Richard L. Baron

The University of Chicago Medical Center, Chicago, Illinois, United States

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Publications (127)480.71 Total impact

  • Richard L. Baron
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    ABSTRACT: LEARNING OBJECTIVES 1) Provide an overview of the AASLD/EASL imaging criteria for noninvasive diagnosis of hepatocellular carcinoma. 2) Provide best practice CT/MR/US Imaging techniques that optimize characterization, detection and staging of primary and metastatic liver tumors. 3) Understand the key role specific findings reported by radiologists have in determining patient treatment options for hepatocellular carcinoma. ABSTRACT
    No preview · Conference Paper · Nov 2012
  • Richard L. Baron
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    ABSTRACT: LEARNING OBJECTIVES View learning objectives under main course title.
    No preview · Conference Paper · Nov 2012
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    Masatoshi Hori · Kenji Suzuki · Mark L Epstein · Richard L Baron
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    ABSTRACT: The purpose of this study was to evaluate the relationship between the slice thickness and the calculated volume in computed tomography (CT) liver volumetry through the comparison of the results from images [including 3-dimensional (3D) images] with various slice thicknesses. Twenty potential adult liver donors (12 men and 8 women) with a mean age of 39 years (range = 24-64 years) underwent CT with a 64-section multidetector row CT scanner after the intravenous injection of a contrast material. Four image sets with slice thicknesses of 0.625, 2.5, 5, and 10 mm were used. First, a program developed in our laboratory for automated liver extraction was applied to the CT images, and the liver boundaries were determined automatically. Then, an abdominal radiologist reviewed all images onto which automatically extracted boundaries had been superimposed and then edited the boundaries on each slice to enhance the accuracy. The liver volumes were determined via the counting of the voxels within the liver boundaries. The mean whole liver volumes estimated with CT were 1322.5 cm(3) from 0.625-mm images, 1313.3 cm(3) from 2.5-mm images, 1310.3 cm(3) from 5-mm images, and 1268.2 cm(3) from 10-mm images. The volumes calculated from 3D (0.625-mm) images were significantly larger than the volumes calculated from thicker images (P < 0.001). The partial liver volumes of right lobes, left lobes, and lateral segments were evaluated in a similar manner. The estimated maximum difference in the calculated volumes of lateral segments was -10.9 cm(3) (-4.63%) between 0.625- and 5-mm images. In conclusion, liver volumes calculated from 2.5-mm-thick or thicker images are significantly smaller than liver volumes calculated from 3D images. If a maximum error of 5% in the calculated graft volume will not have a significant clinical impact, 5-mm-thick images are acceptable for CT volumetry. If the impact is significant, 3D images could be essential.
    Full-text · Article · Dec 2011 · Liver Transplantation
  • Richard L. Baron · Arl Van Moore · Norman Joseph Beauchamp
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    ABSTRACT: LEARNING OBJECTIVES 1) Identify the skills necessary to build and manage an effective radiology organizational structure. 2) Recognize the importanc eof the overall organization mission, and employ means to incorporate mission into the radiology organizational structure. 3) Develop strategies to communicate effectively with other leaders, team members, and external stakeholders. (This course is part of the Leadership Track) ABSTRACT Developing or continuing success within an organization is very dependent on the efforts and skills of leadership at all levels. Leadership is critical for all aspects of operational activities, including mission and goal setting, operational activities, outcome assessments, and communications within and external to the organization. The training to obtain these skill sets is inadequate in medical school and radiology residency, and most leaders, particularly at early levels of responsibility, develop these traits through either observing others or through trial and error experiences. This course will present an overview of the 'traits and states' that one needs to be aware in managing organizations, followed by specific key points to avoid failure in 1) university radiology practices and 2) community radiology practices. The emphasis will be on helpful practical tips to avoid states or traits that frequently are associated with bad outcomes for an organization and/or the involved leaders. ACTIVE HANDOUT http://media.rsna.org/media/abstract/2011/11001890/yams497_11001890_Baron_RC432.pdf
    No preview · Conference Paper · Nov 2011
  • Conference Paper: Conclusive Comments
    Richard L. Baron

    No preview · Conference Paper · Nov 2011
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    ABSTRACT: The purpose of this study was to evaluate automated CT volumetry in the assessment of living-donor livers for transplant and to compare this technique with software-aided interactive volumetry and manual volumetry. Hepatic CT scans of 18 consecutively registered prospective liver donors were obtained under a liver transplant protocol. Automated liver volumetry was developed on the basis of 3D active-contour segmentation. To establish reference standard liver volumes, a radiologist manually traced the contour of the liver on each CT slice. We compared the results obtained with automated and interactive volumetry with those obtained with the reference standard for this study, manual volumetry. The average interactive liver volume was 1553 ± 343 cm(3), and the average automated liver volume was 1520 ± 378 cm(3). The average manual volume was 1486 ± 343 cm(3). Both interactive and automated volumetric results had excellent agreement with manual volumetric results (intraclass correlation coefficients, 0.96 and 0.94). The average user time for automated volumetry was 0.57 ± 0.06 min/case, whereas those for interactive and manual volumetry were 27.3 ± 4.6 and 39.4 ± 5.5 min/case, the difference being statistically significant (p < 0.05). Both interactive and automated volumetry are accurate for measuring liver volume with CT, but automated volumetry is substantially more efficient.
    Full-text · Article · Oct 2011 · American Journal of Roentgenology
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    ABSTRACT: Intraductal papillary neoplasm of the bile duct (IPN-B) is known as a premalignant lesion of invasive cholangiocarcinoma. The purpose of this study was for radiologic-pathologic correlation of morphologic features of IPN-B and to correlate the subclassifications with biological behavior in regard to the bile duct wall invasion. A pathologist classified gross morphology of 75 cases (44 men and 31 women, age range, 39-85) of histopathologically proven IPN-B into polypoid, cast-like, superficial-spreading, and cyst-forming type. Preoperative images were retrospectively reviewed by two observers independently and classified the gross appearance of intraductal tumors into the four types. The pathologist classified macroscopic appearances of 75 cases of IPN-B into polypoid type in 26, cast-like intraductal growth in 17, superficial-spreading growth in 21, and cyst-forming type in 11. Two observers classified image findings in accordance with pathologist's classification in 58 and 57 (77% and 76%) among the 75 cases of IPN-B, respectively; 18 and 19 of 26 cases of polypoid type, 14 and 14 of 17 cases of cast-like growth type, 16 and 19 of 21 cases of superficial-spreading type, 10 and 5 of 11 cases of cyst-forming type, respectively. Interobserver agreement for subclassification of tumor morphology was in the category of good agreement (k = 0.651). There was no correlation between morphological subclassification and tendency to invasive cholangiocarcinoma. IPN-Bs can be classified morphologically into polypoid, cast-like growth, superficial-spreading, and cystic type, but there is no correlation between the types and tendency to invasive cholangiocarcinoma.
    No preview · Article · Aug 2011 · Abdominal Imaging
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    ABSTRACT: Malignant liver tumors such as hepatocellular carcinoma (HCC) account for 1.25 million deaths each year worldwide. Early detection of HCC is sometimes difficult on CT images because the attenuation of HCC is often similar to that of normal liver parenchyma. Our purpose was to develop computer-aided detection (CADe) of HCC using both arterial phase (AP) and portal-venous phase (PVP) of contrast-enhanced CT images. Our scheme consisted of liver segmentation, tumor candidate detection, feature extraction and selection, and classification of the candidates as HCC or non-lesions. We used a 3D geodesic-active-contour model coupled with a level-set algorithm to segment the liver. Both hyper- and hypo-dense tumors were enhanced by a sigmoid filter. A gradient-magnitude filter followed by a watershed algorithm was applied to the tumor-enhanced images for segmenting closed-contour regions as HCC candidates. Seventy-five morphologic and texture features were extracted from the segmented candidate regions in both AP and PVP images. To select most discriminant features for classification, we developed a sequential forward floating feature selection method directly coupled with a support vector machine (SVM) classifier. The initial CADe before the classification achieved a 100% (23/23) sensitivity with 33.7 (775/23) false positives (FPs) per patient. The SVM with four selected features removed 96.5% (748/775) of the FPs without any removal of the HCCs in a leave-one-lesion-out cross-validation test; thus, a 100% sensitivity with 1.2 FPs per patient was achieved, whereas CADe using AP alone produced 6.4 (147/23) FPs per patient at the same sensitivity level.
    No preview · Article · Mar 2011 · Proceedings of SPIE - The International Society for Optical Engineering
  • Masatoshi Hori · Kenji Suzuki · Mark L. Epstein · Richard L. Baron
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    ABSTRACT: PURPOSE Although the use of thinner CT slices may improve the accuracy of CT liver volumetry because of reduced partial volume effects, the increased number of images produces a larger workload. No studies have shown systematically a quantitative estimate for the effects of slice thickness on the accuracy of volume calculations and it is unclear how thin CT images should be for optimal use in clinical routines. In this study, we evaluated the relationship between slice thickness and calculated liver volume on CT liver volumetry for living-related liver transplantation. METHOD AND MATERIALS Twenty adult potential liver donors (12 men, 8 women; mean age, 39 years; range, 24-64 years) underwent CT with a 64-channel multidetector-row CT scanner after intravenous injection of 150 mL of contrast material (300 mgI/mL). Four image sets with slice thicknesses of 0.625 mm (isotropic), 2.5 mm, 5 mm, and 10 mm were used. First, we applied our semi-automated liver extraction software to the four sets of portal venous-phase CT images with different slice thicknesses to obtain the initial liver boundaries. An abdominal radiologist reviewed and edited the initial boundaries on all images to optimize accuracy. Finally, liver volumes were determined by counting of the voxels within the liver boundary. Calculated volumes were compared among the four slice thicknesses. RESULTS The mean liver volumes estimated with CT liver volumetry were 1322.5 ± 259.5 cm3 on 0.625-mm, 1313.3 ± 257.8 cm3 on 2.5-mm, 1310.3 ± 260.0 cm3 on 5-mm, and 1268.2 ± 256.8 cm3 on 10-mm images. The volumes calculated for 0.625-mm images were significantly larger than those for thicker images (Dunnett pairwise multiple comparisons t test, P<.0001 for all comparison pairs). CONCLUSION Liver volumes calculated on 2.5-mm or thicker images were significantly smaller than volumes calculated on 0.625-mm-thick 3D isotropic images. However, if a maximum error of 2% in the calculated liver volume is permitted, 5-mm-thick images are acceptable for CT liver volumetry, and 3D isotropic images are not required. CLINICAL RELEVANCE/APPLICATION The accuracy of CT liver volumetry can be improved by using thinner slices. However, if a maximum error of 2% is permitted, 5-mm-thick images are acceptable, and 3D isotropic images are not required.
    No preview · Conference Paper · Dec 2010
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    ABSTRACT: PURPOSE Intraductal papillary neoplasms of the bile duct (IPN-B) is known as a premalignant lesion of invasive cholangiocarcinoma. The purpose of this study was for radiologic-pathologic correlation of morphologic features of IPN-B and to correlate the subclassifications with biological behavior in regard to the bile duct wall invasion. METHOD AND MATERIALS A pathologist classified gross morphology of 75 cases of histopathologically proven IPN-B into polypoid, castlike, superficial-spreading and cyst-forming type. Preoperative images were retrospectively reviewed by two observers independently and classified the gross appearance of intraductal tumors into the four types. In addition, the correlation of the morphological subclassifications and histological phenotypes as well as transformation of IPN-B to invasive cholangiocarcinoma was assessed. RESULTS The pathologist classified macroscopic appearances of 75 cases of IPN-B into polypoid type in 26, castlike intraductal growth in 17, superficial-spreading growth in 21 and cyst-forming type in 11. Observer 1 classified image findings in accordance with the pathologist’s classification in 58 (77%) among the 75 cases; 18 of 26 cases of polypoid type, 14 of 17 cases of castlike growth type, 16 of 21 cases of superficial-spreading type, and 10 of 11 cases of cyst-forming type. Observer 2 classified image findings in accordance with the pathologist’s classification in 57 (76%) among the 75 cases; 19 of 26 cases of polypoid type, 14 of 17 cases of castlike growth type, 19 of 21 cases of superficial-spreading type and five of 11 cases of cyst-forming type. Interobserver agreement for subclassification of tumor morphology was in the category of good agreement (k = 0.651). CONCLUSION IPN-B can be subclassified by imaging into four distinct morphological types and there was interobserver agreement. However, there was no correlation between morphological subclassification and histopathological phenotypes of IPN-Bs nor any tendency to metamorphose into invasive cholangiocarcinoma. CLINICAL RELEVANCE/APPLICATION Recognition of four distinct morphologic subtypes of IPN-B is important in the diagnosis and treatment of bile duct tumor before malignant transformation.
    No preview · Conference Paper · Dec 2010
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    ABSTRACT: PURPOSE Differentiation between benign and malignant liver tumors at CT can be difficult with overlapping imaging features, leading to required biopsy. Our purpose was to develop a CADf scheme to improve the diagnostic accuracy and reduce “unnecessary” biopsies. METHOD AND MATERIALS Our database consisted of 39 tumors, including 15 hepatocellular carcinomas (HCCs) in 11 patients and 24 hemangiomas in 12 patients, selected to cover a wide spectrum of tumor patterns. All lesions were confirmed either pathologically or with follow-up imaging studies over 2 years. Lesion sizes ranged from 8-69 (mean: 23) mm. Scans were acquired with a multi-detector (16 or 64 rows) CT system with 2-5 mm reconstruction slice thickness. We developed CADf for determining the likelihood of malignancy. First, a geodesic active contour model with level-set algorithms segmented the liver accurately. Then, nonlinear gray-scale conversion enhanced both hyper- and hypo-dense tumors in the segmented liver. A gradient magnitude filter followed by a thinning operator determined the precise locations of the tumor boundaries. A watershed algorithm segmented tumors by using the boundary locations for feature analysis. Based on eight morphologic and texture features selected with stepwise feature selection, artificial neural network regression (ANNR) capable of operating on continuous values was trained to distinguish malignant from benign tumors. We transformed the ANNR’s output values to the likelihood of malignancy with a maximum-likelihood estimated binormal model. The performance of our ANNR was compared with linear discriminant analysis (LDA) as reference. RESULTS Our ANNR-based CADf achieved an area under the receiver operating characteristic curve of 0.96 in differentiation between HCCs and hemangiomas in a leave-one-lesion-out cross-validation test, whereas LDA achieved 0.79. The difference was statistically significant (P=.03). Our scheme provided an accurate likelihood of malignancy for “difficult” cases, e.g., a low value (5%) for an HCC-looking hemangioma. At a specific operating point, our scheme correctly characterized 100% (15/15) of HCCs and 92% (22/24) of hemangiomas. CONCLUSION Our CADf was able accurately to differentiate HCC from hemangioma at CT and provided an accurate likelihood of malignancy. CLINICAL RELEVANCE/APPLICATION CADf could be useful for differentiating malignant from benign liver tumors in CT, potentially reducing unnecessary biopsies.
    No preview · Conference Paper · Nov 2010
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    ABSTRACT: PURPOSE/AIM President Obama recently signed into law the Patient Protection and Affordable Care Act, a reform designed to improve the availability, quality and cost effectiveness of health care in America. This exhibit aims to provide an overview of the Act ,emphasizing the provisions with greatest real and potential impact on radiology practice. CONTENT ORGANIZATION The need for reform Key provisions of the Patient Protection and Affordable Care Act Medicare/Medicaid restructuring Quality improvement Financing Present Impact of the Act on medical imaging Medicare equipment utilization rate Contiguous body parts discount Center for Medicare and Medicaid Innovation and Appropriateness Criteria Self referral disclosure Potential impact of the Act Demand Reimbursement Appropriateness Criteria Radiology practice of the future SUMMARY The Act comprises complex reforms that will change the delivery of medical imaging services. This exhibit summarizes key provisions impacting diagnostic imaging essential for planning future imaging services.
    No preview · Conference Paper · Nov 2010
  • Aytekin Oto · Kirti Kulkarni · Robert Nishikawa · Richard L Baron
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    ABSTRACT: The purpose of this article is to determine whether enhancement of nodular foci within hemangiomas is homogeneous and matches blood vessels at different phases on contrast-enhanced MDCT. Multiphase (unenhanced, arterial, portal venous, and delayed phases) MDCT images of 58 hemangiomas were reviewed by two radiologists. Nodular-enhancing foci within hemangiomas were evaluated for enhancement pattern and were subjectively compared with enhancement of the aorta, inferior vena cava, hepatic vein, and portal vein for each contrast-enhanced phase. Both readers measured CT attenuation of enhancing nodules and vessels at each phase, and enhancement of nodules and vessels was compared. Qualitative analysis showed heterogeneously enhancing nodules in 79.3% and 65.5% of hemangiomas in the arterial phase and in 74.1% and 53.4% of hemangiomas in the portal venous phase, according to readers 1 and 2, respectively. In the arterial phase, 3.8% and 12.3% of nodules showed enhancement similar to that in the aorta. In the portal venous phase, 15.4% and 21.7%, 16.8% and 18.2%, 14.1% and 23.8%, and 19.5% and 25.9% of nodules were scored with enhancement similar to that in the aorta, inferior vena cava, hepatic vein, and portal vein by readers 1 and 2, respectively. Differences between attenuation of nodules and all vessels in the arterial, portal venous, and delayed phases were statistically significant. Statistically significant differences were also noted between attenuation among blood vessels in the arterial and portal venous phases but not in the delayed phase. Attenuation of enhancing foci within hemangiomas does not match vessel density qualitatively or quantitatively. No common blood pool density exists in the arterial or portal venous phase. Although persistent enhancement without washout is a useful CT criterion, specific criteria to match the blood pool cannot be used to confirm a diagnosis of hemangioma.
    No preview · Article · Aug 2010 · American Journal of Roentgenology
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    ABSTRACT: Automatic liver segmentation on CT images is challenging because the liver often abuts other organs of a similar density. Our purpose was to develop an accurate automated liver segmentation scheme for measuring liver volumes. We developed an automated volumetry scheme for the liver in CT based on a 5 step schema. First, an anisotropic smoothing filter was applied to portal-venous phase CT images to remove noise while preserving the liver structure, followed by an edge enhancer to enhance the liver boundary. By using the boundary-enhanced image as a speed function, a fastmarching algorithm generated an initial surface that roughly estimated the liver shape. A geodesic-active-contour segmentation algorithm coupled with level-set contour-evolution refined the initial surface so as to more precisely fit the liver boundary. The liver volume was calculated based on the refined liver surface. Hepatic CT scans of eighteen prospective liver donors were obtained under a liver transplant protocol with a multi-detector CT system. Automated liver volumes obtained were compared with those manually traced by a radiologist, used as "gold standard." The mean liver volume obtained with our scheme was 1,520 cc, whereas the mean manual volume was 1,486 cc, with the mean absolute difference of 104 cc (7.0%). CT liver volumetrics based on an automated scheme agreed excellently with "goldstandard" manual volumetrics (intra-class correlation coefficient was 0.95) with no statistically significant difference (p(F<=f)=0.32), and required substantially less completion time. Our automated scheme provides an efficient and accurate way of measuring liver volumes.
    Full-text · Article · Mar 2010 · Proceedings of SPIE - The International Society for Optical Engineering
  • Richard L. Baron
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    ABSTRACT: LEARNING OBJECTIVES 1) Develop CQI process that can be integrated into routine department operations. 2) Identify appropriate CQI projects that can be successfully implemented. 3) Be able to identify and overcome barriers to successful CQI implementation. URL's http://radiology.uchicago.edu/?q=baronlecture
    No preview · Conference Paper · Dec 2009
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    ABSTRACT: PURPOSE Liver cancer is the 3rd leading cause of cancer death worldwide. Early detection of hepatocellular carcinoma (HCC), the most common primary malignant tumor of the liver, can be difficult especially when attenuation difference between HCC and normal parenchyma is small. Our purpose was to develop a computer-aided diagnostic (CADe) scheme for early detection of HCC in contrast-enhanced CT. METHOD AND MATERIALS Our CADe scheme consisted of segmentation of the liver, detection of tumor candidates, feature analysis, and classification of the candidates into HCC or non-HCC lesions. Liver segmentation was performed by using a 3D geodesic-active-contour model coupled with a level-set algorithm. A nonlinear gray-scale conversion filter enhanced both hyper- and hypo-dense tumors in the segmented liver. A gradient magnitude filter was then applied, followed by image thresholding and thinning, in order to obtain precise tumor boundaries. A watershed algorithm was employed for segmenting closed-contoured regions as HCC candidates. Eight morphologic and texture features were extracted from the segmented candidates. We used our linear-output artificial neural network (LOANN) for final classification based on these features. We compared its performance with linear discriminant analysis (LDA). Our database consisted of arterial-phase hepatic CT scans of 28 patients acquired with a multi-detector-row CT system with a 16, 40, or 64 channel detector (Brilliance, Philips Medical Systems, Netherlands). Reconstructed CT slices used for CADe were 512x512 pixels in size with 3-5 mm slice thickness. Among 28 cases, 15 HCCs were found in 10 patients. All HCCs were confirmed pathologically. HCC sizes ranged from 15-43 mm with a mean of 22 mm. RESULTS The initial CADe scheme before the classification step detected 100% (15/15) of HCCs with 12.2 (342/28) false positives (FPs) per patient. Our LOANN removed 36% (124/342) of the FPs without any loss of true positive in a leave-one-out cross-validation test; thus, it yielded 100% (15/15) sensitivity with 7.8 (218/28) FPs per patient, whereas LDA yielded 80% (12/15) sensitivity at the same FP rate. CONCLUSION Our CADe scheme achieved 100% sensitivity for detection of HCCs in contrast-enhanced hepatic CT with a reasonable number of FPs. CLINICAL RELEVANCE/APPLICATION CAD could be useful for detecting HCCs in CT; thus, it would potentially improve radiologists’ sensitivity for HCCs.
    No preview · Conference Paper · Nov 2009
  • Masatoshi Hori · Aytekin Oto · Sarah Orrin · Kenji Suzuki · Richard L Baron
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    ABSTRACT: To retrospectively determine the additional value of diffusion-weighted magnetic resonance imaging (MRI) to T2-weighted imaging in the evaluation of anal fistulae in comparison with gadolinium (Gd)-enhanced imaging. Thirteen patients (mean age, 35.2 years) with 20 anal fistulae were included. The protocol consisted of fat-suppressed T2-weighted fast spin-echo, diffusion-weighted single-shot echo-planar (b factors 0 and 800 s/mm(2)), and fat-suppressed Gd-enhanced T1-weighted gradient echo sequences. Two radiologists evaluated images in consensus. Eighteen (90%) fistulae were detected on T2-weighted images, and 19 (95%) and 19 (95%) were detected on diffusion-weighted and T2-weighted images combined and on Gd-enhanced and T2-weighted images combined, respectively. There was no statistically significant difference in sensitivity of the techniques (P > 0.5 for all comparison pairs). Confidence scores with diffusion-weighted and T2-weighted images combined or those with Gd-enhanced and T2-weighted images combined were significantly greater than those with T2-weighted images alone (P = 0.0047 and 0.014, respectively). Diffusion-weighted MRI of anal fistulae is a useful sequence and can be a helpful adjunct to T2-weighted imaging, especially in patients with risk factors for contrast agents.
    No preview · Article · Nov 2009 · Journal of Magnetic Resonance Imaging
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    ABSTRACT: Purpose: Measuring the liver volume by manual tracing of the liver boundary on arterial‐phase CTimages is time‐consuming. Our purpose was to develop an automated liver extraction scheme based on a 3D level‐set segmentation technique for measuring liver volumes. Material and Methods: Hepatic CT scans of eighteen prospective liver donors were obtained under a liver transplant protocol. We developed an automated liver segmentation scheme for volumetry of the liver in CT. Our scheme consisted of five steps. First, a 3D anisotropic smoothing filter was applied to CTimages for removing noise while preserving the structures in the liver, followed by an edge enhancement filter and a nonlinear gray‐scale enhancement filter for enhancing the liver boundary. By using the boundary‐enhanced image as a speed function, a 3D fast‐marching algorithm generated an initial surface that roughly estimated the shape of the liver. A 3D level‐set segmentation algorithm refined the initial surface so as to fit the liver boundary more accurately. Automated volumes were compared to manually determined liver volumes. Results: The mean liver volume obtained with our scheme was 1598 cc (range: 1002–2415 cc), whereas the mean manual volume was 1535 cc (range: 1007–2435 cc). The mean absolute difference between automated and manual volumes was 128 cc (9.5%). The two volumetrics reached an excellent agreement (the intra‐class correlation coefficient was 0.89) with no statistically significant difference (P=0.13). The processing time by the automated method was 2–5 min. per case (Intel, Xeon, 2.7 GHz), whereas that by manual segmentation was approximately 50–60 min. per case. Conclusion:CTliver volumetrics based on an automated scheme agreed excellently with manual volumetrics, and required substantially less completion time. Our automated scheme provides an efficient and accurate way of measuring liver volumes in CT; thus, it would be useful for radiologists in their measurement of liver volumes.
    No preview · Article · Jun 2009 · Medical Physics
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    ABSTRACT: The objective of this study was to assess the long-term natural history of focal confluent fibrosis in cirrhotic liver with CT. Two radiologists retrospectively reviewed in consensus 118 liver CT examinations in 26 patients (19 men, seven women; age range, 32-68 years; mean age, 50 years) performed over approximately 6 years. Helical CT scans were obtained before and 30-35 and 65-70 seconds after injection of 125-150 mL of contrast medium at a rate of 4-5 mL/s. Proof of cirrhosis was based on liver transplantation (n = 6), biopsy (n = 9), or imaging findings (n = 11). The number, location, and attenuation of fibrotic lesions and presence of trapped vessels were evaluated. Variation of hepatic retraction associated with the development of focal confluent fibrosis lesions was assessed using the ellipsoid volume formula and an arbitrary retraction index. Each radiologist identified 41 focal confluent fibrosis lesions. All lesions were identified by both radiologists. Twelve patients (46%) had a single lesion, 13 (50%) had two lesions, and one (4%) had three lesions. Thirty-four (83%) of 41 lesions were located in segment IV, VII, or VIII. Thirty-two lesions (78%) were hypoattenuating on unenhanced images, 25 lesions (61%) were hypoattenuating on hepatic arterial phase images, and 20 lesions (49%) were isoattenuating on portal venous phase images. Seven lesions (17%) were or became hyperattenuating at follow-up on portal venous phase images. Trapped vessels were found in six lesions (15%). The retraction index showed a significant increase over time (r = 0.423, p < or = 0.0001). The degree of capsule retraction associated with focal confluent fibrosis evolves with time and relates to the natural evolution of cirrhosis.
    No preview · Article · Jun 2009 · American Journal of Roentgenology
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    ABSTRACT: Purpose: Hepatocellular carcinoma (HCC) is one of the most common primary malignant tumors of the liver. We developed a computer‐aided diagnostic (CAD) scheme for detection of HCC in arterial‐phase hepatic CT in order to assist radiologists in their HCC detection. Materials and Methods: Our database consists of isotropic arterial‐phase CT scans of 14 patients acquired with a multi‐detector‐row CT system with a 64 channel detector scanner, which includes 16 radiologist‐determined HCCs that were confirmed pathologically. Lesion sizes ranged 6–50 mm, with a mean of 22.6 mm. We developed a CAD scheme consisting of 1) liver segmentation, using fast‐marching rough‐segmentation followed by geodesic‐active‐contour fine‐segmentation coupled with a level‐set algorithm, 2) detection of HCC candidates from the segmented liver, based on a watershed segmentation algorithm, 3) calculation of both 2D and 3D morphologic‐, intensity‐ and texture‐based features of the detected candidates, and 4) classification of HCC candidates by means of linear discriminant analysis (LDA) of the calculated features, with a stepwise feature selection method based on the Wilks. lambda and the F value. The performance of the CAD scheme was evaluated by free‐response receiver‐operating‐characteristic analysis. Results: With the candidate selection segment of the detection scheme, we achieved 100% sensitivity with 15.1 false positives per patient. Of the 254 calculated features, the stepwise LDA selected 45 “useful” features for candidate characterization. Using these 45 features, we decreased the false positive rate to 6.1 false positives per patient, with no loss in sensitivity. Conclusion: In contrast‐enhanced arterial‐phase hepatic CT, our CAD scheme achieved a 100% sensitivity in detection of HCCs with 6.1 false positives per patient. This detection scheme could assist radiologists in detecting HCCs; thus, it would potentially improve radiologists' detection sensitivity for HCCs.
    No preview · Article · May 2009 · Medical Physics

Publication Stats

5k Citations
480.71 Total Impact Points

Institutions

  • 2011
    • The University of Chicago Medical Center
      • Department of Radiology
      Chicago, Illinois, United States
  • 2003-2011
    • University of Chicago
      • Department of Radiology
      Chicago, Illinois, United States
  • 1992-2001
    • University of Pittsburgh
      • • Department of Radiology
      • • Department of Medicine
      Pittsburgh, Pennsylvania, United States
  • 1997
    • University of Texas Health Science Center at San Antonio
      • Department of Radiology
      San Antonio, Texas, United States
  • 1988-1992
    • University of Washington Seattle
      • Department of Radiology
      Seattle, Washington, United States
  • 1991
    • United States Department of Veterans Affairs
      Бедфорд, Massachusetts, United States
  • 1990
    • City University of Seattle
      Seattle, Washington, United States