Medical physics最新文献

{"title":"Detection of an internal density change in an anthropomorphic head phantom via tracking of charged nuclear fragments in carbon-ion radiotherapy","authors":"Luisa Schweins, Rebekka Kirchgässner, Pamela Ochoa-Parra, Marcus Winter, Semi Harrabi, Andrea Mairani, Oliver Jäkel, Jürgen Debus, Mária Martišíková, Laurent Kelleter","doi":"10.1002/mp.17590","DOIUrl":"10.1002/mp.17590","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Carbon-ion radiotherapy provides steep dose gradients that allow the simultaneous application of high tumor doses as well as the sparing of healthy tissue and radio-sensitive organs. However, even small anatomical changes may have a severe impact on the dose distribution because of the finite range of ion beams.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>An in-vivo monitoring method based on secondary-ion emission could potentially provide feedback about the patient anatomy and thus the treatment quality. This work aims to prove that a clinically relevant anatomical change in an anthropomorphic head phantom may be detected via charged-fragment tracking during a treatment fraction.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>A clinically representative carbon-ion treatment plan was created for a skull-base tumor in an anthropomorphic head phantom. In order to imitate an inter-fractional anatomical change — for example, through tissue swelling or mucous accumulation — a piece of silicone was inserted into the nasopharynx. Fragment distributions with and without the silicone insert were subsequently acquired with a mini-tracker made of four hybrid silicon pixel detectors. Experimental irradiations were carried out at the Heidelberg Ion Beam Therapy Centre (HIT, Germany). FLUKA Monte Carlo simulations were performed to support the interpretation of the experimental results.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>It was found that the silicone causes a significant change in the fragment emission that was clearly distinguishable from statistical fluctuations and setup uncertainties. Two regions of fragment loss were observed upstream and downstream of the silicone with similar amplitude in both the measurement and the simulation. Monte Carlo simulations showed that the observed signature is a consequence of a complex interplay of fragment production, scattering, and absorption.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Carbon-ion therapy monitoring with charged nuclear fragments was shown to be capable of detecting clinically relevant density changes in an anthropomorphic head phantom under realistic clinic-like conditions. The complexity of the observed signal requires the development of advanced analysis techniques and underscores the importance of Monte Carlo simulations. The findings have strong implications for the ongoing InViMo clinical trial at HIT, which investigates the feasibility of ","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 4","pages":"2399-2411"},"PeriodicalIF":3.2,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mp.17590","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142879345","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 study of using next-generation reservoir computing (NG-RC) model to estimate liver tumor motion from external breathing signals","authors":"Payam Samadi Miandoab, Saeed Setayeshi, Oliver Blanck, Shahyar Saramad","doi":"10.1002/mp.17595","DOIUrl":"10.1002/mp.17595","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>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.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>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.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>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).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The NG-RC model combined with strategy C resulted in better estimation accuracy than the reported CyberKnife cases (Wilcoxon signed rank <i>p</i> < 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.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>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":"Coupling of state space modules and attention mechanisms: An input-aware multi-contrast MRI synthesis method","authors":"Shuai Chen, Ruoyu Zhang, Huazheng Liang, Yunzhu Qian, Xuefeng Zhou","doi":"10.1002/mp.17598","DOIUrl":"10.1002/mp.17598","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Medical imaging plays a pivotal role in the real-time monitoring of patients during the diagnostic and therapeutic processes. However, in clinical scenarios, the acquisition of multi-modal imaging protocols is often impeded by a number of factors, including time and economic costs, the cooperation willingness of patients, imaging quality, and even safety concerns.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>We proposed a learning-based medical image synthesis method to simplify the acquisition of multi-contrast MRI.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We redesigned the basic structure of the Mamba block and explored different integration patterns between Mamba layers and Transformer layers to make it more suitable for medical image synthesis tasks. Experiments were conducted on the IXI (a total of 575 samples, training set: 450 samples; validation set: 25 samples; test set: 100 samples) and BRATS (a total of 494 samples, training set: 350 samples; validation set: 44 samples; test set: 100 samples) datasets to assess the synthesis performance of our proposed method in comparison to some state-of-the-art models on the task of multi-contrast MRI synthesis.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Our proposed model outperformed other state-of-the-art models in some multi-contrast MRI synthesis tasks. In the synthesis task from T1 to PD, our proposed method achieved the peak signal-to-noise ratio (PSNR) of 33.70 dB (95% CI, 33.61, 33.79) and the structural similarity index (SSIM) of 0.966 (95% CI, 0.964, 0.968). In the synthesis task from T2 to PD, the model achieved a PSNR of 33.90 dB (95% CI, 33.82, 33.98) and SSMI of 0.971 (95% CI, 0.969, 0.973). In the synthesis task from FLAIR to T2, the model achieved PSNR of 30.43 dB (95% CI, 30.29, 30.57) and SSIM of 0.938 (95% CI, 0.935, 0.941).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Our proposed method could effectively model not only the high-dimensional, nonlinear mapping relationships between the magnetic signals of the hydrogen nucleus in tissues and the proton density signals in tissues, but also of the recovery process of suppressed liquid signals in FLAIR. The model proposed in our work employed distinct mechanisms in the synthesis of images belonging to normal and lesion samples, which demonstrated that our model had a profound comprehension of the input data. We also proved that in a hierarchical network, only the deeper self-attention layers were responsible for directing more attention on lesion areas.</p>\u0000 </section>\u0000 ","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 4","pages":"2269-2278"},"PeriodicalIF":3.2,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142879296","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":"Investigation of cardiac substructure automatic segmentation methods on synthetically generated 4D cone-beam CT images","authors":"Mark Gardner, Robert N. Finnegan, Owen Dillon, Vicky Chin, Tess Reynolds, Paul J. Keall","doi":"10.1002/mp.17596","DOIUrl":"10.1002/mp.17596","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>STereotactic Arrhythmia Radioablation (STAR) is a novel noninvasive method for treating arrythmias in which external beam radiation is directed towards subregions of the heart. Challenges for accurate STAR targeting include small target volumes and relatively large patient motion, which can lead to radiation related patient toxicities. 4D Cone-beam CT (CBCT) images are used for stereotactic lung treatments to account for respiration-related patient motion. 4D-CBCT imaging could similarly be used to account for respiration-related patient motion in STAR; however, the poor contrast of heart tissue in CBCT makes identifying cardiac substructures in 4D-CBCT images challenging. If cardiac structures can be identified in pre-treatment 4D-CBCT images, then the location of the target volume can be more accurately identified for different phases of the respiration cycle, leading to more accurate targeting and a reduction in patient toxicities.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>The aim of this simulation study is to investigate the accuracy of different cardiac substructure segmentation methods for 4D-CBCT images.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Repeat 4D-CT scans from 13 lung cancer patients were obtained from The Cancer Imaging Archive. Synthetic 4D-CBCT images for each patient were simulated by forward projecting and reconstructing each respiration phase of a chosen “testing” 4D-CT scan. Eighteen cardiac structures were segmented from each respiration phase image in the testing 4D-CT using the previously validated <i>platipy</i> toolkit. The <i>platipy</i> segmentations from the testing 4D-CT were defined as the ground truth segmentations for the synthetic 4D-CBCT images. Five different 4D-CBCT cardiac segmentation methods were investigated: 3D Rigid Alignment, 4D Rigid Alignment, Direct CBCT Segmentation, Contour Transformation, and Synthetic CT Segmentation methods. For all methods except the Direct CBCT segmentation method, a separate 4D-CT (Planning CT) was used to assist in generating 4D-CBCT segmentations. Segmentation performance was measured using the Dice similarity coefficient (DSC), Hausdorff distance (HD), mean surface distance (MSD), and volume ratio (VR) metrics.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The mean ± standard deviation DSC for all cardiac substructures for the 3D Rigid Alignment, 4D Rigid Alignment, Direct CBCT Segmentation, Contour Transformation, and Synthetic CT Segmentation methods were 0.48 ± 0.29, 0.52 ± 0.29, 0.37 ± 0.32, 0.53 ± 0.29, 0.57 ± 0.28, respectively. Similarly, the HD values were 10.9 ± 3.6 , 9.9 ± 2.6 , 17.3 ± 5.3 , 9.9 ±","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 4","pages":"2224-2237"},"PeriodicalIF":3.2,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142879376","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":"A qBOLD-based clinical radiomics-integrated model for predicting isocitrate dehydrogenase-1 mutation in gliomas","authors":"Jingzhi Wu,&nbsp;Jun Qiu,&nbsp;Ying Yang,&nbsp;Wen Sun,&nbsp;Peng Wang,&nbsp;Panpan Hu,&nbsp;Yidong Yang,&nbsp;Ying Liu,&nbsp;Jie Wen","doi":"10.1002/mp.17578","DOIUrl":"10.1002/mp.17578","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Quantitative blood oxygenation level–dependent (qBOLD) technique can be applied to detect tissue damage and changes in hemodynamic in gliomas. It is not known whether qBOLD-based radiomics approaches can improve the prediction of isocitrate dehydrogenase-1 (IDH-1) mutation.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>To establish a qBOLD-based clinical radiomics-integrated model for predicting IDH-1 mutation in gliomas.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>A total of 125 patients of grade II–IV glioma (IDH1 mutation: IDH1 wild-type = 50:75) were divided into a training group (<i>n</i> = 87) and a validation group (<i>n</i> = 38). Contrast enhanced T1-weighted (CE-T1W), T2-weighted (T2W), and 3D multi-gradient-recalled-echo (MGRE) images were acquired. Radiomics features were extracted from the region of interests of each image. The feature selection and support vector machine radiomics models were established for each sequence. A clinical radiomics–integrated model was finally constructed combining the best radiomics model with age. The predictive effectiveness of the models was evaluated by area under the receiver operating characteristic curve (AUC). Brier score was used to assess overall predictive performance. Decision curve analysis and calibration curve were also conducted.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The best radiomics model was CE-T1W + T2W + qBOLD with AUCs of 0.823 (95% confidence interval [CI]: 0.743–0.831) in the training group and 0.751 (95% CI: 0.655–0.794) in the validation group, respectively. The clinical radiomics–integrated model, incorporating the best radiomics model with age, showed the best predictive effectiveness with AUCs of 0.851 (95% CI 0.759–0.918) in the training group and 0.786 (95% CI 0.622–0.902) in the validation group.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>A clinical radiomics-integrated model that combined qBOLD parametric maps, CE-T1W, and T2W images with age achieved promising performance for predicting IDH1 mutation in glioma patients.</p>\u0000 </section>\u0000 </div>","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 4","pages":"2247-2256"},"PeriodicalIF":3.2,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142866829","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":"Improved deep learning-based IVIM parameter estimation via the use of more “realistic” simulated brain data","authors":"Lu Wang,&nbsp;Jiechao Wang,&nbsp;Qinqin Yang,&nbsp;Congbo Cai,&nbsp;Zhen Xing,&nbsp;Zhong Chen,&nbsp;Dairong Cao,&nbsp;Shuhui Cai","doi":"10.1002/mp.17583","DOIUrl":"10.1002/mp.17583","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Due to the low signal-to-noise ratio (SNR) and the limited number of <i>b</i>-values, precise parameter estimation of intravoxel incoherent motion (IVIM) imaging remains an open issue to date, especially for brain imaging where the relatively small difference between <i>D</i> and <i>D^*</i> easily leads to outliers and obvious graininess in estimated results.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>To propose a synthetic data driven supervised learning method (SDD-IVIM) for improving precision and noise robustness in IVIM parameter estimation without relying on real-world data for neural network training.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>On account of the absence of standard IVIM parametric maps from real-world data, a novel model-based method for generating synthetic human brain IVIM data was introduced. Initially, the parameter values of synthetic IVIM parametric maps were sampled from the complex distributions composed of a series of simple and uniform distributions. Subsequently, these parametric maps were modulated with human brain texture to imitate brain tissue structure. Finally, they were used to generate synthetic human brain multi-<i>b</i>-value diffusion-weighted (DW) images based on the IVIM bi-exponential model. With the proposed data synthesis method, an ordinary U-Net with spatial smoothness was employed for IVIM parameter mapping within a supervised learning framework. The performance of SDD-IVIM was evaluated on both numerical phantom and 20 glioma patients. The estimated IVIM parametric maps were compared to those derived from five state-of-the-art methods.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>In numerical phantom experiments, SDD-IVIM method produces IVIM parametric maps with lower mean absolute error, lower mean bias, and higher structural similarity compared to the other five methods, especially when the SNR of DW images is low. In glioma patient experiments, SDD-IVIM method offers lower coefficient of variation and more reasonable contrast-to-noise ratio between tumor and contralateral normal appearing white matter than the other five methods.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>Our method owns superior performance in parametric map quality, parameter estimation precision, and lesion characterization in IVIM parameter estimation, with strong resistance to noise.</p>\u0000 </section>\u0000 </div>","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 4","pages":"2279-2294"},"PeriodicalIF":3.2,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142866834","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":"Automated CT-based measurements of radial and longitudinal expansion of airways due to breathing-related lung volume change","authors":"Syed Ahmed Nadeem,&nbsp;Alejandro P. Comellas,&nbsp;Kung-Sik Chan,&nbsp;Eric A. Hoffman,&nbsp;Sean B. Fain,&nbsp;Punam K. Saha","doi":"10.1002/mp.17592","DOIUrl":"10.1002/mp.17592","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 function is impaired in chronic obstructive pulmonary disease (COPD). Automation of multi-volume CT-based measurements of different components of breathing-related airway deformations will help understand multi-pathway impairments in respiratory mechanics in COPD.&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 develop and evaluate multi-volume chest CT-based automated measurements of breathing-related radial and longitudinal expansion of individual airways between inspiratory and expiratory lung volumes.&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;We developed a method to compute breathing-related airway deformation metrics and applied it to total lung capacity (TLC) and functional residual capacity (FRC) chest CT scans. The computational pipeline involves: (1) segmentation of airways; (2) skeletonization of airways; (3) labeling of anatomical airway segments at TLC and FRC; and (4) computation of radial and longitudinal expansion metrics of individual airways across lung volumes. Radial expansion (∆CSA) of an airway is computed as the percent change of its cross-sectional area (CSA) between two lung volumes. Longitudinal expansion (∆L) of an airway is computed as the percent change in its airway path-length from the carina between lung volumes. These measures are summarized at different airway anatomic generations. Agreement of automated measures with their manually derived values was examined in terms of concordance correlation coefficient (CCC) of automated measures with those derived using manual outlining. Intra-class correlation coefficient (ICC) of automated measures from repeat CT scans (&lt;i&gt;n&lt;/i&gt; = 37) was computed to assess repeatability. The method was also applied to a set of participants from the Genetic Epidemiology of COPD (COPDGene) Iowa cohort, distributed across COPD severity groups (&lt;i&gt;n&lt;/i&gt; = 4 × 60).&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 CCC values for the automated ∆CSA measure with manually derived values were 0.930 at the trachea, 0.898 at primary bronchi, and greater than 0.95 at pre-segmental and segmental airways; these CCC values were consistently greater than 0.95 for ∆L at all airway generations. ICC values for repeatability of ∆CSA were 0.974, 0.950, 0.943, and 0.901 at trachea, primary bronchi, pre-segmental, and segmental airways, respectively; these ICC values for ∆L were 0.973, 0.954, and 0.952 at primary bronchi, pre-segmental, and segmental airways, respectively. ∆CSA values were significantly reduced (&lt;i&gt;p&lt;/i&gt; &lt; 0.001) with increasing COPD se","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 4","pages":"2316-2329"},"PeriodicalIF":3.2,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mp.17592","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142866831","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":"Parameter map guided explainable segmentation framework for breast cancer using amide proton transfer weighted imaging","authors":"Qiuhui Yang,&nbsp;Meng Wang,&nbsp;Weiqiang Dou,&nbsp;Ya Ren,&nbsp;Tianyu Zhang,&nbsp;Long Qian,&nbsp;Yi Xu,&nbsp;Kefeng Li,&nbsp;Mingwei Wang,&nbsp;Yue Sun,&nbsp;Zhou Liu,&nbsp;Tao Tan","doi":"10.1002/mp.17574","DOIUrl":"10.1002/mp.17574","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Amide proton transfer weighted (APTw) imaging has demonstrated extensive clinical applications in diagnosing, treating evaluating, and prognosis prediction of breast cancer. There is a pressing need to automatically segment breast lesions on APTw original images to facilitate downstream quantification, which is however challenging.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>To build a segmentation model on the original images of APTw imaging sequence by leveraging the varying contrasts between breast lesions and their surrounding glandular and fat tissues displayed on the original images of APTw imaging at different frequency offsets.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>This paper proposes a network with multiple tasks, including a breast lesion segmentation model (task I) incorporating multiple images at different frequencies with different contrasts between tumor and surrounding tissues, an automatic classification of pathological task (task II), and an APTw parameter map fitting (task III).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Compared with these advanced segmentation methods such as U-Net, segment anything model (SAM), segment anything in medical images (Med-SAM), and transfomer for MRI brain tumor segmentation (TransBTS), our method achieves higher accuracy (ACC). Furthermore, the model's interpretability facilitates the evaluation of how maps with varying gray contrasts contribute to the segmentation. Moreover, improving the ACC of segmentation can be accomplished through tasks such as pathological classification and parametric map fitting.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>The pathological classification task and parameter fitting task could improve the ACC of segmentation.</p>\u0000 </section>\u0000 </div>","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 4","pages":"2384-2398"},"PeriodicalIF":3.2,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142857414","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}