Journal of Medical Imaging最新文献

{"title":"Estimation of the absorbed dose in simultaneous digital breast tomosynthesis and mechanical imaging.","authors":"Anna Bjerkén, Hanna Tomic, Sophia Zackrisson, Magnus Dustler, Predrag R Bakic, Anders Tingberg","doi":"10.1117/1.JMI.12.S1.S13003","DOIUrl":"10.1117/1.JMI.12.S1.S13003","url":null,"abstract":"<p><strong>Purpose: </strong>Use of mechanical imaging (MI) as complementary to digital mammography (DM), or in simultaneous digital breast tomosynthesis (DBT) and MI - DBTMI, has demonstrated the potential to increase the specificity of breast cancer screening and reduce unnecessary biopsies compared with DM. The aim of this study is to investigate the increase in the radiation dose due to the presence of an MI sensor during simultaneous image acquisition when automatic exposure control is used.</p><p><strong>Approach: </strong>A radiation dose study was conducted on clinically available breast imaging systems with and without an MI sensor present. Our estimations were based on three approaches. In the first approach, exposure values were compared in paired clinical DBT and DBTMI acquisitions in 97 women. In the second approach polymethyl methacrylate (PMMA) phantoms of various thicknesses were used, and the average glandular dose (AGD) values were compared. Finally, a rectangular PMMA phantom with a 45 mm thickness was used, and the AGD values were estimated based on air kerma measurements with an electronic dosemeter.</p><p><strong>Results: </strong>The relative increase in exposure estimated from digital imaging and communications in medicine headers when using an MI sensor in clinical DBTMI was <math><mrow><mn>11.9</mn> <mo>%</mo> <mo>±</mo> <mn>10.4</mn></mrow> </math> . For the phantom measurements of various thicknesses of PMMA, the relative increases in the AGD for DM and DBT measurements were, on average, <math><mrow><mn>10.7</mn> <mo>%</mo> <mo>±</mo> <mn>3.1</mn></mrow> </math> and <math><mrow><mn>11.4</mn> <mo>%</mo> <mo>±</mo> <mn>3.0</mn></mrow> </math> , respectively. The relative increase in the AGD using the electronic dosemeter was <math><mrow><mn>11.2</mn> <mo>%</mo> <mo>±</mo> <mo><</mo> <mn>0.001</mn></mrow> </math> in DM and <math><mrow><mn>12.2</mn> <mo>%</mo> <mo>±</mo> <mo><</mo> <mn>0.001</mn></mrow> </math> in DBT. The average difference in dose between the methods was <math><mrow><mn>11.5</mn> <mo>%</mo> <mo>±</mo> <mn>3.3</mn></mrow> </math> .</p><p><strong>Conclusions: </strong>Our measurements suggest that the use of simultaneous breast radiography and MI increases the AGD by an average of <math><mrow><mn>11.5</mn> <mo>%</mo> <mo>±</mo> <mn>3.3</mn></mrow> </math> . The increase in dose is within the acceptable values for mammography screening recommended by European guidelines.</p>","PeriodicalId":47707,"journal":{"name":"Journal of Medical Imaging","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11266811/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141761688","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Breathing motion compensation in chest tomosynthesis: evaluation of the effect on image quality and presence of artifacts.","authors":"Maral Mirzai, Jenny Nilsson, Patrik Sund, Rauni Rossi Norrlund, Micael Oliveira Diniz, Bengt Gottfridsson, Ida Häggström, Åse A Johnsson, Magnus Båth, Angelica Svalkvist","doi":"10.1117/1.JMI.12.S1.S13004","DOIUrl":"https://doi.org/10.1117/1.JMI.12.S1.S13004","url":null,"abstract":"<p><strong>Purpose: </strong>Chest tomosynthesis (CTS) has a relatively longer acquisition time compared with chest X-ray, which may increase the risk of motion artifacts in the reconstructed images. Motion artifacts induced by breathing motion adversely impact the image quality. This study aims to reduce these artifacts by excluding projection images identified with breathing motion prior to the reconstruction of section images and to assess if motion compensation improves overall image quality.</p><p><strong>Approach: </strong>In this study, 2969 CTS examinations were analyzed to identify examinations where breathing motion has occurred using a method based on localizing the diaphragm border in each of the projection images. A trajectory over diaphragm positions was estimated from a second-order polynomial curve fit, and projection images where the diaphragm border deviated from the trajectory were removed before reconstruction. The image quality between motion-compensated and uncompensated examinations was evaluated using the image quality criteria for anatomical structures and image artifacts in a visual grading characteristic (VGC) study. The resulting rating data were statistically analyzed using the software VGC analyzer.</p><p><strong>Results: </strong>A total of 58 examinations were included in this study with breathing motion occurring either at the beginning or end ( <math><mrow><mi>n</mi> <mo>=</mo> <mn>17</mn></mrow> </math> ) or throughout the entire acquisition ( <math><mrow><mi>n</mi> <mo>=</mo> <mn>41</mn></mrow> </math> ). In general, no significant difference in image quality or presence of motion artifacts was shown between the motion-compensated and uncompensated examinations. However, motion compensation significantly improved the image quality and reduced the motion artifacts in cases where motion occurred at the beginning or end. In examinations where motion occurred throughout the acquisition, motion compensation led to a significant increase in ripple artifacts and noise.</p><p><strong>Conclusions: </strong>Compensation for respiratory motion in CTS by excluding projection images may improve the image quality if the motion occurs mainly at the beginning or end of the examination. However, the disadvantages of excluding projections may outweigh the benefits of motion compensation.</p>","PeriodicalId":47707,"journal":{"name":"Journal of Medical Imaging","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11399550/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142298677","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"<i>In-silico</i> study of the impact of system design parameters on microcalcification detection in wide-angle digital breast tomosynthesis.","authors":"Xiaoyu Duan, Hailiang Huang, Wei Zhao","doi":"10.1117/1.JMI.12.S1.S13002","DOIUrl":"10.1117/1.JMI.12.S1.S13002","url":null,"abstract":"<p><strong>Purpose: </strong>Accurate detection of microcalcifications ( <math><mrow><mi>μ</mi> <mi>Calcs</mi></mrow> </math> ) is crucial for the early detection of breast cancer. Some clinical studies have indicated that digital breast tomosynthesis (DBT) systems with a wide angular range have inferior <math><mrow><mi>μ</mi> <mi>Calc</mi></mrow> </math> detectability compared with those with a narrow angular range. This study aims to (1) provide guidance for optimizing wide-angle (WA) DBT for improving <math><mrow><mi>μ</mi> <mi>Calcs</mi></mrow> </math> detectability and (2) prioritize key optimization factors.</p><p><strong>Approach: </strong>An <i>in-silico</i> DBT pipeline was constructed to evaluate <math><mrow><mi>μ</mi> <mi>Calc</mi></mrow> </math> detectability of a WA DBT system under various imaging conditions: focal spot motion (FSM), angular dose distribution (ADS), detector pixel pitch, and detector electronic noise (EN). Images were simulated using a digital anthropomorphic breast phantom inserted with <math><mrow><mn>120</mn> <mtext> </mtext> <mi>μ</mi> <mi>m</mi></mrow> </math> <math><mrow><mi>μ</mi> <mi>Calc</mi></mrow> </math> clusters. Evaluation metrics included the signal-to-noise ratio (SNR) of the filtered channel observer and the area under the receiver operator curve (AUC) of multiple-reader multiple-case analysis.</p><p><strong>Results: </strong>Results showed that FSM degraded <math><mrow><mi>μ</mi> <mi>Calcs</mi></mrow> </math> sharpness and decreased the SNR and AUC by 5.2% and 1.8%, respectively. Non-uniform ADS increased the SNR by 62.8% and the AUC by 10.2% for filtered backprojection reconstruction with a typical clinical filter setting. When EN decreased from 2000 to 200 electrons, the SNR and AUC increased by 21.6% and 5.0%, respectively. Decreasing the detector pixel pitch from 85 to <math><mrow><mn>50</mn> <mtext>  </mtext> <mi>μ</mi> <mi>m</mi></mrow> </math> improved the SNR and AUC by 55.6% and 7.5%, respectively. The combined improvement of a <math><mrow><mn>50</mn> <mtext> </mtext> <mi>μ</mi> <mi>m</mi></mrow> </math> pixel pitch and EN200 was 89.2% in the SNR and 12.8% in the AUC.</p><p><strong>Conclusions: </strong>Based on the magnitude of impact, the priority for enhancing <math><mrow><mi>μ</mi> <mi>Calc</mi></mrow> </math> detectability in WA DBT is as follows: (1) utilizing detectors with a small pixel pitch and low EN level, (2) allocating a higher dose to central projections, and (3) reducing FSM. The results from this study can potentially provide guidance for DBT system optimization in the future.</p>","PeriodicalId":47707,"journal":{"name":"Journal of Medical Imaging","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11266813/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141761687","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of patient habitus and acquisition protocol on iodine quantification in dual-source photon-counting computed tomography.","authors":"Leening P Liu, Rizza Pua, Michael Dieckmeyer, Nadav Shapira, Pooyan Sahbaee, Grace J Gang, Harold I Litt, Peter B Noël","doi":"10.1117/1.JMI.11.S1.S12806","DOIUrl":"10.1117/1.JMI.11.S1.S12806","url":null,"abstract":"<p><strong>Purpose: </strong>Evaluation of iodine quantification accuracy with varying iterative reconstruction level, patient habitus, and acquisition mode on a first-generation dual-source photon-counting computed tomography (PCCT) system.</p><p><strong>Approach: </strong>A multi-energy CT phantom with and without its extension ring equipped with various iodine inserts (0.2 to 15.0 mg/ml) was scanned over a range of radiation dose levels ( <math> <mrow> <msub><mrow><mi>CTDI</mi></mrow> <mrow><mi>vol</mi></mrow> </msub> </mrow> </math> 0.5 to 15.0 mGy) using two tube voltages (120, 140 kVp) and two different source modes (single-, dual-source). To assess the agreement between nominal and measured iodine concentrations, iodine density maps at different iterative reconstruction levels were utilized to calculate root mean square error (RMSE) and generate Bland-Altman plots by grouping radiation dose levels (ultra-low: <math><mrow><mo><</mo> <mn>1.5</mn></mrow> </math> ; low: 1.5 to 5; medium: 5 to 15 mGy) and iodine concentrations (low: <math><mrow><mo><</mo> <mn>5</mn></mrow> </math> ; high: 5 to 15 mg/mL).</p><p><strong>Results: </strong>Overall, quantification of iodine concentrations was accurate and reliable even at ultra-low radiation dose levels. RMSE ranged from 0.25 to 0.37, 0.20 to 0.38, and 0.25 to 0.37 mg/ml for ultra-low, low, and medium radiation dose levels, respectively. Similarly, RMSE was stable at 0.31, 0.28, 0.33, and 0.30 mg/ml for tube voltage and source mode combinations. Ultimately, the accuracy of iodine quantification was higher for the phantom without an extension ring (RMSE 0.21 mg/mL) and did not vary across different levels of iterative reconstruction.</p><p><strong>Conclusions: </strong>The first-generation PCCT allows for accurate iodine quantification over a wide range of iodine concentrations and radiation dose levels. Stable accuracy across iterative reconstruction levels may allow further radiation exposure reductions without affecting quantitative results.</p>","PeriodicalId":47707,"journal":{"name":"Journal of Medical Imaging","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11278921/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141789483","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spectral optimization using fast kV switching and filtration for photon counting CT with realistic detector responses: a simulation study.","authors":"Sen Wang, Yirong Yang, Debashish Pal, Zhye Yin, Jonathan S Maltz, Norbert J Pelc, Adam S Wang","doi":"10.1117/1.JMI.11.S1.S12805","DOIUrl":"10.1117/1.JMI.11.S1.S12805","url":null,"abstract":"<p><strong>Purpose: </strong>Photon counting CT (PCCT) provides spectral measurements for material decomposition. However, the image noise (at a fixed dose) depends on the source spectrum. Our study investigates the potential benefits from spectral optimization using fast kV switching and filtration to reduce noise in material decomposition.</p><p><strong>Approach: </strong>The effect of the input spectra on noise performance in both two-basis material decomposition and three-basis material decomposition was compared using Cramer-Rao lower bound analysis in the projection domain and in a digital phantom study in the image domain. The fluences of different spectra were normalized using the CT dose index to maintain constant dose levels. Four detector response models based on Si or CdTe were included in the analysis.</p><p><strong>Results: </strong>For single kV scans, kV selection can be optimized based on the imaging task and object size. Furthermore, our results suggest that noise in material decomposition can be substantially reduced with fast kV switching. For two-material decomposition, fast kV switching reduces the standard deviation (SD) by <math><mrow><mo>∼</mo> <mn>10</mn> <mo>%</mo></mrow> </math> . For three-material decomposition, greater noise reduction in material images was found with fast kV switching (26.2% for calcium and 25.8% for iodine, in terms of SD), which suggests that challenging tasks benefit more from the richer spectral information provided by fast kV switching.</p><p><strong>Conclusions: </strong>The performance of PCCT in material decomposition can be improved by optimizing source spectrum settings. Task-specific tube voltages can be selected for single kV scans. Also, our results demonstrate that utilizing fast kV switching can substantially reduce the noise in material decomposition for both two- and three-material decompositions, and a fixed Gd filter can further enhance such improvements for two-material decomposition.</p>","PeriodicalId":47707,"journal":{"name":"Journal of Medical Imaging","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11272100/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141789484","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Number of energy windows for photon counting detectors: is more actually more?","authors":"Katsuyuki Taguchi","doi":"10.1117/1.JMI.11.S1.S12807","DOIUrl":"10.1117/1.JMI.11.S1.S12807","url":null,"abstract":"<p><strong>Purpose: </strong>It has been debated whether photon counting detectors (PCDs) with moderate numbers of energy windows ( <math> <mrow><msub><mi>N</mi> <mi>E</mi></msub> </mrow> </math> ) perform better than PCDs with higher <math> <mrow><msub><mi>N</mi> <mi>E</mi></msub> </mrow> </math> . A higher <math> <mrow><msub><mi>N</mi> <mi>E</mi></msub> </mrow> </math> results in fewer photons in each energy window, which degrades the signal-to-noise ratio of each datum. Unlike energy-integrating detectors, PCDs add very little electronic noise to measured counts; however, there exists electronic noise on the pulse train, to which multiple energy thresholds are applied to count photons. The noise may increase the uncertainty of counts within energy windows; however, this effect has not been studied in the context of spectral imaging tasks. We aim to investigate the effect of <math> <mrow><msub><mi>N</mi> <mi>E</mi></msub> </mrow> </math> on the quality of the spectral information in the presence of electronic noise.</p><p><strong>Approach: </strong>We obtained the following three types of PCD data with various <math> <mrow><msub><mi>N</mi> <mi>E</mi></msub> </mrow> </math> (= 2 to 24) and noise levels using a Monte Carlo simulation: (A) A PCD with no electronic noise; (B) realistic PCDs with electronic noise added to the pulse train; and (C) hypothetical PCDs with electronic noise added to each energy window's output, similar to energy-integrating detectors. We evaluated the Cramér-Rao lower bound (CRLB) of estimation for the following two spectral imaging tasks: (a) water-bone material decomposition and (b) K-edge imaging.</p><p><strong>Results: </strong>For both the e-noise-free and realistic PCDs, the CRLB improved monotonically with increasing <math> <mrow><msub><mi>N</mi> <mi>E</mi></msub> </mrow> </math> for both tasks. In contrast, a moderate <math> <mrow><msub><mi>N</mi> <mi>E</mi></msub> </mrow> </math> provided the best CRLB for the hypothetical PCDs, and the optimal <math> <mrow><msub><mi>N</mi> <mi>E</mi></msub> </mrow> </math> was smaller when electronic noise was larger. Adding one energy window to the minimum necessary <math> <mrow><msub><mi>N</mi> <mi>E</mi></msub> </mrow> </math> for a given task gained 66.2% to 68.7% of the improvement <math> <mrow><msub><mi>N</mi> <mi>E</mi></msub> <mo>=</mo> <mn>24</mn></mrow> </math> provided.</p><p><strong>Conclusion: </strong>For realistic PCDs, the quality of the spectral information monotonically improves with increasing <math> <mrow><msub><mi>N</mi> <mi>E</mi></msub> </mrow> </math> .</p>","PeriodicalId":47707,"journal":{"name":"Journal of Medical Imaging","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11413649/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142298697","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Augmented reality for point-of-care ultrasound-guided vascular access in pediatric patients using Microsoft HoloLens 2: a preliminary evaluation.","authors":"Gesiren Zhang, Trong N Nguyen, Hadi Fooladi-Talari, Tyler Salvador, Kia Thomas, Daragh Crowley, R Scott Dingeman, Raj Shekhar","doi":"10.1117/1.JMI.11.6.062604","DOIUrl":"https://doi.org/10.1117/1.JMI.11.6.062604","url":null,"abstract":"<p><strong>Significance: </strong>Conventional ultrasound-guided vascular access procedures are challenging due to the need for anatomical understanding, precise needle manipulation, and hand-eye coordination. Recently, augmented reality (AR)-based guidance has emerged as an aid to improve procedural efficiency and potential outcomes. However, its application in pediatric vascular access has not been comprehensively evaluated.</p><p><strong>Aim: </strong>We developed an AR ultrasound application, HoloUS, using the Microsoft HoloLens 2 to display live ultrasound images directly in the proceduralist's field of view. We presented our evaluation of the effect of using the Microsoft HoloLens 2 for point-of-care ultrasound (POCUS)-guided vascular access in 30 pediatric patients.</p><p><strong>Approach: </strong>A custom software module was developed on a tablet capable of capturing the moving ultrasound image from any ultrasound machine's screen. The captured image was compressed and sent to the HoloLens 2 via a hotspot without needing Internet access. On the HoloLens 2, we developed a custom software module to receive, decompress, and display the live ultrasound image. Hand gesture and voice command features were implemented for the user to reposition, resize, and change the gain and the contrast of the image. We evaluated 30 (15 successful control and 12 successful interventional) cases completed in a single-center, prospective, randomized study.</p><p><strong>Results: </strong>The mean overall rendering latency and the rendering frame rate of the HoloUS application were 139.30 ms <math><mrow><mo>(</mo> <mi>σ</mi> <mo>=</mo> <mn>32.02</mn> <mtext>  </mtext> <mi>ms</mi> <mo>)</mo></mrow> </math> and 30 frames per second, respectively. The average procedure completion time was 17.3% shorter using AR guidance. The numbers of puncture attempts and needle redirections were similar between the two groups, and the number of head adjustments was minimal in the interventional group.</p><p><strong>Conclusion: </strong>We presented our evaluation of the results from the first study using the Microsoft HoloLens 2 that investigates AR-based POCUS-guided vascular access in pediatric patients. Our evaluation confirmed clinical feasibility and potential improvement in procedural efficiency.</p>","PeriodicalId":47707,"journal":{"name":"Journal of Medical Imaging","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11393663/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142298700","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of monocular and binocular contrast perception on virtual reality head-mounted displays.","authors":"Khushi Bhansali, Miguel A Lago, Ryan Beams, Chumin Zhao","doi":"10.1117/1.JMI.11.6.062605","DOIUrl":"https://doi.org/10.1117/1.JMI.11.6.062605","url":null,"abstract":"<p><strong>Purpose: </strong>Visualization of medical images on a virtual reality (VR) head-mounted display (HMD) requires binocular fusion of a stereoscopic pair of graphical views. However, current image quality assessment on VR HMDs for medical applications has been primarily limited to time-consuming monocular optical bench measurement on a single eyepiece.</p><p><strong>Approach: </strong>As an alternative to optical bench measurement to quantify the image quality on VR HMDs, we developed a WebXR test platform to perform contrast perceptual experiments that can be used for binocular image quality assessment. We obtained monocular and binocular contrast sensitivity responses (CSRs) from participants on a Meta Quest 2 VR HMD using varied interpupillary distance (IPD) configurations.</p><p><strong>Results: </strong>The perceptual result shows that contrast perception on VR HMDs is primarily affected by optical aberration of the VR HMD. As a result, monocular CSR degrades at a high spatial frequency greater than 4 cycles per degree when gazing at the periphery of the display field of view, especially for mismatched IPD settings consistent with optical bench measurements. On the contrary, binocular contrast perception is dominated by the monocular view with superior image quality measured by the contrast.</p><p><strong>Conclusions: </strong>We developed a test platform to investigate monocular and binocular contrast perception by performing perceptual experiments. The test method can be used to evaluate monocular and/or binocular image quality on VR HMDs for potential medical applications without extensive optical bench measurements.</p>","PeriodicalId":47707,"journal":{"name":"Journal of Medical Imaging","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11401613/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142298701","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Investigating the use of signal detection information in supervised learning-based image denoising with consideration of task-shift.","authors":"Kaiyan Li, Hua Li, Mark A Anastasio","doi":"10.1117/1.JMI.11.5.055501","DOIUrl":"10.1117/1.JMI.11.5.055501","url":null,"abstract":"<p><strong>Purpose: </strong>Recently, learning-based denoising methods that incorporate task-relevant information into the training procedure have been developed to enhance the utility of the denoised images. However, this line of research is relatively new and underdeveloped, and some fundamental issues remain unexplored. Our purpose is to yield insights into general issues related to these task-informed methods. This includes understanding the impact of denoising on objective measures of image quality (IQ) when the specified task at inference time is different from that employed for model training, a phenomenon we refer to as \"task-shift.\"</p><p><strong>Approach: </strong>A virtual imaging test bed comprising a stylized computational model of a chest X-ray computed tomography imaging system was employed to enable a controlled and tractable study design. A canonical, fully supervised, convolutional neural network-based denoising method was purposely adopted to understand the underlying issues that may be relevant to a variety of applications and more advanced denoising or image reconstruction methods. Signal detection and signal detection-localization tasks under signal-known-statistically with background-known-statistically conditions were considered, and several distinct types of numerical observers were employed to compute estimates of the task performance. Studies were designed to reveal how a task-informed transfer-learning approach can influence the tradeoff between conventional and task-based measures of image quality within the context of the considered tasks. In addition, the impact of task-shift on these image quality measures was assessed.</p><p><strong>Results: </strong>The results indicated that certain tradeoffs can be achieved such that the resulting AUC value was significantly improved and the degradation of physical IQ measures was statistically insignificant. It was also observed that introducing task-shift degrades the task performance as expected. The degradation was significant when a relatively simple task was considered for network training and observer performance on a more complex one was assessed at inference time.</p><p><strong>Conclusions: </strong>The presented results indicate that the task-informed training method can improve the observer performance while providing control over the tradeoff between traditional and task-based measures of image quality. The behavior of a task-informed model fine-tuning procedure was demonstrated, and the impact of task-shift on task-based image quality measures was investigated.</p>","PeriodicalId":47707,"journal":{"name":"Journal of Medical Imaging","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11376226/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142156370","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting peritumoral glioblastoma infiltration and subsequent recurrence using deep-learning-based analysis of multi-parametric magnetic resonance imaging.","authors":"Sunwoo Kwak, Hamed Akbari, Jose A Garcia, Suyash Mohan, Yehuda Dicker, Chiharu Sako, Yuji Matsumoto, MacLean P Nasrallah, Mahmoud Shalaby, Donald M O'Rourke, Russel T Shinohara, Fang Liu, Chaitra Badve, Jill S Barnholtz-Sloan, Andrew E Sloan, Matthew Lee, Rajan Jain, Santiago Cepeda, Arnab Chakravarti, Joshua D Palmer, Adam P Dicker, Gaurav Shukla, Adam E Flanders, Wenyin Shi, Graeme F Woodworth, Christos Davatzikos","doi":"10.1117/1.JMI.11.5.054001","DOIUrl":"10.1117/1.JMI.11.5.054001","url":null,"abstract":"<p><strong>Purpose: </strong>Glioblastoma (GBM) is the most common and aggressive primary adult brain tumor. The standard treatment approach is surgical resection to target the enhancing tumor mass, followed by adjuvant chemoradiotherapy. However, malignant cells often extend beyond the enhancing tumor boundaries and infiltrate the peritumoral edema. Traditional supervised machine learning techniques hold potential in predicting tumor infiltration extent but are hindered by the extensive resources needed to generate expertly delineated regions of interest (ROIs) for training models on tissue most and least likely to be infiltrated.</p><p><strong>Approach: </strong>We developed a method combining expert knowledge and training-based data augmentation to automatically generate numerous training examples, enhancing the accuracy of our model for predicting tumor infiltration through predictive maps. Such maps can be used for targeted supra-total surgical resection and other therapies that might benefit from intensive yet well-targeted treatment of infiltrated tissue. We apply our method to preoperative multi-parametric magnetic resonance imaging (mpMRI) scans from a subset of 229 patients of a multi-institutional consortium (Radiomics Signatures for Precision Diagnostics) and test the model on subsequent scans with pathology-proven recurrence.</p><p><strong>Results: </strong>Leave-one-site-out cross-validation was used to train and evaluate the tumor infiltration prediction model using initial pre-surgical scans, comparing the generated prediction maps with follow-up mpMRI scans confirming recurrence through post-resection tissue analysis. Performance was measured by voxel-wised odds ratios (ORs) across six institutions: University of Pennsylvania (OR: 9.97), Ohio State University (OR: 14.03), Case Western Reserve University (OR: 8.13), New York University (OR: 16.43), Thomas Jefferson University (OR: 8.22), and Rio Hortega (OR: 19.48).</p><p><strong>Conclusions: </strong>The proposed model demonstrates that mpMRI analysis using deep learning can predict infiltration in the peri-tumoral brain region for GBM patients without needing to train a model using expert ROI drawings. Results for each institution demonstrate the model's generalizability and reproducibility.</p>","PeriodicalId":47707,"journal":{"name":"Journal of Medical Imaging","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11363410/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142113462","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}