A method for sensitivity analysis of deviation factor for geometric correction of cone-beam CT system / 南方医科大学学报

OBJECTIVE@#To propose a sensitivity test method for geometric correction position deviation of cone-beam CT systems.@*METHODS@#We proposed the definition of center deviation and its derivation. We analyzed the influence of the variation of the three-dimensional spatial center of the steel ball point, the projection center and the size of the steel ball point on the deviation of geometric parameters and the reconstructed image results by calculating the geometric correction parameters based on the Noo analytical method using the FDK reconstruction algorithm for image reconstruction.@*RESULTS@#The radius of the steel ball point was within 3 mm. The deviation of the center of the calibration parameter was within the order of magnitude and negligible. A 10% Gaussian perturbation of a single pixel in the 3D spatial coordinates of the steel ball point produced a deviation of about 3 pixel sizes, while the same Gaussian perturbation of the 2D projection coordinates of the steel ball point produced a deviation of about 2 pixel sizes.@*CONCLUSION@#The geometric correction is more sensitive to the deviation generated by the three-dimensional spatial coordinates of the steel ball point with limited sensitivity to the deviation generated by the two-dimensional projection coordinates of the steel ball point. The deviation sensitivity of a small diameter steel ball point can be ignored.

Influence of 4D CT-based respiratory signal acquisition methods on delineation of moving tumor targets / 中国辐射卫生

@#<b>Objective</b> To compare the effects of different respiratory signal acquisition methods on the delineation of moving tumor targets. <b>Methods</b> A cube phantom containing a sphere was placed on a motion platform to simulate respiratory movement by setting motion period, frequency, and direction. Respiratory signal was acquired by real-time position management (RPM) method and GE method independently. Target delineation was conducted using the maximum intensity projection (MIP) sequence. The difference between the reconstructed volume and the theoretical moving volume was compared under the two respiratory signal acquisition methods for cube and sphere targets. <b>Results</b> Under the same respiratory signal acquisition method, the same respiratory amplitude, and different respiratory frequencies, reconstructed volume changes were relatively small. For the sphere target, the deviation between the reconstructed volume and the theoretical moving volume was −1.5% to 5.7% with the RPM method and −1.3% to −13.8% with the GE method (both <i>P</i> < 0.05). For the cube target, the deviation between the reconstructed volume and the theoretical moving volume was 0.2% to 0.9% with the RPM method and −2.6% to 0.9% with the GE method, with no statistical significance. <b>Conclusion</b> For small-volume sphere targets, the target volumes obtained from MIP images by the two respiratory signal acquisition methods are both smaller than the actual moving volume. For large-volume cube targets, there is no significant difference between the reconstructed and theoretical results with any respiratory signal acquisition method. The RPM method produces smaller deviation and better image quality when reconstructing small-volume targets.

Application of generative adversarial network in magnetic resonance image reconstruction / 生物医学工程学杂志

Magnetic resonance imaging (MRI) is an important medical imaging method, whose major limitation is its long scan time due to the imaging mechanism, increasing patients' cost and waiting time for the examination. Currently, parallel imaging (PI) and compress sensing (CS) together with other reconstruction technologies have been proposed to accelerate image acquisition. However, the image quality of PI and CS depends on the image reconstruction algorithms, which is far from satisfying in respect to both the image quality and the reconstruction speed. In recent years, image reconstruction based on generative adversarial network (GAN) has become a research hotspot in the field of magnetic resonance imaging because of its excellent performance. In this review, we summarized the recent development of application of GAN in MRI reconstruction in both single- and multi-modality acceleration, hoping to provide a useful reference for interested researchers. In addition, we analyzed the characteristics and limitations of existing technologies and forecasted some development trends in this field.

Evaluation of PET Mainstream Scattering Correction Methods / 中国医疗器械杂志

OBJECTIVE@#Current mainstream PET scattering correction methods are introduced and evaluated horizontally, and finally, the existing problems and development direction of scattering correction are discussed.@*METHODS@#Based on NeuWise Pro PET/CT products of Neusoft Medical System Co. Ltd. , the simulation experiment is carried out to evaluate the influence of radionuclide distribution out of FOV (field of view) on the scattering estimation accuracy of each method.@*RESULTS@#The scattering events produced by radionuclide out of FOV have an obvious impact on the spatial distribution of scattering, which should be considered in the model. The scattering estimation accuracy of Monte Carlo method is higher than single scatter simulation (SSS).@*CONCLUSIONS@#Clinically, if the activity of the adjacent parts out of the FOV is high, such as brain, liver, kidney and bladder, it is likely to lead to the deviation of scattering estimation. Considering the Monte Carlo scattering estimation of the distribution of radionuclide out of FOV, it's helpful to improve the accuracy of scattering distribution estimation.

Application of deep learning image reconstruction algorithm in low-dose abdominal CT / 西安交通大学学报(医学版)

【Objective】 To investigate the value of deep learning image reconstruction (DLIR) in improving image quality and reducing beam-hardening artifacts of low-dose abdominal CT. 【Methods】 For this study we prospectively enrolled 26 patients (14 males and 12 females, mean age of 60.35±10.89 years old) who underwent CT urography between October 2019 and June 2020. All the patients underwent conventional-dose unenhanced CT and contrast-enhanced CT in the portal venous phase (noise index of 10; volume computed tomographic dose index: 9.61 mGy) and low-dose CT in the excretory phase(noise index of 23; volume computed tomographic dose index: 2.95 mGy). CT images in the excretory phase were reconstructed using four algorithms: ASiR-V 50%, DLIR-L, DLIR-M, and DLIR-H. Repeated measures ANOVA and Kruskal-Wallis H test were used to compare the quantitative (skewness, noise, SNR, CNR) and qualitative (image quality, noise, beam-hardening artifacts) values among the four image groups. Post hoc comparisons were performed using Bonferroni test. 【Results】 In either quantitative or qualitative evaluation, the SNR, CNR, overall image quality score, and noise of DLIR images were similar or better than ASiR-V 50%. In addition, the SNR, CNR, and overall image quality scores increased as the DLIR weight increased, while the noise decreased. There was no statistically significant difference in the distortion artifacts (P=0.776) and contrast-induced beam-hardening artifacts (P=0.881) scores among these groups. 【Conclusion】 Compared with the ASiR-V 50% algorithm, DLIR algorithm, especially DLIR-M and DLIR-H, can significantly improve the image quality of low-dose abdominal CT, but has limitations in reducing contrast-induced beam-hardening artifacts.

Compression of images of dermatological manifestations of paucisymptomatic COVID-19 patients through periodic principal components / Compresión de imágenes de manifestaciones dermatológicas de pacientes paucisintomáticos de COVID-19 mediante componentes principales periódicos

Introduction: The management of medical images has been gaining followers based on the advantages it offers for the diagnosis of diseases, which, like COVID-19, present with clinical manifestations that can be captured in the form of images. Objective: Take advantage of the quasi-periodicity of the principal components (PCs) in the decomposition into PCs of medical images, which represent dermatological manifestations in paucisymptomatic patients of COVID-19. Methods: Here, a set of photos was taken of one of the most frequent patterns in COVID-19, the maculopapular pattern, characterized by an erythmatopapular rash, and compression of one of the medical images was performed. Said compression was carried out in different ways: (1) using two PCs, (2) using both a periodic PC and a non-periodic PC, (3) using two periodic PCs, (4) using a single PC, and (5) using a single periodic PC. Result: The results of this research proved that it is possible to work with acceptable reconstructions of compressed images in the field of dermatology, without losing the quality and characteristics that allow to reach a correct diagnosis. In addition, this achievement permits to correctly classify many diseases without fear of being wrong. Conclusion: With the method presented, the use of a robust medical image compression technique that could be very useful in the field of health is proposed. The images allow the diagnosis of diseases such as COVID-19 in paucisymptomatic patients, understanding them allows minimizing their weight without losing quality, which facilitates their use and storage.

Introducción: El empleo de imágenes médicas en el diagnóstico de enfermedades ha ido ganando adeptos. Un ejemplo es la COVID-19 que cursa con manifestaciones clínicas dermatológicas. Objetivo: Aprovechar la cuasi-periodicidad de los componentes principales de la descomposición en imágenes médicas, que representan manifestaciones dermatológicas en pacientes paucisintomáticos de COVID-19. Métodos: Se tomó un conjunto de fotografías de uno de los patrones más frecuentes en la COVID-19 (el patrón maculopapular), caracterizado por un exantema eritematopapular, y se realizó la compresión de una de las imágenes médicas. Dicha compresión se realizó de diferentes formas: (1) usando dos componentes principales, (2) usando tanto un componente principal periódico como no periódico, (3) dos componentes principales periódicos, (4) un único componente principal, y (5) un solo componente principal periódico. Resultados: Es posible trabajar con reconstrucciones aceptables de imágenes comprimidas en el campo de la dermatología, sin perder la calidad y características que permitan llegar a un diagnóstico correcto. Además, este logro permite clasificar correctamente muchas enfermedades sin miedo a equivocarse. Conclusiones: Con el método presentado se propone el uso de una técnica robusta de compresión de imágenes médicas que podría ser de gran utilidad en el campo de la salud. Las imágenes permiten el diagnóstico de enfermedades como la COVID-19 en pacientes paucisintomáticos, con cuya compresión se minimiza su peso sin perder la calidad, lo que facilita su uso y almacenamiento.

Effect of deep learning image reconstruction algorithm on CT image quality and detectability of hypovascular hepatic metastases at low radiation dose levels / 中华放射学杂志

Objective:To investigate the efficiency of deep learning image reconstruction (DLIR) algorithm in the image quality and detection of hypovascular hepatic metastases under low radiation doses in comparison with adaptive statistical iterative construction-V (ASiR-V).Methods:Fifty-six patients with suspected hypovascular hepatic metastases who needed abdominal enhanced CT scans were collected prospectively in the First Affiliated Hospital of Zhengzhou University from January to April 2021. The patients received conventional radiation dose with tube current-time products of 400 mA CT scans in the first venous phase, low-dose CT scans in the second venous phase, which were set as tube current-time products of 280 mA for group A (19 cases), 200 mA for group B (19 cases) and 120 mA for group C (18 case), respectively. The images of first venous phase and 3 groups of second venous phase were both reconstructed with ASiR-V60% and high-DLIR (DLIR-H). Quantitative parameters [image noise, liver and portal vein signal to noise ratio (SNR), contrast to noise ratio (CNR)] and qualitative parameters (overall image quality, lesion conspicuity, diagnostic confidence) were compared between ASiR-V60% and DLIR-H images, and the effective radiation dose (ED) and the lesion detectability of each group was recorded. The paired t test was used to compare quantitative parameters, whereas the Wilcoxon signed-rank test of paired data was used to compare qualitative parameters. Results:In the second venous phase, ED was (5.56±0.35) mSv in group A, (3.88±0.23) mSv in group B, and (2.42±0.23) mSv in group C, with a decrease of 30%, 50% and 70% compared with the first venous phase, respectively. Moreover, with the decrease of radiation dose, the noise gradually increased, and the CNR lesions, SNR liver and SNR portal vein all gradually decreased. DLIR-H images had statistically better quantitative scores than ASiR-V60% images when the same radiation dose was applied (all P<0.001). Furthermore, the qualitative parameters of each group decreased with the decrease of radiation dose. Under the same radiation dose, the overall image quality, lesion conspicuity and diagnostic confidence of DLIR-H were higher than those of ASiR-V60% (all P<0.001). All lesions [100% (84/84)] were detected by ASIR-V60% and DLIR-H in group A, 92.0% (75/81) in group B, and 88.0% (79/89) in group C. Conclusions:Compared with ASiR-V60%, DLIR-H could reduce image noise, improve overall image quality and lesion conspicuity of hypovascular hepatic metastases as well as increase diagnostic confidence under different radiation doses.

Value of the deep learning image reconstruction algorithm combined with low-kV technique in improving small vessel display level at routine abdominal contrast enhanced CT imaging / 中华放射学杂志

Objective:To evaluate the presentation of small arteries in abdominal contrast-enhanced CT late-arterial images using the deep learning image reconstruction (DLIR) combined with low tube voltage (kV) technique relative to the adaptive statistical iterative reconstruction V (ASiR-V) algorithm.Methods:Patients who were admitted to Peking University People′s Hospital from December 2021 to January 2022 and needed to be screened for abdominal diseases and receive abdominal and pelvic contrast-enhanced CT scan were prospectively collected. The patients were divided into low-voltage (LV) with 80 kV and high-voltage (HV) with 120 kV groups. According to two different reconstruction algorithms, each group was further divided into DLIR-H (D) subgroup and ASiR-V 50% (A) subgroup. The automatic tube current adjustment technique was used for CT enhanced scanning of patients, and the noise index value was uniformly set to 9. Subjective and objective evaluations were performed on the late-arterial images with a constructed slice thickness of 0.625 mm, and the radiation doses were recorded.Results:A total of 168 patients were included, including 76 males and 92 females, aged 18-85 (53±15) years old, body mass index (24±3) kg/m 2; 91 patients in the LV group and 77 in the HV group. The CT values of the aorta and common hepatic artery in the LV group were significantly higher than those in the HV group ( t=-14.20, P<0.001; t=-0.95, P<0.001). When the tube voltage was the same, the late-arterial image noise in subgroup D was significantly lower than that in subgroup A, and the signal to noise ratio (SNR) and contrast to noise ratio (CNR) of the liver, aorta and common hepatic artery were significantly higher than those in subgroup A (all P<0.001). The SNR and CNR of the aorta and common hepatic artery in the LV-D subgroup were significantly better than those in the LV-A, HV-D, and HV-A subgroups (all P<0.001). In the subjective evaluation of abdominal vascular display, the special resolution of the common hepatic artery, inferior mesenteric artery and the edge of the ascending branch of the left colic artery, and the contrast of the ascending branch of the left colic artery in the LV-D subgroup were significantly better than those of the LV-A, HV-D, and HV-A subgroups ( P<0.05). Moreover, the presentation rate of margin artery of splenic region (54.9%, 50/91) in the LV-D subgroup was significantly higher than those in the HV-D subgroup (24.7%, 19/77) and HV-A subgroup (32.5%, 25/77) (adjusted P<0.05). There was no significant difference in the radiation doses between LV and HV groups [(4.91±1.97) mSv vs (5.43±1.78) mSv, P>0.05]. Conclusion:The contrast-enhanced CT scan of abdomen with low tube voltage combined with DLIR algorithm can effectively improve the display level of the ascending vessel of left colonic artery from the inferior mesenteric artery and the margin artery, which brings more possibilities for the evaluation of similar small blood vessels.

Application of three-dimensional image reconstruction technology based on 3D-slicer software in urology / 国际外科学杂志

Objective:To investigate the application value of three-dimensional image reconstruction technology based on 3D-slicer software in urology.Methods:The data of 36 patients with urinary tract diseases admitted to Beijing Friendship Hospital, Capital Medical University from May 2019 to December 2021 were retrospectively analyzed, including 20 males and 16 females; the median age was 53.50(41.75, 66.25) years. There were 10 relative kidney transplant donors, 12 cases with renal tumors, 6 cases with hydronephrosis and 8 patients with urinary calculi. The CT urography data of 36 cases were reconstructed into three-dimensional image models based on 3D-slicer software, and the morphology of the target tissue was measured.Results:In the urinary system model of 10 relative kidney transplant donors constructed in this study, the type of donor renal artery was single artery in 7 cases and accessory renal artery in 3 cases; In the three-dimensional model of 12 tumor kidneys, 4 tumors were located at the upper part of the kidney (2 near ventral and 2 near dorsal), 5 tumors were located at the middle part of the kidney (2 near ventral and 3 near dorsal), and 3 tumors were located at the lower part of the kidney near ventral. The average maximum diameter of the tumors was (27.3 ± 9.63) mm, and the tumor volume was (15.89 ± 5.93) cm 2. The study also successfully constructed a three-dimensional image model of the urinary system in 6 patients with hydronephrosis and 8 patients with urinary calculi (without hydronephrosis). Three-dimensional model image reconstructed by 3D-slicer software clearly showed the spatial structure of renal parenchyma, blood vessels, renal pelvis, calyces and ureter. The diameter, position and direction of ureters and blood vessels can be observed clearly based on the three-dimensional reconstruction model, and clinicians could also evaluate the location, shape, size and adjacent relationship with surrounding tissues of renal cysts, tumors, stones or other masses. Conclusion:3D-slicer software platform can assist clinicians to reconstruct the three-dimensional model of urinary system, which is worthy of further clinical application.

Deep Learning aplicado en imágenes fotoacústicas para la Identificación del cáncer de seno / Deep Learning Applied in Photoacoustic Images for the Identification of Breast Cancer

La Imagen Fotoacústica (PAI por sus siglas en inglés), es una modalidad de imagen híbrida que fusiona la iluminación óptica y la detección por ultrasonido. Debido a que los métodos de imágenes ópticas puras no pueden mantener una alta resolución, la capacidad de lograr imágenes de contraste óptico de alta resolución en tejidos biológicos hace que la fotoacústica (PA por sus siglas en inglés) sea una técnica prometedora para varias aplicaciones de imágenes clínicas. En la actualidad el Aprendizaje Profundo (Deep Learning) tiene el enfoque más reciente en métodos basados en la PAI, donde existe una gran cantidad de aplicaciones en análisis de imágenes, en especial en el área del campo biomédico, como lo es la adquisición, segmentación y reconstrucciones de imágenes de tomografía computarizada. Esta revisión describe las últimas investigaciones en PAI y un análisis sobre las técnicas y métodos basados en Deep Learning, aplicado en diferentes modalidades para el diagnóstico de cáncer de seno(AU)

Photoacoustic Imaging (PAI) is a hybrid imaging modality that combines optical illumination and ultrasound detection. Because pure optical imaging methods cannot maintain high resolution, the ability to achieve high resolution optical contrast images in biological tissues makes Photoacoustic (PA) a promising technique for various clinical imaging applications. At present, Deep Learning has the most recent approach of methods based on PAI where there are a large number of applications in image analysis especially in the area of ​​the biomedical field, such as acquisition, segmentation and reconstructions of computed tomography imaging. This review describes the latest research in PAI and an analysis of the techniques and methods based on Deep Learning applied in different modalities for the diagnosis of breast cancer(AU)

Application of preoperative three-dimensional image reconstruction in the treatment of ureteropelvic junction obstruction / 北京大学学报(医学版)

OBJECTIVE@#To investigate the value of preoperative three-dimensional image reconstruction in the treatment of ureteropelvic junction obstruction (UPJO).@*METHODS@#We reviewed data on 40 patients (22 male cases, and 18 female cases) diagnosed with UPJO in Peking University First Hospital from May 2017 to April 2019. The median age was 26.5 years (IQR 23.25-38.75) years. There were 11 patients complicated with ectopic vessels, 14 patients with kidney stones, 3 patients with horseshoe kidney, and 6 patients with obstruction after pyeloplasty. All the patients underwent preoperative enhanced CT scan, and the CT data were reconstructed into three-dimensional image models. The obstruction position of ureteropelvic junction and the relationship between ureteropelvic junction and blood vessels and organs were observed by three-dimensional models to assist planning surgery. Thirty-seven patients underwent laparoscopic pyeloplasty (including 3 cases combined with pyelolithotomy with flexible cystoscope, 1 case combined with pyelolithotomy by sun-style cystoscope, 1 case with laparoscopic ureter resection and anastomosis, 3 cases of laparoscopic pyeloplasty of horseshoe kidney), 2 patients underwent laparoscopic ventral onlay lingual mucosal graft ureteroplasty, and 1 patient underwent robot-assisted laparoscopic pyeloplasty.@*RESULTS@#Three-dimensional CT image clearly showed the relationship between the obstruction of ureteropelvic junction and blood vessels and organs after three-dimensional reconstruction. The type, diameter, position and direction of the ectopic vessels could be observed clearly before operation according to the three-dimensional reconstruction model, and the number, size, location and shape of renal calculi or other masses, the number of involved renal calyces and the anatomical distribution in the renal pelvis and calyces could be also evaluated preoperatively. After comprehensive analysis of the above information, individualized operation plans were performed on the patients, all the 40 cases were successfully completed with the surgery without any transfer to open surgery. The average operative time was (129.91±37.90) min (range: 75 to 273), the average blood loss was (48.1±78.0) mL (range: 10 to 400), the average hospitality was (5.04±1.99) d (range: 2 to 10), and the average postoperative drainage time was (3.8±1.4) d (range: 2 to 8).@*CONCLUSION@#The preoperative three-dimensional image reconstruction has a high clinical value in the treatment of ureteropelvic junction obstruction, and it is of great help to assist surgery planning and is worthy of further clinical promotion and application.

A precise method of marking pulmonary nodules based on body surface mesh and three-dimensional image reconstruction / 中国胸心血管外科临床杂志

@#Objective    To explore the safety and effectiveness of a precise marking method based on body surface mesh and three-dimensional (3D) image reconstruction. Methods    We retrospectively analyzed the clinical data of 22 patients in our hospital from October 2018 to October 2019. There were 13 males and 9 females aged 58.5 (37-72) years. All patients underwent a precise marking of pulmonary nodules based on body surface mesh and 3D image reconstruction. Then, video-assisted thoracoscopic surgery (VATS) was performed to resect the nodules. The clinical data, including positioning success rate and operation time were analyzed. Results    A total of 22 small pulmonary nodules were removed. The average diameter of small nodules was 12±3 mm, and the average distance from the visceral pleura was 17±6 mm. The localization success rate was 86.4%. The operation time was 110±43 min, and there was no surgery-related complication. Conclusion    The method of marking pulmonary nodules based on body surface mesh and 3D image reconstruction is a safe and reliable technology, which reduces the risk of hemopneumothorax caused by CT-guided lung puncture.

A multifrequency time-difference electrical impedance tomography algorithm using spectral constraints / 生物医学工程学杂志

This study aims to propose a multifrequency time-difference algorithm using spectral constraints. Based on the knowledge of tissue spectrum in the imaging domain, the fraction model was used in conjunction with the finite element method (FEM) to approximate a conductivity distribution. Then a frequency independent parameter (volume or area fraction change) was reconstructed which made it possible to simultaneously employ multifrequency time-difference boundary voltage data and then reduce the degrees of freedom of the reconstruction problem. Furthermore, this will alleviate the illness of the EIT inverse problem and lead to a better reconstruction result. The numerical validation results suggested that the proposed time-difference fraction reconstruction algorithm behaved better than traditional damped least squares algorithm (DLS) especially in the noise suppression capability. Moreover, under the condition of low signal-to-noise ratio, the proposed algorithm had a more obvious advantage in reconstructions of targets shape and position. This algorithm provides an efficient way to simultaneously utilize multifrequency measurement data for time-difference EIT, and leads to a more accurate reconstruction result. It may show us a new direction for the development of time-difference EIT algorithms in the case that the tissue spectrums are known.

Application of holographic image navigation in urological laparoscopic and robotic surgery / 中华泌尿外科杂志

Objective To evaluate the clinical value of holographic image navigation in urological laparoscopic and robotic surgery.Methods The data of patients were reviewed retrospectively for whom accepted holographic image navigation laparoscopic and robotic surgery from Jan.2019 to Dec.2019 in Beijing United Family Hospital and other 18 medical centers,including 78 cases of renal tumor,2 cases of bladder cancer,2 cases of adrenal gland tumor,1 cases of renal cyst,1 case of prostate cancer,1 case of sweat gland carcinoma with lymph node metastasis,1 case of pelvic metastasis after radical cystectomy.All the patients underwent operations.In the laparoscopic surgery group,there were 27 cases of partial nephrectomy,1 case of radical prostatectomy,2 cases of radical cystectomy and 2 cases of adrenalectomy.In the da Vinci robotic surgery group of 54 cases,there were 51 cases of partial nephrectomy,1 case of retroperitoneal lymph node dissection,1 case of retroperitoneal bilateral renal cyst deroofing and 1 case of resection of pelvic metastasis.There were 41 partial nephrectomy patients with available clinical data for statistic,with a median age of 53.5 years (range 24-76),including 26 males and 15 females.The median R.E.N.A.L score was 7.8 (range 4-11).Before the operation,the engineers established the holographic image based on the contrast CT images and reports.The surgeon applied the holographic image for preoperative planning.During the operation,the navigation was achieved by real time fusing holographic images with the laparoscopic surgery images in the screen.Results All the procedures had been complete uneventfully.The holographic images helped surgeon in understanding the visual three-dimension structure and relation of vessels supplying tumor or resection tissue,lymph nodes and nerves.By manipulating the holographic images extracorporeally,the fused image guide surgeons about location vessel,lymph node and other important structure and then facilitate the delicate dissection.For the 41 cases with available clinical data including 23 cases of robotic-assisted partial nephrectomy and 18 cases of laparoscopic nephrectomy,the median operation time was 140 (range 50-225) min,the median warm ischemia time was 23 (range 14-60) min,the median blood loss was 80(range 5-1 200) ml.In the robotic surgery group,the median operation time was 140 (range 50-215)min,the median warm i schemia time was 21 (range 17-40)min,the median blood loss was 150(range 30-1 200)ml.In the laparoscopic surgery group,the median operation time was 160(range 80-225)min,the median warm ischemia time was 25 (range 14-60)min,the median blood loss was 50 (range 5-1 200) ml.All the patients had no adjacent organ injury during operation.There were 2 cases with Clavien Ⅱ complications.One required transfusion and the other one suffered hematoma post-operation.However,the tumors were located in the renal hilus for these 2 cases and the R.E.N.A.L scores were both 11.Conclusions Holographic image navigation can help location and recognize important anatomic structures during the surgical procedures..This technique will reduce the tissue injury,decrease the complications and improve the success rate of surgery.

Advanced Methods in Dynamic Contrast Enhanced Arterial Phase Imaging of the Liver

Dynamic contrast enhanced (DCE) magnetic resonance (MR) imaging plays an important role in non-invasive detection and characterization of primary and metastatic lesions in the liver. Recently, efforts have been made to improve spatial and temporal resolution of DCE liver MRI for arterial phase imaging. Review of recent publications related to arterial phase imaging of the liver indicates that there exist primarily two approaches: breath-hold and free-breathing. For breath-hold imaging, acquiring multiple arterial phase images in a breath-hold is the preferred approach over conventional single-phase imaging. For free-breathing imaging, a combination of three-dimensional (3D) stack-of-stars golden-angle sampling and compressed sensing parallel imaging reconstruction is one of emerging techniques. Self-gating can be used to decrease respiratory motion artifact. This article introduces recent MRI technologies relevant to hepatic arterial phase imaging, including differential subsampling with Cartesian ordering (DISCO), golden-angle radial sparse parallel (GRASP), and X-D GRASP. This article also describes techniques related to dynamic 3D image reconstruction of the liver from golden-angle stack-of-stars data.

Progresses of photoacoustic-ultrasonic combined imaging reconstruction / 中国医学影像技术

Ultrasonic computed tomography (USCT) and photoacoustic computed tomography (PACT) are two kinds of complementary imaging techniques. Photoacoustic-ultrasonic (PAUS) imaging combines PACT with USCT into one system and can obtain structural images and optical absorption distribution images of the target simultaneously. The combined images can display the acoustic discontinuity and optical absorption properties of the tissue. The diseased tissue can be accurately identified and located, and the functional components can also be quantitatively measured. The research progresses of the methods of joint images reconstruction for PAUS were reviewed in this paper.

Review on photoacoustic tomographic image reconstruction for acoustically heterogeneous tissues / 生物医学工程学杂志

Acoustic properties of biological tissues usually vary inhomogeneously in space. Tissues with different chemical composition often have different acoustic properties. The assumption of acoustic homogeneity may lead to blurred details, misalignment of targets and artifacts in the reconstructed photoacoustic tomography (PAT) images. This paper summarizes the main solutions to PAT imaging of acoustically heterogeneous tissues, including the variable sound speed and acoustic attenuation. The advantages and limits of the methods are discussed and the possible future development is prospected.

Effects of energy level, reconstruction kernel, and tube rotation time on Hounsfield units of hydroxyapatite in virtual monochromatic images obtained with dual-energy CT

PURPOSE: This study was performed to investigate the effects of energy level, reconstruction kernel, and tube rotation time on Hounsfield unit (HU) values of hydroxyapatite (HA) in virtual monochromatic images (VMIs) obtained with dual-energy computed tomography (DECT) (Siemens Healthineers, Erlangen, Germany).MATERIALS AND METHODS: A bone density calibration phantom with 3 HA inserts of different densities (CTWATER®; 0, 100, and 200 mg of HA/cm³) was scanned using a twin-beam DECT scanner at 120 kVp with tube rotation times of 0.5 and 1.0 seconds. The VMIs were reconstructed by changing the energy level (with options of 40 keV, 70 keV, and 140 keV). In order to investigate the impact of the reconstruction kernel, virtual monochromatic images were reconstructed after changing the kernel from body regular 40 (Br40) to head regular 40 (Hr40) in the reconstruction phase. The mean HU value was measured by placing a circular region of interests (ROIs) in the middle of each insert obtained from the VMIs. The HU values were compared with regard to energy level, reconstruction kernel, and tube rotation time.RESULTS: Hydroxyapatite density was strongly correlated with HU values (correlation coefficient=0.678, P<0.05). For the HA 100 and 200 inserts, HU decreased significantly at increased energy levels (correlation coefficient= −0.538, P<0.05) but increased by 70 HU when using Hr40 rather than Br40 (correlation coefficient=0.158, P<0.05). The tube rotation time did not significantly affect the HU (P>0.05).CONCLUSION: The HU values of hydroxyapatite were strongly correlated with hydroxyapatite density and energy level in VMIs obtained with DECT.

Coronary CT Angiography with Knowledge-Based Iterative Model Reconstruction for Assessing Coronary Arteries and Non-Calcified Predominant Plaques

OBJECTIVE: To assess the effects of iterative model reconstruction (IMR) on image quality for demonstrating non-calcific high-risk plaque characteristics of coronary arteries. MATERIALS AND METHODS: This study included 66 patients (53 men and 13 women; aged 39–76 years; mean age, 55 ± 13 years) having single-vessel disease with predominantly non-calcified plaques evaluated using prospective electrocardiogram-gated 256-slice CT angiography. Paired image sets were created using two types of reconstruction: hybrid iterative reconstruction (HIR) and IMR. Plaque characteristics were compared using the two algorithms. The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of the images and the CNR between the plaque and adjacent adipose tissue were also compared between the two reformatted methods. RESULTS: Seventy-seven predominantly non-calcified plaques were detected. Forty plaques showed napkin-ring sign with the IMR reformatted method, while nineteen plaques demonstrated napkin-ring sign with HIR. There was no statistically significant difference in the presentation of positive remodeling, low attenuation plaque, and spotty calcification between the HIR and IMR reconstructed methods (all p > 0.5); however, there was a statistically significant difference in the ability to discern the napkin-ring sign between the two algorithms (χ2 = 12.12, p < 0.001). The image noise of IMR was lower than that of HIR (10 ± 2 HU versus 12 ± 2 HU; p < 0.01), and the SNR and CNR of the images and the CNR between plaques and surrounding adipose tissues on IMR were better than those on HIR (p < 0.01). CONCLUSION: IMR can significantly improve image quality compared with HIR for the demonstration of coronary artery and atherosclerotic plaques using a 256-slice CT.

CT Image Conversion among Different Reconstruction Kernels without a Sinogram by Using a Convolutional Neural Network

OBJECTIVE: The aim of our study was to develop and validate a convolutional neural network (CNN) architecture to convert CT images reconstructed with one kernel to images with different reconstruction kernels without using a sinogram. MATERIALS AND METHODS: This retrospective study was approved by the Institutional Review Board. Ten chest CT scans were performed and reconstructed with the B10f, B30f, B50f, and B70f kernels. The dataset was divided into six, two, and two examinations for training, validation, and testing, respectively. We constructed a CNN architecture consisting of six convolutional layers, each with a 3 × 3 kernel with 64 filter banks. Quantitative performance was evaluated using root mean square error (RMSE) values. To validate clinical use, image conversion was conducted on 30 additional chest CT scans reconstructed with the B30f and B50f kernels. The influence of image conversion on emphysema quantification was assessed with Bland–Altman plots. RESULTS: Our scheme rapidly generated conversion results at the rate of 0.065 s/slice. Substantial reduction in RMSE was observed in the converted images in comparison with the original images with different kernels (mean reduction, 65.7%; range, 29.5–82.2%). The mean emphysema indices for B30f, B50f, converted B30f, and converted B50f were 5.4 ± 7.2%, 15.3 ± 7.2%, 5.9 ± 7.3%, and 16.8 ± 7.5%, respectively. The 95% limits of agreement between B30f and other kernels (B50f and converted B30f) ranged from −14.1% to −2.6% (mean, −8.3%) and −2.3% to 0.7% (mean, −0.8%), respectively. CONCLUSION: CNN-based CT kernel conversion shows adequate performance with high accuracy and speed, indicating its potential clinical use.