Browsing by Author "Schwartz, Fides R"
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Item Open Access Correlation of pre-operative imaging characteristics with donor outcomes and operative difficulty in laparoscopic donor nephrectomy.(American journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons, 2019-09-25) Schwartz, Fides R; Shaw, Brian I; Lerebours, Reginald; Vernuccio, Federica; Rigiroli, Francesca; Gonzalez, Fernando; Luo, Sheng; Rege, Aparna S; Vikraman, Deepak; Hurwitz-Koweek, Lynne; Marin, Daniele; Ravindra, KadiyalaThis study aimed to understand the relationship of pre-operative measurements and risk factors on operative time and outcomes of laparoscopic donor nephrectomy. 242 kidney donors between 2010 and 2017 were identified. Patient's demographic, anthropomorphic and operative characteristics were abstracted from the electronic medical record. Glomerular filtration rates (GFR) were documented before surgery, within 24 hours, 6, 12 and 24 months after surgery. Standard radiological measures and kidney volumes, subcutaneous and perinephric fat thicknesses were assessed by three radiologists. Data were analyzed using standard statistical measures. There was significant correlation between cranio-caudal and latero-lateral diameters (p<0.0001) and kidney volume. The left kidney was transplanted in 92.6% of cases and the larger kidney in 69.2%. Kidney choice (smaller vs larger) had no statistically significant impact on the rate of change of donor kidney function over time adjusting for age, sex and race (p=0.61). Perinephric fat thickness (+4.08 min) and surgery after 2011 were significantly correlated with operative time (p≤0.01). In conclusion, cranio-caudal diameters can be used as a surrogate measure for volume in the majority of donors. Size may not be a decisive factor for long-term donor kidney function. Perinephric fat around the donor kidney should be reported to facilitate operative planning.Item Open Access CT Radiomic Features of Superior Mesenteric Artery Involvement in Pancreatic Ductal Adenocarcinoma: A Pilot Study.(Radiology, 2021-09-07) Rigiroli, Francesca; Hoye, Jocelyn; Lerebours, Reginald; Lafata, Kyle J; Li, Cai; Meyer, Mathias; Lyu, Peijie; Ding, Yuqin; Schwartz, Fides R; Mettu, Niharika B; Zani, Sabino; Luo, Sheng; Morgan, Desiree E; Samei, Ehsan; Marin, DanieleBackground Current imaging methods for prediction of complete margin resection (R0) in patients with pancreatic ductal adenocarcinoma (PDAC) are not reliable. Purpose To investigate whether tumor-related and perivascular CT radiomic features improve preoperative assessment of arterial involvement in patients with surgically proven PDAC. Materials and Methods This retrospective study included consecutive patients with PDAC who underwent surgery after preoperative CT between 2012 and 2019. A three-dimensional segmentation of PDAC and perivascular tissue surrounding the superior mesenteric artery (SMA) was performed on preoperative CT images with radiomic features extracted to characterize morphology, intensity, texture, and task-based spatial information. The reference standard was the pathologic SMA margin status of the surgical sample: SMA involved (tumor cells ≤1 mm from margin) versus SMA not involved (tumor cells >1 mm from margin). The preoperative assessment of SMA involvement by a fellowship-trained radiologist in multidisciplinary consensus was the comparison. High reproducibility (intraclass correlation coefficient, 0.7) and the Kolmogorov-Smirnov test were used to select features included in the logistic regression model. Results A total of 194 patients (median age, 66 years; interquartile range, 60-71 years; age range, 36-85 years; 99 men) were evaluated. Aside from surgery, 148 patients underwent neoadjuvant therapy. A total of 141 patients' samples did not involve SMA, whereas 53 involved SMA. A total of 1695 CT radiomic features were extracted. The model with five features (maximum hugging angle, maximum diameter, logarithm robust mean absolute deviation, minimum distance, square gray level co-occurrence matrix correlation) showed a better performance compared with the radiologist assessment (model vs radiologist area under the curve, 0.71 [95% CI: 0.62, 0.79] vs 0.54 [95% CI: 0.50, 0.59]; P < .001). The model showed a sensitivity of 62% (33 of 53 patients) (95% CI: 51, 77) and a specificity of 77% (108 of 141 patients) (95% CI: 60, 84). Conclusion A model based on tumor-related and perivascular CT radiomic features improved the detection of superior mesenteric artery involvement in patients with pancreatic ductal adenocarcinoma. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Do and Kambadakone in this issue.Item Open Access Deep learning based spectral extrapolation for dual-source, dual-energy x-ray computed tomography.(Medical physics, 2020-09) Clark, Darin P; Schwartz, Fides R; Marin, Daniele; Ramirez-Giraldo, Juan C; Badea, Cristian TPurpose
Data completion is commonly employed in dual-source, dual-energy computed tomography (CT) when physical or hardware constraints limit the field of view (FoV) covered by one of two imaging chains. Practically, dual-energy data completion is accomplished by estimating missing projection data based on the imaging chain with the full FoV and then by appropriately truncating the analytical reconstruction of the data with the smaller FoV. While this approach works well in many clinical applications, there are applications which would benefit from spectral contrast estimates over the larger FoV (spectral extrapolation)-e.g. model-based iterative reconstruction, contrast-enhanced abdominal imaging of large patients, interior tomography, and combined temporal and spectral imaging.Methods
To document the fidelity of spectral extrapolation and to prototype a deep learning algorithm to perform it, we assembled a data set of 50 dual-source, dual-energy abdominal x-ray CT scans (acquired at Duke University Medical Center with 5 Siemens Flash scanners; chain A: 50 cm FoV, 100 kV; chain B: 33 cm FoV, 140 kV + Sn; helical pitch: 0.8). Data sets were reconstructed using ReconCT (v14.1, Siemens Healthineers): 768 × 768 pixels per slice, 50 cm FoV, 0.75 mm slice thickness, "Dual-Energy - WFBP" reconstruction mode with dual-source data completion. A hybrid architecture consisting of a learned piecewise linear transfer function (PLTF) and a convolutional neural network (CNN) was trained using 40 scans (five scans reserved for validation, five for testing). The PLTF learned to map chain A spectral contrast to chain B spectral contrast voxel-wise, performing an image domain analog of dual-source data completion with approximate spectral reweighting. The CNN with its U-net structure then learned to improve the accuracy of chain B contrast estimates by copying chain A structural information, by encoding prior chain A, chain B contrast relationships, and by generalizing feature-contrast associations. Training was supervised, using data from within the 33-cm chain B FoV to optimize and assess network performance.Results
Extrapolation performance on the testing data confirmed our network's robustness and ability to generalize to unseen data from different patients, yielding maximum extrapolation errors of 26 HU following the PLTF and 7.5 HU following the CNN (averaged per target organ). Degradation of network performance when applied to a geometrically simple phantom confirmed our method's reliance on feature-contrast relationships in correctly inferring spectral contrast. Integrating our image domain spectral extrapolation network into a standard dual-source, dual-energy processing pipeline for Siemens Flash scanner data yielded spectral CT data with adequate fidelity for the generation of both 50 keV monochromatic images and material decomposition images over a 30-cm FoV for chain B when only 20 cm of chain B data were available for spectral extrapolation.Conclusions
Even with a moderate amount of training data, deep learning methods are capable of robustly inferring spectral contrast from feature-contrast relationships in spectral CT data, leading to spectral extrapolation performance well beyond what may be expected at face value. Future work reconciling spectral extrapolation results with original projection data is expected to further improve results in outlying and pathological cases.Item Open Access Evaluating renal lesions using deep-learning based extension of dual-energy FoV in dual-source CT-A retrospective pilot study.(European journal of radiology, 2021-06) Schwartz, Fides R; Clark, Darin P; Ding, Yuqin; Ramirez-Giraldo, Juan Carlos; Badea, Cristian T; Marin, DanielePurpose
Dual-source (DS) CT, dual-energy (DE) field of view (FoV) is limited to the size of the smaller detector array. The purpose was to establish a deep learning-based approach to DE extrapolation by estimating missing image data using data from both tubes to evaluate renal lesions.Method
A DE extrapolation deep-learning (DEEDL) algorithm had been trained on DECT data of 50 patients using a DSCT with DE-FoV = 33 cm (Somatom Flash). Data from 128 patients with known renal lesions falling within DE-FoV was retrospectively collected (100/140 kVp; reference dataset 1). A smaller DE-FoV = 20 cm was simulated excluding the renal lesion of interest (dataset 2) and the DEEDL was applied to this dataset. Output from the DEEDL algorithm was evaluated using ReconCT v14.1 and Syngo.via. Mean attenuation values in lesions on mixed images (HU) were compared calculating the root-mean-squared-error (RMSE) between the datasets using MATLAB R2019a.Results
The DEEDL algorithm performed well reproducing the image data of the kidney lesions (Bosniak 1 and 2: 125, Bosniak 2F: 6, Bosniak 3: 1 and Bosniak 4/(partially) solid: 32) with RSME values of 10.59 HU, 15.7 HU for attenuation, virtual non-contrast, respectively. The measurements performed in dataset 1 and 2 showed strong correlation with linear regression (r2: attenuation = 0.89, VNC = 0.63, iodine = 0.75), lesions were classified as enhancing with an accuracy of 0.91.Conclusion
This DEEDL algorithm can be used to reconstruct a full dual-energy FoV from restricted data, enabling reliable HU value measurements in areas not covered by the smaller FoV and evaluation of renal lesions.Item Open Access Evaluation of the impact of a novel denoising algorithm on image quality in dual-energy abdominal CT of obese patients.(European radiology, 2023-04) Schwartz, Fides R; Clark, Darin P; Rigiroli, Francesca; Kalisz, Kevin; Wildman-Tobriner, Benjamin; Thomas, Sarah; Wilson, Joshua; Badea, Cristian T; Marin, DanieleObjectives
Evaluate a novel algorithm for noise reduction in obese patients using dual-source dual-energy (DE) CT imaging.Methods
Seventy-nine patients with contrast-enhanced abdominal imaging (54 women; age: 58 ± 14 years; BMI: 39 ± 5 kg/m2, range: 35-62 kg/m2) from seven DECT (SOMATOM Flash or Force) were retrospectively included (01/2019-12/2020). Image domain data were reconstructed with the standard clinical algorithm (ADMIRE/SAFIRE 2), and denoised with a comparison (ME-NLM) and a test algorithm (rank-sparse kernel regression). Contrast-to-noise ratio (CNR) was calculated. Four blinded readers evaluated the same original and denoised images (0 (worst)-100 (best)) in randomized order for perceived image noise, quality, and their comfort making a diagnosis from a table of 80 options. Comparisons between algorithms were performed using paired t-tests and mixed-effects linear modeling.Results
Average CNR was 5.0 ± 1.9 (original), 31.1 ± 10.3 (comparison; p < 0.001), and 8.9 ± 2.9 (test; p < 0.001). Readers were in good to moderate agreement over perceived image noise (ICC: 0.83), image quality (ICC: 0.71), and diagnostic comfort (ICC: 0.6). Diagnostic accuracy was low across algorithms (accuracy: 66, 63, and 67% (original, comparison, test)). The noise received a mean score of 54, 84, and 66 (p < 0.05); image quality 59, 61, and 65; and the diagnostic comfort 63, 68, and 68, respectively. Quality and comfort scores were not statistically significantly different between algorithms.Conclusions
The test algorithm produces quantitatively higher image quality than current standard and existing denoising algorithms in obese patients imaged with DECT and readers show a preference for it.Clinical relevance statement
Accurate diagnosis on CT imaging of obese patients is challenging and denoising algorithms can increase the diagnostic comfort and quantitative image quality. This could lead to better clinical reads.Key points
• Improving image quality in DECT imaging of obese patients is important for accurate and confident clinical reads, which may be aided by novel denoising algorithms using image domain data. • Accurate diagnosis on CT imaging of obese patients is especially challenging and denoising algorithms can increase quantitative and qualitative image quality. • Image domain algorithms can generalize well and can be implemented at other institutions.Item Open Access Image Quality of Photon Counting and Energy Integrating Chest CT – Prospective Head-to-Head Comparison on Same Patients(European Journal of Radiology, 2023-07) Schwartz, Fides R; Ria, Francesco; McCabe, Cindy; Zarei, Mojtaba; Rajagopal, Jayasai; Molvin, Lior; Marin, Daniele; O'Sullivan-Murphy, Bryan; Kalisz, Kevin R; Tailor, Tina D; Washington, Lacey; Henry, Travis; Samei, EhsanItem Open Access Myelography Using Energy-Integrating Detector CT Versus Photon-Counting Detector CT for Detection of CSF-Venous Fistulas in Patients With Spontaneous Intracranial Hypotension.(AJR. American journal of roentgenology, 2024-01) Schwartz, Fides R; Kranz, Peter G; Malinzak, Michael D; Cox, David N; Ria, Francesco; McCabe, Cindy; Harrawood, Brian; Leithe, Linda G; Samei, Ehsan; Amrhein, Timothy JBackground: CSF-venous fistulas (CVFs) are an increasingly recognized cause of spontaneous intracranial hypotension (SIH) that are often diminutive in size and exceedingly difficult to detect by conventional imaging. Objective: This study's objective was to compare EID-CT myelography and PCD-CT myelography in terms of image quality and diagnostic performance for detecting CVFs in patients with SIH. Methods: This retrospective study included 38 patients (15 men, 23 women; mean age, 55±10 years) with SIH who underwent both clinically indicated EID-CT myelography (slice thickness, 0.625 mm) and PCD-CT myelography (slice thickness, 0.2 mm; performed in ultrahigh-resolution mode) to assess for CSF leak. Three blinded radiologists reviewed examinations in random order, assessing image noise, discernibility of spinal nerve root sleeves, and overall image quality using 0-100 scales (100=highest quality), and recording locations of CVFs. Definite CVFs were defined as CVFs described in CT myelography reports using unequivocal language and showing attenuation >70 HU. Results: For all readers, PCD-CT myelography, in comparison with EID-CT myelography, showed higher image noise (reader 1: 69±19 vs 38±15; reader 2: 59±9 vs 49±13; reader 3: 57±13 vs 43±15), higher nerve root sleeve discernibility (reader 1: 84±19 vs 30±14; reader 2: 84±19 vs 70±19; reader 3: 60±13 vs 52±12), and higher overall image quality (reader 1: 84±21 vs 40±15; reader 2: 81±10 vs 72±20; reader 3: 58±11 vs 53±11) (all p<.05). Eleven patients had a definite CVF. Sensitivity and specificity for detection of definite CVF for EID-CT myelography and PCD-CT myelography for reader 1 were 45% and 96% versus 64% and 85; for reader 2 were 36% and 100% versus 55% and 96%; and for reader 3 were 45% and 100% versus 55% and 93%. For all readers, PCD-CT myelography, in comparison with EID-CT myelography, showed significantly higher sensitivity (all p<.05), without significant difference in specificity (all p>.05). Conclusion: In comparison with EID-CT myelography, PCD-CT myelography yielded significantly improved image quality with significantly higher sensitivity for CVFs without significant loss of specificity. Clinical Impact: The findings support a potential role of PCD-CT myelography in facilitating earlier diagnosis and targeted treatment of SIH, avoiding high morbidity during potentially prolonged diagnostic workups.Item Open Access Technology Characterization Through Diverse Evaluation Methodologies: Application to Thoracic Imaging in Photon-Counting Computed Tomography.(J Comput Assist Tomogr, 2024-04-15) Rajagopal, Jayasai R; Schwartz, Fides R; McCabe, Cindy; Farhadi, Faraz; Zarei, Mojtaba; Ria, Francesco; Abadi, Ehsan; Segars, Paul; Ramirez-Giraldo, Juan Carlos; Jones, Elizabeth C; Henry, Travis; Marin, Daniele; Samei, EhsanOBJECTIVE: Different methods can be used to condition imaging systems for clinical use. The purpose of this study was to assess how these methods complement one another in evaluating a system for clinical integration of an emerging technology, photon-counting computed tomography (PCCT), for thoracic imaging. METHODS: Four methods were used to assess a clinical PCCT system (NAEOTOM Alpha; Siemens Healthineers, Forchheim, Germany) across 3 reconstruction kernels (Br40f, Br48f, and Br56f). First, a phantom evaluation was performed using a computed tomography quality control phantom to characterize noise magnitude, spatial resolution, and detectability. Second, clinical images acquired using conventional and PCCT systems were used for a multi-institutional reader study where readers from 2 institutions were asked to rank their preference of images. Third, the clinical images were assessed in terms of in vivo image quality characterization of global noise index and detectability. Fourth, a virtual imaging trial was conducted using a validated simulation platform (DukeSim) that models PCCT and a virtual patient model (XCAT) with embedded lung lesions imaged under differing conditions of respiratory phase and positional displacement. Using known ground truth of the patient model, images were evaluated for quantitative biomarkers of lung intensity histograms and lesion morphology metrics. RESULTS: For the physical phantom study, the Br56f kernel was shown to have the highest resolution despite having the highest noise and lowest detectability. Readers across both institutions preferred the Br56f kernel (71% first rank) with a high interclass correlation (0.990). In vivo assessments found superior detectability for PCCT compared with conventional computed tomography but higher noise and reduced detectability with increased kernel sharpness. For the virtual imaging trial, Br40f was shown to have the best performance for histogram measures, whereas Br56f was shown to have the most precise and accurate morphology metrics. CONCLUSION: The 4 evaluation methods each have their strengths and limitations and bring complementary insight to the evaluation of PCCT. Although no method offers a complete answer, concordant findings between methods offer affirmatory confidence in a decision, whereas discordant ones offer insight for added perspective. Aggregating our findings, we concluded the Br56f kernel best for high-resolution tasks and Br40f for contrast-dependent tasks.