Medical physics最新文献

{"title":"A unified deep-learning framework for enhanced patient-specific quality assurance of intensity-modulated radiation therapy plans","authors":"Hui Khee Looe, Philipp Reinert, Julius Carta, Björn Poppe","doi":"10.1002/mp.17601","DOIUrl":"10.1002/mp.17601","url":null,"abstract":"<div>\u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Modern radiation therapy techniques, such as intensity-modulated radiation therapy (IMRT) and volumetric-modulated arc therapy (VMAT), use complex fluence modulation strategies to achieve optimal patient dose distribution. Ensuring their accuracy necessitates rigorous patient-specific quality assurance (PSQA), traditionally done through pretreatment measurements with detector arrays. While effective, these methods are labor-intensive and time-consuming. Independent calculation-based methods leveraging advanced dose algorithms provide a reduced workload but cannot account for machine performance during delivery.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>This study introduces a novel unified deep-learning (DL) framework to enhance PSQA. The framework can combine the strengths of measurement- and calculation-based approaches.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>A comprehensive artificial training dataset, comprising 400,000 samples, was generated based on a rigorous mathematical model that describes the physical processes of radiation transport and interaction within both the medium and detector. This artificial data was used to pretrain the DL models, which were subsequently fine-tuned with a measured dataset of 400 IMRT segments to capture the machine-specific characteristics. Additional measurements of five IMRT plans were used as the unseen test dataset. Within the unified framework, a forward prediction model uses plan parameters to predict the measured dose distributions, while the backward prediction model reconstructs these parameters from actual measurements. The former enables a detailed control point (CP)-wise analysis. At the same time, the latter facilitates the reconstruction of treatment plans from the measurements and, subsequently, dose recalculation in the treatment planning system (TPS), as well as an independent second check software (VERIQA). This method has been tested with an OD 1600 SRS and an OD 1500 detector array with distinct spatial resolution and detector arrangement in combination with a dedicated upsampling model for the latter.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The final models could deliver highly accurate predictions of the measurements in the forward direction and the actual delivered plan parameters in the backward direction. In the forward direction, the test plans reached median gamma passing rates better than 94% for the OD 1600 SRS measurements. The upsampled OD 1500 measurements show similar performance with similar median gamma passing rates but a slightly ","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 3","pages":"1878-1892"},"PeriodicalIF":3.2,"publicationDate":"2024-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mp.17601","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142883980","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Characterization of LiF TLD-100 in carbon ion beams for remote audits","authors":"Paige A. Taylor, Alfredo Mirandola, Mario Ciocca, Shannon Hartzell, Giuseppe Magro, Paola Alvarez, Christine B. Peterson, Christopher R. Peeler, Eugene J. Koay, Rebecca M. Howell, Stephen F. Kry","doi":"10.1002/mp.17605","DOIUrl":"10.1002/mp.17605","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>A passive dosimeter framework for the measurement of dose in carbon ion beams has yet to be characterized or implemented for regular use.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>This work determined the dose calculation correction factors for absorbed dose in thermoluminescent dosimeters (TLDs) in a therapeutic carbon ion beam. TLD could be a useful tool for remote audits, particularly in the context of clinical trials as new protocols are developed for carbon ion radiotherapy.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>TLD-100 were irradiated in a carbon ion beam at the Centro Nazionale di Adroterapia Oncologica (CNAO) in Pavia, Italy. The dose correction factors for linearity, fading, and beam quality were characterized. Fading was characterized from 5 to 100 days post-irradiation. For linearity, the TLDs were irradiated to absorbed doses ranging from 1 to 15 Gy in both the entrance of a high-energy pristine carbon ion peak and the center of a 2 cm spread-out Bragg peak. For beam quality, the TLD was irradiated to the same absorbed dose (3 Gy) in several pristine carbon ion Bragg peaks, as well as in several spread-out Bragg peaks. Each correction factor was calculated and compared to photon correction factors. The correction factors were also compared between high and low dose-averaged linear energy transfer (LET<sub>D</sub>) in the carbon ion beams. The absorbed dose was compared between ion chamber and TLD-100 in the several tissue substitute phantom materials, applying the carbon ion TLD correction factors.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>There was no statistically significant difference in the TLD fading correction factor between photons, low LET<sub>D</sub> carbon ion beams, or high LET<sub>D</sub> carbon ion beams. The TLD linearity correction factor did differ between photons, low LET<sub>D</sub> carbon ions, and high LET<sub>D</sub> carbon ions. The beam quality correction factor was large and changed linearly with LET<sub>D</sub>. The overall uncertainty of the carbon ion absorbed dose calculation was 3.9% at the one-sigma level, driven largely by a 3.5% uncertainty in the beam quality correction. TLD measurements were within 1.2% of ion chamber measurements in the phantom material for polyethylene, solid water (Gammex and Sun Nuclear), acrylic, blue water, and techtron HPV. TLD measurements in balsa wood were within 3.0% and cork was 6.6% low compared to ion chamber.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>TLD-100 can be used for passive dosimetry in a therapeutic car","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 3","pages":"1858-1866"},"PeriodicalIF":3.2,"publicationDate":"2024-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142883963","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An AI dose-influence matrix engine for robust pencil beam scanning protons therapy","authors":"Yaoying Liu, Xuying Shang, Nan Li, Zishen Wang, Chunfeng Fang, Yue Zou, Xiaoyun Le, Gaolong Zhang, Shouping Xu","doi":"10.1002/mp.17602","DOIUrl":"10.1002/mp.17602","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Rapid planning is of tremendous value in proton pencil beam scanning (PBS) therapy in overcoming range uncertainty. However, the dose calculation of the dose influence matrix (D<sub>ij</sub>) in robust PBS plan optimization is time-consuming and requires substantial acceleration to enhance efficiency.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>To accelerate the D<sub>ij</sub> calculations in PBS therapy, we developed an AI-D<sub>ij</sub> engine integrated into our in-house treatment planning system (TPS).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>The AI-D<sub>ij</sub> engine calculates spot dose using a transformer-based spot dose calculation model (SDM), which takes CT volumes (CT-bars, 256 <span></span><math>\u0000 <semantics>\u0000 <mo>×</mo>\u0000 <annotation>$ times $</annotation>\u0000 </semantics></math> 16 <span></span><math>\u0000 <semantics>\u0000 <mo>×</mo>\u0000 <annotation>$ times $</annotation>\u0000 </semantics></math> 16 voxels, 3 mm resolution) and energy (a float value) as inputs and outputs the spot dose distribution (256 <span></span><math>\u0000 <semantics>\u0000 <mo>×</mo>\u0000 <annotation>$ times $</annotation>\u0000 </semantics></math> 16 <span></span><math>\u0000 <semantics>\u0000 <mo>×</mo>\u0000 <annotation>$ times $</annotation>\u0000 </semantics></math> 16). The SDM was trained on over 200 000 CT-bars and Monte Carlo (MC) spot dose (spanning energy levels from 70 to 225 MeV). Clinical-implemented treatment plans for the head, lung, and liver, initially created on Raystation, were replanned using our AI-D<sub>ij</sub> engine under identical gantry angles and uncertainties settings. After optimizing the spot weight, each in-house plan was recalculated using MCsquare for MC dose evaluation. The dose-volume histogram (DVH) metrics from the in-house TPS and Raystation were compared, evaluating both the optimized and MC doses.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>In optimization, the differences of DVH metrics (%, Value<sub>in-house</sub>—Value<sub>Raystation</sub>) across all uncertainty scenarios between the in-house and Raystation plans were 0.93 ± 2.04% for clinical target volume (CTV) and −5.94 ± 12.19% for organ at risks (OARs). For the MC doses, the differences were 2.48 ± 2.78% for CTV and −5.47 ± 14.16% for OARs. The time cost of a robust AI-D<sub>ij</sub> calculation can be within 2s on an RTX3090 GPU.</p>\u0000 </section>\u0000 ","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 3","pages":"1903-1913"},"PeriodicalIF":3.2,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142879351","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automatic Pavlov ratio measurement method based on spinal landmarks identification by a deep-learning model","authors":"Yongli Wang,&nbsp;Chi Huang,&nbsp;Junhao Zhou,&nbsp;Xueyuan Zhang,&nbsp;Fei Ren,&nbsp;Benbo Zhang,&nbsp;Xiaowen Wang,&nbsp;Xiyue Cheng,&nbsp;Kai Cao,&nbsp;Yibo Dou,&nbsp;Peng Cao","doi":"10.1002/mp.17594","DOIUrl":"10.1002/mp.17594","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Cervical canal stenosis is one of the important pathogenic factors of cervical spondylosis. The accuracy of the Pavlov ratio measurement is crucial for the diagnosis and treatment of cervical spinal stenosis. Manual measurement is influenced by observer variability, accompanied by its inefficiency, which affects clinical evaluation.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>To automatically and accurately measure the Pavlov ratio, we develop a novel deep-learning model by detecting keypoints of cervical spine and measure the Pavlov ratio on plain lateral cervical spine radiographs.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We developed a two-stage deep-learning model; in the first stage, we employ the YOLOX model as the object detection network to locate the ROIs containing the vertebral bodies and spinous processes; in the second stage, we introduce the high-resolution net (HRNet) as keypoint detection network and a series of deconvolutional networks (DNs) as the heatmap-based regressor. Based on the mentioned combining algorithms, we can rapidly detect the 38 keypoints in plain lateral cervical spine radiographs, and then measure the Pavlov ratio of the cervical spine. Radiographs from Shanghai Changhai Hospital (a total of 874) were split into training and test subsets (787 and 87 radiographs, respectively). One hundred twelve cases from Shanghai Changzheng Hospital and 108 cases from Shanghai Fourth People's Hospital are used as external validation datasets.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Our proposed model successfully achieved the objective of automating the recognition of spinal landmarks with the mean absolute error (MAE)ranged from 0.05 to 0.08, and the symmetric mean absolute percentage error (SMAPE) ranged from 4.54% to 6.43%. The achieved accuracy is comparable to that of seasoned medical professionals and notably surpasses the performance of junior physicians (SMAPE ranged from 8.74% to 26.19%). Furthermore, our model demonstrated excellent accuracy in external validation experiments (SMAPE ranged from 4.40% to 5.95%).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>This study presents a novel YOLOX-HRNet-DN model to assist landmarks identification on lateral cervical spine radiographs and demonstrates excellent accuracy in measuring the Pavlov ratio. The proposed method could provide a potential tool for the automatic estimation of the Pavlov ratio to improve the efficiency and accuracy of the treatment workflow.</p>\u0000 </section>\u0000 </div>","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 3","pages":"1536-1545"},"PeriodicalIF":3.2,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142883961","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep denoising approach to improve shear wave phase velocity map reconstruction in ultrasound elastography","authors":"Phidakordor Sahshong,&nbsp;Akash Chandra,&nbsp;Karla P. Mercado-Shekhar,&nbsp;Manish Bhatt","doi":"10.1002/mp.17581","DOIUrl":"10.1002/mp.17581","url":null,"abstract":"&lt;div&gt;\u0000 \u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Background&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Measurement noise often leads to inaccurate shear wave phase velocity estimation in ultrasound shear wave elastography. Filtering techniques are commonly used for denoising the shear wavefields. However, these filters are often not sufficient, especially in fatty tissues where the signal-to-noise ratio (SNR) can be very low.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Purpose&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;The purpose of this study is to develop a deep learning approach for denoising shear wavefields in ultrasound shear wave elastography. This may lead to improved reconstruction of shear wave phase velocity image maps.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Methods&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;The study addresses noise by transforming particle velocity data into a time-frequency representation. A neural network with encoder and decoder convolutional blocks effectively decomposes the input and extracts the signal of interest, improving the SNR in high-noise scenarios. The network is trained on simulated phantoms with elasticity values ranging from 3  to 60 kPa. A total of 1 85 570 samples with 80%–20&lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mo&gt;%&lt;/mo&gt;\u0000 &lt;annotation&gt;$%$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; split were used for training and validation. The approach is tested on experimental phantom and ex-vivo goat liver tissue data. Performance was compared with the traditional filtering methods such as bandpass, median, and wavelet filtering. Kruskal–Wallis one-way analysis of variance was performed to check statistical significance. Multiple comparisons were performed using the Mann–Whitney U test and Holm–Bonferroni adjustment of &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;p&lt;/mi&gt;\u0000 &lt;mo&gt;−&lt;/mo&gt;\u0000 &lt;mi&gt;values&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$p-{rm values}$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Results&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;The results are evaluated using SNR and the percentage of pixels that can be reconstructed in the phase velocity maps. The SNR levels in experimental data improved from –2 to 9.9 dB levels to 15.6 to 30.3 dB levels. Kruskal–Wallis one-way analysis showed statistical significance (&lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;p&lt;/mi&gt;\u0000 &lt;mo&gt;&lt;&lt;/mo&gt;\u0000 &lt;mn&gt;0.05&lt;/mn&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$p&lt;0.05$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;). Multiple comparisons wit","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 3","pages":"1481-1499"},"PeriodicalIF":3.2,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142879324","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Feasibility study of using next-generation reservoir computing (NG-RC) model to estimate liver tumor motion from external breathing signals","authors":"Payam Samadi Miandoab,&nbsp;Saeed Setayeshi,&nbsp;Oliver Blanck,&nbsp;Shahyar Saramad","doi":"10.1002/mp.17595","DOIUrl":"10.1002/mp.17595","url":null,"abstract":"&lt;div&gt;\u0000 \u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Background&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Respiratory motion is a challenge for accurate radiotherapy that may be mitigated by real-time tracking. Commercial tracking systems utilize a hybrid external-internal correlation model (ECM), integrating continuous external breathing monitoring with sparse X-ray imaging of the internal tumor position.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Purpose&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;This study investigates the feasibility of using the next generation reservoir computing (NG-RC) model as a hybrid ECM to transform measured external motions into estimated 3D internal motions.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Methods&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;The NG-RC model utilizes the nonlinear vector autoregressive (NVAR) machine to account for the hysteresis or phase differences between external and internal motions. The datasets used to evaluate the efficacy of the NG-RC model include 57 motion traces from the CyberKnife system. The datasets were divided into three regions (central, lower, and upper livers) and three motion patterns. These patterns include linear and nonlinear motion patterns (Group A), hysteresis motion patterns (Group B), and all motion patterns (Group C). Moreover, various updating techniques were examined, such as continuously updating the NG-RC model using the first-in-first-out (FIFO) approach and sampling the internal tumor position every 0 s (strategy A), 60 s (strategy B), 30 s (strategy C), and 50 s (strategy D).&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Results&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;The NG-RC model combined with strategy C resulted in better estimation accuracy than the reported CyberKnife cases (Wilcoxon signed rank &lt;i&gt;p&lt;/i&gt; &lt; 0.05). For linear and nonlinear motion patterns, the 3D radial estimation accuracy (mean ± SD) using the NG-RC model combined with strategy C and the CyberKnife system was 1.20 ± 0.78 and 1.1 ± 0.20 mm in the central liver, 0.66 ± 0.25 and 1.49 ± 0.50 mm in the lower liver, and 1.73 ± 0.86 and 1.61 ± 0.42 mm in the upper liver. For hysteresis motion patterns, the corresponding values were 1.13 ± 0.37 and 1.45 ± 0.33 mm, 1.43 ± 1.30 and 1.67 ± 0.42 mm, and 1.20 ± 0.68 and 1.46 ± 0.54 mm in the central, lower, and upper livers, respectively.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Conclusion&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;This study proposed a new hybrid correlation model for real-time tumor tracking, which can be used to account for both linear and nonlinear motion patterns, as well as hysteresis motion patterns. Additionally, the NG-RC model required shorter training data sets (15 s) during pre-treatment and short internal motion sampling (every 30 s) during treatment compared to other ","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 3","pages":"1416-1429"},"PeriodicalIF":3.2,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142879371","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"On the origin of MTF reduction in grating-based x-ray differential phase contrast CT imaging","authors":"Yuhang Tan,&nbsp;Jiecheng Yang,&nbsp;Hairong Zheng,&nbsp;Dong Liang,&nbsp;Peiping Zhu,&nbsp;Yongshuai Ge","doi":"10.1002/mp.17593","DOIUrl":"10.1002/mp.17593","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>The complementary absorption contrast CT (ACT) and differential phase contrast CT (DPCT) can be generated simultaneously from an x-ray computed tomography (CT) imaging system incorporated with grating interferometer. However, it has been reported that ACT images exhibit better spatial resolution than DPCT images. By far, the primary cause of such discrepancy remains unclear.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>The purpose of this study is to investigate the underlying cause of the resolution discrepancy between ACT and DPCT in a grating interferometer CT imaging system.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>In this study, theoretical derivations were performed with a <span></span><math>\u0000 <semantics>\u0000 <mi>π</mi>\u0000 <annotation>$pi$</annotation>\u0000 </semantics></math>-phase Talbot–Lau grating interferometer system to model the signal formation mechanism of absorption imaging and phase imaging, respectively. In addition, physical, and numerical experiments were conducted to verify the theoretical findings and assess the resolution discrepancy between ACT and DPCT under various conditions. Herein, the ACT and DPCT images were reconstructed from the filtered-back-projection algorithm using a standard Ramp filter and a standard Hilbert filter, respectively.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Experiments demonstrated that the spatial resolution of ACT and DPCT images are primarily impacted by the beam diffraction induced signal splitting. In particular, lower modulation transfer function (MTF) was observed for DPCT than ACT due to the opposite-superposition of phase signals. In addition, factors such as focal spot size, beam spectra, object composition, sample size, and detector pixel size were found to have minor impacts on the MTFs of both ACT and DPCT.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>In conclusion, this study reveals that the opposite-superposition of split phase signals causes the spatial resolution reduction in DPCT imaging.</p>\u0000 </section>\u0000 </div>","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 3","pages":"1546-1555"},"PeriodicalIF":3.2,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142866837","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting early stage lung cancer recurrence and survival from combined tumor motion amplitude and radiomics on free-breathing 4D-CT","authors":"Emilie Ouraou,&nbsp;Marion Tonneau,&nbsp;William T. Le,&nbsp;Edith Filion,&nbsp;Marie-Pierre Campeau,&nbsp;Toni Vu,&nbsp;Robert Doucet,&nbsp;Houda Bahig,&nbsp;Samuel Kadoury","doi":"10.1002/mp.17586","DOIUrl":"10.1002/mp.17586","url":null,"abstract":"&lt;div&gt;\u0000 \u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Background&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Cancer control outcomes of lung cancer are hypothesized to be affected by several confounding factors, including tumor heterogeneity and patient history, which have been hypothesized to mitigate the dose delivery effectiveness when treated with radiation therapy. Providing an accurate predictive model to identify patients at risk would enable tailored follow-up strategies during treatment.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Purpose&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Our goal is to demonstrate the added prognostic value of including tumor displacement amplitude in a predictive model that combines clinical features and computed tomography (CT) radiomics for 2-year recurrence and survival in non-small-cell lung cancer (NSCLC) patients treated with curative-intent stereotactic body radiation therapy.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Methods&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;A cohort of 381 patients treated for primary lung cancer with radiotherapy was collected, each including a planning CT with a dosimetry plan, 4D-CT, and clinical information. From this cohort, 101 patients (26.5%) experienced cancer progression (locoregional/distant metastasis) or death within 2 years of the end of treatment. Imaging data was analyzed for radiomics features from the tumor segmented image, as well as tumor motion amplitude measured on 4D-CT. A random forest (RF) model was developed to predict the overall outcomes, which was compared to three other approaches — logistic regression, support vector machine, and convolutional neural networks.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Results&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;A 6-fold cross-validation study yielded an area under the receiver operating characteristic curve of 72% for progression-free survival when combining clinical data with radiomics features and tumor motion using a RF model (72% sensitivity and 81% specificity). The combined model showed significant improvement compared to standard clinical data. Model performances for loco-regional recurrence and overall survival sub-outcomes were established at 73% and 70%, respectively. No comparative methods reached statistical significance in any data configuration.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Conclusions&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Combined tumor respiratory motion and radiomics features from planning CT showed promising predictive value for 2-year tumor control and survival, indicating the potential need for improving motion management strategies in future studies using machine learning-based prognosis models.&lt;/p&gt;\u0000 &lt;/sectio","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 3","pages":"1926-1940"},"PeriodicalIF":3.2,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mp.17586","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142866843","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Photon counting CT versus energy-integrating CT: A comparative evaluation of advances in image resolution, noise, and dose efficiency","authors":"Björn Heismann,&nbsp;Björn Kreisler,&nbsp;Robert Fasbender","doi":"10.1002/mp.17591","DOIUrl":"10.1002/mp.17591","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Photon counting computed tomography (PCCT) employs direct and spectrally resolved counting of individual x-ray quanta, enhancing image quality compared to the standard energy-integrating CT (EICT).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>To evaluate the quantitative improvements in CT image quality metrics by comparing the first medical PCCT with a state-of-the-art EICT.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>The PCCT versus EICT noise improvement ratio <i>R</i> was derived from the quantum statistics of the measurement process and measured across the clinical x-ray flux range for both systems. Detector and system modulation transfer functions (MTFs) were obtained using tilted-slit and wire phantom measurements. Image root mean square (RMS) noise, noise power spectrum (NPS), and x-ray patient dose were compared using a CatPhan phantom at two identical clinical target resolutions.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The measurement of the PCCT noise improvement ratio <i>R</i> showed an elimination of electronic noise and a 10% noise transfer advantage. The PCCT detector MTF exhibited 3x higher angular resolution limits in comparison to EICT and close to ideal sinc behavior due to the electromagnetic formation of pixels in the PCCT semiconductor detector. This translated to 3.5x enhancements in CT system MTF ratios at 10 LP/cm, reflecting a significant improvement in millimeter range CT imaging. Both the improved quantum detection and the system MTF ratio improvement contribute to the measured 3x enhancements in image NPS at 10 LP/cm for identical image target resolution. An improvement of up to 1.7x in RMS image noise was observed accordingly. For low and ultra-low dose imaging with image filtering, dose efficiency increased between 2x and 10x, demonstrating the PCCT's capability to advance CT ultra-low dose imaging.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>The direct counting detection in PCCT has been shown to significantly improve sinogram noise and detector MTF ratios compared to energy integrating EICT. The observed translations into CT system MTF, image NPS, image noise, and dose ratios reflect a paradigm shift for CT image quality and dose efficiency.</p>\u0000 </section>\u0000 </div>","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 3","pages":"1526-1535"},"PeriodicalIF":3.2,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mp.17591","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142866840","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Feasibility of volumetric-modulated arc therapy gating for cardiac radioablation using real-time ECG signal acquisition and a dynamic phantom","authors":"Cristiano Q. M. Reis,&nbsp;Alex Cross,&nbsp;Jennifer M. Borsavage,&nbsp;Greg Berryhill,&nbsp;Scott Karnas,&nbsp;James L. Robar,&nbsp;Stewart Gaede","doi":"10.1002/mp.17582","DOIUrl":"10.1002/mp.17582","url":null,"abstract":"&lt;div&gt;\u0000 \u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Background&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Stereotactic arrythmia radioablation (STAR) is a noninvasive technique to treat ventricular tachycardia (VT). Management of cardiorespiratory motion plays an essential role in VT-STAR treatments to improve treatment outcomes by reducing positional uncertainties and increasing dose conformality. Use of an electrocardiogram (ECG) signal, acquired in real-time, as a surrogate to gate the beam has the potential to fulfil that intent.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Purpose&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;To investigate the gated delivery of volumetric-modulated arc therapy (VMAT) for STAR on a TrueBeam linear accelerator (linac) using a real-time acquired ECG signal and a dynamic cardiac phantom.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Methods and materials&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Dosimetric characteristics of a 6 MV flattening filter free (FFF) beam from a Varian TrueBeam linac were initially evaluated under high-frequency gating scenarios relevant to cardiac rhythms with respect to dose linearity, beam output, and energy quality. A microcontroller board was used to interface and gate the linac, sending a beam on/off signal. For real-time cardiac gated measurements, an AD8232 Heart Monitor board was used to acquire the ECG signal and synchronize the VMAT delivery to an ArcCHECK detector to a specific phase of the cardiac cycle. Gated dose distributions were compared against those acquired for a non-gated delivery mode. An in-house dynamic cardiac phantom was developed to simulate cardiorespiratory motion that correlates target position with the signal to gate the beam. Measured dose distributions using gafchromic film were also compared against the static (reference) mode in different scenarios with and without gating.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Results&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Maximum difference in dose per monitor unit (MU) was found to be no greater than 1% as compared to static mode with variation in the chamber response within 0.2% in the range of 50 MUs to 200 MUs. Maximum percentage differences for the beam output and beam qualiy index (TPR&lt;sub&gt;20,10&lt;/sub&gt;) between gated and non-gated modes were 0.91% and -0.44%, respectively. Comparison of delivered dose distributions for the VMAT plan without gating versus ECG synchronized gating modes provided a passing rate 98% for the gamma analysis with 1% relative dose difference, 1 mm distance-to-agreement criteria. For the synchronization of dose delivery with target position, passing rates were 98%, 97%, and 99% for the axial, coronal, and sagittal planes, respectively, when gating the beam based on target position for cardiac","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 3","pages":"1758-1768"},"PeriodicalIF":3.2,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mp.17582","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142857411","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}