Detection of abnormal diffuse perfusion in SPECT using a normal brain atlas
Bibliographic record
Abstract
Despite the advent of sophisticated image analysis algorithms, most SPECT (Single Photon Emission Computerized Tomography)cerebral perfusion studies are assessed visually, leading to unavoidable and significant inter and intra-observer variability. Here, we present an automatic method for evaluating SPECT studies based on a computerized atlas of normal regional cerebral bloodflow(rCBF). To generate the atlas, normal(screened volunteers)brain SPECT studies are registered with an affine transformation to one of them arbitrarily selected as reference to remove any size and orientation variations that are assumed irrelevant for our analysis. Then a smooth non-linear registration is performed to reveal the local activity pattern displacement among the normal subjects. By computing and applying the mean displacement to the reference SPECT image, one obtain the atlas that is the normal mean distribution of the rCBF(up to an affine transformation difference). To complete the atlas we add the intensity variance with the displacement mean and variance of the activity pattern. To investigate a patient's condition, we proceed similarly to the atlas construction phase. We first register the patient's SPECT volume to the atlas with an affine transformation. Then the algorithm computes the non-linear 3D displacement of each voxel needed for an almost perfect shape (but not intensity)fit with the atlas. For each brain voxel, if the intensity difference between the atlas and the registered patient is higher than normal differences then this voxel is counted as "abnormal" and similarly if the 3D motion necessary to move the voxel to its registered position is not within the normal displacements. Our hypothesis is that this number of abnormal voxels discriminates between normal and abnormal studies. A Markovian segmentation algorithm that we have presented elsewhere is also used to identify the white and gray matters for regional analysis. We validated this approachusing 23 SPECT perfusion studies (99mTc ECD)selected visually for clear diffuse anomalies (a much more stringent test than "easy" focal lesions detection) and 21 normal studies. A leave-one-out strategy was used to test our approach to avoid any bias. Based on the number of "abnormal" voxels, two simple supervised classifiers were tested:(1)minimum distance-to-mean and (2)Bayesian. A voxel was considered "abnormal" if its P value with respect to the atlas was lower that 0.01(1%). The results show that for the whole brain, a combination of the number of intensity and displacement "abnormal" voxel is a powerful discriminant with a 91% classification rate. If we focus only on the voxels in the segmented gray matter the rates are slighty higher.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".