Protocol of a prospective study on the diagnostic value of transcranial duplex scanning of the substantia nigra in patients with parkinsonian symptoms

<p>Abstract</p> <p>Background</p> <p>Parkinson's disease (PD) is the second most common neurodegenerative disorder. As there is no definitive diagnostic test, its diagnosis is based on clinical criteria. Recently transcranial duplex scanning (TCD) of the substantia nigra in the brainstem has been proposed as an instrument to diagnose PD. We and others have found that TCD scanning of substantia nigra duplex is a relatively accurate diagnostic instrument in patients with parkinsonian symptoms. However, all studies on TCD so far have involved well-defined, later-stage PD patients, which will obviously lead to an overestimate of the diagnostic accuracy of TCD.</p> <p>We have therefore set out to conduct a prospective study testing the diagnostic accuracy of TCD in patients with a parkinsonism of unclear origin.</p> <p>Methods/Design</p> <p>We will enrol 250 consecutive patients, who are referred to neurology outpatient clinics of two teaching hospitals, for analysis of clinically unclear parkinsonism. Patients, whose parkinsonism is clearly diagnosable at the first visit, will be excluded from the study. All patients will undergo a TCD of the substantia nigra. As a surrogate gold standard we will use the consensus clinical diagnosis reached by two independent, blinded, movement disorder specialist neurologists after 2 years follow-up. At the time of TCD, patients will also undergo a SPECT scan of the brain.</p> <p>Discussion</p> <p>As this prospective trial enrols only patients with an early-stage parkinsonism, it will yield data on the diagnostic accuracy of TCD that is relevant to daily clinical practice: The neurologist needs a diagnostic tool that provides additional information in patients with a clinically indefinable parkinsonian syndrome. The above described observational longitudinal study was designed to explicitly study this aspect in the diagnostic process.</p> <p>Trial registration</p> <p><b>(ITRSCC) NCT00368199</b></p

Texture Analysis Using Gabor filter Based on Transcranial Sonography Image

Abstract. Transcranial sonography (TCS) is a new tool for the diagnosis of Parkinson’s disease (PD) at a very early state. The TCS image of the mesencephalon shows a distinct hyperechogenic pattern in about 90 % PD patients. This pattern is usually manually segmented and the substantia nigra (SN) region can be used as an early PD indicator. However this method is based on manual evaluation of examined images. We propose a texture analysis method using Gabor filters for the early PD risk assessment. The features are based on the local spectrum, which is obtained by a bank of Gabor filters, and the performance of these features is evaluated by feature selection method. The results show that the accuracy of the classification with the feature subset is reaching 92.73 %.

Transcranial sonography as a diagnostic tool for Parkinson's disease: a pilot study in the city of Rio de Janeiro, Brazil

In Brazil there is no systematic study on Transcranial Sonography (TCS), a neuroimaging method that depicts echogenic deep brain structures using ultrasound. OBJECTIVE: To establish the percentage of subjects with permissive temporal windows and to address the ability of TCS of the substantia nigra (SN) to distinguish parkinsonian patients in a Brazilian sample. METHOD: We performed TCS using the Acuson X300 (Siemens, Germany) in 37 individuals: 23 with Parkinson's disease (PD) and 14 healthy controls. RESULTS: 10.8% of subjects had insufficient temporal acoustic bone windows. SN echogenic areas were larger in patients (mean±SD, 0.31±0.08cm²) compared to controls (mean±SD, 0.17±0.02cm²). TCS accurately identified 88.2% of PD patients. CONCLUSION: A large proportion of Brazilians seem to be eligible for TCS. An expressive number of PD patients could be diagnosed by TCS based on an expanded SN echogenic area. However, the current data is preliminary and must be corroborated by larger studies