Plantar fasciitis (PFS) is one of the most common causes of heel pain, estimated to affect 10% of the general population during their lifetime. Ultrasound (US) imaging technique is increasingly being used to assess plantar fascia (PF) thickness, monitor the effect of different interventions and guide therapeutic interventions in patients with PFS. The purpose of the present study was to systematically review previously published studies concerning the application of US in the assessment of PF in patients with PFS. A literature search was performed for the period 2000-2012 using the Science Direct, Scopus, PubMed, CINAHL, Medline, Embase and Springer databases. The key words used were: ultrasound, sonography, imaging techniques, ultrasonography, interventional ultrasonography, plantar fascia and plantar fasciitis. The literature search yielded 34 relevant studies. Sixteen studies evaluated the effect of different interventions on PF thickness in patients with PFS using US; 12 studies compared PF thickness between patients with and without PFS using US; 6 studies investigated the application of US as a guide for therapeutic intervention in patients with PFS. There were variations among studies in terms of methodology used. The results indicated that US can be considered a reliable imaging technique for assessing PF thickness, monitoring the effect of different interventions and guiding therapeutic interventions in patients with PFS.

Serum ferritin and liver iron content may not be good indicators of brain iron deposition in patients with β thalassemia major. Nevertheless, the quantitative T2* MRI technique is useful for evaluation of brain iron overload in β thalassemia major patients.

Neuromyelitis optica (NMO) exhibits substantial similarities to multiple sclerosis (MS) in clinical manifestations and imaging results and has long been considered a variant of MS. With the advent of a specific biomarker in NMO, known as anti-aquaporin 4, this assumption has changed; however, the differential diagnosis remains challenging and it is still not clear whether a combination of neuroimaging and clinical data could be used to aid clinical decision-making. Computer-aided diagnosis is a rapidly evolving process that holds great promise to facilitate objective differential diagnoses of disorders that show similar presentations. In this study, we aimed to use a powerful method for multi-modal data fusion, known as a multi-kernel learning and performed automatic diagnosis of subjects. We included 30 patients with NMO, 25 patients with MS and 35 healthy volunteers and performed multi-modal imaging with T1-weighted high resolution scans, diffusion tensor imaging (DTI) and resting-state functional MRI (fMRI). In addition, subjects underwent clinical examinations and cognitive assessments. We included 18 a priori predictors from neuroimaging, clinical and cognitive measures in the initial model. We used 10-fold cross-validation to learn the importance of each modality, train and finally test the model performance. The mean accuracy in differentiating between MS and NMO was 88%, where visible white matter lesion load, normal appearing white matter (DTI) and functional connectivity had the most important contributions to the final classification. In a multi-class classification problem we distinguished between all of 3 groups (MS, NMO and healthy controls) with an average accuracy of 84%. In this classification, visible white matter lesion load, functional connectivity, and cognitive scores were the 3 most important modalities. Our work provides preliminary evidence that computational tools can be used to help make an objective differential diagnosis of NMO and MS.