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Register a new userOptimized planning target volume margin in helical tomotherapy for prostate cancer: is there a preferred method?
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To compare the dosimetrical differences between plans generated by helical tomotherapy using 2D or 3D margining technique in in prostate cancer. Ten prostate cancer patients were included in this study. For 2D plans, planning target volume (PTV) was created by adding 5 mm (lateral/anterior-posterior) to clinical target volume (CTV). For 3D plans, 5 mm margin was added not only in lateral/anterior-posterior, but also in superior-inferior to CTV. Various dosimetrical indices, including the prescription isodose to target volume (PITV) ratio, conformity index (CI), homogeneity index (HI), target coverage index (TCI), modified dose homogeneity index (MHI), conformation number (CN), critical organ scoring index (COSI), and quality factor (QF) were determined to compare the different treatment plans. Differences between 2D and 3D PTV indices were not significant except for CI (p = 0.023). 3D margin plans (11195 MUs) resulted in higher (13.0%) monitor units than 2D margin plans (9728 MUs). There were no significant differences in any OARs between the 2D and 3D plans. Overall, the average 2D plan dose was slightly lower than the 3D plan dose. Compared to the 2D plan, the 3D plan increased average treatment time by 1.5 minutes; however, this difference was not statistically significant (p = 0.082). We confirmed that 2D and 3D margin plans are not significantly different with regard to various dosimetric indices such as PITV, CI, and HI for PTV, and OARs with tomotherapy.
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Prostate cancer is the most common disease in men and the second leading cause of death from cancer. Generic large imaging instruments used in cancer diagnosis have sensitivity, spatial resolution, and contrast inadequate for the task of imaging details of a small organ such as the prostate. In addition, multimodality imaging can play a significant role merging anatomical and functional details coming from simultaneous PET and MRI. Indeed, multi-parametric PET/MRI was demonstrated to improve diagnosis, but it suffers from too many false positives. In order to address the above limits of the current techniques, we have proposed, built and tested, thanks to the TOPEM project funded by Italian National Institute of Nuclear Phisics a prototype of an endorectal PET-TOF/MRI probe. In the applied magnification PET geometry, performance is dominated by a high-resolution detector placed closer to the source. The expected spatial resolution in the selected geometry is about 1.5 mm FWHM and efficiency a factor of 2 with respect to what obtained with the conventional PET scanner. In our experimental studies, we have obtained timing resolution of ~ 320 ps FWHM and at the same time Depth of Interaction (DOI) resolution of under 1 mm. Tests also showed that mutual adverse PET-MR effects are minimal. In addition, the matching endorectal RF coil was designed, built and tested. In the next planned studies, we expect that benefiting from the further progress in scintillator crystal surface treatment, in SiPM technology and associated electronics would allow us to significantly improve TOF resolution
Prostate cancer (PCa) is the second most common cancer in men worldwide and the most frequently diagnosed cancer among men in more developed countries. The prognosis of PCa is excellent if detected at an early stage, making early screening crucial for detection and treatment. In recent years, a new form of diffusion magnetic resonance imaging called correlated diffusion imaging (CDI) was introduced, and preliminary results show promise as a screening tool for PCa. In the largest study of its kind, we investigate the relationship between PCa presence and a new variant of CDI we term synthetic correlated diffusion imaging (CDI$^s$), as well as its performance for PCa delineation compared to current standard MRI techniques (T2-weighted (T2w) imaging, diffusion-weighted imaging (DWI), and dynamic contrast-enhanced (DCE) imaging) across a cohort of 200 patient cases. Statistical analyses reveal that hyperintensity in CDI$^s$ is a strong indicator of PCa presence and achieves strong delineation of clinically significant cancerous tissue compared to T2w, DWI, and DCE. These results suggest that CDI$^s$ hyperintensity may be a powerful biomarker for the presence of PCa, and may have a clinical impact as a diagnostic aid for improving PCa screening.
High dose-rate brachytherapy (HDRBT) is widely used for gynecological cancer treatment. Although commercial treatment planning systems (TPSs) have inverse optimization modules, it takes several iterations to adjust planning objectives to achieve a satisfactory plan. Interactive plan-modification modules enable modifying the plan and visualizing results in real time, but they update plans based on simple geometrical or heuristic algorithms, which cannot ensure resulting plan optimality. This project develops an interactive plan optimization module for HDRBT of gynecological cancer. By efficiently solving an optimization problem in real time, it allows a user to visualize a plan and interactively modify it to improve quality. We formulated an optimization problem with an objective function containing a weighted sum of doses to normal organs subject to user-specified target coverage. A user interface was developed that allows a user to adjust organ weights using scroll bars. With a simple mouse click, the optimization problem is solved in seconds with a highly efficient alternating-direction method of multipliers and a warm start optimization strategy. Resulting clinically relevant D2cc of organs are displayed immediately. This allows a user to intuitively adjust plans with satisfactory quality. We tested the effectiveness of our development in cervix cancer cases treated with a tandem-and-ovoid applicator. It took a maximum of 3 seconds to solve the optimization problem in each instance. With interactive optimization capability, a satisfactory plan can be obtained in <1 min. In our clinic, although the time for plan adjustment was typically <10min with simple interactive plan modification tools in TPS, the resulting plans do not ensure optimality. Our plans achieved on average 5% lower D2cc than clinical plans, while maintaining target coverage.
Statistical iterative reconstruction is expected to improve the image quality of megavoltage computed tomography (MVCT). However, one of the challenges of iterative reconstruction is its large computational cost. The purpose of this work is to develop a fast iterative reconstruction algorithm by combining several iterative techniques and by optimizing reconstruction parameters. Megavolt projection data was acquired from a TomoTherapy system and reconstructed using our statistical iterative reconstruction. Total variation was used as the regularization term and the weight of the regularization term was determined by evaluating signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and visual assessment of spatial resolution using Gammex and Cheese phantoms. Gradient decent with an adaptive convergence parameter, ordered subset expectation maximization (OSEM), and CPU/GPU parallelization were applied in order to accelerate the present reconstruction algorithm. The SNR and CNR of the iterative reconstruction were several times better than that of filtered back projection (FBP). The GPU parallelization code combined with the OSEM algorithm reconstructed an image several hundred times faster than a CPU calculation. With 500 iterations, which provided good convergence, our method produced a 512$times$512 pixel image within a few seconds. The image quality of the present algorithm was much better than that of FBP for patient data. An image from the iterative reconstruction in TomoTherapy can be obtained within few seconds by fine-tuning the parameters. The iterative reconstruction with GPU was fast enough for clinical use, and largely improve the MVCT images.
Magnetic resonance imaging technique known as DWI (diffusion-weighted imaging) allows measurement of water diffusivity on a pixel basis for evaluating pathology throughout the body and is now routinely incorporated into many body MRI protocols, mainly in oncology. Indeed water molecules motion reflects the interactions with other molecules, membranes, cells, and in general the interactions with the environment. Microstructural changes as e.g. cellular organization and/or integrity then affect the motion of water molecules, and consequently alter the water diffusion properties measured by DWI. Then DWI technique can be used to extract information about tissue organization at the cellular level indirectly from water motion. In general the signal intensity in DWI can be quantified by using a parameter known as ADC (Apparent Diffusion Coefficient) emphasizing that it is not the real diffusion coefficient, which is a measure of the average water molecular motion. In the simplest models, the distribu- tion of a water molecule diffusing in a certain period of time is considered to have a Gaussian form with its width proportional to the ADC. However, water in biological structures often displays non-Gaussian diffusion behavior, consequently the DWI signal shows a more complex behavior that need to be modeled following different approaches. In this work we explore the possibility to quantify the degree to which water diffusion in biologic tissues is non-Gaussian introducing the AKC parameter (Apparent Kurtosis Coefficient). In this work we have realized DWI non-Gaussian diffusion maps to be used in the clinical routine along with standard ADC maps, giving to the radiologist another tool to explore how much structure inside a voxel is organized. In particular in this work some prostate DWI examples have been analyzed and will be shown.
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