Physica Medica-European Journal of Medical Physics最新文献

Ultrasound imaging plays an important role in the early detection and management of breast cancer. This study aimed to evaluate the imaging performance of a range of clinically-used breast ultrasound systems using a set of novel spherical lesion contrast-detail (C-D) and anechoic-target (A-T) phantoms.

Methods: C-D and A-T phantoms were imaged using a range of clinical breast ultrasound systems and imaging modes. A novel sensitive imaging performance metric, the Detectability Score (DS), was proposed which encompasses the Lesion Contrast to Noise Ratio (L_CNR) weighted by the lesion depth and diameter. A geometry-based theoretical model comparing L_CNR measured using spherical and cylindrical phantom anechoic/lesion targets was developed to investigate the influence of slice thickness on focal lesion detectability.

Results: L_CNR and DS metrics derived from phantom image measurments were capable of differentiating the imaging performance of a range of ultrasound systems and advanced imaging modes, with the -2 dB contrast lesion targets offered as the most challenging to resolve. The geometry-based theoretical model, validated against phantom measurements, demonstrated the significant influence of slice thickness on focal lesion detectability, highlighting the need for increased availability of low contrast resolution spherical target phantoms for clinically realistic performance evaluation.

Conclusions: The performance metrics coupled with the -1 dB contrast targets provide scope for evaluating future technological improvements in ultrasound systems. Given the high dependence of breast cancer care on high quality ultrasound imaging techniques, there is a need for evaluating imaging performance using clinically relevant test objects.

Purpose: Even with modern immobilisation devices, some amount of intrafraction patient motion is likely to occur during stereotactic radiosurgery (SRS) delivery. The aim of this work was to investigate how robustness of plans to intrafraction motion is affected by plan geometry and complexity.

Methods: In 2018, the Trans-Tasman Radiation Oncology Group conducted a multiple-target SRS international planning challenge, the data from which was utilised in this study. Patient geometry included five intracranial targets with a prescription of 20 Gy. A previously validated in-house algorithm was used to simulate realistic intrafraction patient motion for these plans. Three scenario types were simulated: translational intrafraction motion; rotational motion; and simultaneous rotational and translational motion. Dosimetric impact was assessed using: dose covering 98 % of planning target volume, dose covering 99 % of gross tumour volume (GTV D99%), volume of normal brain receiving 12 Gy and maximum dose covering 0.03 cc brainstem.

Results: GTV D99% was reduced by up to 70 %, with the strongest correlations between planning factors and robustness to intrafraction motion found for plan complexity. Despite only moderate correlation strength at r = 0.4, lower complexity plans had, on average, 5 % - 9 % less intrafraction motion scenarios with failing targets compared to the highest complexity plans.

Conclusions: SRS plans with lower complexity, in particular larger mean multi-leaf collimator (MLC) gap and MLC aperture irregularity, were shown to improve plan robustness to intrafraction patient motion.

Purpose: To propose comprehensive characterization methods of additive manufacturing (AM) materials for MV photon and MeV electron radiotherapy.

Methodology: This study investigated 15 AM materials using CT machines. Geometrical accuracy, tissue-equivalence, uniformity, and fabrication parameters were considered. Selected soft tissue equivalent filaments were used to fabricate slab phantoms and compared with water equivalent RW3 phantom by delivering planar 6 & 10 MV photons and 6, 9, 12, 15, & 18 MeV electrons. Finally, a 3D printed CT-Electron Density characterization phantom was fabricated.

Results: Materials used to print test objects can simulate tissues from adipose (relative electron density, ρ_e=0.72) up to near inner bone-equivalent (ρ_e=1.08). Lower densities such as breast and lung can be simulated using infills from 90 % down to 30 %, respectively. The gyroid infill pattern shows the lowest CT number variation and is recommended for low infill percentage printing. CT number uniformity can be observed from 40 % up to 100 % infill, while printing orientation does not significantly affect the CT number. The measured doses using the 3D printed phantoms show to have good agreement with TPS calculated dose for photon (< 1 % difference) and electron (< 5 % difference). Varying the printed slab thicknesses shows very similar response (< 3 % difference) compared with RW3 slabs except for 6 MeV electrons. Lastly, the fabricated CT-ED phantom generally matches the lung- up to the soft tissue- equivalence.

Conclusion: The proposed methods give the outline for characterization of AM materials as tissue-equivalent substitute. Printing parameters affect the radiological quality of 3D-printed object.

Purpose: Adaptive MRgRT by 1.5 T MR-linac requires independent verification of the plan-of-the-day by the primary TPS (Monaco^TM) (M). Here we validated a Monte Carlo-based dose-check including the magnetostatic field, SciMoCa^TM (S).

Methods: M and S were validated first in water, by comparison with commissioning-dosimetry. PDD(2x2cm²) through a lung(air)-equivalent virtual-slab was then calculated. Clinical validation retrospectively included 161 SBRT plans, from five patients per-site: Pelvic-Nodes, Prostate, Liver, Pancreas, and Lungs. S-minus-M percentage differences (Δ%) were computed for target- and OARs-related dose-volume metrics. In-phantom dose verification per-patient was performed.

Results: γ(2 %,1mm)-passing-rates (PR%) of in-water-computed PDD and transverse-dose-profiles vs. commissioning-dosimetry were (99.1 ± 2.0)% for M, and (99.3 ± 1.5)% for S. Calculated output-factors (OF) were typically within 1 % from measurements, except for OF(1x1cm²) which was misestimated by -4.4 % and + 2.2 %, by M and S respectively. Dose spikes (valleys) on the PDD(2x2cm²) by S across the lung-equivalent virtual-slab were slightly reduced with respect to M. In clinical plans, S computed higher V95% (p <0.05*, for pancreas and lung) and D2% (p <0.05*, for all sites) for the target, while D%>2% resulted for duodenal D(1cm³), in Pancreas-SBRT, and for mean-lung-dose, in Lung-SBRT. All mostly due to the underestimated OF(1x1cm²) by M. In-phantom dose verifications showed an average 1% increase in PR% by S vs. M.

Conclusions: Beam-model quality in S resulted equivalent to M, thus making S useful both for an independent validation of the same beam-model in M, and for a daily validation of the M-based online approval decisions, without significantly delaying the clinical workflow (2-3 min).

Background: The American Association of Physicists in Medicine (AAPM) Task group 282 (TG282) in collaboration with the European Federation for Organisations of Medical Physics (EFOMP) have developed a novel breast dosimetry model intended as a single international standard.

Purpose: To explore the impact of TG282 dosimetry on estimates of average Mean Glandular Dose (MGD) in the United Kingdom (UK) National Health Service (NHS) Breast Screening Programmes (BSP).

Methods: MGDs were estimated, using the TG282 dosimetry model, for the most recent UK NHSBSP dose survey. This dataset included MGDs estimated using the Dance dosimetry model for 439,916 Full Field Digital Mammography (FFDM) exposures of 111,132 women and 10,831 Digital Breast Tomosynthesis (DBT) exposures of 5,113 women. Direct comparisons of the two models were made and differences explored using this large-scale real world dataset.

Results: TG282 model MGDs were on average approximately 20 % and 15 % lower than Dance model values for FFDM and DBT respectively. For the UK National Diagnostic Reference Level (NDRL) breast thickness range of 50 mm to 60 mm inclusive differences were smaller at approximately 13 % and 10 % respectively. The difference between dosimetry models was shown to depend on the properties of the imaged population and X-ray equipment used. Average differences of up to 63.1 % were observed at higher CBT values for FFDM.

Conclusion: On average, the TG282 dosimetry model resulted in lower estimates for MGD in UK mammography. The differences were more pronounced for women with larger than average compressed breast thickness.

Purpose: Digital Breast Tomosynthesis (DBT) is an advanced mammography technique for which there are currently no internationally agreed methods and reference values for image quality assessment. The aim of this multicentre study was to evaluate a simple method to assess the technical image quality of reconstructed and synthetic 2D (SM) images of different models of DBT systems using commercially available phantoms.

Methods: The signal difference to noise ratio (SDNR) was chosen as an index of technical image quality and was evaluated for three commercial phantoms, Tomophan, Tormam and CIRS model 015, on 55 DBT systems (six vendors, nine models).

Results: SDNR was found to depend on several factors: detail size, average glandular dose (AGD), reconstruction algorithm, software version and applied post-processing. In particular, an increase in SDNR was observed with increasing detail size, AGD, as well as with the use of contrast-enhanced post-processing and iterative reconstruction algorithms. Most systems showed higher SDNR values in SM images respect to DBT for the largest details and a decrease for smaller details.

Conclusions: This study proposes a straightforward method to assess the technical image quality of reconstructed DBT and SM images using three different commercial phantoms. The algorithms used to generate SM and DBT images might perform differently when applied to phantoms with homogeneous backgrounds compared to clinical breast structures. However this method could be used to establish reference values for technical image quality during quality control (QC) which could be used to monitor the consistency of the system's performance over time.

Purpose: With the increasing use of proton therapy, there is a growing emphasis on including radiation quality, often quantified by linear energy transfer, as a treatment plan optimization factor. The Timepix detectors offer energy-sensitive particle tracking useful for the characterization of proton linear energy transfer. To improve the detector's performance in mixed radiation fields produced in proton therapy, we customized the detector settings and performed the per-pixel energy calibration.

Methods: The detection threshold and per-pixel signal shaping time (I_Krum current) were customized, and energy calibration was performed for MiniPIX Timepix3. The detector calibration was verified using α source and clinical proton beams, as well as Monte Carlo simulations. The effects on the detector's performance, in terms of spectral saturation and pixel occupancy, were evaluated.

Results: Measurements with proton beams showed a good agreement with simulations. With the customized settings, the measurable energy range in the detector data-driven mode was extended, and the signal duration time was reduced by 80%, while the yield of pixel time occupancy reduction depends on the number of occupied pixels. For performed measurements with proton beams, the number of occupied pixels was further reduced up to 40% due to the increased threshold.

Conclusions: Customized detector configuration of the Timepix3 detector allowed for reduced pixel occupancy and mitigation of signal saturation in a data-driven mode without significantly interfering with the energy deposition measurement. The presented approach enables the extension of the operational range, including higher intensities and mixed-radiation fields in particle radiotherapy environments.

Purpose: Material decomposition induces substantial noise in basis images and their synthesized computed tomography (CT) images. A likelihood-based bilateral filter was previously developed as a neighborhood filter that effectively reduces noise. However, this method is sensitive to image contrast, and the noise texture needs improvement. It is also necessary to address how to optimally combine filtered basis images to synthesize CT images. This study addressed these issues by introducing total likelihood and a noise-matched condition.

Methods: The experimental feasibility of the proposed method was demonstrated in a benchtop photon-counting CT (PCCT) system using the following steps: (1) A calibration process for forward modeling, (2) maximum likelihood (ML)-based material decomposition, which is accurate but suffers from substantial noise, (3) noise reduction by applying a total-likelihood-based filter, and (4) CT image synthesis using the noise-matched condition. The proposed method was compared with conventional neighborhood filters and statistical iterative reconstruction with edge-preserving regularization.

Results: The local noise and task-based modulation transfer function (TTF) were analyzed using a test phantom, and the proposed method was found to preserve the spatial resolution better than the other methods, especially in low-contrast regions. In the chicken leg experiment, the proposed method improved the fine structures and background textures in the denoised images and exhibited superior properties in analyzing the noise power spectrum.

Conclusion: The proposed method is effective and computationally efficient for noise reduction in PCCT and can potentially replace conventional iterative edge-preserved regularization approaches.

Purpose: Automatic planning (AP) has been compared to manual planning (MP) in lung stereotactic body radiation therapy (SBRT) to validate the former and to implement it in clinical practice.

Methods: A new developing Guided Planning System (GPS) engine was used to reoptimize 20 lung SBRT plans with the RayStation™ treatment planning system (TPS). The original manual plans were optimized to deliver 60 Gy in 5 or 8 fractions to the target with constraints on organs at risk (OARs) based on an internal protocol. AP plans were compared to MP based on (i) planning target volume (PTV) and OARs dosimetric evaluation, (ii) clinician's blind plan comparison, (iii) Plan QA results, and (iv) plan quality metrics. Differences between continuous variables were explored through the Mann-Whitney test (p < 0.05).

Results: Target and OARs dosimetry showed no significant difference, with the only exception of the spinal cord maximum dose that was significantly lower for AP in the 5 fractions scheme (MP: 8.93 Gy ± 3.94 Gy vs AP: 6.45 Gy ± 2.72 Gy, p = 0.034). In the blind comparison, AP was preferred in 45 % of cases while MP in 35 % of cases (no preference was expressed in 20 % of cases). A trend towards lower monitor units (MUs) was found for AP in the 5 fractions scheme (MP: 3383 ± 943 vs AP: 2662 ± 588, p = 0.059). No significant difference was found in any of the plan quality metrics.

Conclusions: AP plans were not inferior to MP plans; therefore, GPS is ready for clinical use in a pulmonary SBRT setting, reducing the planning workload and harmonizing the planning procedure.

Purpose: To train and validate KB prediction models by merging a large multi-institutional cohort of whole breast irradiation (WBI) plans using tangential fields.

Methods: Ten institutions (INST1-INST10, 1481 patients) developed their KB-institutional models for left/right WBI (ten models for right and eight models for left). The transferability of models among centers was assessed based on the overlap of the geometric Principal Component (PC1) of each model when applied to other institutions and/or on the presence of significantly different optimization policies. Centers corresponding to transferable models were asked to join the building of two KB-benchmark models for right/left breast. Dose-volume histogram (DVH) prediction bands (lung/heart) were compared against those of the KB-institutional models.

Results: All models were transferable except INST6 (right breast) and INST1 (left breast). Planning data from 6 institutions for right breast and 5 institutions for left breast (out of 9 and 7 institutions with transferable models, respectively) were combined, totaling data from 850 patients. Prediction bands on the test cohorts (n = 30/25 right/left) showed a large overlap with bands of each institution model: for the right-breast, the KB-benchmark model predicts slightly lower lung D_mean when compared to KB-institution models, except for INST7. Regarding the left-breast, even greater similarity between KB-benchmark and KB-institution model predictions was found.

Conclusions: Multi-institutional KB-benchmark models for WBI were successfully generated. They may be employed by other users, representing the performances reached in a multi-institutional context of experienced centers. KB-benchmark models can also have significant applications for large-scale automatic plan optimization, QA/audit and tutoring/education purposes.