Physica Medica-European Journal of Medical Physics最新文献

Purpose: This study aimed to develop a statistical approach for edge spread function (ESF) phase alignment to improve the accuracy of modulation transfer function (MTF) measurements at the edges of computed tomography (CT) images.

Methods: A statistical approach to ESF phase alignment was initiated by collecting ESF samples with poor phase alignment. One ESF sample was selected as the reference ESF and the other as the treated ESF. The treated ESF was then shifted by 10-pixels in the right and left directions with a 1-pixel increment at each shift. The mean squared error (MSE) for each shift was calculated between the shifted and reference ESF, and the position with the minimum MSE indicated the best phase alignment between the two ESFs. All shifted ESFs were averaged and differentiated to obtain a single-line spread function (LSF). The MTF was generated by Fourier transformation of the LSF. The MTFs from the shifted ESF and the non-shifted MTF from images of the ACR CT, point-computational, CTDI, and anthropomorphic phantoms were compared.

Results: The MTF curves obtained after the phase alignment showed higher and more consistent results than those obtained before the alignment. The MTF curves obtained after phase alignment were comparable to those obtained from a point computational phantom. Our method showed improved accuracy in measuring spatial resolution compared to those without the edge-shifting method.

Conclusions: The results showed that a statistical approach for ESF phase alignment can overcome poor phase alignment and produce a more accurate MTF curve.

Purpose: Inflammatory breast cancer (IBC) is a rare and aggressive type of breast cancer, as many physicians may not be aware of it in terms of symptoms and diagnosis. Mammography is the first choice in breast screenings and diagnosis. Because of a lack of expertise and imaging datasets, IBC portrayal and machine learning-based diagnosis systems have not yet been studied thoroughly. Developing scanning and diagnosis tools can close the knowledge gap and barriers to a timely IBC diagnosis.

Materials and methods: The dataset includes 20 women aged 34-75, of whom 10 were clinically diagnosed with IBC and 10 with non-IBC. A breast mapping and scanning model was developed. Gray-level co-occurrence matrices were used to characterize skin thickening, edema, breast density, microcalcifications, and breast size asymmetry in bilateral mammographic images.

Results: A one-way analysis of variance (ANOVA) test was performed to evaluate differences between mammogram breasts with IBC, non-IBC, and healthy breasts. Higher breast density variations were calculated in breasts with IBC in the anterior (P = 0.0147) and middle (P = 0.0026) regions. Breasts with IBC showed higher microcalcifications (P = 0.0472) than the other breasts, and bilateral analyses showed higher variations (P = 0.1367). Breast size asymmetry (P = 0.9833) was not significantly different between the groups.

Conclusion: Skin thickening, edema, and breast density-related parameters were found to be associated with IBC. This study thus lays the foundation of machine learning diagnosis models for IBC.

Purpose: The purpose of this study is to investigate the accuracy of few-time-points (FTP) time-integrated activity coefficients (TIACs) in peptide-receptor radionuclide therapy (PRRT) using non-linear mixed-effects (NLME) modeling.

Methods: Biokinetic data of [¹¹¹In]In-DOTA-TATE in kidneys at T-1 = (2.9 ± 0.6) h, T-2 = (4.6 ± 0.4) h, T-3 = (22.8 ± 1.6) h, T-4 = (46.7 ± 1.7) h, and T-5 = (70.9 ± 1.0) h after injection were obtained from eight patients using planar imaging. The Sum-Of-Exponentials (SOE) function with four parameters was used, which was selected as the best model for the renal biokinetic data of [¹¹¹In]In-DOTA-TATE. The parameters of the SOE function were fitted to the all-time-point data in the NLME framework to derive reference (rTIACs). FTP fits, which consist of all combinations of time points, are done to calculate the estimated TIACs (eTIACs). The accuracy of the FTP-NLME TIACs calculations was assessed by calculating the relative deviations (RDs) and relative root-mean-square errors (RMSEs) between the eTIACs and rTIACs.

Results: The lowest (mean ± SD) of RDs for the single-, two-, three-, four-time point FTPs were (0 ± 8) % (T-4), (1 ± 6) % (T-3 and T-4), (3 ± 5) % (T-2, T-3 and T-4), and (0 ± 2) % (T-2, T-3, T-4, and T-5), respectively. The lowest RMSEs for the one-, two-, three-, and four-time point FTPs were 8 % (T-4), 6 % (T-3 and T-4), 5 % (T-2, T-3 and T-4), and 2 % (T-2, T-3, T-4, and T-5), respectively.

Conclusion: Our results showed that FTP-NLME in an example of [¹¹¹In]In-DOTA-TATE could lead to a high accuracy of eTIAC across various time points, when incorporating time point T-4 = (46.7 ± 1.7) h.

Purpose: The aim of our study is to verify the reliability of Diffusion Kurtosis Imaging (DKI) parameters through correlation with perfusion metrics obtained by Dynamic Contrast Enhanced (DCE)- and Dynamic Susceptibility Contrast (DSC)-MRI techniques in histologic-proven primary central nervous system lymphoma (PCNSL).

Methods: A total of 15 lesions were analyzed in patient with neo-diagnosis of Epstein-Barr Virus negative PCNSL. DKI was acquired using 5b values from 0 to 2500 s/mm². DCE-MRI was acquired with a long scan time (up to 10 min) and with a temporal resolution of 5 s. DSC was acquired with a T2*-weighted sequence made up by 40 dynamic volumes, with a total scan time of 75 s. The correlation between the DCE- and DSC-MRI metrics and the DWI/DKI parameters was assessed with the Spearman's Rho correlation test. A p-value < 0.05 was considered statistically significant.

Results: A strong inverse relationship between D_app and K_ep (Rho = -0.56; p-value = 0.034) and between ADC and the K_ep (Rho = -0.52; p = 0.049) was found. A strong relationship emerged between K_app and relCBV (Rho = 0.62; p = 0.016). Even though a correlation has been detected between DKI and DWI parameters.

Conclusions: DKI seem to provide additional information compared to the standard diffusion model, this can be inferred from the results obtained through this study as DKI parameters correlates with both DCE and DSC parameters while ADC only correlates with K_ep and in a statistically less significant manner.

Purpose: To evaluate the impact of a range of shielding strategies on the rate of false positive detections by a simulated detector for application in Neutron Capture Enhanced Particle Therapy (NCEPT).

Methods: In this work, we extend a previously published method for neutron capture detection and discrimination. A Geant4 Monte Carlo model was designed, with the simulated irradiation of a poly(methyl methacrylate) phantom and cubic ¹⁰B insert with carbon and helium ion beams and various shielding configurations.

Results: In the free-space configuration, shielding the crystal actually decreases the ratio of true/false positive detections (R_TF) by more than 50% and increases the activation of the detector. In a closed-space configuration with a model of the beamline neutron fluence, R_TF also decreases with shielding, although activation decreases in this case. However, for a detector with boron present in the printed circuit boards (PCBs), shielding with a thin layer of Gd₂O₃ improves R_TF by up to 21%.

Conclusions: Shielding of the detector crystal itself is unnecessary as shielding actually degrades discrimination accuracy relative to the unshielded detector. However, if the detector PCBs contain boron, then shielding the electronics provides a valuable increase in overall detector selectivity.

Introduction: Consensus contouring guidelines for intensity-modulated-radiation-therapy (IMRT) of patients with locally advanced cervix cancer (LACC) advise including the whole uterus in the target volume and adding generous planning-target-volumes (PTVs) to account for motion uncertainties of the gross-tumor-volume (GTV). The primary objective of this analysis was to assess the interfractional GTV motions using a magnetic-resonance-image (MRI) guided-Radiation-Therapy (MRgRT) system to investigate the margins required for MRgRT treatments.

Methods: 125 daily set-up MRIs from five patients with LACC who received MRgRT were analyzed. The GTV, bladder, uterus, and rectum were contoured on all 125 MRIs. Tumor volume changes were calculated in cubic-centimeters (cc). The positional and volume changes of organs-at-risk (OARs) were calculated to assess their effect on GTV interfractional motion, these data were used to calculate adequate PTV margins.

Results: The tumor volume decreased in size during the course of MRgRT for all patients, from 34.0 % to 85.2 %. The interfractional average GTV displacement ranged from 0.46 cm to 0.94 cm. The PTV margins required were: 0.78 cm Left-Right, 1.31 cm Anterior-Posterior and 1.38 cm for the Superior-Inferior directions. The proposed PTV margins, compared to those recommended by consensus guidelines, reduce the PTV by 38 % sparing both the sigmoid and bowel OARs.

Conclusions: By utilizing daily onboard MRI guidance, the GTV becomes readily visualized, allowing for margin reduction and potentially excluding a portion of the uterine fundus from the PTV. The amount of interfractional motion demonstrated in this study is considerable and clinically significant with the goal of decreasing treatment toxicity while maintaining tumor control.

Summary: Daily pretreatment magnetic resonance images (MRIs) from patients with locally advanced cervix cancer (LACC) treated with on-board MR-guided radiation therapy (MRgRT) were analyzed to quantify the range of interfractional motion and develop target volume guidelines for adaptive MRgRT. MRI-guidance leads to better tumor visualization in comparison to cone beam computed tomography (CBCT), and online adaptive planning can account for the interfraction motion of the tumor and surrounding tissue. MRI's ability to better visualize the disease and pelvic anatomy along with adaptive on-board MRgRT could allow for a reduction in the required setup margins as well as potentially excluding non-diseased portions of the uterus from the target volumes. These changes will lead to reduced treatment volumes and may lead to decreased treatment toxicities and allow for dose escalation in certain circumstances.

Medical Physics departments primarily concentrate on clinical operations and regulatory compliance, which often restricts their ability to improve technical efficiencies. Nonetheless, developing technical capabilities is crucial as the healthcare sector increasingly depends on advanced technologies. A part-time software engineer was successfullyrecruited and integrated into the medical physics team to address operational needs and provide technical solutions. The engineer designed tailored systems, established automated dose tracking to ensure regulatory compliance, and worked alongside clinical staff for effective data management. Furthermore, they created a standardised operating environment for research initiatives, provided computational infrastructure for machine learning endeavours, and optimised workflows through automation. The integration improved workflow efficiency, expanded research capacity, and enhanced system integration, illustrating the significant benefits of incorporating technical expertise within medical physics teams.

Purpose: Automated treatment plan generation is essential for magnetic resonance imaging (MRI)-guided adaptive radiotherapy (MRIgART) to ensure standardized treatment-plan quality. We proposed a novel cross-technique transfer learning (CTTL)-based strategy for online MRIgART autoplanning.

Method: We retrospectively analyzed the data from 210 rectal cancer patients. A source dose prediction model was initially trained using a large volume of volumetric-modulated arc therapy data. Subsequently, a single patient's pretreatment data was employed to construct a CTTL-based dose prediction model (CTTL_M) for each new patient undergoing MRIgART. The CTTL_M predicted dose distributions for subsequent treatment fractions. We optimized an auto plan using the parameters based on dose prediction. Performance of our CTTL_M was assessed using dose-volume histogram and mean absolute error (MAE). Our auto plans were compared with clinical plans regarding plan quality, efficiency, and complexity.

Results: CTTL_M significantly improved the dose prediction accuracy, particularly in planning target volumes (median MAE: 1.27 % vs. 7.06 %). The auto plans reduced high-dose exposure to the bladder (D_0.1cc: 2,601.93 vs. 2,635.43 cGy, P < 0.001) and colon (D_0.1cc: 2,593.22 vs. 2,624.89 cGy, P < 0.001). The mean colon dose decreased from 1,865.08 to 1,808.16 cGy (P = 0.035). The auto plans maintained similar planning time, monitor units, and plan complexity as clinical plans.

Conclusions: We proposed an online ART autoplanning method for generating high-quality plans with improved organ sparing. Its high degree of automation can standardize planning quality across varying expertise levels, mitigating subjective assessment and errors.

Introduction: Targeted alpha therapies show great potential for cancer treatment due to their high linear energy transfer (LET) and low range. ²¹¹At is currently employed in clinical trials. Targeted alpha therapies (TAT) are effective as an adjuvant treatment for cancer or to treat micrometastases and diffuse cancers. A deeper understanding of the induced initial damage is crucial to enhance treatment planning.

Methods: This study shows Geant4(-DNA)-based simulations to calculate absorbed dose profiles and DNA damaging potential in intravenously administered TAT with ²¹¹At. It assumes radionuclide decay on the blood vessel wall, and calculates the DNA damage in the surrounding tissue.

Results: The calculated dosimetric quantities show that the effect of such treatment is mainly due to the emitted alpha particles, and is localised in a region of up to 80μm from the blood vessel. The RBE of the treatment is in the range 2.5-4, and is calculated as a function of the number of double-strand breaks.

Conclusions: Targeted therapies with ²¹¹At are effective within the range of the emitted alpha particles. With its capacity to induce complex DNA damage in such a short range, it is very promising for localised treatment of small tumour cells or micrometastases.

A preliminary study was conducted using electronic portal imaging device (EPID) based dose verification in pre-treatment and in vivo dose reconstruction modes for breast cancer intensity-modulated radiation therapy (IMRT) technique with known repositioning set-up errors. For 43 IMRT plans, the set-up errors were determined from 43 sets of EPID images and 258 sets of cone beam computed tomography images. In-house developed Edose software was used to reconstruct the dose distribution using the pre-treatment and on-treatment (in vivo) EPID acquired fluence maps. The maximum setup error was < 3.5 mm. For 43 pre-treatment cases, the γ pass rate (3 %/3 mm) is 98.49 % ± 1.15 %. The chest wall target ΔV_98%P, ΔV_95%P, andΔV_90%P are all < 5 %, while the majority of the ipsilateral lung ΔV_5Gy, ΔV_20Gy, and ΔV_30Gy are also < 5 %. For 258 in vivo cases, the γ pass rate is 90.98 % ± 6.53 %, with the chest wall target ΔV_90%P and ipsilateral lung ΔV_30Gy both < 5 %, while the other volume differences all exceed 5 %. The γ pass rate for in vivo verification is significantly lower than pre-treatment values. Although the in vivo γ verification satisfies the medical physics requirements, the reconstructed coverage of the chest wall target is far below the clinical dosimetry requirements. In vivo 3D dose reconstruction directly predicts changes in the planning target volume to aid clinicians better understand the actual dose received by patients with intra-fractional motion and anatomical changes.