Adaptive Voxel Matching for Temporal CT Subtraction.

Temporal subtraction (TS) technique calculates a subtraction image between a pair of registered images acquired from the same patient at different times. Previous studies have shown that TS is effective for visualizing pathological changes over time; therefore, TS should be a useful tool for radiologists. However, artifacts caused by partial volume effects degrade the quality of thick-slice subtraction images, even with accurate image registration. Here, we propose a subtraction method for reducing artifacts in thick-slice images and discuss its implementation in high-speed processing. The proposed method is based on voxel matching, which reduces artifacts by considering gaps in discretized positions of two images in subtraction calculations. There are two different features between the proposed method and conventional voxel matching: (1) the size of a searching region to reduce artifacts is determined based on discretized position gaps between images and (2) the searching region is set on both images for symmetrical subtraction. The proposed method is implemented by adopting an accelerated subtraction calculation method that exploit the nature of liner interpolation for calculating the signal value at a point among discretized positions. We quantitatively evaluated the proposed method using synthetic data and qualitatively using clinical data interpreted by radiologists. The evaluation showed that the proposed method was superior to conventional methods. Moreover, the processing speed using the proposed method was almost unchanged from that of the conventional methods. The results indicate that the proposed method can improve the quality of subtraction images acquired from thick-slice images.

Detection of lung carcinoma with predominant ground-glass opacity on CT using temporal subtraction method.

PURPOSE: To evaluate the usefulness of the CT temporal subtraction (TS) method for the detection of the lung cancer with predominant ground-glass opacity (LC-pGGO). MATERIALS AND METHODS: Twenty-five pairs of CT and their TS images in patients with LC-pGGO (31 lesions) and 25 pairs of those in patients without nodules were used for an observer performance study. Eight radiologists participated and the statistical significance of differences with and without the CT-TS was assessed by JAFROC analysis. RESULTS: The average figure-of-merit (FOM) values for all radiologists increased to a statistically significant degree, from 0.861 without CT-TS to 0.912 with CT-TS (p < .001). The average sensitivity for detecting the actionable lesions improved from 73.4 % to 85.9 % using CT-TS. The reading time with CT-TS was not significantly different from that without. CONCLUSION: The use of CT-TS improves the observer performance for the detection of LC-pGGO. KEY POINTS: • CT temporal subtraction can improve the detection accuracy of lung cancer. • Reading time with temporal subtraction is not different from that without. • CT temporal subtraction improves observer performance for ground-glass/subsolid nodule detection.

Performance of computer-based analysis using temporal subtraction to assess joint space narrowing progression in rheumatoid patients.

Our computer-based method can detect the chronological change in joint space width between baseline and follow-up images as the joint space difference index (JSDI). The aim of this study was to verify the sensitivity and specificity of our computer-based method in assessment of joint space narrowing progression in rheumatoid patients. Twenty-seven patients (24 women and 3 men) with rheumatoid arthritis underwent radiography of the bilateral hand at baseline and at 1 year. The joint space narrowing (JSN) of a total of 252 metacarpophalangeal (MCP) joints and 229 carpal joints was assessed by our computer-based method, setting the Sharp/van der Heijde method as the gold standard. We constructed a receiver operating characteristic curve by using the Sharp/van der Heijde method as the gold standard and set the optimal cutoff on JSDI for MCP, carpal, and MCP/carpal joints. We then calculated the sensitivity and specificity for each cutoff in assessment of JSN progression. At the most discriminant cutoff, the sensitivity and specificity of the computer-based method for MCP joints was 78.6 versus 85.3 %, respectively (AUC = 0.837; P < 0.001). Carpal joints revealed a lower sensitivity and specificity with 64.7 and 86.8 % (AUC = 0.775; P < 0.001). Furthermore, the sensitivity and specificity for MCP/carpal joints was 71.0 versus 83.6 %, respectively (AUC = 0.778; P < 0.001). The computer-based method presented a reliable assessment of JSN progression with high sensitivity and specificity and may be useful in follow-up assessment of the joint damage in rheumatoid patients.

Evaluation of the clinical utility of temporal subtraction using bone suppression processing in digital chest radiography.

Rationale and objectives: To evaluate the usefulness of temporal subtraction using the bone suppression method in digital chest radiography for the detection of pulmonary lesions. Materials and methods: The images of 31 patients with pulmonary lesions and 19 normal cases were included in the study. Conventional and bone suppression temporal subtraction were performed in the 50 cases selected and used for an observer performance study. Five radiologists participated in the study, and the differences between using conventional and bone suppression temporal subtraction were assessed using jackknife free-response receiver operating characteristic analysis. Results: The average figure-of-merit values for all radiologists increased significantly using the bone suppression method, from 0.619 (conventional) to 0.696 (p = 0.032). The average sensitivity for detecting pulmonary lesions improved from 67.9% to 75.4%, and the average number of false-positive per case decreased from 0.336 to 0.252 using bone suppression temporal subtraction. Conclusion: Bone suppression temporal subtraction processing can assist with the detection of subtle pulmonary lesions in digital chest radiographs.

Comparative evaluation of temporal subtraction computed tomography and non-echoplanar diffusion-weighted imaging for the mastoid extension of middle ear cholesteatoma.

OBJECTIVE: Preoperative imaging assessment influences the decision to perform mastoidectomy for the mastoid extension of middle ear cholesteatoma. This study compared the performance of temporal subtraction CT (TSCT) and non-echoplanar diffusion-weighted imaging (non-EP DWI) in evaluating such mastoid extensions. METHODS: We retrospectively evaluated 239 consecutive patients with surgically proven middle ear cholesteatoma between April 2016 and April 2021. The diagnostic performance of TSCT, wherein the presence of black color indicated progressive bone erosion, and non-EP DWI, wherein high signal intensity in the mastoid region suggested mastoid extension, was compared using Fisher's exact test. RESULTS: In 34 patients with evaluable TSCT images, black color was significantly more common in patients with mastoid extension than in those without; the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy of TSCT were 1.00, 0.95, 0.94, 1.00, and 0.97, respectively. In 90 patients with evaluable non-EP DWI, high signal intensity was significantly more common in patients with mastoid extension than in those without; the sensitivity, specificity, PPV, NPV, and accuracy of non-EP DWI were 0.88, 0.85, 0.91, 0.81, and 0.87, respectively. In 16 patients with both evaluable TSCT and non-EP DWI, the diagnostic performance of the TSCT was slightly superior to that of the non-EP DWI for predicting mastoid extension, although the difference was not significant. CONCLUSIONS: TSCT images generated using consecutively acquired preoperative high-resolution CT images are useful for predicting mastoid extension of middle ear cholesteatoma, and the diagnostic performance of TSCT is non-inferior to that of non-EP DWI.

Automatic Breast Mass Segmentation and Classification Using Subtraction of Temporally Sequential Digital Mammograms.

OBJECTIVE: Cancer remains a major cause of morbidity and mortality globally, with 1 in 5 of all new cancers arising in the breast. The introduction of mammography for the radiological diagnosis of breast abnormalities, significantly decreased their mortality rates. Accurate detection and classification of breast masses in mammograms is especially challenging for various reasons, including low contrast and the normal variations of breast tissue density. Various Computer-Aided Diagnosis (CAD) systems are being developed to assist radiologists with the accurate classification of breast abnormalities. METHODS: In this study, subtraction of temporally sequential digital mammograms and machine learning are proposed for the automatic segmentation and classification of masses. The performance of the algorithm was evaluated on a dataset created especially for the purposes of this study, with 320 images from 80 patients (two time points and two views of each breast) with precisely annotated mass locations by two radiologists. RESULTS: Ninety-six features were extracted and ten classifiers were tested in a leave-one-patient-out and k-fold cross-validation process. Using Neural Networks, the detection of masses was 99.9% accurate. The classification accuracy of the masses as benign or suspicious increased from 92.6%, using the state-of-the-art temporal analysis, to 98%, using the proposed methodology. The improvement was statistically significant (p-value < 0.05). CONCLUSION: These results demonstrate the effectiveness of the subtraction of temporally consecutive mammograms for the diagnosis of breast masses. Clinical and Translational Impact Statement: The proposed algorithm has the potential to substantially contribute to the development of automated breast cancer Computer-Aided Diagnosis systems with significant impact on patient prognosis.

Development of a novel detection method for changes in lung conditions during radiotherapy using a temporal subtraction technique.

We aimed to develop a novel method of detecting changes in lung conditions during radiotherapy using temporal subtraction technique. Twenty patients who underwent radiotherapy were retrospectively assessed by calculating optimal direct similarity error (ODSE) between initial and mid-treatment registered images. Patients were grouped according to region in tumor size and atelectasis for lung of < 20 or ≥ 20 cm3, which analyzed two field regions (1024 × 768 pixels, 512 × 512 pixels). Correlations between ODSE and changes in lung conditions were analyzed based on effect of radiation dose; receiver operating characteristic (ROC) analysis was performed to evaluate whether changes can be detected during treatment period. The ODSE of 1024 × 768 pixels was changed to 1.00 (0.28-3.48) for lung lesion size of < 20 cm3 and 1.86 (0.55-6.58) for the ≥ 20 cm3 lung lesion size. ODSE of 512 × 512 pixels was 1.03 (0.40-2.12) for the region in tumor size and atelectasis of < 20 cm3 and 1.90 (0.39-27.8) for the ≥ 20 cm3 lung lesion size. The region under the curve values from ROC analysis were 0.796 (1024 × 768 pixels) and 0.983 (512 × 512 pixels). A novel method can visually and numerically help to detect changes in lung condition at early treatment stages. Using this method, difference between plan and actual positional relationship for target and risk organs that cannot be predicted at the time of planning can be avoided, ensuring high safety and accuracy in lung radiotherapy.

Evaluation of the depiction ability of similar subtraction images using digital chest radiographs of different patients.

The temporal subtraction (TS) technique requires the same patient's chest radiographs (CXRs) acquired on different dates, whereas the similar subtraction (SS) technique can be used in patients who have no previous CXR, using similar CXRs from different patients. This study aimed to examine the depiction ability of SS images with simulated nodules in comparison with that of TS images with 2- and 7-year acquisition intervals. One hundred patients were randomly selected from our image database. The most recently acquired images of the patients were used as target images for subtraction. The simulated nodule was superimposed on each target image to examine the usefulness of the SS technique. The most (Top 1) and ten most (Top 10) similar images for each target image were identified in the 24,254-image database using a template-matching technique, and used for the SS technique. SS and TS images were obtained using a previously developed nonlinear image-warping technique. The depiction ability of SS and TS images was evaluated using the contrast-to-noise ratio (CNR). The proportion of Top 1 SS images showing higher CNR than that of the TS images with 2- and 7-year acquisition intervals was 28% (28/100) and 33% (33/100), respectively. Moreover, the proportion of cases that had any of the Top 10 SS images with higher CNRs than those of TS images with 2- and 7-year acquisition intervals was 56% (56/100) and 72% (72/100), respectively. Our study indicates that the SS technique can potentially be used to detect lung nodules on CXRs.

CT temporal subtraction method for detection of sclerotic bone metastasis in the thoracolumbar spine.

PURPOSE: To assess the effectiveness of a CT temporal subtraction (TS) method on radiologists' performance in sclerotic metastasis detection in the thoracolumbar spine. MATERIALS AND METHODS: 20 pairs (current and previous CTs) of standard-dose CT and their TS images in patients with sclerotic bone metastasis and 20 pairs (current and previous CTs) of those in patients without bone metastasis were used for an observer performance study. A total of 135 lesions were identified as the reference standard of actionable lesions (sclerotic metastasis newly appeared or increased in size or in attenuation). 4 attending radiologists and 4 radiology residents participated in this observer study. Ratings and locations of "lesions" determined by the observers were utilized for assessing the statistical significance of differences between radiologists' performances without and with the CT-TS images in JAFROC analysis. The statistical significance of differences in the reviewing time was determined by a two-tailed paired t-test. RESULTS: The average figure-of-merit (FOM) values for all but one radiologist increased to a statistically significant degree, from 0.856 without the CT-TS images to 0.884 with the images (P = .037). The average sensitivity for detecting the actionable lesions was improved from 60.7 % to 72.5% at a false-positive rate of 0.15 per case by use of the CT-TS images. The average reading time with CT-TS images was significantly shorter than that without (150.6 s vs. 166.5 s, P = .004). CONCLUSION: The use of CT-TS would improve the observer performance for the detection of the sclerotic bone metastasis in the thoracolumbar spine.

Phantom-based study exploring the effects of different scatter correction approaches on the reconstructed images generated by contrast-enhanced stationary digital breast tomosynthesis.

Stationary digital breast tomosynthesis (sDBT) is an emerging technology in which the single rotating x-ray tube is replaced by a fixed array of multiple carbon nanotube-enabled sources, providing a higher spatial and temporal resolution. As such, sDBT offers a promising platform for contrast-enhanced (CE) imaging. However, given the minimal enhancement above background with standard operational tube settings and iodine dosing, CE breast imaging requires additional acquisition steps to isolate the iodine signal, using either temporal or dual energy subtraction (TS or DES) protocols. Also, correcting for factors that limit contrast is critical, and scatter and noise pose unique challenges during tomosynthesis. This phantom-based study of CE sDBT compared different postacquisition scatter correction approaches on the quality of the reconstructed image slices. Beam-pass collimation was used to sample scatter indirectly, from which an interpolated scatter map was obtained for each projection image. Scatter-corrected projections provided the information for reconstruction. Comparison between the application of different scatter maps demonstrated the significant effect that processing has on the contrast-to-noise ratio and feature detectability ([Formula: see text]) in the final displayed images and emphasized the critical importance of scatter correction during DES.

Automatic classification of lung nodules on MDCT images with the temporal subtraction technique.

PURPOSE: A temporal subtraction (TS) image is obtained by subtracting a previous image, which is warped to match the structures of the previous image and the related current image. The TS technique removes normal structures and enhances interval changes such as new lesions and substitutes in existing abnormalities from a medical image. However, many artifacts remaining on the TS image can be detected as false positives. METHOD: This paper presents a novel automatic segmentation of lung nodules using the Watershed method, multiscale gradient vector flow snakes and a detection method using the extracted features and classifiers for small lung nodules (20 mm or less). RESULT: Using the proposed method, we conduct an experiment on 30 thoracic multiple-detector computed tomography cases including 31 small lung nodules. CONCLUSION: The experimental results indicate the efficiency of our segmentation method.

Optimization of contrast-enhanced breast imaging: Analysis using a cascaded linear system model.

PURPOSE: Contrast-enhanced (CE) breast imaging involves the injection contrast agents (i.e., iodine) to increase conspicuity of malignant lesions. CE imaging may be used in conjunction with digital mammography (DM) or digital breast tomosynthesis (DBT) and has shown promise in improving diagnostic specificity. Both CE-DM and CE-DBT techniques require optimization as clinical diagnostic tools. Physical factors including x-ray spectra, subtraction technique, and the signal from iodine contrast, must be considered to provide the greatest object detectability and image quality. We developed a cascaded linear system model (CLSM) for the optimization of CE-DM and CE-DBT employing dual energy (DE) subtraction or temporal (TE) subtraction. METHODS: We have previously developed a CLSM for DBT implemented with an a-Se flat panel imager (FPI) and filtered backprojection (FBP) reconstruction algorithm. The model is used to track image quality metrics - modulation transfer function (MTF) and noise power spectrum (NPS) - at each stage of the imaging chain. In this study, the CLSM is extended for CE breast imaging. The effect of x-ray spectrum (varied by changing tube potential and the filter) and DE and TE subtraction techniques on breast structural noise was measured was studied and included as a deterministic source of noise in the CLSM. From the two-dimensional (2D) and three-dimensional (3D) MTF and NPS, the ideal observer signal-to-noise ratio (SNR), also known as the detectability index (d'), may be calculated. Using d' as a FOM, we discuss the optimization of CE imaging for the task of iodinated contrast object detection within structured backgrounds. RESULTS: Increasing x-ray energy was determined to decrease the magnitude of structural noise and not its correlation. By performing DE subtraction, the magnitude of the structural noise was further reduced at the expense of increased stochastic (quantum and electronic) noise. TE subtraction exhibited essentially no residual structural noise at the expense of increased quantum noise, even over that of the DE case. For DE subtraction, optimization of dose weighting to the HE view (fh ) results in the minimization of quantum noise. Both subtraction weighting factor (wSub ) and the iodine contrast signal were dependent on the LE and HE x-ray spectra. To best detect a 5 mm Gaussian lesion with 5 mg/ml of iodine within a 4 cm thick breast, it was found that the high energy (HE) view should be acquired with a tube potential of 47 kVp (W/Ti spectrum) and the low energy (LE) view with a potential of 23 kVp (W/Rh spectrum). Due to the complete removal of structural noise, TE subtraction produced much higher d' than DE subtraction both as a function of mean glandular dose and iodine concentration. CONCLUSIONS: We have shown the effect of increasing x-ray energy as well as projection domain subtraction on breast structural noise. Further, we have exhibited the utility of the CLSM for DE and TE subtraction CE imaging in the optimization of imaging parameters such as x-ray energy, fh , and wSub as well as guiding the understanding of their effects on image contrast and noise.