Physica Medica-European Journal of Medical Physics最新文献

{"title":"Working thresholds for in-vivo dosimetry in EPIGray based on a clinical, anatomically-stratified study","authors":"Sandra Williamson Puente,&nbsp;Miguel Cámara Gallego,&nbsp;David Sevillano Martínez,&nbsp;Rafael Colmenares Fernández,&nbsp;Juan David García Fuentes,&nbsp;Ana Belén Capuz Suárez,&nbsp;Rafael Morís Pablos,&nbsp;María José Béjar Navarro,&nbsp;Daniel Prieto Morán,&nbsp;Pablo Galiano Fernández,&nbsp;Rubén Chillida Rey,&nbsp;Carlos Rodríguez-Manzaneque Sosa,&nbsp;Feliciano García-Vicente","doi":"10.1016/j.ejmp.2025.104933","DOIUrl":"10.1016/j.ejmp.2025.104933","url":null,"abstract":"<div><h3>Purpose</h3><div>To obtain tolerance levels for working with the EPID-based EPIgray in vivo dosimetry system.</div></div><div><h3>Methods</h3><div>Dose differences between planned and delivered treatments in various anatomical areas, including the gastro-intestinal, urological, rectum and anal canal, gynecological, breast, head and neck, and lung regions, were analyzed across 5,791 fractions. Whether or not the dose differences at each location are symmetrical with respect to zero and adhere to a normal distribution is then checked. Linear regression analysis was applied to check for temporal drift in lung and head and neck treatments. A water equivalent phantom and another with a water-polystyrene interface is used to estimate the dose difference intrinsic to the measurement system. Furthermore, appropriate dose distribution in two treatments is verified using radiochoromic film.</div></div><div><h3>Results</h3><div>Normal distribution was not observed in any region, and only two showed symmetry around zero. The mean dose differences were: (0.33 ± 6.32) % for the gastro-intestinal system, (−1.31 ± 3.16) % for the gynaecological area, (0.79 ± 4.55) % for VMAT-breast, (3.48 ± 4.00) % for 3DCRT-breast, (0.70 ± 3.20) % for head and neck, (5.63 ± 5.48)% for lung, (−1.36 ± 2.98) % for rectum and anal canal, and (0.13 ± 3.53) % for the urological system.</div></div><div><h3>Conclusion</h3><div>EPIgray should support tolerance levels asymmetric with respect to zero, given the positive deviation observed in mean dose for lung, breast, and head and neck regions. Additionally, the system’s ability to detect dose variations during treatment could help identify changes in tumor volume.</div></div>","PeriodicalId":56092,"journal":{"name":"Physica Medica-European Journal of Medical Physics","volume":"131 ","pages":"Article 104933"},"PeriodicalIF":3.3,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143421921","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessment and improvement of the quality of radiotherapy treatment planning CT images using a clinically validated phantom based method and a multicentre intercomparison","authors":"Anne T. Davis ,&nbsp;Andrew Bird ,&nbsp;Lorraine Cowley ,&nbsp;Oliver Donnelly ,&nbsp;Mostafa ELHaddad ,&nbsp;Cheryl Evans ,&nbsp;Tracey Kearton ,&nbsp;Rachel Morrison ,&nbsp;David Nash ,&nbsp;Joshua Naylor ,&nbsp;Joel Palmer ,&nbsp;Katherine Potterton ,&nbsp;Anand M. Ravindran ,&nbsp;Daniel Sandys ,&nbsp;Athina Sdrolia ,&nbsp;Antonio de Stefano ,&nbsp;Maja Uherek ,&nbsp;Zoe Walker ,&nbsp;Antony L. Palmer ,&nbsp;Andrew Nisbet","doi":"10.1016/j.ejmp.2025.104912","DOIUrl":"10.1016/j.ejmp.2025.104912","url":null,"abstract":"<div><h3>Purpose</h3><div>To develop a phantom method of image quality assessment for radiotherapy planning CT protocols (head and neck (H&amp;N) and prostate) and validate results against clinical image quality. Test with data from different scanners and suggest protocol adjustments.</div></div><div><h3>Methods</h3><div>Macros measured patient water-equivalent diameter and noise from clinical CT images. Target transfer function (TTF), contrast, noise-power spectrum (NPS), detectability index and the edge visibility of a low contrast target were measured using Catphan 604 and bespoke phantoms. Ten centres scanned the phantoms with modified clinical protocols and collected data from patient images using the macros. Clinical experts, ranked the quality of images for contouring and correlated results against phantom metrics.</div></div><div><h3>Results</h3><div>Clinical image review showed a large range of results from different scanners for H&amp;N scans and fewer differences for prostate. The phantom metrics best correlated with high clinical image scores were, for H&amp;N: high TTF50 (r = 0.73, <em>p</em> = 0.003), contrast (r = 0.58, p = 0.003) and target edge visibility (r = 0.70, <em>p</em> = 0.004); for prostate: high TTF50 (r = 0.83, <em>p</em> = 0.002), low noise (r = 0.37, <em>p</em> = 0.26) and target edge visibility (r = 0.59, <em>p</em> = 0.05). Hence, optimal contrast, resolution and noise are important for good contouring image quality. Reconstruction kernel, field of view and noise, or X-ray tube current and rotation time, are possible parameters for adjustment.</div></div><div><h3>Conclusions</h3><div>This phantom method (using Catphan 604) was a good surrogate for clinical quality assessment of CT images for radiotherapy contouring. Results identified the poorest performing scanners, allowing recommendations for image quality improvement and confirming scan protocol optimisation is necessary in some centres.</div></div>","PeriodicalId":56092,"journal":{"name":"Physica Medica-European Journal of Medical Physics","volume":"131 ","pages":"Article 104912"},"PeriodicalIF":3.3,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143403432","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dosimetry audit service in Italy: Results of the partnership between Italian Association of Medical Physics (AIFM) and Italian National Institute of Ionizing Radiation Metrology (ENEA-INMRI)","authors":"Paola Martucci ,&nbsp;Alessia Embriaco ,&nbsp;Maria Pimpinella ,&nbsp;Vanessa De Coste ,&nbsp;Serenella Russo ,&nbsp;Pierino De Felice ,&nbsp;Michele Stasi ,&nbsp;Christian Fiandra","doi":"10.1016/j.ejmp.2025.104925","DOIUrl":"10.1016/j.ejmp.2025.104925","url":null,"abstract":"<div><h3>Purpose</h3><div>The Italian National Institute of Ionizing Radiation Metrology (ENEA-INMRI) and the Italian Association of Medical Physics (AIFM) offer a certified audit service to radiotherapy (RT) centers for reference dosimetry.</div></div><div><h3>Methods</h3><div>Audits are provided for photon beams in the range 6–18 MV including flattening filter free C-arm Linac, CyberKnife and TomoTherapy beams. A dosimeter consisting of a set of TLD chips embedded in a PMMA waterproof holder is used. TLDs are calibrated in terms of absorbed dose to water in the ENEA-INMRI reference <span><math><mrow><msup><mrow><mspace></mspace></mrow><mn>60</mn></msup><mi>C</mi><mi>o</mi></mrow></math></span> γ-beam. Correction factors accounting for energy dependence, signal reproducibility and response stability are applied to evaluate absorbed dose. For each beam audited, irradiation of two dosimeters with 2 Gy is required in reference conditions, according to the international dosimetry protocols. Audit performance is evaluated in terms of the<!--> <!-->E_n <!-->score: it is satisfactory if |E_n| ≤ 1.0.</div></div><div><h3>Results</h3><div>Audit was successfully performed for 94<!--> <!-->beams in 34 Italian<!--> <!-->RT<!--> <!-->centers. Nominal beam energies analysed were 6 MV (38.3 %), 6 MV FFF (33.0 %), 10 MV (12.8 %), 10 MV FFF (4.2 %), 15 MV (9.6 %) and 18 MV (2.1 %). The<!--> <!-->E_n <!-->scores are normally distributed with 95 % of data between −0.54 and 0.7; 99.5 % of values are in the range [−1.0,1.0] and 81.1 % are in the optimal range [−0.5,0.5]. As for the single unsatisfactory result, data from the form filled in by the RT center allowed ENEA-INMRI to identify an error in the measurement setup.</div></div><div><h3>Conclusions</h3><div>Results of remote audits have shown excellent performance of Italian RT centers.</div></div>","PeriodicalId":56092,"journal":{"name":"Physica Medica-European Journal of Medical Physics","volume":"131 ","pages":"Article 104925"},"PeriodicalIF":3.3,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143403433","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development and clinical application of a probabilistic robustness evaluation tool for pencil beam scanning proton therapy treatments","authors":"Francesco Fracchiolla ,&nbsp;Lamberto Widesott ,&nbsp;Roberto Righetto ,&nbsp;Carlo Algranati ,&nbsp;Dante Amelio ,&nbsp;Annalisa Trianni ,&nbsp;Edmond Sterpin ,&nbsp;Stefano Lorentini","doi":"10.1016/j.ejmp.2025.104938","DOIUrl":"10.1016/j.ejmp.2025.104938","url":null,"abstract":"<div><h3>Purpose</h3><div>to implement a probabilistic-Robustness-Evaluation (pRE) tool for proton therapy treatments and to correlate these results with the worst-case approach (wRE) implemented in commercial TPS for clinical applications.</div></div><div><h3>Materials and Methods</h3><div>12 skull base patients were planned with a robust multiple field optimization (MFO) approach. 10 years of machine QA were analysed to derive the uncertainties of our treatment system (beam delivery and patient positioning system). For a large cohort of patients, post-treatment imaging was acquired to determine the intra-fraction uncertainty. The pRE, considered explicitly all these uncertainties, the fractionation and range uncertainty. For each plan a wRE with different combinations of range and setup uncertainties was simulated. wRE results were then compared, in terms of target coverage and OAR dose limits, with pRE results.</div></div><div><h3>Results</h3><div>43,400 dose distributions were analysed. pRE simulations lasted 18.6 h (±11.5 h). The results showed that the combination of wRE uncertainty parameters that surrogated the best pRE results with a confidence level of 95 % were (1.0 mm/3.5 %). The median OAR’s dose indexes difference (D₁/D_1cc) between pRE and wRE was 1.90 (±1.49) GyRBE, while for target D₉₈ and D₉₅ it was −0.66(±0.95) and −0.67 (±0.52) GyRBE, respectively.</div></div><div><h3>Conclusion</h3><div>A tool able to explicitly simulate the source of treatment uncertainties and the effect of the fractionation was implemented to have a more realistic evaluation of plan robustness. This tool was used to find the best wRE parameters that surrogate the pRE results while maintaining clinically acceptable timing. These results are now used in our clinical workflow.</div></div>","PeriodicalId":56092,"journal":{"name":"Physica Medica-European Journal of Medical Physics","volume":"131 ","pages":"Article 104938"},"PeriodicalIF":3.3,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143396257","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparative analysis of machine learning models for predicting pathological complete response to neoadjuvant chemotherapy in breast cancer: An MRI radiomics approach","authors":"Alessia D’Anna ,&nbsp;Carlo Aranzulla ,&nbsp;Carlo Carnaghi ,&nbsp;Francesco Caruso ,&nbsp;Gaetano Castiglione ,&nbsp;Roberto Grasso ,&nbsp;Anna Maria Gueli ,&nbsp;Carmelo Marino ,&nbsp;Francesco Pane ,&nbsp;Alfredo Pulvirenti ,&nbsp;Giuseppe Stella","doi":"10.1016/j.ejmp.2025.104931","DOIUrl":"10.1016/j.ejmp.2025.104931","url":null,"abstract":"<div><h3>Purpose</h3><div>The aim of this work is to compare different machine learning models for predicting pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in breast cancer using radiomics features from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI).</div></div><div><h3>Method</h3><div>The study included 55 patients with breast cancer, among whom 18 achieved pCR and 37 did not respond completely to NAC (non-pCR). After some pre-processing steps, 1446 features were extracted and corrected for batch effects using ComBat. Five machine learning algorithms, namely random forest (RF), decision tree (DT), logistic regression (LR), k-nearest neighbors (k-NN), and extreme gradient boosting (XGB), were evaluated using area under the receiver operating characteristic curve (AUC), accuracy, precision, recall, and F1 score as classification metrics. A Leave-Group-Out cross validation (LGOCV) was applied in the outer loop.</div></div><div><h3>Results</h3><div>RF and DT models exhibited the highest performances compared to the other algorithms. DT achieved an accuracy of 0.96 ± 0.07, and RF achieved 0.95 ± 0.05. The AUC values for RF and DT were 0.98 ± 0.06 and 0.94 ± 0.07, respectively. LR and k-NN demonstrated lower performance across all metrics, while XGB showed competitive results but slightly lower than RF and DT.</div></div><div><h3>Conclusions</h3><div>This study demonstrates the potential of radiomics and machine learning for predicting pCR to NAC in breast cancer. RF and DT models proved to be the most effective in capturing underlying patterns in radiomics data. Further research is required to validate and strengthen the proposed approach and explore its applicability in diverse radiomics datasets and clinical scenarios.</div></div>","PeriodicalId":56092,"journal":{"name":"Physica Medica-European Journal of Medical Physics","volume":"131 ","pages":"Article 104931"},"PeriodicalIF":3.3,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143387570","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A high magnetic resonance imaging (MRI) contrast agar/silica-based phantom for evaluating focused ultrasound (FUS) protocols","authors":"Antria Filippou,&nbsp;Nikolas Evripidou,&nbsp;Christakis Damianou","doi":"10.1016/j.ejmp.2025.104932","DOIUrl":"10.1016/j.ejmp.2025.104932","url":null,"abstract":"<div><h3>Purpose</h3><div>Thermal ablation therapies require tissue mimicking phantoms for evaluating novel systems. Herein, an agar phantom exhibiting high magnetic resonance imaging (MRI) contrast to noise ratio (CNR) was developed for testing focused ultrasound (FUS) protocols.</div></div><div><h3>Methods</h3><div>Four agar based phantoms (6 % w/v) were fabricated with varied silica concentrations (0, 2, 4, or 6 % w/v) and subjected to FUS inside a 3 T MRI. T2-Weighted Fast Spin Echo (T2-W FSE) images were acquired after sonications to assess the effect of varied silica on CNR of inflicted lesions. The highest CNR phantom was sonicated and its proton resonance frequency (PRF) coefficient, thermal dose denaturation threshold and ability to sustain good lesion CNR 0–44 min post exposures were assessed.</div></div><div><h3>Results</h3><div>T2-W median lesion CNR between 1.5–453.5 was observed, exponentially increasing with increased silica concentration. High CNR was achieved with 4 % w/v silica, with the PRF coefficient of the phantom calculated at −0.00954 ppm/°C. The thermal dose denaturation threshold was revealed at 2 × 10⁶ CEM43°C by comparing thermal dose maps with T2-W FSE lesion hyperenhancement. Progressive lesion CNR loss was observed, with CNR lost 28 min after sonications.</div></div><div><h3>Conclusions</h3><div>The proposed phantom possesses excellent T2-W contrast of inflicted lesions while exhibiting a tissue like PRF coefficient and can thus constitute an inexpensive reusable tool for validating FUS systems and protocols.</div></div>","PeriodicalId":56092,"journal":{"name":"Physica Medica-European Journal of Medical Physics","volume":"131 ","pages":"Article 104932"},"PeriodicalIF":3.3,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143387568","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessment of the use of synthetic CT produced by deformable image registration of planning CT and CBCT in adaptive radiotherapy treatments of head and neck cancers","authors":"Sanju,&nbsp;Ashutosh Mukherji,&nbsp;Sambit S. Nanda,&nbsp;Shubham Dokania,&nbsp;Alka Kataria,&nbsp;Narender Kumar,&nbsp;Vinay Saini,&nbsp;Sanjay Barman,&nbsp;Dandpani R. Epili,&nbsp;Ajay Choubey,&nbsp;Ninad H. Patil,&nbsp;Ajay Krishnan","doi":"10.1016/j.ejmp.2025.104929","DOIUrl":"10.1016/j.ejmp.2025.104929","url":null,"abstract":"<div><h3>Introduction</h3><div>The present study uses deformable image registration to produce synthetic CT (sCT) images and investigate their use in treatment planning and improving clinical judgment in assessing the need for adaptive radiotherapy (ART).</div></div><div><h3>Methods</h3><div>A total of 30 patients with squamous cell carcinoma of the head and neck (HNSCC) who underwent ART were included in this study. The patients considered for adaptive planning, were re-simulated within 1–2 days. Both the Day 1 planning CT (pCT) and acquired CBCT were imported in Velocity® and a new sCT was created. The new treatment plan on re-simulation CT (rCT) which was planned for treatment delivery was recalculated on sCT. The geometric differences (Volume, Dice Similarity coefficient (DSC), mean distance to agreement (MDA)) in structures and dosimetric differences (Gamma analysis, mean doses and other DVH parameters) in treatment plans between the two images (sCT and rCT) were compared.</div></div><div><h3>Results</h3><div>The evaluation between sCT and rCT revealed that the average DSC and MDA for all the structures obtained was 0.86(0.05) and 1.15(0.20) respectively. Global gamma passing rate for 3 %, 3 mm was 96.85 ± 2.10 %. Mean dose for OARs and PTVs were found to be similar (difference within 3 %) in the two images.</div></div><div><h3>Conclusion</h3><div>sCT can be used to predict the per fraction dose delivered to the patient and could be a better alternative than only relying on clinical judgments, to take the patient for ART. Further work needs to be done on the use of sCT images to replace rCT images for ART.</div></div>","PeriodicalId":56092,"journal":{"name":"Physica Medica-European Journal of Medical Physics","volume":"131 ","pages":"Article 104929"},"PeriodicalIF":3.3,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143387567","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning paradigms in lung cancer diagnosis: A methodological review, open challenges, and future directions","authors":"Aryan Nikul Patel,&nbsp;Kathiravan Srinivasan","doi":"10.1016/j.ejmp.2025.104914","DOIUrl":"10.1016/j.ejmp.2025.104914","url":null,"abstract":"<div><div>Lung cancer is the leading cause of global cancer-related deaths, which emphasizes the critical importance of early diagnosis in enhancing patient outcomes. Deep learning has demonstrated significant promise in lung cancer diagnosis, excelling in nodule detection, classification, and prognosis prediction. This methodological review comprehensively explores deep learning models’ application in lung cancer diagnosis, uncovering their integration across various imaging modalities. Deep learning consistently achieves state-of-the-art performance, occasionally surpassing human expert accuracy. Notably, deep neural networks excel in detecting lung nodules, distinguishing between benign and malignant nodules, and predicting patient prognosis. They have also led to the development of computer-aided diagnosis systems, enhancing diagnostic accuracy for radiologists. This review follows the specified criteria for article selection outlined by PRISMA framework. Despite challenges such as data quality and interpretability limitations, this review emphasizes the potential of deep learning to significantly improve the precision and efficiency of lung cancer diagnosis, facilitating continued research efforts to overcome these obstacles and fully harness neural network’s transformative impact in this field.</div></div>","PeriodicalId":56092,"journal":{"name":"Physica Medica-European Journal of Medical Physics","volume":"131 ","pages":"Article 104914"},"PeriodicalIF":3.3,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143378689","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Direct comparison of PMT and SiPM PET systems using modified NEMA IEC Body phantom","authors":"Jaroslav Ptáček ,&nbsp;Pavel Karhan ,&nbsp;Gregor Horňák ,&nbsp;Libuše Quinn","doi":"10.1016/j.ejmp.2025.104919","DOIUrl":"10.1016/j.ejmp.2025.104919","url":null,"abstract":"<div><h3>Purpose</h3><div>This study aimed to compare the image quality of the Siemens Biograph mCT40 (photomultiplier-based system – PMT) and the Siemens Vision600 (silicon photomultiplier-based system – SiPM) using a modified NEMA IEC Body phantom (Data Spectrum).</div></div><div><h3>Methods</h3><div>SiPM-based Vision600 has a smaller crystal size (3.2 × 3.2 mm vs. 4.0 × 4.0 mm in the PMT-based mCT40), resulting in better spatial resolution. Enhanced time-of-flight (TOF) timing and higher sensitivity leads to nearly four times higher effective sensitivity. The standard NEMA IEC Body phantom was modified with a 3D-printed holder to accommodate also Hollow and Micro Hollow Spheres of 15.4 mm, 12.4 mm, 7.9 mm, 6.2 mm, 5.0 mm, and 4.0 mm. Each of the three acquisition sessions per scanner included 18 time points and spanned 5.6 half-lives to assess system performance at varying activity concentrations in the field of view.</div></div><div><h3>Results</h3><div>Recovery curves for both systems were similar when identical post-reconstruction filters were applied. The SiPM-based Vision600 system detected smaller sources at significantly lower activity concentrations, and the variations in standardized uptake value (SUV_max, SUV_A50) measurements were generally smaller compared to those of the PMT-based system. The two smallest sources became undetectable below 63 MBq and 16 MBq on the PMT system, versus 20 MBq and 6.5 MBq on the SiPM system.</div></div><div><h3>Conclusions</h3><div>SiPM technology demonstrated superior performance compared to PMT in detecting small sources in low-activity scenarios and provided more robust quantification results. It is recommended to use averaged SUV metrics, such as SUV_A50 or SUV_peak.</div></div>","PeriodicalId":56092,"journal":{"name":"Physica Medica-European Journal of Medical Physics","volume":"131 ","pages":"Article 104919"},"PeriodicalIF":3.3,"publicationDate":"2025-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143372128","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Response surface methodology for predicting optimal conditions in very low-dose chest CT imaging","authors":"Eléonore Pouget ,&nbsp;Véronique Dedieu ,&nbsp;Marie Lemery Magnin ,&nbsp;Marie Biard ,&nbsp;Guillaume Lienemann ,&nbsp;Jean-Marc Garcier ,&nbsp;Benoît Magnin","doi":"10.1016/j.ejmp.2025.104916","DOIUrl":"10.1016/j.ejmp.2025.104916","url":null,"abstract":"<div><h3>Objectives</h3><div>Dose reduction techniques, such as new reconstruction algorithms and automated exposure control systems vary with manufacturer and scanner models, complicating the optimization and standardization procedures. We investigated the feasibility of using the design of experiments in CT protocols optimization.</div></div><div><h3>Materials &amp; Methods</h3><div>A Doehlert matrix was used to define the experiments to carry out. Measurements were conducted on a 128-slice CT scanner using an anthropomorphic chest phantom with a 5 mm diameter lesion that has a HU of −800. CT images were reconstructed using iterative (ASIR-V) and deep learning-based reconstruction techniques at low (DLIR-L) and high (DLIR-H) strengths. Lesion detectability was assessed using two self-supervised learning-based model observers and six human observers. Second-order polynomial functions have been established to model the combined effect of noise index (NI) and percentage of ASIR-V on dose and model observers’ performances. The analysis of agreement between model and human observers was performed using correlation coefficients and Bland-Altman test.</div></div><div><h3>Results</h3><div>The optimal conditions predicted by this method were NI = 64, % ASIR-V = 60 and DLIR-H reconstruction. They were found in good agreement with the experimental results obtained by the average human observer, as showed by the Bland-Altman plot with a mean absolute difference of −0.01 <span><math><mo>±</mo></math></span> 3.16. Compared to 60 % ASIR-V, these results suggested an approximately 64 % dose reduction potential for DLIR-H without compromising lesion detection.</div></div><div><h3>Conclusion</h3><div>The proposed method can predict the optimal conditions that ensure diagnostic quality of low-dose chest CT examinations, while minimizing the number of experiments to carry out.</div></div>","PeriodicalId":56092,"journal":{"name":"Physica Medica-European Journal of Medical Physics","volume":"131 ","pages":"Article 104916"},"PeriodicalIF":3.3,"publicationDate":"2025-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143372127","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}