Interstitial lung diseases (ILD) involve several abnormal imaging patterns observed in computed tomography (CT) images. Accurate classification of these patterns plays a significant role in precise clinical decision making of the extent and nature of the diseases. Therefore, it is important for developing automated pulmonary computer-aided detection systems. Conventionally, this task relies on experts’ manual identification of regions of interest (ROIs) as a prerequisite to diagnose potential diseases. This protocol is time consuming and inhibits fully automatic assessment. In this paper, we present a new method to classify ILD imaging patterns on CT images. The main difference is that the proposed algorithm uses the entire image as a holistic input. By circumventing the prerequisite of manual input ROIs, our problem set-up is significantly more difficult than previous work but can better address the clinical workflow. Qualitative and quantitative results using a publicly available ILD database demonstrate state-of-the-art classification accuracy under the patch-based classification and shows the potential of predicting the ILD type using holistic image.

Abstract. Computed tomography imaging is a standard modality for detecting and assessing lung cancer. In order to evaluate the malignancy of lung nodules, clinical practice often involves expert qualitative ratings on several criteria describing a nodule's appearance and shape. Translating these features for computer-aided diagnostics is challenging due to their subjective nature and the difficulties in gaining a complete description. In this paper, we propose a computerized approach to quantitatively evaluate both appearance distinctions and 3D surface variations. Nodule shape was modeled and parameterized using spherical harmonics, and appearance features were extracted using deep convolutional neural networks. Both sets of features were combined to estimate the nodule malignancy using a random forest classifier. The proposed algorithm was tested on the publicly available Lung Image Database Consortium dataset, achieving high accuracy. By providing lung nodule characterization, this method can provide a robust alternative reference opinion for lung cancer diagnosis.

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We present a new image quality assessment method for determining whether reducing radiation dose impairs the image quality of computed tomography (CT) in qualitative and quantitative clinical analyses tasks. In this Institutional Review Board-exempt study, we conducted a review of 50 patients (male, 22; female, 28) who underwent reduced-dose CT scanning on the first follow-up after standard-dose multiphase CT scanning. Scans were for surveillance of von Hippel-Lindau disease (N = 26) and renal cell carcinoma (N = 10). We investigated density, morphometric, and structural differences between scans both at tissue (fat, bone) and organ levels (liver, heart, spleen, lung). To quantify structural variations caused by image quality differences, we propose using the following metrics: dice similarity coefficient, structural similarity index, Hausdorff distance, gradient magnitude similarity deviation, and weighted spectral distance. Pearson correlation coefficient and Welch 2-sample t test were used for quantitative comparisons of organ morphometry and to compare density distribution of tissue, respectively. For qualitative evaluation, 2-sided Kendall Tau test was used to assess agreement among readers. Both qualitative and quantitative evaluations were designed to examine significance of image differences for clinical tasks. Qualitative judgment served as an overall assessment, whereas detailed quantifications on structural consistency, intensity homogeneity, and texture similarity revealed more accurate and global difference estimations. Qualitative and quantitative results indicated no significant image quality degradation. Our study concludes that low(er)-dose CT scans can be routinely used because of no significant loss in quantitative image information compared with standard-dose CT scans.

Purpose: The purpose of this study is to quantify the phase lag of superior‐inferior abdominal organ motion relative to an external marker block used to monitor respiratory motion. The diaphragm, liver, spleen, and kidneys were studied. Method and Materials: A 4DCT (GE Medical System, Waukesha, Wisconsin) scan correlated with respiratory motion using the Real‐Time Position Management (RPM) Respiratory Gating System (Varian Medical Systems, Palo Alto, CA) was used to acquire scans of 10 patients. Up to 10 images at each slice location within one breathing cycle were acquired and sorted into respiratory phases evenly distributed in time. The superior and inferior edge of each organ was identified, and the average of these positions was used as the S‐I position of the organ. The anterior edge of the external marker block was also recorded. These positions were identified for all respiratory phases. The data was then fit with a cosine squared function. The argument of the function was the observed respiratory phase plus a starting phase. The starting phase was then adjusted until the value generating the least square deviation among all measurements of the particular organ, diaphragm, or marker block for all phases was found. The difference of starting phase minus that of the marker block is then recorded as the phase lag relative to the marker block. Results: No phase lag is greater than 36° which is the minimum difference between successive phase for a respiratory cycle divided into 10 phases. Conclusion: The external marker block used to monitor respiration is observed to be in phase the motion of the abdominal organs and diaphragm within the measurement accuracy. Conflict of Interest: Software provided by GE Medical Systems.