Objectives: This study is aimed at evaluating the diagnostic performance of clinical predictors and the Doppler ultrasonography in predicting esophageal varices (EV) in patients with hepatitis C-related cirrhosis and exploring the practical predictors of EV.
Methods: We conducted a prospective study from July 2020 to January 2021, enrolling 65 patients with mild hepatitis C-related cirrhosis. We obtained clinical data and performed grayscale and the Doppler ultrasound to explore the predictors of EV. Esophagogastroduodenoscopy (EGD) was performed as the reference test by the gastroenterologist within a week.
Results: The prevalence of EV in the study was 41.5%. Multivariable regression analysis revealed that gender (female, OR = 4.04, p = 0.02), platelet count (<150000 per ml, OR = 3.13, p = 0.09), splenic length (>11 cm, OR = 3.64, p = 0.02), and absent right hepatic vein (RHV) triphasicity (OR = 3.15, p = 0.03) were significant predictors of EV. However, the diagnostic accuracy indices for isolated predictors were not good (AUROC = 0.63-0.66). A combination of these four predictors increases the diagnostic accuracy in predicting the presence of EV (AUROC = 0.80, 95% CI 0.69-0.91). Furthermore, the Doppler assessment of the right hepatic vein waveform showed good reproducibility (κ = 0.76).
Conclusion: Combining clinical and Doppler ultrasound features can be used as a screening test for predicting the presence of EV in patients with hepatitis C-related cirrhosis. The practical predictors identified in this study could serve as an alternative to invasive EGD in EV diagnosis. Further studies are needed to explore the diagnostic accuracy of additional noninvasive predictors, such as elastography, to improve EV screening.
{"title":"Prediction of Esophageal Varices in Viral Hepatitis C Cirrhosis: Performance of Combined Ultrasonography and Clinical Predictors.","authors":"Puwitch Charoenchue, Wittanee Na Chiangmai, Amonlaya Amantakul, Wasuwit Wanchaitanawong, Taned Chitapanarux, Suwalee Pojchamarnwiputh","doi":"10.1155/2023/7938732","DOIUrl":"https://doi.org/10.1155/2023/7938732","url":null,"abstract":"<p><strong>Objectives: </strong>This study is aimed at evaluating the diagnostic performance of clinical predictors and the Doppler ultrasonography in predicting esophageal varices (EV) in patients with hepatitis C-related cirrhosis and exploring the practical predictors of EV.</p><p><strong>Methods: </strong>We conducted a prospective study from July 2020 to January 2021, enrolling 65 patients with mild hepatitis C-related cirrhosis. We obtained clinical data and performed grayscale and the Doppler ultrasound to explore the predictors of EV. Esophagogastroduodenoscopy (EGD) was performed as the reference test by the gastroenterologist within a week.</p><p><strong>Results: </strong>The prevalence of EV in the study was 41.5%. Multivariable regression analysis revealed that gender (female, OR = 4.04, <i>p</i> = 0.02), platelet count (<150000 per ml, OR = 3.13, <i>p</i> = 0.09), splenic length (>11 cm, OR = 3.64, <i>p</i> = 0.02), and absent right hepatic vein (RHV) triphasicity (OR = 3.15, <i>p</i> = 0.03) were significant predictors of EV. However, the diagnostic accuracy indices for isolated predictors were not good (AUROC = 0.63-0.66). A combination of these four predictors increases the diagnostic accuracy in predicting the presence of EV (AUROC = 0.80, 95% CI 0.69-0.91). Furthermore, the Doppler assessment of the right hepatic vein waveform showed good reproducibility (<i>κ</i> = 0.76).</p><p><strong>Conclusion: </strong>Combining clinical and Doppler ultrasound features can be used as a screening test for predicting the presence of EV in patients with hepatitis C-related cirrhosis. The practical predictors identified in this study could serve as an alternative to invasive EGD in EV diagnosis. Further studies are needed to explore the diagnostic accuracy of additional noninvasive predictors, such as elastography, to improve EV screening.</p>","PeriodicalId":47063,"journal":{"name":"International Journal of Biomedical Imaging","volume":"2023 ","pages":"7938732"},"PeriodicalIF":7.6,"publicationDate":"2023-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10516699/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41147595","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}
Hong Chang Tan, Elizabeth Shumbayawonda, Cayden Beyer, Lionel Tim-Ee Cheng, Albert Low, Chin Hong Lim, Alvin Eng, Weng Hoong Chan, Phong Ching Lee, Mei Fang Tay, Stella Kin, Jason Pik Eu Chang, Yong Mong Bee, George Boon Bee Goh
Background: Bariatric surgery is the most effective treatment for morbid obesity and reduces the severity of nonalcoholic fatty liver disease (NAFLD) in the long term. Less is known about the effects of bariatric surgery on liver fat, inflammation, and fibrosis during the early stages following bariatric surgery.
Aims: This exploratory study utilises advanced imaging methods to investigate NAFLD and fibrosis changes during the early metabolic transitional period following bariatric surgery.
Methods: Nine participants with morbid obesity underwent sleeve gastrectomy. Multiparametric MRI (mpMRI) and magnetic resonance elastography (MRE) were performed at baseline, during the immediate (1 month), and late (6 months) postsurgery period. Liver fat was measured using proton density fat fraction (PDFF), disease activity using iron-correct T1 (cT1), and liver stiffness using MRE. Repeated measured ANOVA was used to assess longitudinal changes and Dunnett's method for multiple comparisons.
Results: All participants (Age 45.1 ± 9.0 years, BMI 39.7 ± 5.3 kg/m2) had elevated hepatic steatosis at baseline (PDFF >5%). In the immediate postsurgery period, PDFF decreased significantly from 14.1 ± 7.4% to 8.9 ± 4.4% (p = 0.016) and cT1 from 826.9 ± 80.6 ms to 768.4 ± 50.9 ms (p = 0.047). These improvements continued to the later postsurgery period. Bariatric surgery did not reduce liver stiffness measurements.
Conclusion: Our findings support using MRI as a noninvasive tool to monitor NAFLD in patient with morbid obesity during the early stages following bariatric surgery.
{"title":"Multiparametric Magnetic Resonance Imaging and Magnetic Resonance Elastography to Evaluate the Early Effects of Bariatric Surgery on Nonalcoholic Fatty Liver Disease.","authors":"Hong Chang Tan, Elizabeth Shumbayawonda, Cayden Beyer, Lionel Tim-Ee Cheng, Albert Low, Chin Hong Lim, Alvin Eng, Weng Hoong Chan, Phong Ching Lee, Mei Fang Tay, Stella Kin, Jason Pik Eu Chang, Yong Mong Bee, George Boon Bee Goh","doi":"10.1155/2023/4228321","DOIUrl":"https://doi.org/10.1155/2023/4228321","url":null,"abstract":"<p><strong>Background: </strong>Bariatric surgery is the most effective treatment for morbid obesity and reduces the severity of nonalcoholic fatty liver disease (NAFLD) in the long term. Less is known about the effects of bariatric surgery on liver fat, inflammation, and fibrosis during the early stages following bariatric surgery.</p><p><strong>Aims: </strong>This exploratory study utilises advanced imaging methods to investigate NAFLD and fibrosis changes during the early metabolic transitional period following bariatric surgery.</p><p><strong>Methods: </strong>Nine participants with morbid obesity underwent sleeve gastrectomy. Multiparametric MRI (mpMRI) and magnetic resonance elastography (MRE) were performed at baseline, during the immediate (1 month), and late (6 months) postsurgery period. Liver fat was measured using proton density fat fraction (PDFF), disease activity using iron-correct T1 (cT1), and liver stiffness using MRE. Repeated measured ANOVA was used to assess longitudinal changes and Dunnett's method for multiple comparisons.</p><p><strong>Results: </strong>All participants (Age 45.1 ± 9.0 years, BMI 39.7 ± 5.3 kg/m<sup>2</sup>) had elevated hepatic steatosis at baseline (PDFF >5%). In the immediate postsurgery period, PDFF decreased significantly from 14.1 ± 7.4% to 8.9 ± 4.4% (<i>p</i> = 0.016) and cT1 from 826.9 ± 80.6 ms to 768.4 ± 50.9 ms (<i>p</i> = 0.047). These improvements continued to the later postsurgery period. Bariatric surgery did not reduce liver stiffness measurements.</p><p><strong>Conclusion: </strong>Our findings support using MRI as a noninvasive tool to monitor NAFLD in patient with morbid obesity during the early stages following bariatric surgery.</p>","PeriodicalId":47063,"journal":{"name":"International Journal of Biomedical Imaging","volume":"2023 ","pages":"4228321"},"PeriodicalIF":7.6,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10372298/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9919473","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}
Otman Sarrhini, Pedro D'Orléans-Juste, Jacques A Rousseau, Jean-François Beaudoin, Roger Lecomte
We propose an enhanced method to accurately retrieve time-activity curves (TACs) of blood and tissue from dynamic 2-deoxy-2-[18F]fluoro-D-glucose ([18F]FDG) positron emission tomography (PET) cardiac images of mice. The method is noninvasive and consists of using a constrained nonnegative matrix factorization algorithm (CNMF) applied to the matrix (A) containing the intensity values of the voxels of the left ventricle (LV) PET image. CNMF factorizes A into nonnegative matrices H and W, respectively, representing the physiological factors (blood and tissue) and their associated weights, by minimizing an extended cost function. We verified our method on 32 C57BL/6 mice, 14 of them with acute myocardial infarction (AMI). With CNMF, we could break down the mouse LV into myocardial and blood pool images. Their corresponding TACs were used in kinetic modeling to readily determine the [18F]FDG influx constant (Ki) required to compute the myocardial metabolic rate of glucose. The calculated Ki values using CNMF for the heart of control mice were in good agreement with those published in the literature. Significant differences in Ki values for the heart of control and AMI mice were found using CNMF. The values of the elements of W agreed well with the LV structural changes induced by ligation of the left coronary artery. CNMF was compared with the recently published method based on robust unmixing of dynamic sequences using regions of interest (RUDUR). A clear improvement of signal separation was observed with CNMF compared to the RUDUR method.
{"title":"Enhanced Extraction of Blood and Tissue Time-Activity Curves in Cardiac Mouse FDG PET Imaging by Means of Constrained Nonnegative Matrix Factorization.","authors":"Otman Sarrhini, Pedro D'Orléans-Juste, Jacques A Rousseau, Jean-François Beaudoin, Roger Lecomte","doi":"10.1155/2023/5366733","DOIUrl":"https://doi.org/10.1155/2023/5366733","url":null,"abstract":"<p><p>We propose an enhanced method to accurately retrieve time-activity curves (TACs) of blood and tissue from dynamic 2-deoxy-2-[<sup>18</sup>F]fluoro-D-glucose ([<sup>18</sup>F]FDG) positron emission tomography (PET) cardiac images of mice. The method is noninvasive and consists of using a constrained nonnegative matrix factorization algorithm (CNMF) applied to the matrix (<i>A</i>) containing the intensity values of the voxels of the left ventricle (LV) PET image. CNMF factorizes <i>A</i> into nonnegative matrices <i>H</i> and <i>W</i>, respectively, representing the physiological factors (blood and tissue) and their associated weights, by minimizing an extended cost function. We verified our method on 32 C57BL/6 mice, 14 of them with acute myocardial infarction (AMI). With CNMF, we could break down the mouse LV into myocardial and blood pool images. Their corresponding TACs were used in kinetic modeling to readily determine the [<sup>18</sup>F]FDG influx constant (<i>K</i><sub><i>i</i></sub>) required to compute the myocardial metabolic rate of glucose. The calculated <i>K</i><sub><i>i</i></sub> values using CNMF for the heart of control mice were in good agreement with those published in the literature. Significant differences in <i>K</i><sub><i>i</i></sub> values for the heart of control and AMI mice were found using CNMF. The values of the elements of <i>W</i> agreed well with the LV structural changes induced by ligation of the left coronary artery. CNMF was compared with the recently published method based on robust unmixing of dynamic sequences using regions of interest (RUDUR). A clear improvement of signal separation was observed with CNMF compared to the RUDUR method.</p>","PeriodicalId":47063,"journal":{"name":"International Journal of Biomedical Imaging","volume":"2023 ","pages":"5366733"},"PeriodicalIF":7.6,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10287520/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9716473","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}
Thore Dietrich, Stephan Theodor Bujak, Thorsten Keller, Bernhard Schnackenburg, Riad Bourayou, Rolf Gebker, Kristof Graf, Eckart Fleck
The usefulness of perfluorocarbon nanoemulsions for the imaging of experimental myocarditis has been demonstrated in a high-field 9.4 Tesla MRI scanner. Our proof-of-concept study investigated the imaging capacity of PFC-based 19F/1H MRI in an animal myocarditis model using a clinical field strength of 1.5 Tesla. To induce experimental myocarditis, five male rats (weight ~300 g, age ~50 days) were treated with one application per week of doxorubicin (2 mg/kg BW) over a period of six weeks. Three control animals received the identical volume of sodium chloride 0.9% instead. Following week six, all animals received a single 4 ml injection of an 20% oil-in-water perfluorooctylbromide nanoemulsion 24 hours prior to in vivo1H/19F imaging on a 1.5 Tesla MRI. After euthanasia, cardiac histology and immunohistochemistry using CD68/ED1 macrophage antibodies were performed, measuring the inflamed myocardium in μm2 for further statistical analysis to compare the extent of the inflammation with the 19F-MRI signal intensity. All animals treated with doxorubicin showed a specific signal in the myocardium, while no myocardial signal could be detected in the control group. Additionally, the doxorubicin group showed a significantly higher SNR for 19F and a stronger CD68/ED1 immunhistoreactivity compared to the control group. This proof-of-concept study demonstrates that perfluorocarbon nanoemulsions could be detected in an in vivo experimental myocarditis model at a currently clinically relevant field strength.
全氟碳纳米乳对实验性心肌炎成像的有用性已在高场9.4特斯拉MRI扫描仪中得到证实。我们的概念验证研究考察了基于pfc的19F/1H MRI在动物心肌炎模型中使用1.5特斯拉临床场强的成像能力。为了诱导实验性心肌炎,将5只体重~300 g、年龄~50日龄的雄性大鼠,每周1次给予阿霉素(2 mg/kg BW),持续6周。而对照组的三只动物则注射了相同体积的0.9%氯化钠。第六周后,所有动物在1.5特斯拉MRI 1h /19F成像前24小时接受单次4 ml 20%水包油全氟辛基溴纳米乳注射。安乐死后采用CD68/ED1巨噬细胞抗体进行心脏组织学和免疫组化,以μm2为单位测量炎症心肌,进一步统计分析炎症程度与19F-MRI信号强度的比较。阿霉素处理的所有动物心肌均有特异信号,而对照组未检测到心肌信号。此外,与对照组相比,阿霉素组显示出更高的19F信噪比和更强的CD68/ED1免疫组化活性。这项概念验证研究表明,全氟碳纳米乳剂可以在体内实验心肌炎模型中以当前临床相关的场强检测到。
{"title":"In Vivo Fluorine Imaging Using 1.5 Tesla MRI for Depiction of Experimental Myocarditis in a Rodent Animal Model.","authors":"Thore Dietrich, Stephan Theodor Bujak, Thorsten Keller, Bernhard Schnackenburg, Riad Bourayou, Rolf Gebker, Kristof Graf, Eckart Fleck","doi":"10.1155/2023/4659041","DOIUrl":"https://doi.org/10.1155/2023/4659041","url":null,"abstract":"<p><p>The usefulness of perfluorocarbon nanoemulsions for the imaging of experimental myocarditis has been demonstrated in a high-field 9.4 Tesla MRI scanner. Our proof-of-concept study investigated the imaging capacity of PFC-based <sup>19</sup>F/<sup>1</sup>H MRI in an animal myocarditis model using a clinical field strength of 1.5 Tesla. To induce experimental myocarditis, five male rats (weight ~300 g, age ~50 days) were treated with one application per week of doxorubicin (2 mg/kg BW) over a period of six weeks. Three control animals received the identical volume of sodium chloride 0.9% instead. Following week six, all animals received a single 4 ml injection of an 20% oil-in-water perfluorooctylbromide nanoemulsion 24 hours prior to <i>in vivo</i><sup>1</sup>H/<sup>19</sup>F imaging on a 1.5 Tesla MRI. After euthanasia, cardiac histology and immunohistochemistry using CD68/ED1 macrophage antibodies were performed, measuring the inflamed myocardium in <i>μ</i>m<sup>2</sup> for further statistical analysis to compare the extent of the inflammation with the <sup>19</sup>F-MRI signal intensity. All animals treated with doxorubicin showed a specific signal in the myocardium, while no myocardial signal could be detected in the control group. Additionally, the doxorubicin group showed a significantly higher SNR for <sup>19</sup>F and a stronger CD68/ED1 immunhistoreactivity compared to the control group. This proof-of-concept study demonstrates that perfluorocarbon nanoemulsions could be detected in an <i>in vivo</i> experimental myocarditis model at a currently clinically relevant field strength.</p>","PeriodicalId":47063,"journal":{"name":"International Journal of Biomedical Imaging","volume":"2023 ","pages":"4659041"},"PeriodicalIF":7.6,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10361831/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9855524","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}
Pub Date : 2022-12-22eCollection Date: 2022-01-01DOI: 10.1155/2022/5318447
Rumana Islam, Mohammed Tarique
This paper presents an automated and noninvasive technique to discriminate COVID-19 patients from pneumonia patients using chest X-ray images and artificial intelligence. The reverse transcription-polymerase chain reaction (RT-PCR) test is commonly administered to detect COVID-19. However, the RT-PCR test necessitates person-to-person contact to administer, requires variable time to produce results, and is expensive. Moreover, this test is still unreachable to the significant global population. The chest X-ray images can play an important role here as the X-ray machines are commonly available at any healthcare facility. However, the chest X-ray images of COVID-19 and viral pneumonia patients are very similar and often lead to misdiagnosis subjectively. This investigation has employed two algorithms to solve this problem objectively. One algorithm uses lower-dimension encoded features extracted from the X-ray images and applies them to the machine learning algorithms for final classification. The other algorithm relies on the inbuilt feature extractor network to extract features from the X-ray images and classifies them with a pretrained deep neural network VGG16. The simulation results show that the proposed two algorithms can extricate COVID-19 patients from pneumonia with the best accuracy of 100% and 98.1%, employing VGG16 and the machine learning algorithm, respectively. The performances of these two algorithms have also been collated with those of other existing state-of-the-art methods.
本文介绍了一种利用胸部 X 光图像和人工智能区分 COVID-19 患者和肺炎患者的自动化、无创技术。逆转录聚合酶链反应(RT-PCR)测试是检测 COVID-19 的常用方法。然而,RT-PCR 检测需要人与人之间的接触才能进行,产生结果所需的时间不固定,而且价格昂贵。此外,这种检测方法仍无法惠及全球大量人口。胸部 X 光图像在这方面可以发挥重要作用,因为任何医疗机构都有 X 光机。然而,COVID-19 和病毒性肺炎患者的胸部 X 光图像非常相似,往往会导致主观误诊。这项研究采用了两种算法来客观地解决这一问题。一种算法使用从 X 光图像中提取的低维编码特征,并将其应用于机器学习算法进行最终分类。另一种算法则依靠内置的特征提取器网络从 X 光图像中提取特征,并通过预训练的深度神经网络 VGG16 进行分类。仿真结果表明,采用 VGG16 和机器学习算法,所提出的两种算法可将 COVID-19 患者从肺炎中解救出来,准确率分别达到 100%和 98.1%。这两种算法的性能还与其他现有的先进方法进行了比较。
{"title":"Chest X-Ray Images to Differentiate COVID-19 from Pneumonia with Artificial Intelligence Techniques.","authors":"Rumana Islam, Mohammed Tarique","doi":"10.1155/2022/5318447","DOIUrl":"10.1155/2022/5318447","url":null,"abstract":"<p><p>This paper presents an automated and noninvasive technique to discriminate COVID-19 patients from pneumonia patients using chest X-ray images and artificial intelligence. The reverse transcription-polymerase chain reaction (RT-PCR) test is commonly administered to detect COVID-19. However, the RT-PCR test necessitates person-to-person contact to administer, requires variable time to produce results, and is expensive. Moreover, this test is still unreachable to the significant global population. The chest X-ray images can play an important role here as the X-ray machines are commonly available at any healthcare facility. However, the chest X-ray images of COVID-19 and viral pneumonia patients are very similar and often lead to misdiagnosis subjectively. This investigation has employed two algorithms to solve this problem objectively. One algorithm uses lower-dimension encoded features extracted from the X-ray images and applies them to the machine learning algorithms for final classification. The other algorithm relies on the inbuilt feature extractor network to extract features from the X-ray images and classifies them with a pretrained deep neural network VGG16. The simulation results show that the proposed two algorithms can extricate COVID-19 patients from pneumonia with the best accuracy of 100% and 98.1%, employing VGG16 and the machine learning algorithm, respectively. The performances of these two algorithms have also been collated with those of other existing state-of-the-art methods.</p>","PeriodicalId":47063,"journal":{"name":"International Journal of Biomedical Imaging","volume":"2022 ","pages":"5318447"},"PeriodicalIF":3.3,"publicationDate":"2022-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9800093/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10464881","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}
S. Raeymaeckers, Yannick De Brucker, Maurizio Tosi, N. Buls, J. Mey
A multiphase 4DCT technique can be useful for the detection of parathyroid adenomas. Up to 16 different phases can be obtained without significant increase of exposure dose using wide beam axial scanning. This technique also allows for the calculation of perfusion parameters in suspected lesions. We present data on 19 patients with histologically proven parathyroid adenomas. We find a strong correlation between 2 perfusion parameters when comparing parathyroid adenomas and thyroid tissue: parathyroid adenomas show a 55% increase in blood flow (BF) (p < 0.001) and a 50% increase in blood volume (BV) (p < 0.001) as compared to normal thyroid tissue. The analysis of the ROC curve for the different perfusion parameters demonstrates a significantly high area under the curve for BF and BV, confirming these two perfusion parameters to be a possible discriminating tool to discern between parathyroid adenomas and thyroid tissue. These findings can help to discern parathyroid from thyroid tissue and may aid in the detection of parathyroid adenomas.
{"title":"Relative Perfusion Differences between Parathyroid Adenomas and the Thyroid on Multiphase 4DCT","authors":"S. Raeymaeckers, Yannick De Brucker, Maurizio Tosi, N. Buls, J. Mey","doi":"10.1155/2022/2984789","DOIUrl":"https://doi.org/10.1155/2022/2984789","url":null,"abstract":"A multiphase 4DCT technique can be useful for the detection of parathyroid adenomas. Up to 16 different phases can be obtained without significant increase of exposure dose using wide beam axial scanning. This technique also allows for the calculation of perfusion parameters in suspected lesions. We present data on 19 patients with histologically proven parathyroid adenomas. We find a strong correlation between 2 perfusion parameters when comparing parathyroid adenomas and thyroid tissue: parathyroid adenomas show a 55% increase in blood flow (BF) (p < 0.001) and a 50% increase in blood volume (BV) (p < 0.001) as compared to normal thyroid tissue. The analysis of the ROC curve for the different perfusion parameters demonstrates a significantly high area under the curve for BF and BV, confirming these two perfusion parameters to be a possible discriminating tool to discern between parathyroid adenomas and thyroid tissue. These findings can help to discern parathyroid from thyroid tissue and may aid in the detection of parathyroid adenomas.","PeriodicalId":47063,"journal":{"name":"International Journal of Biomedical Imaging","volume":" ","pages":""},"PeriodicalIF":7.6,"publicationDate":"2022-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47701953","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The methods of compressed sensing magnetic resonance imaging (CS-MRI) can be divided into two categories roughly based on the number of target variables. One group devotes to estimating the complex-valued MRI image. And the other calculates the magnitude and phase parts of the complex-valued MRI image, respectively, by enforcing separate penalties on them. We propose a new CS-based method based on dual-tree complex wavelet (DT CWT) sparsity, which is under the frame of the second class of CS-MRI. Owing to the separate regularization frame, this method reduces the impact of the phase jumps (that means the jumps or discontinuities of phase values) on magnitude reconstruction. Moreover, by virtue of the excellent features of DT CWT, such as nonoscillating envelope of coefficients and multidirectional selectivity, the proposed method is capable of capturing more details in the magnitude and phase images. The experimental results show that the proposed method recovers the image contour and edges information well and can eliminate the artifacts in magnitude results caused by phase jumps.
{"title":"MRI Reconstruction with Separate Magnitude and Phase Priors Based on Dual-Tree Complex Wavelet Transform","authors":"W. He, Linman Zhao","doi":"10.1155/2022/7251674","DOIUrl":"https://doi.org/10.1155/2022/7251674","url":null,"abstract":"The methods of compressed sensing magnetic resonance imaging (CS-MRI) can be divided into two categories roughly based on the number of target variables. One group devotes to estimating the complex-valued MRI image. And the other calculates the magnitude and phase parts of the complex-valued MRI image, respectively, by enforcing separate penalties on them. We propose a new CS-based method based on dual-tree complex wavelet (DT CWT) sparsity, which is under the frame of the second class of CS-MRI. Owing to the separate regularization frame, this method reduces the impact of the phase jumps (that means the jumps or discontinuities of phase values) on magnitude reconstruction. Moreover, by virtue of the excellent features of DT CWT, such as nonoscillating envelope of coefficients and multidirectional selectivity, the proposed method is capable of capturing more details in the magnitude and phase images. The experimental results show that the proposed method recovers the image contour and edges information well and can eliminate the artifacts in magnitude results caused by phase jumps.","PeriodicalId":47063,"journal":{"name":"International Journal of Biomedical Imaging","volume":" ","pages":""},"PeriodicalIF":7.6,"publicationDate":"2022-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49417825","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Dr. MANOHARAN SUBRAMANIAN, Velmurugan Lingamuthu, Chandran Venkatesan, S. Perumal
In this paper, a new approach for Content-Based Image Retrieval (CBIR) has been addressed by extracting colour, gray, advanced texture, and shape features for input query images. Contour-based shape feature extraction methods and image moment extraction techniques are used to extract the shape features and shape invariant features. The informative features are selected from extracted features and combined colour, gray, texture, and shape features by using PSO. The target image has been retrieved for the given query image by training the random forest classifier. The proposed colour, gray, advanced texture, shape feature, and random forest classifier with optimized PSO (CGATSFRFOPSO) provide efficient retrieval of images in a large-scale database. The main objective of this research work is to improve the efficiency and effectiveness of the CBIR system by extracting the features like colour, gray, texture, and shape from database images and query images. These extracted features are processed in various levels like removing redundancy by optimal feature selection and fusion by optimal weighted linear combination. The Particle Swarm Optimization algorithm is used for selecting the informative features from gray and colour and texture features. The matching accuracy and the speed of image retrieval are improved by an ensemble of machine learning algorithms for the similarity search.
{"title":"Content-Based Image Retrieval Using Colour, Gray, Advanced Texture, Shape Features, and Random Forest Classifier with Optimized Particle Swarm Optimization","authors":"Dr. MANOHARAN SUBRAMANIAN, Velmurugan Lingamuthu, Chandran Venkatesan, S. Perumal","doi":"10.1155/2022/3211793","DOIUrl":"https://doi.org/10.1155/2022/3211793","url":null,"abstract":"In this paper, a new approach for Content-Based Image Retrieval (CBIR) has been addressed by extracting colour, gray, advanced texture, and shape features for input query images. Contour-based shape feature extraction methods and image moment extraction techniques are used to extract the shape features and shape invariant features. The informative features are selected from extracted features and combined colour, gray, texture, and shape features by using PSO. The target image has been retrieved for the given query image by training the random forest classifier. The proposed colour, gray, advanced texture, shape feature, and random forest classifier with optimized PSO (CGATSFRFOPSO) provide efficient retrieval of images in a large-scale database. The main objective of this research work is to improve the efficiency and effectiveness of the CBIR system by extracting the features like colour, gray, texture, and shape from database images and query images. These extracted features are processed in various levels like removing redundancy by optimal feature selection and fusion by optimal weighted linear combination. The Particle Swarm Optimization algorithm is used for selecting the informative features from gray and colour and texture features. The matching accuracy and the speed of image retrieval are improved by an ensemble of machine learning algorithms for the similarity search.","PeriodicalId":47063,"journal":{"name":"International Journal of Biomedical Imaging","volume":"2022 1","pages":""},"PeriodicalIF":7.6,"publicationDate":"2022-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44197574","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
R. K. Hapsari, Miswanto, R. Rulaningtyas, H. Suprajitno, H. Gan
Iris has specific advantages, which can record all organ conditions, body construction, and psychological disorders. Traces related to the intensity or deviation of organs caused by the disease are recorded systematically and patterned on the iris and its surroundings. The pattern that appears on the iris can be recognized by using image processing techniques. Based on the pattern in the iris image, this paper aims to provide an alternative noninvasive method for the early detection of DM and HC. In this paper, we perform detection based on iris images for two diseases, DM and HC simultaneously, by developing the invariant Haralick feature on quantized images with 256, 128, 64, 32, and 16 gray levels. The feature extraction process does early detection based on iris images. Researchers and scientists have introduced many methods, one of which is the feature extraction of the gray-level co-occurrence matrix (GLCM). Early detection based on the iris is done using the volumetric GLCM development, namely, 3D-GLCM. Based on 3D-GLCM, which is formed at a distance of d = 1 and in the direction of 0°, 45°, 90°, 135°, 180°, 225°, 270°, and 315°, it is used to calculate Haralick features and develop Haralick features which are invariant to the number of quantization gray levels. The test results show that the invariant feature with a gray level of 256 has the best identification performance. In dataset I, the accuracy value is 97.92, precision is 96.88, and recall is 95.83, while in dataset II, the accuracy value is 95.83, precision is 89.69, and recall is 91.67. The identification of DM and HC trained on invariant features showed higher accuracy than the original features.
{"title":"Modified Gray-Level Haralick Texture Features for Early Detection of Diabetes Mellitus and High Cholesterol with Iris Image","authors":"R. K. Hapsari, Miswanto, R. Rulaningtyas, H. Suprajitno, H. Gan","doi":"10.1155/2022/5336373","DOIUrl":"https://doi.org/10.1155/2022/5336373","url":null,"abstract":"Iris has specific advantages, which can record all organ conditions, body construction, and psychological disorders. Traces related to the intensity or deviation of organs caused by the disease are recorded systematically and patterned on the iris and its surroundings. The pattern that appears on the iris can be recognized by using image processing techniques. Based on the pattern in the iris image, this paper aims to provide an alternative noninvasive method for the early detection of DM and HC. In this paper, we perform detection based on iris images for two diseases, DM and HC simultaneously, by developing the invariant Haralick feature on quantized images with 256, 128, 64, 32, and 16 gray levels. The feature extraction process does early detection based on iris images. Researchers and scientists have introduced many methods, one of which is the feature extraction of the gray-level co-occurrence matrix (GLCM). Early detection based on the iris is done using the volumetric GLCM development, namely, 3D-GLCM. Based on 3D-GLCM, which is formed at a distance of d = 1 and in the direction of 0°, 45°, 90°, 135°, 180°, 225°, 270°, and 315°, it is used to calculate Haralick features and develop Haralick features which are invariant to the number of quantization gray levels. The test results show that the invariant feature with a gray level of 256 has the best identification performance. In dataset I, the accuracy value is 97.92, precision is 96.88, and recall is 95.83, while in dataset II, the accuracy value is 95.83, precision is 89.69, and recall is 91.67. The identification of DM and HC trained on invariant features showed higher accuracy than the original features.","PeriodicalId":47063,"journal":{"name":"International Journal of Biomedical Imaging","volume":" ","pages":""},"PeriodicalIF":7.6,"publicationDate":"2022-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44414554","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
El-Sayed H Ibrahim, Luba Frank, Dhiraj Baruah, V Emre Arpinar, Andrew S Nencka, Kevin M Koch, L Tugan Muftuler, Orhan Unal, Jadranka Stojanovska, Jason C Rubenstein, Sherry-Ann Brown, John Charlson, Elizabeth M Gore, Carmen Bergom
Cardiac magnetic resonance imaging (CMR) is considered the gold standard for measuring cardiac function. Further, in a single CMR exam, information about cardiac structure, tissue composition, and blood flow could be obtained. Nevertheless, CMR is underutilized due to long scanning times, the need for multiple breath-holds, use of a contrast agent, and relatively high cost. In this work, we propose a rapid, comprehensive, contrast-free CMR exam that does not require repeated breath-holds, based on recent developments in imaging sequences. Time-consuming conventional sequences have been replaced by advanced sequences in the proposed CMR exam. Specifically, conventional 2D cine and phase-contrast (PC) sequences have been replaced by optimized 3D-cine and 4D-flow sequences, respectively. Furthermore, conventional myocardial tagging has been replaced by fast strain-encoding (SENC) imaging. Finally, T1 and T2 mapping sequences are included in the proposed exam, which allows for myocardial tissue characterization. The proposed rapid exam has been tested in vivo. The proposed exam reduced the scan time from >1 hour with conventional sequences to <20 minutes. Corresponding cardiovascular measurements from the proposed rapid CMR exam showed good agreement with those from conventional sequences and showed that they can differentiate between healthy volunteers and patients. Compared to 2D cine imaging that requires 12-16 separate breath-holds, the implemented 3D-cine sequence allows for whole heart coverage in 1-2 breath-holds. The 4D-flow sequence allows for whole-chest coverage in less than 10 minutes. Finally, SENC imaging reduces scan time to only one slice per heartbeat. In conclusion, the proposed rapid, contrast-free, and comprehensive cardiovascular exam does not require repeated breath-holds or to be supervised by a cardiac imager. These improvements make it tolerable by patients and would help improve cost effectiveness of CMR and increase its adoption in clinical practice.
{"title":"Value CMR: Towards a Comprehensive, Rapid, Cost-Effective Cardiovascular Magnetic Resonance Imaging.","authors":"El-Sayed H Ibrahim, Luba Frank, Dhiraj Baruah, V Emre Arpinar, Andrew S Nencka, Kevin M Koch, L Tugan Muftuler, Orhan Unal, Jadranka Stojanovska, Jason C Rubenstein, Sherry-Ann Brown, John Charlson, Elizabeth M Gore, Carmen Bergom","doi":"10.1155/2021/8851958","DOIUrl":"https://doi.org/10.1155/2021/8851958","url":null,"abstract":"<p><p>Cardiac magnetic resonance imaging (CMR) is considered the gold standard for measuring cardiac function. Further, in a single CMR exam, information about cardiac structure, tissue composition, and blood flow could be obtained. Nevertheless, CMR is underutilized due to long scanning times, the need for multiple breath-holds, use of a contrast agent, and relatively high cost. In this work, we propose a rapid, comprehensive, contrast-free CMR exam that does not require repeated breath-holds, based on recent developments in imaging sequences. Time-consuming conventional sequences have been replaced by advanced sequences in the proposed CMR exam. Specifically, conventional 2D cine and phase-contrast (PC) sequences have been replaced by optimized 3D-cine and 4D-flow sequences, respectively. Furthermore, conventional myocardial tagging has been replaced by fast strain-encoding (SENC) imaging. Finally, T1 and T2 mapping sequences are included in the proposed exam, which allows for myocardial tissue characterization. The proposed rapid exam has been tested in vivo. The proposed exam reduced the scan time from >1 hour with conventional sequences to <20 minutes. Corresponding cardiovascular measurements from the proposed rapid CMR exam showed good agreement with those from conventional sequences and showed that they can differentiate between healthy volunteers and patients. Compared to 2D cine imaging that requires 12-16 separate breath-holds, the implemented 3D-cine sequence allows for whole heart coverage in 1-2 breath-holds. The 4D-flow sequence allows for whole-chest coverage in less than 10 minutes. Finally, SENC imaging reduces scan time to only one slice per heartbeat. In conclusion, the proposed rapid, contrast-free, and comprehensive cardiovascular exam does not require repeated breath-holds or to be supervised by a cardiac imager. These improvements make it tolerable by patients and would help improve cost effectiveness of CMR and increase its adoption in clinical practice.</p>","PeriodicalId":47063,"journal":{"name":"International Journal of Biomedical Imaging","volume":"2021 ","pages":"8851958"},"PeriodicalIF":7.6,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8147553/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9653604","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}