Hydrodissection During Computed Tomography-Guided Cryoablation for Renal Tumors: Where is the Effective Fluid Accumulation Space in the Retroperitoneum?

PURPOSE: This study was performed to investigate the effectiveness of hydrodissection during computed tomography-guided renal cryoablation by evaluation of the fluid distribution based on the retroperitoneal anatomy with the interfascial plane. MATERIALS AND METHODS: Between March 2014 and March 2021, 52 renal tumors were treated by cryoablation with hydrodissection (36 men; mean age 72.5 years). The hydrodissection needle was located in perirenal space. The spreading fluid space based on the retroperitoneal anatomy with the interfascial plane was retrospectively evaluated. The fluid space that most effectively separated the tumor from the adjacent organs was defined. The relationship of the needle tip position in the perirenal space (renal capsule or fascia side) and the most effective fluid space was also evaluated. RESULTS: Cryoablation was successfully completed in all cases with no major complications. Hydrodissection was effective in all cases. The distance between the tumors and the adjacent organs was significantly longer after hydrodissection (from 7.50 ± 7.43 to 22.6 ± 9.86 mm) (P < 0.0001). Although fluid spreading through multiple retroperitoneal spaces was frequently observed, the retromesenteric plane was observed more frequently as the most effective fluid space (67.3%) than the perirenal space (21.2%) (P < 0.0001). Regardless of the needle tip position, the most effective fluid space was also commonly the retromesenteric plane. CONCLUSIONS: The retromesenteric plane could be the most effective fluid space to separate the tumor from the adjacent organ, regardless of where the hydrodissection needle tip is positioned in the perirenal space. LEVEL OF EVIDENCE: 3b.

Frequency and Risk Factors for Air Embolism in Computed Tomography Fluoroscopy-Guided Biopsy of Lung Tumor With the Use of Noncoaxial Automatic Needle.

PURPOSE: The aim of the study is to analyze incidence and risk factors for air embolism during computed tomography (CT) fluoroscopy-guided lung biopsies using noncoaxial automatic needle. MATERIALS AND METHODS: Between February 2014 and December 2019, 204 CT fluoroscopy-guided lung biopsies (127 men; mean age, 70.6 years) using noncoaxial automatic needle under inspiratory breath holding were performed. We retrospectively evaluated the incidence of air embolism as presence of air in the systemic circulation on whole-chest CT images obtained immediately after biopsy. Risk factors of the patient, tumor and procedural factors (size, location and type of nodule, distance from the pleura, the level of the lesion relative to the left atrium, emphysema, patient position, penetration of a pulmonary vein, etc) were analyzed. RESULTS: The technical success rate was 97.1%. Air embolism was radiologically identified in 8 cases (3.92%, 7 males; size, 21.6 ± 18.2 mm; distance to pleura, 11.9 ± 14.5 mm). Two patients showed overt symptoms and the others were asymptomatic. Independent risk factors were needle penetration of the pulmonary vein ( P = 0.0478) and higher location relative to left atrium ( P = 0.0353). Size, location and type of nodule, distance from the pleura, emphysema, patient position, and other variables were not significant risk factors. As other complications, pneumothorax and alveolar hemorrhage were observed in 57.4% and 77.5%, respectively. CONCLUSIONS: In CT fluoroscopy-guided lung biopsy using the noncoaxial automatic needles, radiological incidence of air embolism was 3.92%. Given the frequency of air embolism, it is necessary to incorporate this into postprocedure imaging and clinical evaluation.

An autopsy case of COVID-19 with a sudden death: Clinico-pathological comparison.

Autopsy was performed on a COVID-19 patient, who suddenly died despite the extensive anti-viral and anti-inflammatory therapies. Although moderate subpleural fibrosis was seen, pathology of DAD, a well-known cause for pulmonary failure, was minimum. Instead, severe hemorrhage was observed. Therapeutic effects were indicated; however, why severe hemorrhage occurred was unclear.

A deep learning method for translating 3DCT to SPECT ventilation imaging: First comparison with 81m Kr-gas SPECT ventilation imaging.

PURPOSE: This study aimed to evaluate the accuracy of deep learning (DL)-based computed tomography (CT) ventilation imaging (CTVI). METHODS: A total of 71 cases that underwent single-photon emission CT 81m Kr-gas ventilation (SPECT V) and CT imaging were included. Sixty cases were assigned to the training and validation sets, and the remaining 11 cases were assigned to the test set. To directly transform three-dimensional (3D) CT (free-breathing CT) images to SPECT V images, a DL-based model was implemented based on the U-Net architecture. The input and output data were 3DCT- and SPECT V-masked, respectively, except for whole-lung volumes. These data were rearranged in voxel size, registered rigidly, cropped, and normalized in preprocessing. In addition to a standard estimation method (i.e., without dropout during the estimation process), a Monte Carlo dropout (MCD) method (i.e., with dropout during the estimation process) was used to calculate prediction uncertainty. To evaluate the two models' (CTVIMCD U-Net , CTVIU-Net ) performance, we used fivefold cross-validation for the training and validation sets. To test the final model performances for both approaches, we applied the test set to each trained model and averaged the test prediction results from the five trained models to acquire the mean test result (bagging) for each approach. For the MCD method, the models were predicted repeatedly (sample size = 200), and the average and standard deviation (SD) maps were calculated in each voxel from the predicted results: The average maps were defined as test prediction results in each fold. As an evaluation index, the voxel-wise Spearman rank correlation coefficient (Spearman rs ) and Dice similarity coefficient (DSC) were calculated. The DSC was calculated for three functional regions (high, moderate, and low) separated by an almost equal volume. The coefficient of variation was defined as prediction uncertainty, and these average values were calculated within three functional regions. The Wilcoxon signed-rank test was used to test for a significant difference between the two DL-based approaches. RESULTS: The average indexes with one SD (1SD) between CTVIMCD U-Net and SPECT V were 0.76 ± 0.06, 0.69 ± 0.07, 0.51 ± 0.06, and 0.75 ± 0.04 for Spearman rs , DSChigh , DSCmoderate , and DSClow , respectively. The average indexes with 1SD between CTVIU-Net and SPECT V were 0.72 ± 0.05, 0.66 ± 0.04, 0.48 ± 0.04, and 0.74 ± 0.06 for Spearman rs , DSChigh , DSCmoderate , and DSClow , respectively. These indexes between CTVIMCD U-Net and CTVIU-Net showed no significance difference (Spearman rs , p = 0.175; DSChigh , p = 0.123; DSCmoderate , p = 0.278; DSClow , p = 0.520). The average coefficient of variations with 1SD were 0.27 ± 0.00, 0.27 ± 0.01, and 0.36 ± 0.03 for the high-, moderate-, and low-functional regions, respectively, and the low-functional region showed a tendency to exhibit larger uncertainties than the others. CONCLUSION: We evaluated DL-based framework for estimating lung-functional ventilation images only from CT images. The results indicated that the DL-based approach could potentially be used for lung-ventilation estimation.

K-means clustering for support construction in diffractive imaging.

A method for constructing an object support based on K-means clustering of the object-intensity distribution is newly presented in diffractive imaging. This releases the adjustment of unknown parameters in the support construction, and it is well incorporated with the Gerchberg and Saxton diagram. A simple numerical simulation reveals that the proposed method is effective for dynamically constructing the support without an initial prior support.

Spherical shell structure of distribution of images reconstructed by diffractive imaging.

Image reconstruction from Fourier intensity through phase retrieval was investigated when the intensity was contaminated with Poisson noise. Although different initial conditions and/or the instability of the iterative phase retrieval process led to different reconstructed images, we found that the distribution of the resulting images in both the object and Fourier spaces formed spherical shell structures. Averaging of the images over the distribution corresponds to the position of the image at the sphere center.