Hendrickslundqvist2669

The results of this study show that the correction methods, when using the single photopeak windows, result in increase in image contrast with a significant level of noise. In return, when both the photopeak energy windows are used for imaging, it is possible to achieve the better image characteristics.

The use of the proposed correction methods, by considering both the photopeak windows, leads to improve the image contrast with a reasonable level of image noise.

The use of the proposed correction methods, by considering both the photopeak windows, leads to improve the image contrast with a reasonable level of image noise.

Deep-learning methods are becoming versatile in the field of medical image analysis. The hand-operated examination of smaller nodules from computed tomography scans becomes a challenging and time-consuming task due to the limitation of human vision. A standardized computer-aided diagnosis (CAD) framework is required for rapid and accurate lung cancer diagnosis. The National Lung Screening Trial recommends routine screening with low-dose computed tomography among high-risk patients to reduce the risk of dying from lung cancer by early cancer detection. Epigenetics inhibitor The evolvement of clinically acceptable CAD system for lung cancer diagnosis demands perfect prototypes for segmenting lung region, followed by identifying nodules with reduced false positives. Recently, deep-learning methods are increasingly adopted in medical image diagnosis applications.

In this study, a deep-learning-based CAD framework for lung cancer diagnosis with chest computed tomography (CT) images is built using dilated SegNet and convolutional neefficient lung segmentation and two-dimensional nodule patch classification in CAD system for lung cancer diagnosis with CT screening.

The purpose of present study is to estimate asymmetric margins of prostate target volume based on biological limitations with help of knowledge based fuzzy logic considering the effect of organ motion and setup errors.

A novel application of fuzzy logic modelling technique considering radiotherapy uncertainties including setup, delineation and organ motion was used in this study to derive margins. The new margin was applied in prostate cancer treatment planning and the results compared very well to current techniques Here volumetric modulated arc therapy treatment plans using stepped increments of asymmetric margins of planning target volume (PTV) were performed to calculate the changes in prostate radiobiological indices and results were used to formulate the rule based and membership function for Mamdani-type fuzzy inference system. The optimum fuzzy rules derived from input data, the clinical goals and knowledge-based conditions imposed on the margin limits. The PTV margin obtained using the fuzzy modebased fuzzy logic is that a practical limitation on the margin size is included in the model for limiting the dose received by the critical organs. It uses both physical and radiobiological data to optimize the required margin as per clinical requirement in real time or adaptive planning, which is an improvement on most margin models which mainly rely on physical data only.

The purpose of this study is to evaluate the effects of cone-beam computed tomography (CBCT) on dose distribution and normal tissue complication probability (NTCP) by constructing a comprehensive dose evaluation system for prostate intensity-modulated radiation therapy (IMRT).

A system that could combine CBCT and treatment doses with MATLAB was constructed. Twenty patients treated with prostate IMRT were studied. A mean dose of 78 Gy was prescribed to the prostate region, excluding the rectal volume from the target volume, with margins of 4 mm to the dorsal side of the prostate and 7 mm to the entire circumference. CBCT and treatment doses were combined, and the dose distribution and the NTCP of the rectum and bladder were evaluated.

The radiation dose delivered to 2% and 98% of the target volume increased by 0.90 and 0.74 Gy on average, respectively, in the half-fan mode and on average 0.76 and 0.72 Gy, respectively, in the full-fan mode. The homogeneity index remained constant. The percent volume of the rectum and bladder irradiated at each dose increased slightly, with a maximum increase of <1%. The rectal NTCP increased by approximately 0.07% from 0.46% to 0.53% with the addition of a CBCT dose, while the maximum NTCP in the bladder was approximately 0.02%.

This study demonstrated a method to evaluate a combined dose of CBCT and a treatment dose using the constructed system. The combined dose distribution revealed increases of <1% volume in the rectal and bladder doses and approximately 0.07% in the rectal NTCP.

This study demonstrated a method to evaluate a combined dose of CBCT and a treatment dose using the constructed system. The combined dose distribution revealed increases of less then 1% volume in the rectal and bladder doses and approximately 0.07% in the rectal NTCP.

This study aimed to investigate the influence of cleaned-up knowledge-based treatment planning (KBP) models on the plan quality for volumetric-modulated arc therapy (VMAT) of prostate cancer.

Thirty prostate cancer VMAT plans were enrolled and evaluated according to four KBP modeling methods as follows (1) model not cleaned - trained by fifty other clinical plans (KBP

); (2) cases cleaned by removing plans that did not meet all clinical goals of the dosimetric parameters, derived from dose-volume histogram (DVH) (KBP

); (3) cases cleaned outside the range of ±1 standard deviation through the principal component analysis regression plots (KBP

); and (4) cases cleaned using both methods (2) and (3) (KBP

). Rectal and bladder structures in the training models numbered 34 and 48 for KBP

, 37 and 33 for KBP

, and 26 and 33 for KBP

, respectively. The dosimetric parameters for each model with one-time auto-optimization were compared.

All KBP models improved target dose coverage and conformity and provided comparable sparing of organs at risks (rectal and bladder walls). There were no significant differences in plan quality among the KBP models. Nevertheless, only the KBP

model generated no cases of >1% V

(prescribed dose) to the rectal wall, whereas the KBP

, KBP

, and KBP

models included two, four, and three cases, respectively, which were difficult to overcome with KBP because the planning target volume (PTV) and rectum regions overlapped.

The cleaned-up KBP model based on DVH and regression plots improved plan quality in the PTV-rectum overlap region.

The cleaned-up KBP model based on DVH and regression plots improved plan quality in the PTV-rectum overlap region.