Australasian Physical & Engineering Sciences in Medicine最新文献

A grid is one of the key components of a digital radiography (DR) system because it removes scattered radiation, which arises when X-rays penetrate an object and improves diagnostic accuracy by enhancing image quality. With the widespread use of DR systems, demand for grids with high precision has simultaneously increased. Because unsuitable grids may decrease image quality and lead to misdiagnosis, using optimised grids for DR systems is critical. In this study, we aimed to analyse the quality of X-ray images acquired using grids with different specifications and proposed standardised criteria for grid use on the basis of our results. We measured modulation transfer function (MTF), normalised noise power spectrum (NNPS) and detective quantum efficiency (DQE) using grids with different ratios (10:1, 12:1 and 15:1) with or without implementing poly methyl methacrylate (PMMA) phantoms (0-20 cm). Pixel pitch of the detector used in this experiment was 143 μm. Based on this, a grid with a line frequency of 85 line pairs/cm was selected to prevent distortion caused by implementing unoptimised grids. As a result, the NNPS was found to increase when using the grid, and the difference in MTF and DQE was only measured when the scattered X-ray was generated by stacking the PMMA phantom. However, grids showed a positive effect MTF and DQE when the PMMA phantom was implemented. Specifically, MTF and DQE improved with increase in grid ratio. Thus, it is desirable to use a high-ratio grid to improve image quality.

This paper develops a computationally bio-inspired framework of brain activities based on concepts, such as sensory register (SR), encoding, emotion, short-term memory (STM), selective attention, working memory (WM), forgetting, long-term memory (LTM), sustained memory (SM), and response selection for estimating the depth of anesthesia (DOA) using electroencephalogram (EEG) signals. Different brain regions, such as the thalamus, cortex, neocortex, amygdala, striatum, basal ganglia, cerebellum, and hippocampus, are considered for developing a cognitive architecture and a computationally bio-inspired framework. A clinical study was managed on twenty-two patients corresponding to three anesthetic states, including awake state, moderate anesthesia, and general anesthesia. The proposed approach utilizes a multiple of dynamically reconfigurable neural networks with radial basis function (RBF) and its associated data processing mechanisms. The emotion effect in the model, dynamic RBFs in WM and LTMs, and adjusting the adaptive weights in the last layer are the main innovations of the proposed approach. In the proposed approach, various incoming information is entered into the model. The correct labeling process of EEG signals is performed by qualitative and quantitative analyses of peripheral parameters. Then, an SR is used to accumulate the pre-processed EEG segment for a period of 2.3 s. Feature extraction is performed in the encoding stage as a primary perception. The output of this stage can be transferred to STM and WM with a bottom-up involuntary attentional capture. LTM and SM are a fairly permanent reservoir for information which is passed from WM using a top-down voluntary attention mechanism. Finally, weighting factors in SM and LTMs outputs are determined and then response selection is used by winner-take-all (WTA) strategy. The results indicate that the proposed approach can classify in different anesthetic states with an average accuracy of 89.2%. Results also indicate that the combined use of the above elements can effectively decipher the cognitive process task. A final comparison between the obtained results and the previous method on the same database indicate the effectiveness of the proposed approach for estimating DOA.

Characteristics of a small-animal radiotherapy device, the X-RAD SmART, are described following commissioning of the device for pre-clinical radiotherapy research. Performance characteristics were assessed using published standards and compared with previous results published for similar systems. Operational radiation safety was established. Device X-ray beam quality and output dose-rate were found to be consistent with those reported for similar devices. Output steadily declined over 18 months though remained within tolerance levels. There is considerable variation in output factor across the international installations for the smallest field size (varying by more than 30% for 2.5 mm diameter fields). Measured depth dose and profile data was mostly consistent with that published, with some differences in penumbrae and generally reduced flatness. Target localisation is achieved with an imaging panel and with automatic corrections for panel flex and device mechanical instability, targeting within 0.2 mm is achievable. The small-animal image-guided radiotherapy platform has been implemented and assessed and found to perform as specified. The combination of kV energy and high spatial precision make it suitable for replicating clinical dose distributions at the small-animal scale, though dosimetric uncertainties for the narrowest fields need to be acknowledged.

Respiratory motion has a significant impact on dose delivered to abdominal targets during radiotherapy treatment. Accurate treatment of liver tumours adjacent to the diaphragm is complicated by large respiratory movement, as well as differing tissue densities at the lung-liver interface. This study aims to evaluate the accuracy of dose delivered to superior liver tumours using passive respiratory monitoring, in the absence of gating technology, for a range of treatment techniques. An in-house respiratory phantom was designed and constructed to simulate the lung and liver anatomy. The phantom consisted of adjacent slabs of lung and liver equivalent materials and a cam drive system to emulate respiratory motion. A CC04 ionisation chamber and Gafchromic EBT3 film were used to perform point dose and dose plane measurements respectively. Plans were calculated using an Elekta Monaco treatment planning system (TPS) on exhale phase study sets for conformal, volume modulated arc therapy (VMAT) and intensity modulated radiation therapy (IMRT) techniques, with breathing rates of 8, 14 and 23 bpm. Analysis confirmed the conformal delivery protocol currently used for this site within the department is suitable. The experiments also determined that VMAT is a viable alternative technique for treatment of superior liver lesions undergoing respiratory motion and was superior to IMRT. Furthermore, the measurements highlighted the need for respiratory management in these cases. Displacements due to respiration exceeding planned margins could result in reduced coverage of the clinical target volume and much higher doses to the lung than anticipated.

The inconsistency of volumetric results often seen in MR neuroimaging studies can be partially attributed to small sample sizes and variable data analysis approaches. Increased sample size through multi-scanner studies can tackle the former, but combining data across different scanner platforms and field-strengths may introduce a variability factor capable of masking subtle statistical differences. To investigate the sample size effect on regression analysis between depressive symptoms and grey matter volume (GMV) loss in Alzheimer's disease (AD), a retrospective multi-scanner investigation was conducted. A cohort of 172 AD patients, with or without comorbid depressive symptoms, was studied. Patients were scanned with different imaging protocols in four different MRI scanners operating at either 1.5 T or 3.0 T. Acquired data were uniformly analyzed using the computational anatomy toolbox (CAT12) of the statistical parametric mapping (SPM12) software. Single- and multi-scanner regression analyses were applied to identify the anatomical pattern of correlation between GM loss and depression severity. A common anatomical pattern of correlation between GMV loss and increased depression severity, mostly involving sensorimotor areas, was identified in all patient subgroups imaged in different scanners. Analysis of the pooled multi-scanner data confirmed the above finding employing a more conservative statistical criterion. In the retrospective multi-scanner setting, a significant correlation was also exhibited for temporal and frontal areas. Increasing the sample size by retrospectively pooling multi-scanner data, irrespective of the acquisition platform and parameters employed, can facilitate the identification of anatomical areas with a strong correlation between GMV changes and depression symptoms in AD patients.

The main purpose of the study is to establish correlations for the ablation volume and the ice front as a function of the spray parameters. The ablation volume and the ice front depend upon the nozzle diameter, spraying distance and the freeze duration (spray parameters). The estimation of the ablation volume using the spray parameters shall be useful in surgical practice to ablate the different sizes of tumours. Liquid nitrogen spray cooling is carried out with 0.8 mm, 0.6 mm and 0.4 mm nozzle diameters. The spraying distance is maintained at 9 mm, 18 mm and 27 mm. The spray cooling is carried out for a single freeze-thaw cycle where freezing and thawing consist of 120 s and 130 s duration respectively. A two-dimensional heat flow equation with phase change is considered for the numerical study. The numerically calculated transient temperature (2 mm and 5 mm from the gel surface) and ice front values show confirmatory results with the experimentally measured data. Correlations are obtained to determine the ablation volume (- 50 °C and - 25 °C isothermal surfaces) and ice front (axial and lateral) with a goodness of fit [Formula: see text] 95%. The nozzle diameter has a greater impact on the ablation volume as compared to the spraying distance during 120 s of freezing. The nozzle diameter of 0.8 mm, 0.6 mm and 0.4 mm can be effectively used for cryotherapy with spraying distance up to 27 mm, 18 mm and 9 mm respectively.

Methodologies reported in the existing literature for identification of a region of interest (ROI) in medical thermograms suffer from over- and under-extraction of the abnormal and/or inflammatory region, thereby causing inaccurate diagnoses of the spread of an abnormality. We overcome this limitation by exploiting the advantages of a logarithmic transformation. Our algorithm extends the conventional region growing segmentation technique with a modified similarity criteria and a stopping rule. In this method, the ROI is generated by taking common information from two independent regions produced by two different versions of a region-growing algorithm that use different parameters. An automatic multi-seed selection procedure prevents missed segmentations in the proposed approach. We validate our technique by experimentation on various thermal images of the inflammation of affected knees and abnormal breasts. The images were obtained from three databases, namely the Knee joint dataset, the DBT-TU-JU dataset, and the DMR-IR dataset. The superiority of the proposed technique is established by comparison to the performance of state-of-the-art competing methodologies. This study performed temperature emitted inflammatory area segmentation on thermal images of knees and breasts. The proposed segmentation method is of potential value in thermal image processing applications that require expediency and automation.

The importance of the medical physics profession and medical physicists is widely recognized by the international bodies like ILO, IAEA, EC, etc. The description of a medical physicist's qualification framework, their role and responsibilities have been addressed in the legislative and regulatory frameworks of developed countries like the USA (in 10CFR) and the EC (EC RP 174) and less comprehensively in developing counties like Pakistan. AFOMP has contributed positively in various regulatory and policy matters regarding the medical physics practices in Asian countries. Furthermore, the recommendations of IAEA's regional meeting on "Medical Physics in Europe-Current Status and Future Perspective" in Vienna, 2015, address the need and mechanism of a harmonized framework for medical physicists' qualifications. The lack of a comprehensive professional recognition framework becomes more challenging when we see that hi-tech diagnostic (e.g. PET CT) and therapeutic (e.g. cyberknife, VMAT, tomotherapy, etc.) modalities are now available in many parts of the world, including Pakistan which still have a basic level of medical physics qualification and practices. Therefore, international efforts like the above-mentioned IAEA-EC meeting in 2015; and by AFOMP activities related to training, qualification and recognition of medical physicists can provide a pathway to further improve medical physics practices in the developing world. The objective of this review is to (i) summarize the international practices for the legislation and regulation of medical physics, (ii) provide a brief overview of the medical physics practices in Pakistan and (iii) discuss the applicability of the IAEA-EC meeting's recommendations to the case of Pakistan. The review highlights the areas which are addressed in IAEA-EC meeting and could be beneficial to other nations as well, particularly, for low and middle income countries. The review also presents few suggestions how to progress with the medical physics profession in developing countries in general, and in Pakistan in particular. These suggestions also include further possible pathway the IAEA could consider, like IAEA project or meetings, to further strengthen the medical physics profession globally.

The accuracy of a computed tomography (CT)-relative electron density (RED) curve may have an indirect impact on the accuracy of dose calculation by a treatment planning system (TPS). This effect has not been previously quantified for input of different CT-RED curves from different CT-scan units in the Monaco TPS. This study aims to evaluate the effect of CT-RED curve on the dose calculation by the Monaco radiotherapy TPS. Four CT images of the CIRS phantom were obtained by different CT scanners. The accuracy of the dose calculation in the three algorithms of the Monaco TPS (Monte Carlo, collapse cone, and pencil beam) is also evaluated based on TECDOC 1583. The CT-RED curves from the CT scanners were transferred to the Monaco TPS to audit the different algorithms of the TPS. The dose values were measured with an ionization chamber in the CIRS phantom. Then, the dose values were calculated by the Monaco algorithms in the corresponding points. For the Monaco TPS and based on TECDOC 1583, the accuracy of the dose calculation in all the three algorithms is within the agreement criteria for most of the points evaluated. For low dose regions, the differences between the calculated and measured dose values are higher than the agreement criteria in a number of points. For the majority of the points, the algorithms underestimate the calculated dose values. It was also found that the use of different CT-RED curves can lead to minor discrepancies in the dose calculation by the Monaco TPS, especially in low dose regions. However, it appears that these differences are not clinically significant in most of the cases.

White blood cells play a vital role in monitoring health condition of a person. Change in count and/or appearance of these cells indicate hematological disorders. Manual microscopic evaluation of white blood cells is the gold standard method, but the result depends on skill and experience of the hematologist. In this paper we present a comparative study of feature extraction using two approaches for classification of white blood cells. In the first approach, features were extracted using traditional image processing method and in the second approach we employed AlexNet which is a pre-trained convolutional neural network as feature generator. We used neural network for classification of WBCs. The results demonstrate that, classification result is slightly better for the features extracted using the convolutional neural network approach compared to traditional image processing approach. The average accuracy and sensitivity of 99% was obtained for classification of white blood cells. Hence, any one of these methods can be used for classification of WBCs depending availability of data and required resources.