Liver and bone window settings for soft-copy interpretation of chest and abdominal CT

New liver window width in detecting hepatocellular carcinoma on dynamic contrast-enhanced computed tomography with deep learning reconstruction

Changing a window width (WW) alters appearance of noise and contrast of CT images. The aim of this study was to investigate the impact of adjusted WW for deep learning reconstruction (DLR) in detecting hepatocellular carcinomas (HCCs) on CT with DLR. This retrospective study included thirty-five patients who underwent abdominal dynamic contrast-enhanced CT. DLR was used to reconstruct arterial, portal, and delayed phase images. The investigation of the optimal WW involved two blinded readers. Then, five other blinded readers independently read the image sets for detection of HCCs and evaluation of image quality with optimal or conventional liver WW. The optimal WW for detection of HCC was 119 (rounded to 120 in the subsequent analyses) Hounsfield unit (HU), which was the average of adjusted WW in the arterial, portal, and delayed phases. The average figures of merit for the readers for the jackknife alternative free-response receiver operating characteristic analysis to detect HCC were 0.809 (reader 1/2/3/4/5, 0.765/0.798/0.892/0.764/0.827) in the optimal WW (120 HU) and 0.765 (reader 1/2/3/4/5, 0.707/0.769/0.838/0.720/0.791) in the conventional WW (150 HU), and statistically significant difference was observed between them (p < 0.001). Image quality in the optimal WW was superior to those in the conventional WW, and significant difference was seen for some readers (p < 0.041). The optimal WW for detection of HCC was narrower than conventional WW on dynamic contrast-enhanced CT with DLR. Compared with the conventional liver WW, optimal liver WW significantly improved detection performance of HCC.

Deep learning-based algorithm improved radiologists' performance in bone metastases detection on CT

OBJECTIVES

To develop and evaluate a deep learning-based algorithm (DLA) for automatic detection of bone metastases on CT.

METHODS

This retrospective study included CT scans acquired at a single institution between 2009 and 2019. Positive scans with bone metastases and negative scans without bone metastasis were collected to train the DLA. Another 50 positive and 50 negative scans were collected separately from the training dataset and were divided into validation and test datasets at a 2:3 ratio. The clinical efficacy of the DLA was evaluated in an observer study with board-certified radiologists. Jackknife alternative free-response receiver operating characteristic analysis was used to evaluate observer performance.

RESULTS

A total of 269 positive scans including 1375 bone metastases and 463 negative scans were collected for the training dataset. The number of lesions identified in the validation and test datasets was 49 and 75, respectively. The DLA achieved a sensitivity of 89.8% (44 of 49) with 0.775 false positives per case for the validation dataset and 82.7% (62 of 75) with 0.617 false positives per case for the test dataset. With the DLA, the overall performance of nine radiologists with reference to the weighted alternative free-response receiver operating characteristic figure of merit improved from 0.746 to 0.899 (p < .001). Furthermore, the mean interpretation time per case decreased from 168 to 85 s (p = .004).

CONCLUSION

With the aid of the algorithm, the overall performance of radiologists in bone metastases detection improved, and the interpretation time decreased at the same time.

KEY POINTS

• A deep learning-based algorithm for automatic detection of bone metastases on CT was developed. • In the observer study, overall performance of radiologists in bone metastases detection improved significantly with the aid of the algorithm. • Radiologists' interpretation time decreased at the same time.

Stochastic tissue window normalization of deep learning on computed tomography

Tissue window filtering has been widely used in deep learning for computed tomography (CT) image analyses to improve training performance (e.g., soft tissue windows for abdominal CT). However, the effectiveness of tissue window normalization is questionable since the generalizability of the trained model might be further harmed, especially when such models are applied to new cohorts with different CT reconstruction kernels, contrast mechanisms, dynamic variations in the acquisition, and physiological changes. We evaluate the effectiveness of both with and without using soft tissue window normalization on multisite CT cohorts. Moreover, we propose a stochastic tissue window normalization (SWN) method to improve the generalizability of tissue window normalization. Different from the random sampling, the SWN method centers the randomization around the soft tissue window to maintain the specificity for abdominal organs. To evaluate the performance of different strategies, 80 training and 453 validation and testing scans from six datasets are employed to perform multiorgan segmentation using standard 2D U-Net. The six datasets cover the scenarios, where the training and testing scans are from (1) same scanner and same population, (2) same CT contrast but different pathology, and (3) different CT contrast and pathology. The traditional soft tissue window and nonwindowed approaches achieved better performance on (1). The proposed SWN achieved general superior performance on (2) and (3) with statistical analyses, which offers better generalizability for a trained model.

A machine learning model to predict hepatocellular carcinoma response to transcatheter arterial chemoembolization

PURPOSE

Some patients with hepatocellular carcinoma (HCC) are more likely to experience disease progression despite transcatheter arterial chemoembolization (TACE) treatment, and thus would benefit from early switching to other therapeutic regimens. We sought to evaluate a fully automated machine learning algorithm that uses pre-therapeutic quantitative computed tomography (CT) image features and clinical factors to predict HCC response to TACE.

MATERIALS AND METHODS

Outcome information from 105 patients receiving first-line treatment with TACE was evaluated retrospectively. The primary clinical endpoint was time to progression (TTP) based on follow-up CT radiological criteria (mRECIST). A 14-week cutoff was used to classify patients as TACE-susceptible (TTP ≥14 weeks) or TACE-refractory (TTP <14 weeks). Response to TACE was predicted using a random forest classifier with the Barcelona Clinic Liver Cancer (BCLC) stage and quantitative image features as input as well as the BCLC stage alone as a control.

RESULTS

The model's response prediction accuracy rate was 74.2% (95% CI=64%-82%) using a combination of the BCLC stage plus quantitative image features versus 62.9% (95% CI= 52%-72%) using the BCLC stage alone. Shape image features of the tumor and background liver were the dominant features correlated to the TTP as selected by the Boruta method and were used to predict the outcome.

CONCLUSION

This preliminary study demonstrates that quantitative image features obtained prior to therapy can improve the accuracy of predicting response of HCC to TACE. This approach is likely to provide useful information for aiding HCC patient selection for TACE.

Automated Volumetric Assessment of Hepatocellular Carcinoma Response to Sorafenib: A Pilot Study

PURPOSE

This pilot study evaluates the feasibility of automated volumetric quantification of hepatocellular carcinoma (HCC) as an imaging biomarker to assess treatment response for sorafenib.

METHODS

In this institutional review board-approved, Health Insurance Portability and Accountability Act-compliant retrospective study, a training database of manually labeled background liver, enhancing and nonenhancing tumor tissue was established using pretherapy and first posttherapy multiphasic computed tomography images from a registry of 13 HCC patients. For each patient, Hounsfield density and geometry-based feature images were generated from registered multiphasic computed tomography data sets and used as the input for a random forest-based classifier of enhancing and nonenhancing tumor tissue. Leave-one-out cross-validation of the dice similarity measure was applied to quantify the classifier accuracy. A Cox regression model was used to confirm volume changes as predictors of time to progression (TTP) of target lesions for both manual and automatic methods.

RESULTS

When compared with manual labels, an overall classification accuracy of dice similarity coefficient of 0.71 for pretherapy and 0.66 posttherapy enhancing tumor labels and 0.45 for pretherapy and 0.59 for posttherapy nonenhancing tumor labels was observed. Automated methods for quantifying volumetric changes in the enhancing lesion agreed with manual methods and were observed as a significant predictor of TTP.

CONCLUSIONS

Automated volumetric analysis was determined to be feasible for monitoring HCC response to treatment. The information extracted using automated volumetrics is likely to reproduce labor-intensive manual data and provide a good predictor for TTP. Further work will extend these studies to additional treatment modalities and larger patient populations.

Large-scale medical image annotation with crowd-powered algorithms

Accurate segmentations in medical images are the foundations for various clinical applications. Advances in machine learning-based techniques show great potential for automatic image segmentation, but these techniques usually require a huge amount of accurately annotated reference segmentations for training. The guiding hypothesis of this paper was that crowd-algorithm collaboration could evolve as a key technique in large-scale medical data annotation. As an initial step toward this goal, we evaluated the performance of untrained individuals to detect and correct errors made by three-dimensional (3-D) medical segmentation algorithms. To this end, we developed a multistage segmentation pipeline incorporating a hybrid crowd-algorithm 3-D segmentation algorithm integrated into a medical imaging platform. In a pilot study of liver segmentation using a publicly available dataset of computed tomography scans, we show that the crowd is able to detect and refine inaccurate organ contours with a quality similar to that of experts (engineers with domain knowledge, medical students, and radiologists). Although the crowds need significantly more time for the annotation of a slice, the annotation rate is extremely high. This could render crowdsourcing a key tool for cost-effective large-scale medical image annotation.

Iterative reconstructions in multiphasic CT imaging of the liver: qualitative and task-based analyses of image quality

AIM

To evaluate the clinical benefits on image quality (IQ) of adaptive statistical iterative reconstruction (ASIR) and model-based iterative reconstruction (MBIR) in multiphasic liver CT compared to filtered back-projection (FBP), in patients and on phantoms using a novel task-based metric.

MATERIALS AND METHODS

Image data of 65 patients who underwent a routine multiphasic liver CT during a 1-month period were reconstructed with FBP, ASIR50, ASIR80, and MBIR. IQ was assessed qualitatively by ranking the most distal hepatic artery (HA) and portal vein (PV) visible; and quantitatively by measuring contrast-to-noise ratio (CNR) of the liver parenchyma, HA and PV. IQ was compared between each reconstruction and correlated to CNR and detectability index (d') measurements computed on phantoms scanned with the same CT protocol as for patients.

RESULTS

HA and PV were seen more distally on MBIR and ASIR80 compared to FBP (p≤0.001). The CNR correlated weakly between patient and phantom (r=0.76 and 0.80 for HA and PV, respectively), whereas d' correlated strongly with the division order of HA and PV (r=0.96 and 0.95, respectively).

CONCLUSION

MBIR and ASIR significantly improve the IQ of multiphasic liver CT, especially through better distal detection of HA and PV, in agreement with the adapted task-based metric d' estimated on phantoms.

Methods for optimizing the display conditions of brain magnetic resonance images

PURPOSE

To investigate a method for optimizing the display conditions of brain magnetic resonance (MR) images.

MATERIALS AND METHODS

We retrospectively analyzed brain MR images of 120 adults classified into screening, acute cerebral infarction, and brain tumor groups (n = 40 each). Two observers independently displayed the images on a monitor and optimized the display conditions using the W/L and U/L methods. In the W/L method, the observers manipulated the width and level of the display window, while in the U/L method they manipulated the upper and lower levels of the window. The times required were compared between the two methods. Additionally, the appropriateness of the determined window setting was evaluated visually by the respective observer to exclude the possibility that rough, suboptimal adjustment shortened the adjustment time.

RESULTS

For both observers and all groups, the time required for optimization was significantly shorter for the U/L method than for the W/L method. The appropriateness of the window setting for the U/L method was equal to or better than that for the W/L method.

CONCLUSION

Manipulating the upper and lower levels of the display window appears to improve the efficiency of interpreting brain MR images through rapid optimization of the display condition.

Diagnostic errors when interpreting abdominopelvic computed tomography: a pictorial review

Diagnostic errors remain an inevitable occurrence during abdominopelvic CT (APCT) interpretation, despite advances in imaging technology. The main cause of error is failure to identify a lesion (i.e. perceptual error) and failure to recognize a finding's significance (i.e. interpretive or cognitive error). Awareness and understanding of the causes of errors can reduce their occurrence and may lead to a reduction in morbidity and mortality. This pictorial essay highlights various causes of error in interpreting APCT scans and briefly discusses possible solutions for minimizing these errors.

Thoracic Temporal Subtraction Three Dimensional Computed Tomography (3D-CT): Screening for Vertebral Metastases of Primary Lung Cancers

PURPOSE

We developed an original, computer-aided diagnosis (CAD) software that subtracts the initial thoracic vertebral three-dimensional computed tomography (3D-CT) image from the follow-up 3D-CT image. The aim of this study was to investigate the efficacy of this CAD software during screening for vertebral metastases on follow-up CT images of primary lung cancer patients.

MATERIALS AND METHODS

The interpretation experiment included 30 sets of follow-up CT scans in primary lung cancer patients and was performed by two readers (readers A and B), who each had 2.5 years' experience reading CT images. In 395 vertebrae from C6 to L3, 46 vertebral metastases were identified as follows: osteolytic metastases (n = 17), osteoblastic metastases (n = 14), combined osteolytic and osteoblastic metastases (n = 6), and pathological fractures (n = 9). Thirty-six lesions were in the anterior component (vertebral body), and 10 lesions were in the posterior component (vertebral arch, transverse process, and spinous process). The area under the curve (AUC) by receiver operating characteristic (ROC) curve analysis and the sensitivity and specificity for detecting vertebral metastases were compared with and without CAD for each observer.

RESULTS

Reader A detected 47 abnormalities on CT images without CAD, and 33 of them were true-positive metastatic lesions. Using CAD, reader A detected 57 abnormalities, and 38 were true positives. The sensitivity increased from 0.717 to 0.826, and on ROC curve analysis, AUC with CAD was significantly higher than that without CAD (0.849 vs. 0.902, p = 0.021). Reader B detected 40 abnormalities on CT images without CAD, and 36 of them were true-positive metastatic lesions. Using CAD, reader B detected 44 abnormalities, and 39 were true positives. The sensitivity increased from 0.783 to 0.848, and AUC with CAD was nonsignificantly higher than that without CAD (0.889 vs. 0.910, p = 0.341). Both readers detected more osteolytic and osteoblastic metastases with CAD than without CAD.

CONCLUSION

Our temporal 3D-CT subtraction CAD software easily detected vertebral metastases on the follow-up CT images of lung cancer patients regardless of the osteolytic or osteoblastic nature of the lesions.

Bone Windows for Distinguishing Malignant from Benign Primary Bone Tumors on FDG PET/CT

Objective. The default window setting on PET/CT workstations is soft tissue. This study investigates whether bone windowing and hybrid FDG PET/CT can help differentiate between malignant and benign primary bone tumors. Materials and methods. A database review included 98 patients with malignant (n=64) or benign primary bone (n=34) tumors. The reference standard was biopsy for malignancies and biopsy or >1 year imaging follow-up of benign tumors. Three radiologists and/or nuclear medicine physicians blinded to diagnosis and other imaging viewed the lesions on CT with bone windows (CT-BW) without and then with PET (PET/CT-BW), and separate PET-only images for malignancy or benignity. Three weeks later the tumors were viewed on CT with soft tissue windows (CT-STW) without and then with PET (PET/CT-STW). Results. Mean sensitivity and specificity for identifying malignancies included: CT-BW: 96%, 90%; CT-STW: 90%, 90%; PET/CT-BW: 95%, 85%, PET/CT-STW: 95%, 86% and PET-only: 96%, 75%, respectively. CT-BW demonstrated higher specificity than PET-only and PET/CT-BW (p=0.0005 and p=0.0103, respectively) and trended toward higher sensitivity than CT-STW (p=0.0759). Malignant primary bone tumors were more avid than benign lesions overall (p<0.0001) but the avidity of benign aggressive lesions (giant cell tumors and Langerhans Cell Histiocytosis) trended higher than the malignancies (p=0.08). Conclusion. Bone windows provided high specificity for distinguishing between malignant and benign primary bone tumors and are recommended when viewing FDG PET/CT.

The value of "liver windows" settings in the detection of small renal cell carcinomas on unenhanced computed tomography

OBJECTIVE

To assess if "liver window" settings improve the conspicuity of small renal cell carcinomas (RCC).

METHODS

Patients were analysed from our institution's pathology-confirmed RCC database that included the following: (1) stage T1a RCCs, (2) an unenhanced computed tomography (CT) abdomen performed ≤ 6 months before histologic diagnosis, and (3) age ≥ 17 years. Patients with multiple tumours, prior nephrectomy, von Hippel-Lindau disease, and polycystic kidney disease were excluded. The unenhanced CT was analysed, and the tumour locations were confirmed by using corresponding contrast-enhanced CT or magnetic resonance imaging studies. Representative single-slice axial, coronal, and sagittal unenhanced CT images were acquired in "soft tissue windows" (width, 400 Hounsfield unit (HU); level, 40 HU) and liver windows (width, 150 HU; level, 88 HU). In addition, single-slice axial, coronal, and sagittal unenhanced CT images of nontumourous renal tissue (obtained from the same cases) were acquired in soft tissue windows and liver windows. These data sets were randomized, unpaired, and were presented independently to 3 blinded radiologists for analysis. The presence or absence of suspicious findings for tumour was scored on a 5-point confidence scale.

RESULTS

Eighty-three of 415 patients met the study criteria. Receiver operating characteristics (ROC) analysis, t test analysis, and kappa analysis were used. ROC analysis showed statistically superior diagnostic performance for liver windows compared with soft tissue windows (area under the curve of 0.923 vs 0.879; P = .0002). Kappa statistics showed "good" vs "moderate" agreement between readers for liver windows compared with soft tissue windows.

CONCLUSION

Use of liver windows settings improves the detection of small RCCs on the unenhanced CT.

Quando a fase de equilíbrio pode ser suprimida nos exames de tomografia computadorizada de abdome?

OBJETIVO: Avaliar a necessidade de realização da fase de equilíbrio nos exames de tomografia computadorizada de abdome. MATERIAIS E MÉTODOS: Realizou-se estudo retrospectivo, transversal e observacional, avaliando 219 exames consecutivos de tomografia computadorizada de abdome com contraste intravenoso, realizados num período de três meses, com diversas indicações clínicas. Para cada exame foram emitidos dois pareceres, um avaliando o exame sem a fase de equilíbrio (primeira análise) e o outro avaliando todas as fases em conjunto (segunda análise). Ao final de cada avaliação, foi estabelecido se houve mudança nos diagnósticos principais e secundários, entre a primeira e a segunda análise. Foi utilizada a extensão do teste exato de Fisher para avaliar a modificação dos diagnósticos principais (p < 0,05 como significante). RESULTADOS: Entre os 219 casos avaliados, a supressão da fase de equilíbrio provocou alteração no diagnóstico principal em apenas um exame (0,46%; p > 0,999). Com relação aos diagnósticos secundários, cinco exames (2,3%) foram modificados. CONCLUSÃO: Para indicações clínicas como estadiamento tumoral, abdome agudo e pesquisa de coleção abdominal, a fase de equilíbrio não acrescenta contribuição diagnóstica expressiva, podendo ser suprimida dos protocolos de exame.

Incidental lung nodules on CT examinations of the abdomen: prevalence and reporting rates in the PACS era

OBJECTIVES

To retrospectively evaluate prevalence, reporting rates and clinical implications of incidental pulmonary nodules detected in multidetector computed tomography (MDCT) abdominal studies.

MATERIALS AND METHODS

Abdominal MDCT studies of 243 consecutive patients, 94 of whom had a history of cancer, were evaluated. Lung bases included in the scan were reviewed on a PACS workstation with different window settings and post-processing techniques. Nodules were classified according to their density (calcified, solid noncalcified, non-solid, part-solid) and size (<4mm; 4-6mm; 6-8mm; >8mm). The study findings were compared with the corresponding radiologic reports. Previous of following CT studies, when available from the PACS, were also reviewed to evaluate changes in number and size of the detected nodules.

RESULTS

An average of 8.2 cm of lung parenchyma was imaged in each patient. 213 noncalcified nodules (NCNs) were identified in 95 patients (39.1%) but only 8 patients (8.4%) had it mentioned in the final report. Comparison CT studies were available for 44 out of the 95 positive patients showing disappearance of the nodules in 2 cases, no interval change in 26 and progression in size and/or number in 16 patients, in whom a final diagnosis of metastasis or primary lung cancers was achieved.

CONCLUSION

Radiologists tend to overlook lung portions on abdominal CT studies. Underreporting may affect patient care and have medico-legal implications since images are permanently stored in digital format on PACS and CD-ROMs. Management of the discovered nodules should be tailored to the clinical situation of the patient, and particular care should be reserved to patients with oncologic history.

Is targeted reconstruction necessary for evaluating contrast-enhanced chest computed tomography using a liquid crystal display monitor?

PURPOSE

The aim of this study was to examine whether 20-cm field-of-view (FOV) targeted reconstruction (TR) on contrast-enhanced (CE) chest computed tomography (CT) might improve the diagnostic value compared with simple zooming (SZ) from whole-thorax FOV images using a 2 million (2M)-pixel liquid crystal display (LCD) monitor.

MATERIALS AND METHODS

We prospectively evaluated 44 patients. SZ images were magnified from a FOV of 26-34 cm (mean 29.7 cm). Parameters were 512 x 512 matrix and 3 mm thickness and interval. Images were reconstructed using a soft-tissue kernel. Three radiologists evaluated contour, spiculation, notch, pleural tag, invasion, and internal characteristics of the lesions using 5-scale scores. We also performed a phantom study to evaluate the spatial resolution of images.

RESULTS

The diagnostic value of the TR images was similar to that of the SZ images, with the findings identified in 88%-100% of the cases. Artifacts from high-density structures deteriorated the image quality in six (14%), and the SZ images were judged to be preferable in five of them. In the phantom study, there was little difference in spatial resolution between the two images.

CONCLUSION

The SZ images from whole-thorax FOV on CE chest CT were similar in quality to TR images using a 2M-pixel LCD monitor.

Distinguishing benign from malignant pulmonary nodules with helical chest CT in children with malignant solid tumors

PURPOSE

To retrospectively assess whether computed tomographic (CT) findings can indicate the benign or malignant nature of pulmonary nodules in pediatric patients with malignant solid primary tumors.

MATERIALS AND METHODS

With institutional review board approval, waived parental and patient consent, and HIPAA compliance, the authors determined the incidence of malignancy among 81 pulmonary nodules that were sampled at biopsy within 3 weeks after chest CT (January 1999 to September 2003) in 41 young patients with malignant solid tumors. Three radiologists independently and retrospectively reviewed these scans and the available previously obtained scans, classifying nodules as benign, malignant, or indeterminate on the basis of their number, unilateral versus bilateral distribution, size, margins (indistinct vs distinct), calcification, growth, and associated adenopathy. These classifications were compared with nodule histologic type, and interreviewer agreement was assessed.

RESULTS

The median patient age was 14.8 years (mean, 13.7 years; range, 5-21 years). Twenty-four of the 41 patients (58%) had at least one biopsy-proved malignant nodule. Four (10%) patients had both benign and malignant nodules; 17 (42%) had only benign nodules. Reviewer 1 classified 65% (39 of 60) of nodules correctly; reviewer 2, 57% (37 of 65); and reviewer 3, 67% (43 of 64). Interreviewer agreement was slight to moderate (kappa </= 0.43, P </= .03). In contrast to findings in adults, sharply defined nodules in younger individuals were more likely to be malignant (P = .03) and nodule size was not associated with malignancy (P >/= .32).

CONCLUSION

The frequency of benign nodules and the inconsistency of predictions based on CT features suggest the need for better predictors of pulmonary nodules being malignant or benign, so as to reduce unnecessary thoracotomy in pediatric patients with solid malignancy. .

Liver Metastases on Serial Contrast-enhanced Multidetector Computed Tomography Examinations

OBJECTIVE

To verify the earliest detectability of liver metastases in patients who underwent serial multidetector computed tomography (MDCT) examinations.

METHODS

We selected 12 patients with known primary cancer who underwent 4 or more contrast-enhanced, 4-detector MDCTs. When metastases had been reported, an evaluation of the preceding MDCT was done to define whether the lesion was detectable, detectable only by minimal signs, undetectable, or detected but misdiagnosed as a benign lesion (MBL).

RESULTS

Eighty-eight lesions were analyzed. Evaluating the preceding examination, we defined detectable (n=8), detectable only by minimal signs (n=5), undetectable (n=74), and MBL (n=1). The group with minimal signs was composed of 4 small hypodense foci and 1 calcification. The MBL was a non-Hodgkin lesion first misdiagnosed as a hemangioma.

CONCLUSION

Approximately 15% of liver metastases were prospectively missed, 9% of them being retrospectively detectable, 6% being retrospectively visible as minimal signs, whereas only 1% of liver metastases were misdiagnosed as a benign lesion.

Evolution of the digital revolution: a radiologist perspective

The transformation from film-based to filmless operation has become more and more challenging, as imaging studies expand in size and complexity. To adapt to these changes, radiologists must proactively develop new workflow strategies to compensate for increasing work demands and the existing workforce shortage. This article addresses the evolutionary changes underway in the radiology interpretation process and reviews changes that have occurred in the past decade. These include a number of developments in soft-copy interpretation, which is migrating from a relatively static process, duplicating film-based interpretation, to a dynamic process, using multi-planar reconstructions, volumetric navigation, and electronic decision support tools. The result is optimization of the human-computer interface with improved productivity, diagnostic confidence, and interpretation accuracy.

Usefulness of coronal reformations in the diagnostic evaluation of infiltrative lung disease

PURPOSE

To evaluate the diagnostic accuracy of coronal thin sections as an alternative to transverse high-resolution computed tomography (HRCT) scans in the diagnostic approach to infiltrative lung disease (ILD) with multislice computed tomography (MSCT).

MATERIALS AND METHODS

Fifty consecutive patients referred for suspicion of ILD underwent MSCT (collimation: 4 mm x 1 mm; pitch: 1.75; scan time: 0.5 seconds; 80 mA per slice) of the entire thorax. Two sets of lung images were systematically reconstructed: 1-mm thick transverse computed tomography (CT) scans (i.e., HRCT scans) (group 1) and 1-mm thick coronal images (group 2). Both series of images were obtained at 10-mm intervals and reconstructed with a high-spatial frequency algorithm. Two observers independently analyzed the overall image quality, the presence and distribution of CT features of ILD, and the diagnostic value of group 1 and group 2 lung images.

RESULTS

Group 1 and group 2 images were coded as interpretable, with minimal respiratory artifacts in the lower lung zones in two cases (4%). Presence of abnormal lung infiltration was found in 38 patients in group 2 with concordant interpretation of group 1 images. No significant difference was found in the identification of CT features of ILD between group 2 and group 1 (nodules: 32% vs. 30%; lines: 14% vs. 16%; increased attenuation: 24% vs. 26%; fibrosis: 48% vs. 50%; distortion: 46% vs. 50%; and abnormal interfaces: 16% in both groups). Distribution of lung abnormalities in central, peripheral, anterior, and/or posterior lung zones was similarly recognized in group 2 and group 1. In patients with extensive lung infiltration, the vertical predominance of lung changes was more precisely assessed in group 2 (n = 12) than in group 1 (n = 4). For a mean coverage of 260 mm in this study group, the mean number of sections to be interpreted was significantly lower in group 2 (19 sections) than in group 1 (28 sections) (P < 0.01).

CONCLUSION

Coronal sections allow a diagnostic approach to ILD as precise as that provided with HRCT scans, based on the interpretation of a significantly reduced number of images.

Accuracy of interpretation of CT scans: comparing PACS monitor displays and hard-copy images

OBJECTIVE

The purpose of this study was to determine the relative diagnostic accuracy of radiologists in the interpretation of CT scans using a computer workstation in comparison with using film.

MATERIALS AND METHODS

Four board-certified radiologists with extensive soft-copy experience interpreted 117 CT scans in four anatomic regions using films displayed on an alternator and images displayed on a four-monitor workstation. The radiologists were asked to interpret the scans in their usual fashion and were aware that both the time required to review the study and the accuracy of the reports were being assessed. The radiologists' diagnostic impressions were compared with those of a consensus panel and scored for accuracy.

RESULTS

Soft-copy interpretation using computer workstations was found to produce statistically significant improvement in combined measurements of sensitivity, specificity, and overall accuracy for chest, brain, and chest-abdominal CT scans compared with film interpretation.

CONCLUSION

PACS (picture archiving and communication system) offers radiologists the potential for improved accuracy in CT interpretation compared with traditional film-based interpretation.

CT detection of hepatic and splenic injuries: usefulness of liver window settings

OBJECTIVE

This study was designed to assess the usefulness of liver window settings when performing abdominal CT for the detection and characterization of hepatic and splenic injuries.

SUBJECTS AND METHODS

We prospectively evaluated helical abdominal CT scans for hepatic and splenic injuries in 300 consecutive patients with blunt abdominal trauma over a 4-month period. There were 204 males and 96 females with a mean age of 34 years (age range, 1-87 years). For each patient, initial CT diagnosis of hepatic or splenic injury was made from images obtained with standard abdominal window settings. CT scans were then immediately reinterpreted using additional images obtained at narrow window width (liver windows). Changes in conspicuity and characterization of injury were recorded. All CT examinations were performed with helical 7-mm collimation at a pitch of 1.5 after oral ingestion of diluted barium and during bolus IV administration of 125 mL of ioversol at a rate of 2-3 mL/sec.

RESULTS

We detected hepatic or splenic injuries in 34 patients (11.3%). There were 19 hepatic injuries and 18 splenic injuries. Three patients had injuries to both liver and spleen. Conspicuity of hepatic or splenic injuries was mildly increased (+1 H) on liver windows in 16 patients, whereas the injury was equally conspicuous on both liver window and standard window images in 19 cases. In no case did review of the liver windows result in a change in grade of injury or reveal an injury that was not seen on standard abdominal window images. The total increased cost for printing liver windows was $5748.

CONCLUSION

Routine use of liver window settings for abdominal CT in trauma patients has little clinical usefulness and is not cost-effective.