Browsing by Author "Marin, Daniele"
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Item Open Access A mathematical framework to quantitatively balance clinical and radiation risk in Computed Tomography(2021-12-01) Ria, Francesco; Zhang, Anru; Lerebours, Reginald; Erkanli, Alaattin; Solomon, justin; Marin, Daniele; Samei, EhsanPurpose: Risk in medical imaging is a combination of radiation risk and clinical risk, which is largely driven by the effective diagnosis. While radiation risk has traditionally been the main focus of Computed Tomography (CT) optimization, such a goal cannot be achieved without considering clinical risk. The purpose of this study was to develop a comprehensive mathematical framework that considers both radiation and clinical risks based on the specific task, the investigated disease, and the interpretive performance (i.e., false positive and false negative rates), tested across a representative clinical CT population. Methods and Materials: The proposed mathematical framework defined the radiation risk to be a linear function of the radiation dose, the population prevalence of the disease, and the false positive rate. The clinical risk was defined to be a function of the population prevalence, the expected life-expectancy loss for an incorrect diagnosis, and the interpretative performance in terms of the AUC as a function of radiation dose. A Total Risk (TR) was defined as the sum of the radiation risk and the clinical risk. With IRB approval, the mathematical function was applied to a dataset of 80 adult CT studies investigating localized stage liver cancer (LLC) for a specific false positive rate of 5% reconstructed with both Filtered Back Projection (FBP) and Iterative Reconstruction (IR) algorithm. Linear mixed effects models were evaluated to determine the relationship between radiation dose and radiation risk and interpretative performance, respectively. Lastly, the analytical minimum of the TR curve was determined and reported. Results: TR is largely affected by clinical risk for low radiation dose whereas radiation risk is dominant at high radiation dose. Concerning the application to the LLC population, the median minimum risk in terms of mortality per 100 patients was 0.04 in FBP and 0.03 in IR images; the corresponding CTDIvol values were 38.5 mGy and 25.7 mGy, respectively. Conclusions: The proposed mathematical framework offers a complete quantitative description of risk in CT enabling a comprehensive risk-to-benefit assessment essential in the effective justification of radiological procedures and in the design of optimal clinical protocols. Clinical Relevance/Application: The quantification of both radiation and clinical risk using comparable units allows the calculation of the overall risk paving the road towards a comprehensive risk-to-benefit assessment in CT.Item Open Access A patient-informed approach to predict iodinated-contrast media enhancement in the liver(European Journal of Radiology, 2022-10) Setiawan, Hananiel; Chen, Chaofan; Abadi, Ehsan; Fu, Wanyi; Marin, Daniele; Ria, Francesco; Samei, EhsanItem Open Access Clinical and radiation risk across one million patients in Computed Tomography: influence of age, size, and race(2023-11-26) Ria, Francesco; Lerebours, Reginald; Zhang, Anru; Erkanli, Alaattin; Abadi, Ehsan; SOLOMON, justin; Marin, Daniele; Samei, EhsanPurpose. We recently developed a mathematical model to balance radiation risk and clinical risk, namely the risk of misdiagnosis due to insufficient image quality. In this work, we applied this model to a population of one million CT imaging cases to evaluate the risk stratification with different ages, sexes, and races. Materials and Methods. The demographics were informed by literature and census information simulating a clinical liver cancer population. The Total Risk (TR) was calculated as the linear combination of radiation risk and clinical risk. The model included factors for the radiation burden for different age and sex; the prevalence of the disease; the false positive rate; the expected life-expectancy loss for an incorrect diagnosis for different ages, sex, and race; and a typical false positive rate of 5%. It was assumed that each case received an average radiologist interpretative performance of 0.75 AUC for a hypothetical lesion without any changes in radiation dose beyond routine practice. We further, for each patient, simulated 2,000 imaging conditions with CTDIvol varying from 0.1 and 200 mGy with 0.1 mGy increments. Per each CTDIvol value, the anticipated AUC was calculated by applying the established asymptotic relationships between CTDIvol and image quality. The AUC distribution was then used to calculate the theoretical minimum total risk (TRmin) per each patient. Results. For the routine practice, the median theoretical total risk was estimated to be 0.058 deaths per 100 patients (range: 0.002 – 0.154) comprising of the median radiation risk of 0.009 (range: 0.001 – 0.069), and of the median clinical risk of 0.049 (range: 7.0x10-5 – 0.094). Considering the varying scanner output conditions, the median TRmin was 0.054 deaths per 100 patients for White male patients, 0.054 for Blacks, 0.057 for Hispanics, and 0.065 for Asians. For female patients, the median TRmin values were 0.049, 0.056, 0.054, and 0.061 deaths per 100 patients, respectively. Conclusion. For each demography condition, the clinical risk was found to largely outweigh the radiation risk by at least 500%. Total risk showed different stratifications with patient age and race. Clinical Relevance Statement. To optimize CT conditions for specific patients and/or population, both radiation risk and clinical risks should be all accounted for together with demographic information. We demonstrated a methodology that allows a complete depiction of total risk in CT, considering radiation and clinical risks at comparable units, and patient demographic.Item Open Access Comparison of image quality of abdominal CT examinations and virtual noncontrast images between photon-counting and energy-integrating detector CT(2023-11-26) Lofino, Ludovica; Schwartz, fides; Ria, francesco; Zarei, Mojtaba; Samei, Ehsan; Abadia, Andres; Marin, DanielePurpose: To compare image quality of portal venous phase (PVP) abdominal CT examinations and virtual non-contrast (VNC) images between photon-counting CT (PCCT) and energy-integratingDetector CT (EID). Methods and Materials: In this HIPAA compliant, IRB-approved, retrospective study, multi-phase CT scans from one commercially available PCCT (NAEOTOM Alpha, Siemens Healthineers) and two EID dual-source dual-energy CT systems (SOMATOM Definition Flash and SOMATOM Force, Siemens Healthineers) were retrieved. A total of 45 BMI-matched patients (21 women, 24 men; mean age58.5 ± 15.3 years, range 19-81 years; mean BMI 29.0 ± 6.8 kg/m2, range 13-47 kg/m2) were included: 15 for PCCT and 15 for each EID system. In vivo image quality parameters (MTFf10, noisemagnitude, Fav, Fpeak, NPSf10) were measured and compared between PCCT and EID for standard PVP and VNC images. A subset analysis was also performed in the overweight patient population(BMI>25 kg/m2). CTDIvol values were recorded for the three scanners. Because scanner tube current modulation adapts to patient size, radiation dose was compared among scanners accounting forBMI using a figure of merit: FOM=1/(BMI*lnCTDIvol). A five-point scale (1=best and 5=worst) was used to assess reader perception of noise, visibility of small structures, and overall image quality. Results: Compared to the two EID systems, PCCT yielded significantly improved resolution and noise magnitude for both PVP (MTFf10 = 0.55 ± 0.08 for PCCT vs. 0.50 ± 0.04 and 0.49 ± 0.03 for Flashand Force, P = 0.02; noise = 9.76 ± 3.10 vs. 15.35 ± 4.14 and 10.70 ± 1.34, P = 0.02) and VNC images (MTFf10 = 0.56 ± 0.01 for PCCT vs. 0.51 ± 0.05 and 0.51 ± 0.03 for Flash and Force, P = 0.02; noise =9.59 ± 2.77 vs. 13.90 ± 3.57 and 10.83 ± 2.83, P = 0.02). A similar statistically significant trend was confirmed in the smaller subset of overweight patients. Our FOM analysis suggests that, for equal radiation exposure levels and comparable patient size, PCCT yields 20% noise reduction compared to the two EID systems, with 18% reduction in overweight patients. Reader’s perceived image noise was significantly lower for PCCT compared to EID for both PVP (1.85 ± 0.88 vs. 2.60 ± 0.88 and 2.70 ± 0.80) and VNC images (1.95 ± 0.83 vs. 3.0 ± 0.97 and 2.90 ±0.85). Of note, overall image quality improved significantly for PCCT compared to EID (1.35 ± 0.67 vs. 2.60 ± 0.82 and 2.45 ± 0.69 for PVP and 1.50 ± 0.67 vs 2.85 ± 0.81 and 2.55 ± 0.60 for VNC). Conclusions: Compared to conventional EID systems, PCCT yields significantly lower radiation dose along with improved image quality on PVP and VNC images of abdominal CT examinations. Clinical Relevance/Application: PCCT has a lower radiation dose compared to EID CT, with better image quality parameters and lower noise magnitude.Item Open Access Comparison of photon-counting and energy-integrating detector CT systems for the characterization of cystic renal lesions on virtual noncontrast imaging(2023-11-26) Lofino, Ludovica; Schwartz, Fides; Al Tarhuni, Mohammed; Abadia, Andres; Ria, Francesco; Samei, Ehsan; Marin, DanielePurpose: The purpose of this study is to compare the absolute CT attenuation errors of cystic renal lesions and abdominal organs on virtual noncontrast images (VNC) between photon-counting (PCCT) and energy-integrating (EID) detector CT systems. Methods and Materials: In this HIPAA compliant, IRB-approved retrospective study, multiphase CT scans from one commercially available PCCT (NAEOTOM Alpha, Siemens Healthineers) and two EID dual-source dual-energy CT systems (SOMATOM Definition Flash and SOMATOM Force, Siemens Healthineers) were retrieved. A total of 56 BMI-matched patients (26 women, 30 men; mean age 58.5 ± 15.3 years; range 19-81 years, mean BMI 29.0 ± 6.8 kg/m2, range 13-47 kg/m2) were included: 16 for PCCT and 20 each per EID systems. Attenuation measurements of abdominal organs (liver, pancreas, spleen, kidney, and aorta) were recorded on VNC and True Noncontrast (TNC) datasets. Furthermore, attenuation measurements of 16 cystic renal lesions (eight for PCCT and eight for EID) were compared on VNC and TNC datasets. Absolute CT attenuation errors |HUVNC-HUTNC| were calculated and compared between PCCT and EID systems for the entire population and a subset of 20 obese patients (BMI: >30 kg/m2), using paired t-tests. Absolute CT attenuation errors were also compared for all cystic renal lesions and for renal lesions <1 cm, separately. *Results: PCCT yielded significantly lower absolute CT attenuation errors than EID using VNC in comparison with TNC images for the liver (4.3 ± 5.4 vs 8.8 ± 10.4), spleen (2.6 ± 6.2 vs 8.0 ± 10.3) and pancreas (4.4 ± 1.8 vs 7.7 ± 9.7) for all patients (P<0.01) and for spleen and pancreas in the obese patient cohort (P<0.05). Furthermore, PCCT yielded significantly lower absolute CT attenuation errors compared to EID for all cystic renal lesions (2.0 ± 1.3 vs. 12.0 ± 8.9; P<0.01) and for renal lesions <1 cm (1.4 ± 0.9 vs. 19.1 ± 6.8; P<0.01). Conclusions: PCCT yields significantly lower absolute CT attenuation errors for abdominal organs and cystic renal lesions in VNC images, compared to two dual-source dual-energy EID systems. Our results were corroborated in a subset of obese patients and small (<1 cm) renal lesions. Clinical Relevance/Application: Reliable CT attenuation values of virtual non-contrast imaging are necessary to replace true non-contrast acquisitions. This can be achieved with photon-counting CT with important implications in radiation dose reduction.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 Unknown 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 Development and Testing of a Clinical Tool to Predict and Optimize Liver Contrast-Enhanced CT Imaging(https://aapm.onlinelibrary.wiley.com/doi/epdf/10.1002/mp.16525, 2023-07-23) Setiawan, Hananiel; Abadi, Ehsan; Marin, Daniele; Molvin, Lior; Ria, Francesco; Samei, EhsanAchieving consistent and sufficient hepatic parenchyma contrast enhancement (HPCE) level can improve diagnostic performance and reduce enhancement variability; this raises the baseline image quality and optimize injection practices, both carries economic and safety implications. Patient factors, Iodine injection and scanning parameters (e.g. tube potential, scanning delay) affect HPCE in CT imaging. In this study, we developed and prospectively tested a clinical graphical user interface (GUI) tool which predicts enhancement level and suggests alternative injection/scanning parameters based on patient attributes (height, weight, sex, age). Methods: This work was based on our retrospectively-validated neural-network prediction model. We built a GUI to combine our model with an optimization algorithm, which suggests alternative injection/scanning parameters for patients with predicted-insufficient enhancement. The system was clinically-deployed and prospectively-tested in 24 patients considering a 110HU+/-10HU target portal-venous HPCE. For each patient, HPCE was calculated as the average HU-value of three ROIs and compared against the target value. Additionally, we compared the outcome with the patient’s previous similarly-protocoled scan to assess improvement and consistency. Results: The system suggested adjustment for 15 patients with median 8.8% and 9.1% reductions to volume and injection rate, respectively. All scan delays were reduced by an average of 42.6%. Comparison with previous scans shows increased consistency (CV=0.21 v. 0.11,p=0.012) while median enhancement remained relatively unchanged (111.3HU v. 108.7HU). The number of under-enhanced patients was halved, and all previously over-enhanced patients received enhancement reductions. Conclusion: We developed and tested a patient-informed clinical framework which predicts optimal patient’s HPCE; and suggests empiric injection/scanning parameters when predicted enhancement is deemed insufficient. The system improved HPCE consistency and decreased the number of under-enhanced patients as compared to their previous scans. This study demonstrated that the patient-informed clinical framework can predict an optimal patient's HPCE and suggest empiric injection/scanning parameters to achieve consistent and sufficient HPCE levels.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 LRP1B mutations are associated with favorable outcomes to immune checkpoint inhibitors across multiple cancer types.(Journal for immunotherapy of cancer, 2021-03) Brown, Landon C; Tucker, Matthew D; Sedhom, Ramy; Schwartz, Eric B; Zhu, Jason; Kao, Chester; Labriola, Matthew K; Gupta, Rajan T; Marin, Daniele; Wu, Yuan; Gupta, Santosh; Zhang, Tian; Harrison, Michael R; George, Daniel J; Alva, Ajjai; Antonarakis, Emmanuel S; Armstrong, Andrew JBackground
Low-density lipoprotein receptor-related protein 1b (encoded by LRP1B) is a putative tumor suppressor, and preliminary evidence suggests LRP1B-mutated cancers may have improved outcomes with immune checkpoint inhibitors (ICI).Methods
We conducted a multicenter, retrospective pan-cancer analysis of patients with LRP1B alterations treated with ICI at Duke University, Johns Hopkins University (JHU) and University of Michigan (UM). The primary objective was to assess the association between overall response rate (ORR) to ICI and pathogenic or likely pathogenic (P/LP) LRP1B alterations compared with LRP1B variants of unknown significance (VUS). Secondary outcomes were the associations with progression-free survival (PFS) and overall survival (OS) by LRP1B status.Results
We identified 101 patients (44 Duke, 35 JHU, 22 UM) with LRP1B alterations who were treated with ICI. The most common tumor types by alteration (P/LP vs VUS%) were lung (36% vs 49%), prostate (9% vs 7%), sarcoma (5% vs 7%), melanoma (9% vs 0%) and breast cancer (3% vs 7%). The ORR for patients with LRP1B P/LP versus VUS alterations was 54% and 13%, respectively (OR 7.5, 95% CI 2.9 to 22.3, p=0.0009). P/LP LRP1B alterations were associated with longer PFS (HR 0.42, 95% CI 0.26 to 0.68, p=0.0003) and OS (HR 0.62, 95% CI 0.39 to 1.01, p=0.053). These results remained consistent when excluding patients harboring microsatellite instability (MSI) and controlling for tumor mutational burden (TMB).Conclusions
This multicenter study shows significantly better outcomes with ICI therapy in patients harboring P/LP versus VUS LRP1B alterations, independently of TMB/MSI status. Further mechanistic and prospective validation studies are warranted.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 Lawn Mower Versus Left Ventricular Assist Device(JACC: Case Reports, 2020-03) Rao, Vishal; Fudim, Marat; Griffin, Andrew; Rymer, Jennifer; Jones, W Schuyler; Koweek, Lynne; Smith, Tony; Marin, Daniele; DeVore, AdamItem Open Access Optimization of imaging as a risk-versus-risk framework of quantitative balance between clinical and radiation risk: a task-based implementation for liver CT in a large demographic population(2022-11-30) Ria, Francesco; Lerebours, Reginald; Zhang, Anru; Erkanli, Alaattin; Marin, Daniele; Samei, EhsanItem 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.Item Open Access Total risk index: a mathematical model for decision making based on clinical and radiation risk assessment in CT(2019-12-04) Ria, Francesco; Smith, Taylor; Hoye, Jocelyn; Marin, Daniele; Samei, EhsanPurpose. Radiological risk is a combination of radiation and clinical risk (likelihood of not delivering a proper diagnosis), which together may be characterized as a total risk index (TRI). While many strategies have been developed to ascertain radiation risk, there has been a paucity of studies assessing the clinical risk. This knowledge gap makes impossible to determine the total radiological procedure risk and, thus, to perform a comprehensive optimization. The purpose of this study was to develop a mathematical model to ascertain TRI and to identify the minimum TRI (mTRI) in a clinical CT population. Materials and Methods. This IRB approved study included 21 adults abdomen exams performed on a dual-source single energy CT at two different dose levels (84 CT series). Virtual liver lesions were inserted into projection data to simulate localized stage liver cancer (LSLC). The detectability index (d') was calculated in each series and converted to percentage of correct observer answers (AUC) in a two-alternative forced-choice model. The AUC was converted into the loss of 5-year relative survival rate (SEER, NCI), considering an upper bound on patient's risk for a misdiagnosis of LSLC (false positive + false negative). Concerning radiation risk, organ doses were estimated using a Monte Carlo method and the Risk Index was calculated and converted in 5-year relative survival rate for cancer. Finally, the two risks were weighted equally into a combined TRI curve per each patient as a function of CTDIvol. The analytical minimum of each TRI curve provided the patient mTRI. Results. The mTRI for LSLC patients that underwent an abdominal CT exhibited a rapid rise at low radiation dose due to enhanced clinical risk of under-dosed examinations. Increasing dose offered less risk with mortality per 100 patients between 2.1 and 6.5 (mean 4.5) at CTDIvol=5mGy, between 1.1 and 5.9 (mean 3.5) at CTDIvol=10mGy and between 0.5 and 5.4 (mean 3.0) at CTDIvol=20 mGy. Conclusion. The clinical risk seems to play a more dominant factor in designing optimum CT protocols. The TRI may provide an objective and quantifiable metric of the interplay of radiation and clinical risks during the optimization of the CT technique for individual patients. Clinical Relevance statement. CT risk-based optimization can be made possible by first quantifying both radiation and clinical risk using comparable units, then calculating an overall risk, and finally minimizing the total risk.Item Open Access Validation of algorithmic CT image quality metrics with preferences of radiologists(MEDICAL PHYSICS, 2019-11-01) Cheng, Yuan; Abadi, Ehsan; Smith, Taylor Brunton; Ria, Francesco; Meyer, Mathias; Marin, Daniele; Samei, EhsanItem Open Access Validation of Algorithmic CT Image Quality Metrics with Preferences of Radiologists.(Medical physics, 2019-08-29) Cheng, Yuan; Abadi, Ehsan; Smith, Taylor Brunton; Ria, Francesco; Meyer, Mathias; Marin, Daniele; Samei, EhsanPURPOSE:Automated assessment of perceptual image quality on clinical Computed Tomography (CT) data by computer algorithms has the potential to greatly facilitate data-driven monitoring and optimization of CT image acquisition protocols. The application of these techniques in clinical operation requires the knowledge of how the output of the computer algorithms corresponds to clinical expectations. This study addressed the need to validate algorithmic image quality measurements on clinical CT images with preferences of radiologists and determine the clinically acceptable range of algorithmic measurements for abdominal CT examinations. MATERIALS AND METHODS:Algorithmic measurements of image quality metrics (organ HU, noise magnitude, and clarity) were performed on a clinical CT image dataset with supplemental measures of noise power spectrum from phantom images using techniques developed previously. The algorithmic measurements were compared to clinical expectations of image quality in an observer study with seven radiologists. Sets of CT liver images were selected from the dataset where images in the same set varied in terms of one metric at a time. These sets of images were shown via a web interface to one observer at a time. First, the observer rank ordered the CT images in a set according to his/her preference for the varying metric. The observer then selected his/her preferred acceptable range of the metric within the ranked images. The agreement between algorithmic and observer rankings of image quality were investigated and the clinically acceptable image quality in terms of algorithmic measurements were determined. RESULTS:The overall rank order agreements between algorithmic and observer assessments were 0.90, 0.98, and 1.00 for noise magnitude, liver parenchyma HU, and clarity, respectively. The results indicate a strong agreement between the algorithmic and observer assessments of image quality. Clinically acceptable thresholds (median) of algorithmic metric values were (17.8, 32.6) HU for noise magnitude, (92.1, 131.9) for liver parenchyma HU, and (0.47, 0.52) for clarity. CONCLUSIONS:The observer study results indicated that these algorithms can robustly assess the perceptual quality of clinical CT images in an automated fashion. Clinically acceptable ranges of algorithmic measurements were determined. The correspondence of these image quality assessment algorithms to clinical expectations paves the way towards establishing diagnostic reference levels in terms of clinically acceptable perceptual image quality and data-driven optimization of CT image acquisition protocols.