We present a prototype for hybrid Compton and positron emission tomography(PET)imaging aimed at enhancing data utilization and enabling concurrent imaging of multiple radiopharmaceuticals.The prototype comprises two d...We present a prototype for hybrid Compton and positron emission tomography(PET)imaging aimed at enhancing data utilization and enabling concurrent imaging of multiple radiopharmaceuticals.The prototype comprises two detectors that utilize LYSO-SiPM and were available in our laboratory.One detector consists of a 50×50 array of LYSO crystals,each measuring 0.9mm×0.9mm×10mm with 1 mm pitches,whereas the other detector comprises a 25×25 array of LYSO crystals,each measuring 1.9mm×1.9mm×10mm with 2 mm pitches.These detectors are mounted on a rotational stage,which enables them to function as either a Compton camera or a PET detector pair.The 64-channel signals from the SiPMs of each detector are processed through a capacitive multiplexing circuit to yield four position-weighted outputs.Distinct energy windows were used to discriminate Compton events from PET events.Energy resolution and energy-channel relationships were calibrated via multiple sources.The measured average energy resolutions(full widths at half maximum,FWHMs)for the detectors at 511 keV were 17.5%and 15.2%,respectively.The initial experimental results indicate an angular resolution(FWHM)of 8.6◦for the system in Compton imaging mode.A V-shaped tube injected with 18 F solution was clearly reconstructed,which further verified the imaging capabilities of the system in Compton imaging mode.The results of simulation and experimental imaging studies show that the system can detect tumors as small as 1 mm in diameter when working in PET imaging mode.Mouse bone PET imaging was successfully conducted,with the results matching well with the corresponding CT images.This technology holds great potential for advancing the development of physiological function modalities.展开更多
Various and intricate varieties of lung disease have made it challenging for computer aided diagnosis to appropriately segment lung lesions utilizing computed tomography(CT)images.This study integrates transfer learni...Various and intricate varieties of lung disease have made it challenging for computer aided diagnosis to appropriately segment lung lesions utilizing computed tomography(CT)images.This study integrates transfer learning with the attention mechanism to construct a deep learning model that can automatically detect new coronary pneumonia on lung CT images.In this study,using VGG16 pre-trained by ImageNet as the encoder,the decoder was established utilizing the U-Net structure.The attention module is incorporated during each concatenate procedure,permitting the model to concentrate on the critical information and identify the crucial components efficiently.The public COVID-19-CT-Seg-Benchmark dataset was utilized for experiments,and the highest scores for Dice,F1,and Accuracy were 0.9071,0.9076,and 0.9965,respectively.The generalization performance was assessed concurrently,with performance metrics including Dice,F1,and Accuracy over 0.8.The experimental findings indicate the feasibility of the segmentation network proposed in this study.展开更多
Objective:In the Radiology Department of Mzuzu Central Hospital(MCH),daily training for radiographers now includes content on Computed Tomography(CT)image quality control and equipment maintenance to ensure the normal...Objective:In the Radiology Department of Mzuzu Central Hospital(MCH),daily training for radiographers now includes content on Computed Tomography(CT)image quality control and equipment maintenance to ensure the normal,continuous,and stable operation of the 16-slice spiral CT scanner.Methods:Through comprehensive analysis of relevant equipment,we have identified key parameters that significantly impact CT image quality.Innovative optimization strategies and solutions targeting these parameters have been developed and integrated into daily training programs.Furthermore,starting from an examination of prevalent failure modes observed in CT equipment,we delve into essential maintenance and preservation techniques that CT technologists must master to ensure optimal system performance.Results:(1)Crucial factors affecting CT image quality include artifacts,noise,partial volume effects,and surrounding gap phenomena,alongside spatial and density resolutions,CT dose,reconstruction algorithms,and human factors during the scanning process.In the daily training for radiographers,emphasis is placed on strictly implementing image quality control measures at every stage of the CT scanning process and skillfully applying advanced scanning and image processing techniques.By doing so,we can provide clinicians with accurate and reliable imaging references for diagnosis and treatment.(2)Strategies for CT equipment maintenance:①Environmental inspection of the CT room to ensure cleanliness and hygiene.②Rational and accurate operation,including calibration software proficiency.③Regular maintenance and servicing for minimizing machine downtime.④Maintenance of the CT X-ray tube.CT technicians can become proficient in equipment maintenance and upkeep techniques through training,which can significantly extend the service life of CT systems and reduce the occurrence of malfunctions.Conclusion:Through the regular implementation of rigorous CT image quality control training for radiology technicians,coupled with diligent and proactive CT equipment maintenance,we have observed profound and beneficial impacts on improving image quality.The accuracy and fidelity of radiological data ultimately leads to more accurate diagnoses and effective treatments.展开更多
This paper aims to develop a nonrigid registration method of preoperative and intraoperative thoracoabdominal CT images in computer-assisted interventional surgeries for accurate tumor localization and tissue visualiz...This paper aims to develop a nonrigid registration method of preoperative and intraoperative thoracoabdominal CT images in computer-assisted interventional surgeries for accurate tumor localization and tissue visualization enhancement.However,fine structure registration of complex thoracoabdominal organs and large deformation registration caused by respiratory motion is challenging.To deal with this problem,we propose a 3D multi-scale attention VoxelMorph(MAVoxelMorph)registration network.To alleviate the large deformation problem,a multi-scale axial attention mechanism is utilized by using a residual dilated pyramid pooling for multi-scale feature extraction,and position-aware axial attention for long-distance dependencies between pixels capture.To further improve the large deformation and fine structure registration results,a multi-scale context channel attention mechanism is employed utilizing content information via adjacent encoding layers.Our method was evaluated on four public lung datasets(DIR-Lab dataset,Creatis dataset,Learn2Reg dataset,OASIS dataset)and a local dataset.Results proved that the proposed method achieved better registration performance than current state-of-the-art methods,especially in handling the registration of large deformations and fine structures.It also proved to be fast in 3D image registration,using about 1.5 s,and faster than most methods.Qualitative and quantitative assessments proved that the proposed MA-VoxelMorph has the potential to realize precise and fast tumor localization in clinical interventional surgeries.展开更多
The accuracy of attenuation correction in positron emission tomography scanners depends mainly on deriving the reliable 511-keV linear attenuation coefficient distribution in the scanned objects. In the PET/CT system,...The accuracy of attenuation correction in positron emission tomography scanners depends mainly on deriving the reliable 511-keV linear attenuation coefficient distribution in the scanned objects. In the PET/CT system, the linear attenu- ation distribution is usually obtained from the intensities of the CT image. However, the intensities of the CT image relate to the attenuation of photons in an energy range of 40 keV-140 keV. Before implementing PET attenuation correction, the intensities of CT images must be transformed into the PET 511-keV linear attenuation coefficients. However, the CT scan parameters can affect the effective energy of CT X-ray photons and thus affect the intensities of the CT image. Therefore, for PET/CT attenuation correction, it is crucial to determine the conversion curve with a given set of CT scan parameters and convert the CT image into a PET linear attenuation coefficient distribution. A generalized method is proposed for con- verting a CT image into a PET linear attenuation coefficient distribution. Instead of some parameter-dependent phantom calibration experiments, the conversion curve is calculated directly by employing the consistency conditions to yield the most consistent attenuation map with the measured PET data. The method is evaluated with phantom experiments and small animal experiments. In phantom studies, the estimated conversion curve fits the true attenuation coefficients accurately, and accurate PET attenuation maps are obtained by the estimated conversion curves and provide nearly the same correction results as the true attenuation map. In small animal studies, a more complicated attenuation distribution of the mouse is obtained successfully to remove the attenuation artifact and improve the PET image contrast efficiently.展开更多
Automatic pancreas segmentation in CT scans is crucial for various medical applications including early disease detection,treatment planning and therapeutic evaluation.However,the pancreas’s small size,irregular morp...Automatic pancreas segmentation in CT scans is crucial for various medical applications including early disease detection,treatment planning and therapeutic evaluation.However,the pancreas’s small size,irregular morphology,and low contrast with surrounding tissues make accurate pancreas segmentation still a challenging task.To address these challenges,we propose a novel RPMS-DSAUnet for accurate automatic pancreas segmentation in abdominal CT images.First,a Residual Pyramid Squeeze Attention module enabling hierarchical multi-resolution feature extraction with dynamic feature weighting and selective feature reinforcement capabilities is integrated into the backbone network,enhancing pancreatic feature extraction and improving localization accuracy.Second,a Multi-Scale Feature Extraction module is embedded into the network to expand the receptive field while preserving feature map resolution,mitigate feature degradation caused by network depth,and maintain awareness of pancreatic anatomical structures.Third,a Dimensional Squeeze Attention module is designed to reduce interference from adjacent organs and highlight useful pancreatic features through spatial-channel interaction,thereby enhancing sensitivity to small targets.Finally,a hybrid loss function combining Dice loss and Focal loss is employed to alleviate class imbalance issues.Extensive evaluation on two public datasets(NIH and MSD)shows that the proposed RPMS-DSAUnet achieves Dice Similarity Coefficients of 85.51%and 80.91%,with corresponding Intersection over Union(IoU)scores of 74.93%and 67.94%on each dataset,respectively.Experimental results demonstrate superior performance of the proposed model over baseline methods and state-of-the-art approaches,validating its effectiveness for CT-based pancreas segmentation.展开更多
The purpose of this study is to introduce a novel empirical iterative algorithm for medical image reconstruction, under the short name MRP-ISWLS (Median Root Prior Image Space Weighted Least Squares). Further, we asse...The purpose of this study is to introduce a novel empirical iterative algorithm for medical image reconstruction, under the short name MRP-ISWLS (Median Root Prior Image Space Weighted Least Squares). Further, we assess the performance of the new algorithm by comparing it to the simultaneous version of known MRP algorithms. All algorithms are compared in terms of cross-correlation and CNRs (Contrast-to-Noise Ratios). As it turns out, MRP-ISWLS presents higher CNRs than the known algorithms for objects of different size. Also MRP-ISWLS has better noise manipulation.展开更多
This study was to assess quantitatively the accuracy of ^(18)F-FDG PET/CT images reconstructed by TOF+PSF and TOF only, considering the noise-matching concept to minimize probable bias in evaluating algorithm performa...This study was to assess quantitatively the accuracy of ^(18)F-FDG PET/CT images reconstructed by TOF+PSF and TOF only, considering the noise-matching concept to minimize probable bias in evaluating algorithm performance caused by noise. PET images of similar noise level were considered. Measurements were made on an inhouse phantom with hot inserts of Φ10–37 mm, and oncological images of 14 patients were analyzed. The PET images were reconstructed using the OSEM, OSEM+TOF and OSEM+TOF+PSF algorithms. Optimal reconstruction parameters including iteration, subset, and FWHM of post-smoothing filter were chosen for both the phantom and patient data. In terms of quantitative accuracy, the recovery coefficient(RC) was calculated for the phantom PET images. The signal-to-noise ratio(SNR),lesion-to-background ratio(LBR), and SUV_(max)were evaluated from the phantom and clinical data. The smallest hot insert(Ф10 mm) with 2:1 activity concentration ratio could be detected in the PET image reconstructed using the TOF and TOF+PSF algorithms, but not the OSEM algorithm. The relative difference for SNR between the TOF+PSF and OSEM showed significantly higher values for smaller sizes, while SNR change was smaller for Ф22–37 mm inserts both 2:1 and 4:1 activity concentration ratio. In the clinical study, SNR gains were 1.6 ± 0.53 and 2.7 ± 0.74 for the TOF and TOF+PSF, while the relative difference of contrast was 17 ± 1.05 and 41.5 ± 1.85% for the TOF only and TOF+PSF, respectively. The impact of TOF+PSF is more significant than that of TOF reconstruction, in smaller inserts with low activity concentration ratio. In the clinical PET/CT images, the use of the TOF+PSF algorithm resulted in better SNR and contrast for lesions, and the highest SUV_(max)was also seen for images reconstructed with the TOF+PSF algorithm.展开更多
基金supported by the National Natural Science Foundation of China(No.12105018)the Beijing Nova Program(Nos.Z211100002121129 and 20230484413)the Beijing Normal University Start-up Grant(No.312232104).
文摘We present a prototype for hybrid Compton and positron emission tomography(PET)imaging aimed at enhancing data utilization and enabling concurrent imaging of multiple radiopharmaceuticals.The prototype comprises two detectors that utilize LYSO-SiPM and were available in our laboratory.One detector consists of a 50×50 array of LYSO crystals,each measuring 0.9mm×0.9mm×10mm with 1 mm pitches,whereas the other detector comprises a 25×25 array of LYSO crystals,each measuring 1.9mm×1.9mm×10mm with 2 mm pitches.These detectors are mounted on a rotational stage,which enables them to function as either a Compton camera or a PET detector pair.The 64-channel signals from the SiPMs of each detector are processed through a capacitive multiplexing circuit to yield four position-weighted outputs.Distinct energy windows were used to discriminate Compton events from PET events.Energy resolution and energy-channel relationships were calibrated via multiple sources.The measured average energy resolutions(full widths at half maximum,FWHMs)for the detectors at 511 keV were 17.5%and 15.2%,respectively.The initial experimental results indicate an angular resolution(FWHM)of 8.6◦for the system in Compton imaging mode.A V-shaped tube injected with 18 F solution was clearly reconstructed,which further verified the imaging capabilities of the system in Compton imaging mode.The results of simulation and experimental imaging studies show that the system can detect tumors as small as 1 mm in diameter when working in PET imaging mode.Mouse bone PET imaging was successfully conducted,with the results matching well with the corresponding CT images.This technology holds great potential for advancing the development of physiological function modalities.
基金the Natural Science Foundation of Zhejiang Province(No.LQ20F020024)。
文摘Various and intricate varieties of lung disease have made it challenging for computer aided diagnosis to appropriately segment lung lesions utilizing computed tomography(CT)images.This study integrates transfer learning with the attention mechanism to construct a deep learning model that can automatically detect new coronary pneumonia on lung CT images.In this study,using VGG16 pre-trained by ImageNet as the encoder,the decoder was established utilizing the U-Net structure.The attention module is incorporated during each concatenate procedure,permitting the model to concentrate on the critical information and identify the crucial components efficiently.The public COVID-19-CT-Seg-Benchmark dataset was utilized for experiments,and the highest scores for Dice,F1,and Accuracy were 0.9071,0.9076,and 0.9965,respectively.The generalization performance was assessed concurrently,with performance metrics including Dice,F1,and Accuracy over 0.8.The experimental findings indicate the feasibility of the segmentation network proposed in this study.
基金supported by the First Affiliated Hospital of Xi’an Jiaotong University Teaching Reform Project(Grant No.JG2023-0206 and JG2022-0324).
文摘Objective:In the Radiology Department of Mzuzu Central Hospital(MCH),daily training for radiographers now includes content on Computed Tomography(CT)image quality control and equipment maintenance to ensure the normal,continuous,and stable operation of the 16-slice spiral CT scanner.Methods:Through comprehensive analysis of relevant equipment,we have identified key parameters that significantly impact CT image quality.Innovative optimization strategies and solutions targeting these parameters have been developed and integrated into daily training programs.Furthermore,starting from an examination of prevalent failure modes observed in CT equipment,we delve into essential maintenance and preservation techniques that CT technologists must master to ensure optimal system performance.Results:(1)Crucial factors affecting CT image quality include artifacts,noise,partial volume effects,and surrounding gap phenomena,alongside spatial and density resolutions,CT dose,reconstruction algorithms,and human factors during the scanning process.In the daily training for radiographers,emphasis is placed on strictly implementing image quality control measures at every stage of the CT scanning process and skillfully applying advanced scanning and image processing techniques.By doing so,we can provide clinicians with accurate and reliable imaging references for diagnosis and treatment.(2)Strategies for CT equipment maintenance:①Environmental inspection of the CT room to ensure cleanliness and hygiene.②Rational and accurate operation,including calibration software proficiency.③Regular maintenance and servicing for minimizing machine downtime.④Maintenance of the CT X-ray tube.CT technicians can become proficient in equipment maintenance and upkeep techniques through training,which can significantly extend the service life of CT systems and reduce the occurrence of malfunctions.Conclusion:Through the regular implementation of rigorous CT image quality control training for radiology technicians,coupled with diligent and proactive CT equipment maintenance,we have observed profound and beneficial impacts on improving image quality.The accuracy and fidelity of radiological data ultimately leads to more accurate diagnoses and effective treatments.
基金supported in part by the National Natural Science Foundation of China[62301374]Hubei Provincial Natural Science Foundation of China[2022CFB804]+2 种基金Hubei Provincial Education Research Project[B2022057]the Youths Science Foundation of Wuhan Institute of Technology[K202240]the 15th Graduate Education Innovation Fund of Wuhan Institute of Technology[CX2023295].
文摘This paper aims to develop a nonrigid registration method of preoperative and intraoperative thoracoabdominal CT images in computer-assisted interventional surgeries for accurate tumor localization and tissue visualization enhancement.However,fine structure registration of complex thoracoabdominal organs and large deformation registration caused by respiratory motion is challenging.To deal with this problem,we propose a 3D multi-scale attention VoxelMorph(MAVoxelMorph)registration network.To alleviate the large deformation problem,a multi-scale axial attention mechanism is utilized by using a residual dilated pyramid pooling for multi-scale feature extraction,and position-aware axial attention for long-distance dependencies between pixels capture.To further improve the large deformation and fine structure registration results,a multi-scale context channel attention mechanism is employed utilizing content information via adjacent encoding layers.Our method was evaluated on four public lung datasets(DIR-Lab dataset,Creatis dataset,Learn2Reg dataset,OASIS dataset)and a local dataset.Results proved that the proposed method achieved better registration performance than current state-of-the-art methods,especially in handling the registration of large deformations and fine structures.It also proved to be fast in 3D image registration,using about 1.5 s,and faster than most methods.Qualitative and quantitative assessments proved that the proposed MA-VoxelMorph has the potential to realize precise and fast tumor localization in clinical interventional surgeries.
基金Project supported by the National Natural Science Foundation of China (Grant Nos.81101070 and 81101175)
文摘The accuracy of attenuation correction in positron emission tomography scanners depends mainly on deriving the reliable 511-keV linear attenuation coefficient distribution in the scanned objects. In the PET/CT system, the linear attenu- ation distribution is usually obtained from the intensities of the CT image. However, the intensities of the CT image relate to the attenuation of photons in an energy range of 40 keV-140 keV. Before implementing PET attenuation correction, the intensities of CT images must be transformed into the PET 511-keV linear attenuation coefficients. However, the CT scan parameters can affect the effective energy of CT X-ray photons and thus affect the intensities of the CT image. Therefore, for PET/CT attenuation correction, it is crucial to determine the conversion curve with a given set of CT scan parameters and convert the CT image into a PET linear attenuation coefficient distribution. A generalized method is proposed for con- verting a CT image into a PET linear attenuation coefficient distribution. Instead of some parameter-dependent phantom calibration experiments, the conversion curve is calculated directly by employing the consistency conditions to yield the most consistent attenuation map with the measured PET data. The method is evaluated with phantom experiments and small animal experiments. In phantom studies, the estimated conversion curve fits the true attenuation coefficients accurately, and accurate PET attenuation maps are obtained by the estimated conversion curves and provide nearly the same correction results as the true attenuation map. In small animal studies, a more complicated attenuation distribution of the mouse is obtained successfully to remove the attenuation artifact and improve the PET image contrast efficiently.
基金supported by the National Natural and Science Foundation of China under Grant No.12301662Zhejiang Provincial Natural Science Foundation of China under Grant No.LQ21F030019.
文摘Automatic pancreas segmentation in CT scans is crucial for various medical applications including early disease detection,treatment planning and therapeutic evaluation.However,the pancreas’s small size,irregular morphology,and low contrast with surrounding tissues make accurate pancreas segmentation still a challenging task.To address these challenges,we propose a novel RPMS-DSAUnet for accurate automatic pancreas segmentation in abdominal CT images.First,a Residual Pyramid Squeeze Attention module enabling hierarchical multi-resolution feature extraction with dynamic feature weighting and selective feature reinforcement capabilities is integrated into the backbone network,enhancing pancreatic feature extraction and improving localization accuracy.Second,a Multi-Scale Feature Extraction module is embedded into the network to expand the receptive field while preserving feature map resolution,mitigate feature degradation caused by network depth,and maintain awareness of pancreatic anatomical structures.Third,a Dimensional Squeeze Attention module is designed to reduce interference from adjacent organs and highlight useful pancreatic features through spatial-channel interaction,thereby enhancing sensitivity to small targets.Finally,a hybrid loss function combining Dice loss and Focal loss is employed to alleviate class imbalance issues.Extensive evaluation on two public datasets(NIH and MSD)shows that the proposed RPMS-DSAUnet achieves Dice Similarity Coefficients of 85.51%and 80.91%,with corresponding Intersection over Union(IoU)scores of 74.93%and 67.94%on each dataset,respectively.Experimental results demonstrate superior performance of the proposed model over baseline methods and state-of-the-art approaches,validating its effectiveness for CT-based pancreas segmentation.
文摘The purpose of this study is to introduce a novel empirical iterative algorithm for medical image reconstruction, under the short name MRP-ISWLS (Median Root Prior Image Space Weighted Least Squares). Further, we assess the performance of the new algorithm by comparing it to the simultaneous version of known MRP algorithms. All algorithms are compared in terms of cross-correlation and CNRs (Contrast-to-Noise Ratios). As it turns out, MRP-ISWLS presents higher CNRs than the known algorithms for objects of different size. Also MRP-ISWLS has better noise manipulation.
基金supported by the Tehran University of Medical Sciences,Tehran,Iran(No.24166)the Masih Daneshvari Hospital,Shahid Beheshti University of Medical Sciences,Tehran,Iran
文摘This study was to assess quantitatively the accuracy of ^(18)F-FDG PET/CT images reconstructed by TOF+PSF and TOF only, considering the noise-matching concept to minimize probable bias in evaluating algorithm performance caused by noise. PET images of similar noise level were considered. Measurements were made on an inhouse phantom with hot inserts of Φ10–37 mm, and oncological images of 14 patients were analyzed. The PET images were reconstructed using the OSEM, OSEM+TOF and OSEM+TOF+PSF algorithms. Optimal reconstruction parameters including iteration, subset, and FWHM of post-smoothing filter were chosen for both the phantom and patient data. In terms of quantitative accuracy, the recovery coefficient(RC) was calculated for the phantom PET images. The signal-to-noise ratio(SNR),lesion-to-background ratio(LBR), and SUV_(max)were evaluated from the phantom and clinical data. The smallest hot insert(Ф10 mm) with 2:1 activity concentration ratio could be detected in the PET image reconstructed using the TOF and TOF+PSF algorithms, but not the OSEM algorithm. The relative difference for SNR between the TOF+PSF and OSEM showed significantly higher values for smaller sizes, while SNR change was smaller for Ф22–37 mm inserts both 2:1 and 4:1 activity concentration ratio. In the clinical study, SNR gains were 1.6 ± 0.53 and 2.7 ± 0.74 for the TOF and TOF+PSF, while the relative difference of contrast was 17 ± 1.05 and 41.5 ± 1.85% for the TOF only and TOF+PSF, respectively. The impact of TOF+PSF is more significant than that of TOF reconstruction, in smaller inserts with low activity concentration ratio. In the clinical PET/CT images, the use of the TOF+PSF algorithm resulted in better SNR and contrast for lesions, and the highest SUV_(max)was also seen for images reconstructed with the TOF+PSF algorithm.
