the edge details from degradation and without added artifacts or noise. These features are essential in CE approaches for a better lesion recognition. A modified version of BDS called MBDS is also implemented in the second part of this paper wherein we have proposed a new method for computing the correction factor. Indeed, with the use of the new correction factor, the entropy has been increased and the contrast is greatly enhanced. MBDS is specially dedicated for very low contrast MRI images. The experimental results proved the effectiveness of developed methods in improving low contrast of MRI images with preservation of brightness level and edge information. Moreover, performances of both proposed BDS and MBDS algorithms exceeded conventional CE methods.

Exceptional Renal Metastasis from Adenoid Cystic Carcinoma of the Nasal Cavity and Literature Review.

Adenoid cystic carcinoma (ACC) is a rare malignant cancer that arises from secretory glands. Slow growth, perineural invasion, and late recurrences are the main characteristics of ACC. Only few cases of kidney metastases from ACC have been reported in the literature. We report here the case of a 66-year-old female patient who presented with bilateral renal metastases from ACC of the nasal cavity, detected 14 years after treatment of primary tumor and 6 years after metastasectomy of lung metastases. Histological examination confirmed diagnosis and the patient was treated with systemic chemotherapy. Radiological evaluation showed stability of the disease. However, a progression with occurrence of metastases in other sites (lung and bones) has been observed after 7 months. She is still receiving second-line chemotherapy. To the best of our knowledge, this is the second case of kidney metastases from ACC of the nasal cavity.

Retropharyngeal edema secondary to superior vena cava syndrome revealing Behcet's disease.

Retropharyngeal edema, uncommon in superior vena cava syndrome, can exceptionally represent the first manifestation of Behcet's disease, which should be taken into account in the differential diagnosis of this condition.

Deep Convolutional Encoder-Decoder algorithm for MRI brain reconstruction.

Compressed Sensing Magnetic Resonance Imaging (CS-MRI) could be considered a challenged task since it could be designed as an efficient technique for fast MRI acquisition which could be highly beneficial for several clinical routines. In fact, it could grant better scan quality by reducing motion artifacts amount as well as the contrast washout effect. It offers also the possibility to reduce the exploration cost and the patient's anxiety. Recently, Deep Learning Neuronal Network (DL) has been suggested in order to reconstruct MRI scans with conserving the structural details and improving parallel imaging-based fast MRI. In this paper, we propose Deep Convolutional Encoder-Decoder architecture for CS-MRI reconstruction. Such architecture bridges the gap between the non-learning techniques, using data from only one image, and approaches using large training data. The proposed approach is based on autoencoder architecture divided into two parts: an encoder and a decoder. The encoder as well as the decoder has essentially three convolutional blocks. The proposed architecture has been evaluated through two databases: Hammersmith dataset (for the normal scans) and MICCAI 2018 (for pathological MRI). Moreover, we extend our model to cope with noisy pathological MRI scans. The normalized mean square error (NMSE), the peak-to-noise ratio (PSNR), and the structural similarity index (SSIM) have been adopted as evaluation metrics in order to evaluate the proposed architecture performance and to make a comparative study with the state-of-the-art reconstruction algorithms. The higher PSNR and SSIM values as well as the lowest NMSE values could attest that the proposed architecture offers better reconstruction and preserves textural image details. Furthermore, the running time is about 0.8 s, which is suitable for real-time processing. Such results could encourage the neurologist to adopt it in their clinical routines. Graphical abstract.

Denoising and contrast-enhancement approach of magnetic resonance imaging glioblastoma brain tumors.

We investigate a new preprocessing approach for MRI glioblastoma brain tumors. Based on combined denoising technique (bilateral filter) and contrast-enhancement technique (automatic contrast stretching based on image statistical information), the proposed approach offers competitive results while preserving the tumor region's edges and original image's brightness. In order to evaluate the proposed approach's performance, quantitative evaluation has been realized through the Multimodal Brain Tumor Segmentation (BraTS 2015) dataset. A comparative study between the proposed method and four state-of-the art preprocessing algorithm attests that the proposed approach could yield a competitive performance for magnetic resonance brain glioblastomas tumor preprocessing. In fact, the result of this step of image preprocessing is very crucial for the efficiency of the remaining brain image processing steps: i.e., segmentation, classification, and reconstruction.

Rank-Two NMF Clustering for Glioblastoma Characterization.

This study investigates a novel classification method for 3D multimodal MRI glioblastomas tumor characterization. We formulate our segmentation problem as a linear mixture model (LMM). Thus, we provide a nonnegative matrix M from every MRI slice in every segmentation process' step. This matrix will be used as an input for the first segmentation process to extract the edema region from T2 and FLAIR modalities. After that, in the rest of segmentation processes, we extract the edema region from T1c modality, generate the matrix M, and segment the necrosis, the enhanced tumor, and the nonenhanced tumor regions. In the segmentation process, we apply a rank-two NMF clustering. We have executed our tumor characterization method on BraTS 2015 challenge dataset. Quantitative and qualitative evaluations over the publicly training and testing dataset from the MICCAI 2015 multimodal brain segmentation challenge (BraTS 2015) attested that the proposed algorithm could yield a competitive performance for brain glioblastomas characterization (necrosis, tumor core, and edema) among several competing methods.

Severe head injury among children: prognostic factors and outcome.

AIM: To determine predictive factors of mortality among children after traumatic brain injury. METHODS: A retrospective study over 8 years of 222 children with severe head injury (Glasgow Coma Scale score < or = 8) admitted to a university hospital (Sfax, Tunisia). Basic demographic, clinical, biological and radiological data were recorded on admission and during intensive care unit stay. RESULTS: The study included 163 boys (73.4%) and 59 girls, with mean age 7.54+/-3.8 years. The main cause of trauma was road traffic accident (75.7%). Mean Glasgow Coma Scale score was 6+/-1.5, mean Injury Severity Score (ISS) was 28.2+/-6.9, mean Paediatric Trauma Score (PTS) was 3.7+/-2.1 and mean Paediatric Risk of Mortality (PRISM) was 14.3+/-8.5; 54 children (24.3%) died. Univariate analysis showed that low PTS on admission, high ISS or PRISM, presence of shock or meningeal haemorrhage or bilateral mydriasis, and serum glucose > 10 mmol l(-1) were associated with mortality rate. Multivariate analysis showed that factors associated with a poor prognosis were PRISM > 20 and bilateral mydriasis on admission. CONCLUSIONS: In Tunisia, head injury is a frequent cause of hospital admission and is most often due to road traffic accidents. Short-term prognosis is poor, with a high mortality rate (24.3%), and is influenced by demographic, clinical, radiological and biological factors.