CT Studies Of Patients Research Articles

Informe radiológico de la TC multimodal en los pacientes con ictus isquémico agudo

Multimodal CT has taken on a fundamental role in the clinical and therapeutic management of patients with acute ischemic strokes. Because CT findings are included in the criteria for deciding on treatment options for patients with acute ischemic strokes in clinical trials, the radiology report must be clear, precise, homogeneous, and fast. The introduction of semi-automatic image-processing software has occasionally led to decision making based on imaging tests that lack the necessary quality and appropriate technical supervision to ensure optimal diagnosis. To fulfill the criteria of quality and usefulness, the radiology report must be thoroughly protocolized. This paper describes a protocolized radiology report for multimodal CT studies in patients with suspected ischemic strokes.

El informe estructurado de la TC en el enfermo politraumatizado

The large number of images and the complexity of the findings in CT studies of patients with multiple trauma make structured reports necessary. Structured reports enable radiologists to evaluate all the radiological and clinical findings in an orderly and systematic way and to highlight those that can be important for determining the treatment approach. This article proposes a three-part radiology report: a first part to describe life-threatening findings; a much-more detailed second part describing the findings according to anatomic regions, in which the report should be adapted to the different medical/surgical classifications normally used in managing trauma; and a third part that aims to improve the quality of the report.

A review of staging chest CT in trunk and extremity soft tissue sarcoma.

To determine the incidence of pulmonary metastases on chest CT in trunk and extremity soft tissue sarcoma based on two size criteria, and to identify factors associated with metastases. Retrospective review of chest CT studies in patients with trunk and extremity soft tissue sarcoma over an 18-month period. Data collected included patient age/sex, tumour location, size and relationship to fascia. All chest CTs were reviewed for the presence of metastases which were diagnosed according to two size criteria: multiple nodules > 5 mm in size or multiple nodules > 10 mm in size. Follow-up CT studies were reviewed in cases initially considered indeterminate. 127 males and 73 females were included (mean age 57.1 years; range 10-90 years). 147 (73.5%) tumours were deep to the fascia and 53 (26.5%) superficial. Tumour size classified according to the 12 AJCC 2019 criteria was: T1 = 52, T2 = 76, T3 = 39, T4 = 33. Based on nodule size >5 mm, 73 (36.5%) patients had no metastases, 42 (21%) had metastases, while 85 (42.5%) studies were indeterminate. Based on nodule size >10 mm, 73 (36.5%) patients had no metastases, 28 (14%) had metastases, while 99 (49.5%) studies were indeterminate. Larger maximum dimension of the primary tumour was a risk factor for pulmonary metastases using both size criteria. The incidence of pulmonary metastases at presentation in trunk and extremity soft tissue sarcoma is 14-21%. 42.5-49.5% of chest CTs were indeterminate. The incidence of pulmonary metastases at presentation in trunk and extremity soft tissue sarcoma is 14-21%. Indeterminate pulmonary nodules are also very common.

Characteristics of the liver parenchyma according to the native CT examinations data at the stages of COVID-19 treatment

Purpose . To assess the liver density according to the data of native CT studies in patients with COVID-19, depending on the severity of the pulmonary parenchyma damage and the prescribed treatment, to compare the data with biochemical indicators, and also to demonstrate changes in density indicators over time. Material and methods . Lung CT data from 200 patients with COVID-19 were retrospectively analyzed. The density of the liver, spleen, and subcutaneous fat tissue was measured in all patients on the images of the upper abdominal cavity that entered the scan area. The ratio of the density of the liver to the spleen and to the density of the fat tissue was assesed. These indicators were compared with each other in two groups of lung tissue damage: CT 1—2 and CT 3—4. The CT 3—4 group was assessed in detail: the density indicators of the liver were studied in dynamics, and their relationship with biochemical indicators — during the initial study. A comparison was also made between two subgroups: patients taking tocilizumab and those without tocilizumab. Results . A decrease in liver density and the ratio of liver density to spleen density was observed in 35.5% and 47.5% of patients respectively. Liver density and the ratio of liver density to spleen density were lower in the CT 3—4 group than in the CT 1—2 group, and amounted to 43.9 HU versus 49.3 HU ( p < 0.008) and 0.9 versus 1.0 respectively ( p < 0.014). In the initial study, there were a moderate ( r = -0.30; p < 0.05) and weak ( r = -0.26; p < 0.05) negative correlation of liver density and the ratio of liver density to spleen density with serum albumin. When assessing the dynamics in patients in the CT 3—4 group, with each subsequent study, an increase in the density of the liver parenchyma and the ratio of liver density to spleen density was noted. The difference between the mean values of liver density at the first and at the fourth CT examinations was 11.85 HU. Liver density values were independent of treatment with tocilizumab. Conclusion . Liver density values were lower in patients with COVID-19 with the degree of lung parenchyma lesion CT 3—4, increased during treatment and did not depend on the prescription of tocilizumab. Evaluation and monitoring of the dynamics of liver density could become a useful parameter in determining the severity of the disease course. No strong relationships were found between the density parameters during primary CT and any of the biochemical parameters. A more detailed analysis of these changes in dynamics is required, which may suggest the prevailing mechanism of liver damage in COVID-19.

Virtual monochromatic reconstructions of dual energy CT in abdominal trauma: optimization of energy level improves pancreas laceration conspicuity and diagnostic confidence.

Pancreatic injury is associated with significant morbidity and mortality. Pancreatic lacerations can be challenging to identify as the pancreas is not scanned at peak enhancement in most trauma CT protocols. This study qualitatively and quantitively assessed pancreatic lacerations with virtual monoenergetic dual-energy CT (DE CT) to establish an optimal energy level for visualization of pancreatic lacerations. Institutional review board approval was obtained. We retrospectively examined 17 contrast-enhanced CT studies in patients with blunt trauma with MRCP, ERCP, or surgically proven pancreatic lacerations. All studies were performed in our Emergency Department from 2016 to 2019 with a 128 slice dual-source DE CT scanner. Conventional 120kVp and noise-optimized virtual monoenergetic imaging (VMI) datasets were created. VMI energy levels were constructed from 40 to 100keV in 10keV increments and analyzed quantitatively and qualitatively. Pancreatic laceration attenuation, background parenchymal attenuation, and noise were calculated. Qualitative assessment was performed by two independent readers. The optimal CNR for the assessment of pancreatic lacerations was observed at VMI-40 in comparison with standard reconstructions and the remaining VMI energy levels (p = 0.001). Readers reported improved contrast resolution, diagnostic confidence, and laceration conspicuity at VMI at 40keV (p = 0.016, p = 0.002, and p = 0.0012 respectively). However, diagnostic acceptability and subjective noise were improved on conventional polyenergentic images (p = 0.0006 and p = 0.001 respectively). Dual energy CT at VMI-40 maximizes the CNR of pancreatic laceration, improves diagnostic confidence, and increases laceration conspicuity.

Performance of machine learning-augmented analysis of radiomics for the head and neck cancer histopathological diagnosis: A systematic review and meta-analysis.

e18526 Background: The imaging criteria used for head and neck cancers (HNC) staging are mostly anatomical with basic quantitative measures, such as size, and admittedly radiologists’ reading of images is dependent on their expertise level. Radiomics, a term referring to extracting and investigating higher dimensional data from images, has been suggested to address these shortcomings. Assisted by machine learning (ML), highly efficient prediction models could revolutionise our diagnostic practices. Our goal was to study the role of ML in the histopathological diagnosis of HNC based on radiomics. Methods: A systematic review and meta-analysis was conducted using electronic databases (PubMed, Scopus, EMBASE, Google Scholar) and including MRI, PET, and CT studies in patients with HNC. Our study was aimed only at diagnosis utilising radiomics and artificial intelligence (ML). A PRISMA diagram retracing the steps of this search process was completed. QUADAS-2 and EQUATOR checklists were completed. A weighted mean, a mean and a median of the performance indicators were recorded. Results: 7 studies were found eligible for meta-analysis. Patient sample sizes ranged between 2-107 patients (median: 18). CT was the most common modality used (4/7 studies). All but one studies were retrospective. Support vector machine and random forest techniques were the main ML techniques used but how the model was built was rarely described. Furthermore, studies did not make clear the exact number of patients in the testing set. Other issues included the reporting of the final model performance with few studies reporting confidence intervals and 2 studies not reporting the exact performance metrics. The accuracy values for the testing set ranged from 58% -94.1%. The meta-analysis showed an overall weighted-mean accuracy of 78.53%, a mean of 82.9% and a median of 84.4%. The weighted mean of the sensitivity was 76.5%, the mean was 83.3%, and for specificity was 83.9% and 88.5%., respectively. The AUC was 0.8. The neuroradiologists’ overall accuracy was 50.4% if weighted, and 54.5% if not, and the corresponding accuracy of the ML classifiers were 78.4% and 79.6%. The ML scored an accuracy of 20% higher than the radiologists. Conclusions: The results are overall encouraging, keeping in perspective the possible calculation biases and small number of studies. There is need for better documentation and standardisation of the applied ML models, which show initially superior performance compared to radiologists.

Artificial intelligence model for COVID-19 detection based on native chest computed tomography

Summary The aim is to develop and evaluate the clinical effectiveness of the artificial intelligence system for analyzing chest CT images, recognizing the leading signs of lung damage caused by coronavirus infection, and determining the damage volume. Materials and methods. We used open source images for the model training and validation, including anonymized complete studies or separate axial sections of chest CT of patients with PCR confirmed coronavirus etiology of lung disease. Algorithm of the model included sequence as follows: axial sections of CT of the chest organs at the entrance, image segmentation into 4 classes, visualization and calculation of the area as a part (in%) occupied by each finding from the total area of the pulmonary fields in all sections available for analysis. In this work, we used a convolutional artificial neural network for segmentation of the form of an encoder-decoder, with a Feature Pyramid Network (FPN) decoder and an encoder based on the EfficientNet-B5 classification neural network. To increase the diversity of the training data set, as well as to protect the neural network model from retraining, input image transformations are used in the learning process. Results. The neural network model with high accuracy (IoU 0.82-0.97) reveals diagnostic signs that determine the severity of lung damage with a new coronavirus infection, on axial sections of native computed tomography of the chest. The diagnostic accuracy of the model for determining the signs of interstitial and alveolar infiltration exceeds the accuracy of a novice radiologist and is comparable to a diagnostician in predicting the shape of pulmonary fields and the presence of pleural effusion. Findings. The model can be used as an effective intelligent radiologist assistant when working with CT studies of patients with suspected coronavirus infection.

КТ-ПАТТЕРНЫ ПРИ COVID-19 АССОЦИИРОВАННЫХ ПНЕВМОНИЯХ – СТАНДАРТИЗАЦИЯ ОПИСАНИЙ ИССЛЕДОВАНИЙ НА ОСНОВЕ ГЛОССАРИЯ ОБЩЕСТВА ФЛЕЙШНЕРА

According to most recent publications, computed tomography (CT) plays the central role in diagnostic, evaluation of severity and follow-up of COVID-19 associated pneumonias. The aim of this article is to unify the assessment of the lung and thorax CT-studies in patients with COVID-19 associated pneumonias based on the glossary of Fleischner Society. The results of CT-studies of verified COVID-19 associated pneumonias are demonstrated in this article. The terminology of Fleischner Society glossary is suggested for the description of CT-studies. The value of different CT-patterns in evaluation of presen-tation, severity grade and prognosis in COVID-19 pneumonias is highlighted. This article was supported by the Medical Faculty of Heidelberg University.

Performance of a Deep Learning Algorithm for Automated Segmentation and Quantification of Traumatic Pelvic Hematomas on CT.

The volume of pelvic hematoma at CT has been shown to be the strongest independent predictor of major arterial injury requiring angioembolization in trauma victims with pelvic fractures, and also correlates with transfusion requirement and mortality. Measurement of pelvic hematomas (unopacified extraperitoneal blood accumulated from time of injury) using semi-automated seeded region growing is time-consuming and requires trained experts, precluding routine measurement at the point of care. Pelvic hematomas are markedly variable in shape and location, have irregular ill-defined margins, have low contrast with respect to viscera and muscle, and reside within anatomically distorted pelvises. Furthermore, pelvic hematomas occupy a small proportion of the entire volume of a chest, abdomen, and pelvis (C/A/P) trauma CT. The challenges are many, and no automated methods for segmentation and volumetric analysis have been described to date. Traditional approaches using fully convolutional networks result in coarse segmentations and class imbalance with suboptimal convergence. In this study, we implement a modified coarse-to-fine deep learning approach-the Recurrent Saliency Transformation Network (RSTN) for pelvic hematoma volume segmentation. RSTN previously yielded excellent results in pancreas segmentation, where low contrast with adjacent structures, small target volume, variable location, and fine contours are also problematic. We have curated a unique single-institution corpus of 253 C/A/P admission trauma CT studies in patients with bleeding pelvic fractures with manually labeled pelvic hematomas. We hypothesized that RSTN would result in sufficiently high Dice similarity coefficients to facilitate accurate and objective volumetric measurements for outcome prediction (arterial injury requiring angioembolization). Cases were separated into five combinations of training and test sets in an 80/20 split and fivefold cross-validation was performed. Dice scores in the test set were 0.71 (SD ± 0.10) using RSTN, compared to 0.49 (SD ± 0.16) using a baseline Deep Learning Tool Kit (DLTK) reference 3D U-Net architecture. Mean inference segmentation time for RSTN was 0.90min (± 0.26). Pearson correlation between predicted and manual labels was 0.95 with p< 0.0001. Measurement bias was within 10mL. AUC of hematoma volumes for predicting need for angioembolization was 0.81 (predicted) versus 0.80 (manual). Qualitatively, predicted labels closely followed hematoma contours and avoided muscle and displaced viscera. Further work will involve validation using a federated dataset and incorporation into a predictive model using multiple segmented features.

Intramural hematoma and penetrating atherosclerotic ulcers of the aorta: uncertainties and controversies.

The natural histories of intramural hematoma (IMH) and penetrating atherosclerotic ulcer (PAU) are highly variable as they may progress to aneurysm formation, rupture, or dissection, or even resolve, in the specific case of IMH. Imaging plays an increasingly important role in clinical and surgical management of IMH and PAU. In contrast to ulcer-like projections, images of intramural blood pools have not been widely reported in CT studies of patients with IMH. Understanding the imaging characteristics and the natural course of each of these entities would help clinicians and surgeons to identify patients at greatest risk for bad prognosis and may improve outcomes. This paper discusses the pathophysiology of these entities, the controversies regarding their natural history, and the prognostic factors that should be identified in CT scans.

Marked Discordance Between Coronary Artery Calcium Score and Framingham Risk Score in Middle Eastern and Veteran American Populations

Background: Conventionally, the Framingham Risk Score (FRS) was used as a tool to risk stratify individuals for cardiovascular (CV) events and death. More recently, the coronary artery calcium score (CACS) has been used as a complimentary method for the assessment of CV risk. This study’s objective is to quantify the magnitude of marked discordance (MD) between FRS and CACS. The subjects were from two different ethnic groups, namely, a Middle Eastern population and a veteran American population. Methods: This is a retrospective observational cohort study of 499 consecutive patients who underwent multidetector cardiac computerized tomography (MDCT) at King Fahd Armed Forces Hospital (KFAFH), Jeddah, Saudi Arabia and Veterans Affairs Loma Linda Health Care System (VALLHCS). CACS was measured using the Agatston method. FRS was calculated by chart review. MD was defined as a CACS of 400 with low risk FRS. Patients had CACS between 100 and 400 were excluded from the analysis. Results: A total of 499 patient CT studies were reviewed with 450 meeting study criteria,130 patients from the VALLHCS (63 ± 12, 95% males) and 320 from KFAFH (27% males). MD was found in 308 (68%) of the total number enrolled. In the American population, of the 62 patients with a CACS 400, 20 patients (29%) had a discordantly low FRS. In the Middle Eastern population, of the 290 patients with a CACS 400, only 5 patients (16.7%) had a discordantly low FRS. Conclusion: The majority of MD in the Middle Eastern population had a high FRS with a low CACS. In contradistinction, the American veterans with MD had a high CACS and a low FRS.

Reducing CT radiation dose with iterative reconstruction algorithms: The influence of scan and reconstruction parameters on image quality and CTDIvol

ObjectivesIn this phantom CT study, we investigated whether images reconstructed using filtered back projection (FBP) and iterative reconstruction (IR) with reduced tube voltage and current have equivalent quality. We evaluated the effects of different acquisition and reconstruction parameter settings on image quality and radiation doses. Additionally, patient CT studies were evaluated to confirm our phantom results. MethodsHelical and axial 256 multi-slice computed tomography scans of the phantom (Catphan®) were performed with varying tube voltages (80–140kV) and currents (30–200mAs). 198 phantom data sets were reconstructed applying FBP and IR with increasing iterations, and soft and sharp kernels. Further, 25 chest and abdomen CT scans, performed with high and low exposure per patient, were reconstructed with IR and FBP. Two independent observers evaluated image quality and radiation doses of both phantom and patient scans. ResultsIn phantom scans, noise reduction was significantly improved using IR with increasing iterations, independent from tissue, scan-mode, tube-voltage, current, and kernel. IR did not affect high-contrast resolution. Low-contrast resolution was also not negatively affected, but improved in scans with doses <5mGy, although object detectability generally decreased with the lowering of exposure. At comparable image quality levels, CTDIvol was reduced by 26–50% using IR. In patients, applying IR vs. FBP resulted in good to excellent image quality, while tube voltage and current settings could be significantly decreased. ConclusionsOur phantom experiments demonstrate that image quality levels of FBP reconstructions can also be achieved at lower tube voltages and tube currents when applying IR. Our findings could be confirmed in patients revealing the potential of IR to significantly reduce CT radiation doses.

Contralateral breast volume measurement during chest CT for postmastectomy breast reconstruction.

Successful breast reconstruction after mastectomy may be guided by knowledge of the contralateral breast volume. Three-dimensional (3D) reconstruction based on a CT examination was used to determine the volume of the contralateral normal breast before postmastectomy breast reconstruction. Seventeen female patients scheduled for postmastectomy breast reconstruction using silicon implant prostheses were using noncontrast CT scans of the chest for metastatic work-up. The CT scans were used to measure the volume of contralateral normal breast. The volume estimates were used to specify the proper implant size for cosmesis. The estimated CT volume was correlated with volume estimates obtained using water displacement, as well as anthropometric measurements performed by a plastic surgeon. Breast volume estimates obtained from CT scans were highly correlated with volumes measured by the two nonradiological methods, yielding a positive linear correlation coefficient (r = 0.99). Volume measurement of the intact breast should be added to reports of routine chest CT studies in patients who undergo mastectomy. CT imaging is a feasible method for contralateral normal breast volume measurement in these patients.

PT405 Measurements of the aortic root from 320 slice cardiac CT studies in patients with aortopathy: Which parameter is most consistent with echocardiography?

PT405 Measurements of the aortic root from 320 slice cardiac CT studies in patients with aortopathy: Which parameter is most consistent with echocardiography?

Prevalence of malignancy in adrenal incidentaloma on CTstudies in patients with unknown primary malignancy

Prevalence of malignancy in adrenal incidentaloma on CTstudies in patients with unknown primary malignancy

WE‐C‐108‐03: CT‐Based 3D Dose Calculation Method Using Artificial Neural Networks (ANN)

Purpose: In this study Artificial Neural Networks (ANN) are trained to develop a fast CT based dose calculation method. The ANN is capable of calculating the 3D dose distribution inside a voxel‐based phantom for prostate I–125 seed implants. The speed allows for accurate CT‐based calculation of the dose distributions during implantation in real time due to plan modifications. Methods: Training the ANN needs a set of accurate sample dose distributions in heterogeneous CT based phantoms. For the purpose of this study, 60000 sample cubicle phantoms were extracted from 3 patient CT studies. The dose distributions of the cubicle phantoms were calculated using MCNP5. The absorption coefficient of each voxel of the cubicle phantom was calculated by analyzing the CT scan. This information was then given to the ANN as the input. The calculated dose by MCNP5 was given to it as the sample output for the purpose of training. The dose distributions calculated by ANNs were then tested for 7000 cubicle phantoms against the MCNP5 results. Finally, the ANN was used to calculate the dose distribution in two real treatment plans. The results were compared against the MCNP5 voxel based results. Results: The regression graphs for individual ANNs show a regression of R=0.99543 for the worst ANN The regression of the best Trained ANN was equal to R=0.9997 for the 7000 cases. The maximum average absolute error of the ANN dose calculation was 3% in comparison with MCNP5. Conclusion: The results of individual ANN calculations and the complete treatment plan calculations verified the accuracy of the ANN method. Capability of the ANN method to calculate the dose distributions for 500 seed positions in less than 10 minutes suggests that this method can be used for model based dose calculation for real‐time plan modifications in the case of LDR brachytherapy.

Calcifications associated with pediatric intracranial arterial aneurysms: incidence and correlation with pathogenetic subtypes

Little is known about calcifications associated with pediatric intracranial arterial aneurysms (IAA). We sought to characterize calcifications associated with pediatric IAA according to aneurysm pathogenetic subtype. Patients with IAA less than 20 years of age were retrospectively identified. Three fellowship-trained neuroradiologists independently reviewed each patient's CT studies for calcifications of the parent artery or aneurysm. Aneurysmal calcification (ANC) was correlated with characteristics of the patient (age, sex) and aneurysm pathogenetic subtype, size, morphology, rupture status, and location. Thirty-three patients (mean age 10 years) with 43 IAA were analyzed. There were no parent artery calcifications. Nine IAA were calcified. IAA in children with non-hemodynamic risk factors (arteriopathy, trauma, infection, tumor) were more commonly calcified than idiopathic IAA (p = 0.029). More than one third of the pediatric IAAs in this group (arteriopathy, infection trauma, tumor) were calcified. IAA ≥ 10 mm were more likely to be calcified (p = 0.03). IAA that were ruptured at presentation were less likely to be calcified (p = 0.03). ANC was not significantly associated with patient age (≤10 years vs. >10 years), sex, morphology (fusiform vs. saccular) or location (anterior vs. posterior circulation). Aneurysmal but not parent artery calcifications are associated with a significant minority of pediatric IAA. Pediatric ANCs are associated with underlying non-hemodynamic vascular risk factors (arteriopathy, infection, trauma, and tumor), size ≥10 mm and non-hemorrhagic presentation.

Relationship between duration of pain and secondary signs of obstruction of the urinary tract on unenhanced helical CT.

The objective of this study was to investigate the relationship between duration of flank pain and the frequency of secondary signs of ureteral obstruction on unenhanced helical CT. The duration of flank pain was prospectively determined in 227 consecutive patients diagnosed with acute ureterolithiasis on unenhanced helical CT. These CT studies were evaluated for the presence or absence of perinephric stranding, ureteral dilatation, perinephric fluid, collecting system dilatation, periureteral stranding, and nephromegaly. The frequency of each sign was determined as a function of the duration of pain. The frequency of moderate or severe perinephric stranding increased from 5% at 1--2 hr to 51% at 7--8 hr (p < 0.001); ureteral dilatation increased from 84% at 1--2 hr to 97% at more than 8 hr (p < 0.03); moderate or severe perinephric fluid increased from 0% at 1--2 hr to 22% at 3--4 hr (p < 0.03); collecting system dilatation increased from 68% at 1--2 hr to 89% at 7-8 hr (p < 0.03); periureteral stranding increased from 35% at 1--2 hr to 76% at 7--8 hr (p < 0.004); and nephromegaly increased from 40% at 1--2 hr to 54% at 7--8 hr (p < 0.36). All CT secondary signs of ureteral obstruction except nephromegaly showed a significant increase in frequency as duration of flank pain increased. This observation may explain why the CT studies of some patients with acute ureterolithiasis show negative findings for some or all CT secondary signs of obstruction. Therefore, knowledge of the duration of pain is important when interpreting unenhanced CT studies in patients with acute ureterolithiasis.

Radioanatomic study of the gastrocolic venous trunk.

The mode of formation, measurements and frequency of occurrence of the gastrocolic venous trunk were studied by the injection-corrosion technique in a series of 54 anatomic specimens and by the analysis of 50 CT studies in patients without hepatic or pancreatic disease. The gastrocolic trunk was found in 51 of the 54 anatomic specimens and in 27 of the 50 CT studies. The great variability in its formation, whether bipodal, tripodal or quadripodal, was noted. With a mean diameter of 4.9 mm, it opened into the anterior, right or antero-left aspects of the superior mesenteric v. at a mean distance of 15 mm below the inferior border of the spleno-mesenteric confluence. The value of preliminary CT assessment before an operation for portal hypertension or a pancreatic tumor or in the diagnosis of a splenic thrombosis is emphasised.

The relationship between enlargement of the temporal horns of the lateral ventricles and dementia in aging patients with Down syndrome

Head CT studies of patients with Alzheimer's disease (AD) show global atrophic changes. Tissue loss is especially prominent in the temporal lobes, with widening of the temporal horns of the lateral ventricles and, usually, widening of the temporal sulci. Some recent studies have found a familial form of AD to be mapped to chromosome 21. Down syndrome (DS) results from the inheritance of three chromosomes 21, and it has been shown that after the age of 35 the brains of patients with DS commonly show neuropathological changes similar to those in patients with AD. CT studies of 25 patients with DS (ages 29-64 years) were examined for tissue loss in the temporal regions, and this was compared to the findings commonly seen in patients with AD. The widths of CSF spaces varied considerably in patients with DS, but after the age of 50 most of them showed significant widening of the temporal horns. In some patients the horns were large enough to suggest obstructive hydrocephalus. Because of a new trend toward deinstitutionalization of patients with DS, radiologists will be seeing more studies on these patients and should familiarize themselves with the unique ways in which they manifest the aging process.