Performance evaluation of the 3D-ring cadmium-zinc-telluride (CZT) StarGuide system according to the NEMA NU 1-2018 standard.

Background: The application of semi-conductor detectors such as cadmium-zinc-telluride (CZT) in nuclear medicine improves extrinsic energy resolution and count sensitivity due to the direct conversion of gamma photons into electric signals. A 3D-ring pixelated CZT system named StarGuide was recently developed and implemented by GE HealthCare for SPECT acquisition. The system consists of 12 detector columns with seven modules of 16 × 16 CZT pixelated crystals, each with an integrated parallel-hole tungsten collimator.

On the necessity of specialized knowledge-based models for SBRT prostate treatments plans.

Purpose: Test whether a well-grounded KBP model trained on moderately hypo-fractionated prostate treatments can be used to satisfactorily drive the optimization of SBRT prostate treatments.

Materials And Methods: A KBP model (SBRT-model) was developed, trained and validated using the first forty-seven clinically treated VMAT SBRT prostate plans (42.7 Gy/7fx or 36.

Prediction models as decision-support tools for virtual patient-specific quality assurance of helical tomotherapy plans.

Background And Purpose: Prediction models may be reliable decision-support tools to reduce the workload associated with the measurement-based patient-specific quality assurance (PSQA) of radiotherapy plans. This study compared the effectiveness of three different models based on delivery parameters, complexity metrics and sinogram radiomics features as tools for virtual-PSQA (vPSQA) of helical tomotherapy (HT) plans.

Materials And Methods: A dataset including 881 RT plans created with two different treatment planning systems (TPSs) was collected.

Updating a clinical Knowledge-Based Planning prediction model for prostate radiotherapy.

Background And Purpose: Clinical knowledge-based planning (KBP) models dedicated to prostate radiotherapy treatment may require periodical updates to remain relevant and to adapt to possible changes in the clinic. This study proposes a paired comparison of two different update approaches through a longitudinal analysis.

Materials And Methods: A clinically validated KBP model for moderately hypofractionated prostate therapy was periodically updated using two approaches: one was targeted at achieving the biggest library size (M), while the other one at achieving the highest mean sample quality (R).

Analysis of clinical patient-specific pre-treatment quality assurance with the new helical tomotherapy platform, following the AAPM TG-218 report.

Purpose: This study presents patient-specific quality assurance (QA) results from the first 395 clinical cases for the new helical TomoTherapy® platform (Radixact) coupled with dedicated Precision TPS.

Methods: The passing rate of the Gamma Index (GP%) of 395 helical QA of patient-specific tomotherapy, acquired with ArcCHECK, is presented, analysed and correlated to various parameters of the plan. Following TG-218 recommendations, the clinic specific action limit (AL) and tolerance limit (TL) were calculated for our clinic and monitored during the analysed period.

Reducing inter- and intra-planner variability in radiotherapy plan output with a commercial knowledge-based planning solution.

Purpose: This study measured to which extent RapidPlan can drive a reduction of the human-caused variability in prostate cancer treatment planning.

Methods: Seventy clinical prostate plans were used to train a RapidPlan model. Seven planners, with different levels of planning experience, were asked to plan a VMAT treatment for fifteen prostate cancer patients with and without RapidPlan assistance.

Efficiently train and validate a RapidPlan model through APQM scoring.

Purpose: The aim of this study was to propose and validate an intuitive method for training and to validate knowledge-based planning (KBP) systems based on a patient-specific plan quality scoring.

Methods: A sample of 80 clinical plans of prostate cancer patients were ranked on the basis of the Adjusted Plan Quality Metric (APQM%). This quality metric was computed normalizing the Plan Quality Metric (PQM%) score to the best possible OAR sparing estimated by the Feasibility DVH (FDVH) algorithm.