Prospective characterization of intestinal MRI intravoxel incoherent motion in pediatric and young adult patients with newly diagnosed small bowel Crohn's disease.

BACKGROUND: MRI diffusion-weighted imaging (DWI) is commonly used in MR enterography protocols for assessment of intestinal inflammation in patients with Crohn's disease. The intravoxel incoherent motion (IVIM) approach to DWI has been proposed as a more objective approach, providing quantitative parameters that reflect water diffusivity (D), blood flow (D*), and perfusion fraction (f). PURPOSE: We aimed to determine if DWI-IVIM metrics from the terminal ileum in patients with newly diagnosed Crohn's disease differ from healthy participants and change in response to biologic medical therapy. METHODS: In this prospective case-control study, 20 consecutive pediatric patients (mean age = 14 years ± 2 [SD]; eight females) with newly diagnosed ileal Crohn's disease and 15 pediatric healthy participants (mean age = 18 years ± 4 [SD]; eight females) underwent research MRI examinations of the small bowel between 12/2018 and 10/2021. Participants with Crohn's disease underwent MR studies at baseline, 6 weeks, and 6 months following initiation of anti-TNF-alpha therapy, whereas control participants underwent one research MRI examination. The MRI protocol included a DWI-IVIM sequence with nine b-values and the IVIM parameters (D, D*, and f) were extracted. Unpaired t-tests and mixed-effects models were used for analyses. RESULTS: Mean IVIM D (P < 0.001), D* (P = 0.004), and f (P = 0.001) metrics were lower for Crohn's patients at the time of diagnosis compared to healthy participants. Mean IVIM f value increased over time in response to medical therapy (mean f at baseline, 22% ± 6%; 6 weeks, 25% ± 7%; 6 months, 29% ± 10%; P = 0.016). Mean IVIM D* value increased over time in response to treatment (mean D* at baseline, 10.9 ± 3.0 × 10-3 mm2/s; 6 weeks, 11.8 ± 2.8 × 10-3 mm2/s; 6 months, 13.3 ± 3.3 × 10-3 mm2/s; P = 0.047), while there was no significant change in mean IVIM D value (P = 0.10). CONCLUSION: MRI DWI-IVIM metrics in patients with ileal Crohn's disease change over time in response to biological therapy and help discriminate these patients from healthy participants.

Deep-Learning Models for Abdominal CT Organ Segmentation in Children: Development and Validation in Internal and Heterogeneous Public Datasets.

Background: Deep-learning abdominal organ segmentation algorithms have shown excellent results in adults; validation in children is sparse. Objective: To develop and validate deep-learning models for liver, spleen, and pancreas segmentation on pediatric CT examinations. Methods: This retrospective study developed and validated deep-learning models for liver, spleen, and pancreas segmentation using 1731 CT examinations (1504 training, 221 testing), derived from three internal institutional pediatric (age ≤18) datasets (n=483) and three public datasets comprising pediatric and adult examinations with various pathologies (n=1248). Three deep-learning model architectures (SegResNet, DynUNet, and SwinUNETR) from the Medical Open Network for AI (MONAI) framework underwent training using native training (NT), relying solely on institutional datasets, and transfer learning (TL), incorporating pre-training on public datasets. For comparison, TotalSegmentator (TS), a publicly available segmentation model, was applied to test data without further training. Segmentation performance was evaluated using mean Dice similarity coefficient (DSC), with manual segmentations as reference. Results: For internal pediatric data, DSC for normal liver was 0.953 (TS), 0.964-0.965 (NT models), and 0.965-0.966 (TL models); normal spleen, 0.914 (TS), 0.942-0.945 (NT models), and 0.937-0.945 (TL models); normal pancreas, 0.733 (TS), 0.774-0.785 (NT models), and 0.775-0.786 (TL models); pancreas with pancreatitis, 0.703 (TS), 0.590-0.640 (NT models), and 0.667-0.711 (TL models). For public pediatric data, DSC for liver was 0.952 (TS), 0.876-0.908 (NT models), and 0.941-0.946 (TL models); spleen, 0.905 (TS), 0.771-0.827 (NT models), and 0.897-0.926 (TL models); pancreas, 0.700 (TS), 0.577-0.648 (NT models), and 0.693-0.736 (TL models). For public primarily adult data, DSC for liver was 0.991 (TS), 0.633-0.750 (NT models), and 0.926-0.952 (TL models); spleen, 0.983 (TS), 0.569-0.604 (NT models), and 0.923-0.947 (TL models); pancreas, 0.909 (TS), 0.148-0.241 (NT models), and 0.699-0.775 (TL models). DynUNet-TL was selected as the best-performing NT or TL model and was made available as an opensource MONAI bundle (https://github.com/cchmc-dll/pediatric_abdominal_segmentation_bundle.git). Conclusion: TL models trained on heterogeneous public datasets and fine-tuned using institutional pediatric data outperformed internal NT models and TotalSegmentator across internal and external pediatric test data. Segmentation performance was better in liver and spleen than in pancreas. Clinical Impact: The selected model may be used for various volumetry applications in pediatric imaging.

Deep Learning for Automated Measurement of Total Cardiac Volume for Heart Transplantation Size Matching.

Total Cardiac Volume (TCV)-based size matching using Computed Tomography (CT) is a novel technique to compare donor and recipient heart size in pediatric heart transplant that may increase overall utilization of available grafts. TCV requires manual segmentation, which limits its widespread use due to time and specialized software and training needed for segmentation. This study aims to determine the accuracy of a Deep Learning (DL) approach using 3-dimensional Convolutional Neural Networks (3D-CNN) to calculate TCV, with the clinical aim of enabling fast and accurate TCV use at all transplant centers. Ground truth TCV was segmented on CT scans of subjects aged 0-30 years, identified retrospectively. Ground truth segmentation masks were used to train and test a custom 3D-CNN model consisting of a DenseNet architecture in combination with residual blocks of ResNet architecture. The model was trained on a cohort of 270 subjects and a validation cohort of 44 subjects (36 normal, 8 heart disease retained for model testing). The average Dice similarity coefficient of the validation cohort was 0.94 ± 0.03 (range 0.84-0.97). The mean absolute percent error of TCV estimation was 5.5%. There is no significant association between model accuracy and subject age, weight, or height. DL-TCV was on average more accurate for normal hearts than those listed for transplant (mean absolute percent error 4.5 ± 3.9 vs. 10.5 ± 8.5, p = 0.08). A deep learning-based 3D-CNN model can provide accurate automatic measurement of TCV from CT images. This initial study is limited as a single-center study, though future multicenter studies may enable generalizable and more accurate TCV measurement by inclusion of more diverse cardiac pathology and increasing the training data.

Pancreas volumes in pediatric patients following index acute pancreatitis and acute recurrent pancreatitis.

BACKGROUND/OBJECTIVES: Pancreas volume derived from imaging may objectively reveal volume loss relevant to identifying sequelae of acute pancreatitis (AP) and ultimately diagnosing chronic pancreatitis (CP). The purposes of this study were to: (1) quantify pancreas volume by imaging in children with either (a) a single episode of AP or (b) acute recurrent pancreatitis (ARP), and (2) compare these volumes to normative volumes. METHODS: This retrospective study was institutional review board approved. A single observer segmented the pancreas (3D Slicer; slicer.org) on n = 30 CT and MRI exams for 23 children selected from a prospective registry of patients with either an index attack of AP or with ARP after a known index attack date. Patients with CP were excluded. Segmented pancreas volumes were compared to published normal values. RESULTS: Mean pancreas volumes normalized to body surface area (BSA) in the index AP and ARP groups were 38.2 mL/m2 (range: 11.8-73.5 mL/m2) and 27.9 mL/m2 (range: 8.0-69.2 mL/m2) respectively. 43 % (6/14) of patients post-AP had volumes below the 25th percentile, 1 (17 %) of which was below the 5th percentile (p = 0.3027 vs. a normal distribution). Post-ARP, 44 % (7/16) of patients had volumes below the 5th percentile (p < 0.001). CONCLUSIONS: A significant fraction (40 %) of children with ARP have pancreas volumes <5th percentile for BSA even in the absence of CP. A similar, but not statistically significant, fraction have pancreas volumes <25th percentile after an index attack of AP. Pancreatic parenchymal volume deserves additional investigation as an objective marker of parenchymal damage from acute pancreatitis and of progressive pancreatitis in children.

Longitudinal changes in quantitative magnetic resonance imaging metrics in children and young adults with autoimmune liver disease.

PURPOSE: To assess longitudinal changes in quantitative MRI metrics in pediatric and young adult patients with autoimmune liver disease (AILD). METHODS: This prospective, IRB-approved study included 20 children and young adults (median age = 15 years) with primary sclerosing cholangitis (PSC)/autoimmune sclerosing cholangitis (ASC) and 19 (median age = 17 years) with autoimmune hepatitis (AIH). At a field strength of 1.5-T, T2*-corrected T1 mapping (cT1), 3D fast spin-echo MRCP, and 2D gradient recalled echo MR elastography (MRE) were performed at baseline, one year, and two years. cT1 and quantitative MRCP were processed using LiverMultiScan and MRCP + , respectively (Perspectum Ltd, Oxford, UK). Linear mixed models were used to assess longitudinal changes in quantitative MRI metrics. Spearman rank-order correlation was used to assess relationships between changes in quantitative MRI metrics. RESULTS: Changes in quantitative MRI metrics greater than established repeatability coefficients were measured in six (cT1) and five (MRE) patients with PSC/ASC as well as in six patients (cT1 and MRE) with AIH, although linear mixed models identified no significant changes for the subgroups as a whole. For PSC/ASC, there were positive correlations between change in liver stiffness and changes in bile duct strictures (ρ = 0.68; p = 0.005) and bile duct dilations (ρ = 0.70; p = 0.004) between baseline and Year 2. CONCLUSION: On average, there were no significant changes in quantitative MRI metrics over a two-year period in children and young adults with AILD. However, worsening cholangiopathy was associated with increasing liver stiffness by MRE in patients with PSC/ASC.

Corrected T1 Mapping in Children and Young Adults With Autoimmune Liver Disease: Correlation With Histology.

The purpose of this study was to assess relationships between liver-corrected T1 (cT1) values (adjusted for T2* effect, MRI system manufacturer, and field strength) and histologic inflammation and fibrosis in 35 participants (15 women and girls, 20 boys and men; median age, 16.0 years) with autoimmune liver disease. At multivariable analysis, inflammation score (ß = 15.5) and sex (ß = 56.0 [female]) were independent predictors of cT1, and fibrosis score (ß = 32.3) and age (ß = 5.5) were independent predictors of cT1 IQR. Liver T1 may have relevance for assessing liver inflammatory activity and fibrosis stage.

Deep Learning for Automated Measurement of Total Cardiac Volume for Heart Transplantation Size Matching.

Background: Total Cardiac Volume (TCV) based size matching using Computed Tomography (CT) is a novel technique to compare donor and recipient heart size in pediatric heart transplant that may increase overall utilization of available grafts. TCV requires manual segmentation, which limits its widespread use due to time and specialized software and training needed for segmentation. Objective: This study aims to determine the accuracy of a Deep Learning (DL) approach using 3-dimensional Convolutional Neural Networks (3D-CNN) to calculate TCV, with the clinical aim of enabling fast and accurate TCV use at all transplant centers. Materials and Methods: Ground truth TCV was segmented on CT scans of subjects aged 0-30 years, identified retrospectively. Ground truth segmentation masks were used to train and test a custom 3D-CNN model consisting of a Dense-Net architecture in combination with residual blocks of ResNet architecture. Results: The model was trained on a cohort of 270 subjects and a validation cohort of 44 subjects (36 normal, 8 heart disease retained for model testing). The average Dice similarity coefficient of the validation cohort was 0.94 ± 0.03 (range 0.84-0.97). The mean absolute percent error of TCV estimation was 5.5%. There is no significant association between model accuracy and subject age, weight, or height. DL-TCV was on average more accurate for normal hearts than those listed for transplant (mean absolute percent error 4.5 ± 3.9 vs. 10.5 ± 8.5, p = 0.08). Conclusion: A deep learning based 3D-CNN model can provide accurate automatic measurement of TCV from CT images.

Bowel wall MRI T1 relaxation estimates for assessment of intestinal inflammation in pediatric Crohn's disease.

PURPOSE: To compare bowel wall T1 relaxation estimates in young patients with newly diagnosed ileal CD to healthy control participants, characterize their change over time in response to biologic medical therapy, and evaluate their associations with clinical markers of intestinal inflammation. MATERIALS AND METHODS: Patients with newly diagnosed ileal CD and healthy control participants were prospectively recruited between December 2018 and October 2021. Patients underwent research MRI examinations of the bowel at baseline and at 6-weeks and 6-months into biologic medical treatment; control participants underwent single MRI examinations. MRI examinations included native T1 relaxometry of the terminal ileum using a modified Look-Locker inversion recovery (MOLLI) sequence. T1 estimates were measured on scanner-generated parametric maps. Clinical markers of intestinal inflammation were recorded at each visit. Group differences were assessed using the Mann-Whitney U test; the Friedman test was used to assess longitudinal changes in T1 estimates. Spearman correlation was used to evaluate associations between T1 estimates and inflammatory markers. RESULTS: Nineteen participants with CD (12 males; median age 14 years) and 15 control participants (7 males; median age 17 years) were included in the study. Bowel wall T1 estimates in CD patients (median 1302 ms) were significantly longer compared to control participants (median 1159 ms) (p < 0.001). In CD patients, T1 estimates changed over time after treatment (p = 0.001), with largest reductions between baseline and 6-weeks (p < 0.001). T1 estimates correlated with inflammatory markers, including erythrocyte sedimentation rate (ρ = 0.35; p = 0.01), c-reactive protein level (ρ = 0.34; p = 0.02), and weighted Pediatric Crohn's Disease Activity Index (ρ = 0.39; p = 0.005). T1 estimates did not correlate with serum albumin (ρ = - 0.28; p = 0.051) and fecal calprotectin (ρ = 0.07; p = 0.63). CONCLUSION: Bowel wall T1 estimates are abnormally increased in newly diagnosed ileal CD patients and decrease in response to medical therapy.

Associations Between Quantitative MRI Metrics and Clinical Risk Scores in Children and Young Adults With Autoimmune Liver Disease.

BACKGROUND. The Mayo risk score and SCOPE (Sclerosing Cholangitis Outcomes in Pediatrics) index are clinical risk scores for monitoring the progression of primary sclerosing cholangitis (PSC) and predicting clinically important endpoints. OBJECTIVE. The purpose of this study was to evaluate relationships between quantitative MRI measures of liver disease and clinical risk scores in children and young adults with autoimmune liver disease (AILD). METHODS. This prospective study included 58 patients (35 male and 23 female patients; mean age, 15.1 ± 1.1 [SD] years [range, 6-24 years]) with AILD (16 with PSC, 30 with autoimmune hepatitis, and 12 with autoimmune sclerosing cholangitis) who underwent research liver MRI examinations including MR elastography, T2*-corrected T1 (cT1), and quantitative MRCP measurements. Associations between quantitative MRI metrics and clinical risk scores were evaluated using Spearman rank-order correlation coefficients and multivariable regression analyses. RESULTS. The mean Mayo risk score was -1.1 ± 0.9 (SD) (range, -2.9 to 1.1); the mean SCOPE index was 2.7 ± 2.2 (range, 0-9). Mean liver stiffness was 2.8 ± 1.1 kPa, whole-liver mean cT1 was 874.7 ± 77.7 ms, and whole-liver cT1 interquartile range (IQR) was 150.8 ± 55.6 ms. The Mayo risk score was significantly correlated with liver stiffness (ρ = 0.56; p < .001), whole-liver mean cT1 (ρ = 0.31; p = .02), whole-liver cT1 IQR (ρ = 0.58; p < .001), and multiple quantitative MRCP measures (ρ = 0.36-0.45, p < .001). SCOPE index was significantly correlated with liver stiffness (ρ = 0.68; p < .001), whole-liver cT1 IQR (ρ = 0.51; p < .001), and multiple quantitative MRCP measures (ρ = 0.47-0.49; p < .001). Multivariable linear regression analyses identified liver stiffness, whole-liver cT1 IQR, and left hepatic duct maximum diameter as significant independent predictors of the Mayo risk score (adjusted R2 = 0.45), and liver stiffness, whole-liver cT1 IQR, maximum common bile duct (CBD) diameter, and median CBD diameter as significant independent predictors of the SCOPE index (adjusted R2 = 0.69). On logistic regression analysis, greater than low risk by SCOPE index was best predicted by liver stiffness [odds ratio [OR] = 49.6; 95% CI, 3.1-793.6) and maximum CBD diameter (OR = 2.5; 95% CI, 1.3-4.7). CONCLUSION. Increased liver stiffness, increased cT1 IQR, and larger bile duct diameters are independently associated with higher (worse) Mayo risk score and SCOPE index among children and young adults with AILD and may be surrogate markers of clinically meaningful endpoints. CLINICAL IMPACT. Multiparametric MRI of the liver incorporating quantitative metrics may serve as a noninvasive diagnostic and prognostic tool in pediatric AILD. TRIAL REGISTRATION. ClinicalTrials.gov: NCT03175471.

Comparison of quantitative 3D magnetic resonance cholangiography measurements obtained using three different image acquisition methods.

PURPOSE: To compare quantitative biliary measurements obtained with three different magnetic resonance cholangiopancreatography (MRCP) acquisition methods. METHODS: This retrospective study was IRB-approved. Patients with combinations of clinically indicated 3D FSE MRCP with sensitivity encoding (SENSE), 3D FSE SENSE MRCP with compressed sensing (CS-FSE; acceleration factor 8), and 3D gradient and spin-echo (GRASE) MRCP, acquired between October 2018 and March 2020, were included. The MRCP + Tuning Threshold algorithm (Perspectum Ltd., Oxford, UK) was used to segment 3D biliary models from MRCP data, with multiple metrics quantified from the models. Single measure, two-way, mixed-effects intra-class correlations, Bland-Altman analyses, and Wilcoxon signed-rank tests were used to compare quantitative measurements. RESULTS: From 160 MRCP datasets (25 3D FSE, 67 3D CS-FSE, 68 3D GRASE) in 69 patients, 48 datasets (7 [28%] 3D FSE, 14 [21%] 3D CS-FSE, 27 [40%] 3D GRASE) failed post-processing due to motion artifacts. The remaining 112 MRCP datasets (18 3D FSE, 53 3D CS-FSE, 41 3D GRASE) from 60 patients were included in the analysis. There was good to excellent agreement between 3D FSE and 3D CS-FSE MRCP for diameter of the left and right hepatic ducts, biliary volume, number and length of ducts, and total length of dilations (ICC: 0.83-0.93). The only metrics that exhibited good agreement between 3D FSE and 3D GRASE MRCP were biliary volume (ICC: 0.75) and total number of dilations (ICC: 0.77). CONCLUSION: 3D CS-FSE MRCP produces comparable biliary diameter metrics and global duct quantification to 3D FSE MRCP at a significantly reduced acquisition time.

Associations between MRI T1 mapping, liver stiffness, quantitative MRCP, and laboratory biomarkers in children and young adults with autoimmune liver disease.

PURPOSE: Define relationships between quantitative magnetic resonance imaging (MRI) metrics and clinical/laboratory data in a pediatric and young adult cohort with autoimmune liver disease (AILD). MATERIALS AND METHODS: This prospective, cross-sectional study was institutional review board-approved. Patients enrolled in an institutional AILD registry were divided into groups: (1) autoimmune hepatitis (AIH) or (2) primary sclerosing cholangitis (PSC)/autoimmune sclerosing cholangitis (ASC). Participants underwent serum liver biochemistry testing and research MRI examinations, including 3D magnetic resonance cholangiopancreatography (MRCP), magnetic resonance elastography (MRE), and iron-corrected T1 mapping (cT1). MRCP + and LiverMultiScan (Perspectum Ltd., Oxford, UK) were used to post-process 3D MRCP and cT1 data. Multiple linear regression models were used to assess relationships. RESULTS: 58 patients, 35 male, median age 16 years were included; 30 in the AIH group, 28 in the PSC/ASC group. After statistical adjustments for patient age, sex, presence of inflammatory bowel disease (IBD), specific diagnosis (PSC/ASC vs. AIH), and time from diagnosis to MRI examination, left hepatic bile duct maximum diameter was a statistically significant predictor of whole liver mean cT1, cT1 interquartile range (IQR), and MRE liver stiffness (p = 0.01-0.04). Seven laboratory values were significant predictors of whole liver cT1 IQR (p < 0.0001-0.04). Eight laboratory values and right hepatic bile duct median and maximum diameter were significant predictors of liver stiffness (p < 0.0001-0.03). CONCLUSIONS: Bile duct diameters and multiple laboratory biomarkers of liver disease are independent predictors of liver stiffness and cT1 IQR in pediatric patients with AILD.

In vivo ultrasound thermal ablation control using echo decorrelation imaging in rabbit liver and VX2 tumor.

The utility of echo decorrelation imaging feedback for real-time control of in vivo ultrasound thermal ablation was assessed in rabbit liver with VX2 tumor. High-intensity focused ultrasound (HIFU) and unfocused (bulk) ablation were performed using 5 MHz linear image-ablate arrays. Treatments comprised up to nine lower-power sonications, followed by up to nine higher-power sonications, ceasing when the average cumulative echo decorrelation within a control region of interest exceeded a predefined threshold (- 2.3, log10-scaled echo decorrelation per millisecond, corresponding to 90% specificity for tumor ablation prediction in previous in vivo experiments). This threshold was exceeded in all cases for both HIFU (N = 12) and bulk (N = 8) ablation. Controlled HIFU trials achieved a significantly higher average ablation rate compared to comparable ablation trials without image-based control, reported previously. Both controlled HIFU and bulk ablation trials required significantly less treatment time than these previous uncontrolled trials. Prediction of local liver and VX2 tumor ablation using echo decorrelation was tested using receiver operator characteristic curve analysis, showing prediction capability statistically equivalent to uncontrolled trials. Compared to uncontrolled trials, controlled trials resulted in smaller thermal ablation regions and higher contrast between echo decorrelation in treated vs. untreated regions. These results indicate that control using echo decorrelation imaging may reduce treatment duration and increase treatment reliability for in vivo thermal ablation.

Tongue Part Movement Trajectories for /r/ Using Ultrasound.

PURPOSE: Because it shows the movement of different parts of the tongue in real time, ultrasound biofeedback therapy is a promising technology for speech research and remediation. One limitation is the difficulty of interpreting real-time ultrasound images of tongue motion. Our image processing system, TonguePART, tracks the tongue surface and allows for the acquisition of quantitative tongue part trajectories. METHOD: TonguePART automatically identifies the tongue contour based on ultrasound image brightness and tracks motion of the tongue root, dorsum, and blade in real time. We present tongue part trajectory data from 2 children with residual sound errors on /r/ and 2 children with typical speech, focusing on /r/ (International Phonetic Alphabet ɹ) in the phonetic context /ɑr/. We compared the tongue trajectories to magnetic resonance images of sustained vowel /ɑ/ and /r/. RESULTS: Measured trajectories show larger overall displacement and greater differentiation of tongue part movements for children with typical speech during the production of /ɑr/, compared to children with residual speech sound disorders. CONCLUSION: TonguePART is a fast, reliable method of tracking articulatory movement of tongue parts for syllables such as /ɑr/. It is extensible to other sounds and phonetic contexts. By tracking tongue parts, clinical researchers can investigate lingual coordination. TonguePART is suitable for real-time data collection and biofeedback. Ultrasound biofeedback therapy users may make more progress using simplified biofeedback of tongue movement.

Optimized Echo Decorrelation Imaging Feedback for Bulk Ultrasound Ablation Control.

Feasibility of controlling bulk ultrasound (US) thermal ablation using echo decorrelation imaging was investigated in ex vivo bovine liver. The first of two ablation and control procedures used a sequence of constant-intensity sonication cycles, ceased when the minimum echo decorrelation within a control region of interest (ROI) exceeded a predetermined threshold. The second procedure used a variable-intensity sonication sequence, with spatially averaged decorrelation as the stopping criterion. US exposures and echo decorrelation imaging were performed by a linear image-ablate array. Based on preliminary experiments, control ROIs and thresholds for the minimum-decorrelation and average-decorrelation criteria were specified. Controlled trials for the minimum-decorrelation and average-decorrelation criteria were compared with uncontrolled trials employing 9 or 18 cycles of matching sonication sequences. Lesion dimensions, treatment times, ablation rates, and areas under receiver operating characteristic curves were statistically compared. Successfully controlled trials using both criteria required significantly shorter treatment times than corresponding 18-cycle treatments, with better ablation prediction performance than uncontrolled 9-cycle and 18-cycle treatments. Either control approach resulted in greater ablation rate than corresponding 9-cycle or 18-cycle uncontrolled approaches. A post hoc analysis studied the effect of exchanging control criteria between the two series of controlled experiments. For either group, the average time needed to exceed the alternative decorrelation threshold approximately matched the average duration of successfully controlled experimental trials. These results indicate that either approach, using minimum-decorrelation or average-decorrelation criteria, is feasible for control of bulk US ablation. In addition, use of a variable-intensity sonication sequence for bulk US thermal ablation can result in larger ablated regions compared to constant-intensity sonication sequences.

Real-Time Spatiotemporal Control of High-Intensity Focused Ultrasound Thermal Ablation Using Echo Decorrelation Imaging in ex Vivo Bovine Liver.

The ability to control high-intensity focused ultrasound (HIFU) thermal ablation using echo decorrelation imaging feedback was evaluated in ex vivo bovine liver. Sonications were automatically ceased when the minimum cumulative echo decorrelation within the region of interest exceeded an ablation control threshold, determined from preliminary experiments as -2.7 (log-scaled decorrelation per millisecond), corresponding to 90% specificity for local ablation prediction. Controlled HIFU thermal ablation experiments were compared with uncontrolled experiments employing two, five or nine sonication cycles. Means and standard errors of the lesion width, area and depth, as well as receiver operating characteristic curves testing ablation prediction performance, were computed for each group. Controlled trials exhibited significantly smaller average lesion area, width and treatment time than five-cycle or nine-cycle uncontrolled trials and also had significantly greater prediction capability than two-cycle uncontrolled trials. These results suggest echo decorrelation imaging is an effective approach to real-time HIFU ablation control.