diff --git a/docs/source/references.bib b/docs/source/references.bib
index 257f2428..e612e36e 100644
--- a/docs/source/references.bib
+++ b/docs/source/references.bib
@@ -396,6 +396,13 @@ @Article {jin-2017-unet
   doi =		 {10.1109/TIP.2017.2713099}
 }
 
+@Book {kak-1988-principles,
+  author = 	 {Avinash C. Kak and Malcolm Slaney},
+  title = 	 {Principles of Computerized Tomographic Imaging},
+  publisher = 	 {IEEE Press},
+  year = 	 1988
+}
+
 @TechReport {kamilov-2016-minimizing,
   author =	 {Ulugbek S. Kamilov},
   title =	 {Minimizing Isotropic Total Variation without
@@ -771,6 +778,7 @@ @Article {zhang-2017-dncnn
   pages =	 {3142--3155}
 }
 
+
 @Article {zhang-2021-plug,
   author =	 {Zhang, Kai and Li, Yawei and Zuo, Wangmeng and
                   Zhang, Lei and Van Gool, Luc and Timofte, Radu},
diff --git a/scico/linop/xray/_xray.py b/scico/linop/xray/_xray.py
index 770bf627..127b4e7e 100644
--- a/scico/linop/xray/_xray.py
+++ b/scico/linop/xray/_xray.py
@@ -124,6 +124,69 @@ def back_project(self, y: ArrayLike) -> snp.Array:
         """Compute X-ray back projection"""
         return XRayTransform2D._back_project(y, self.x0, self.dx, self.nx, self.y0, self.angles)
 
+    def fbp(self, y: ArrayLike) -> snp.Array:
+        """Compute Filter Back Projection inverse of projection.
+
+        Compute the Filter Back Projection inverse by filtering each row
+        of the sinogram with the filter defined in (61) in
+        :cite:`kak-1988-principles` and then back projecting. The
+        projection angles are assumed to be evenly spaced: poor results
+        may be obtained if this assumption is violated.
+
+        Args:
+            y: Input projection, (num_angles, N).
+
+        Returns:
+            Filtered Back Projection inverse of projection.
+        """
+
+        N = y.shape[1]
+        nvec = np.arange(N) - (N - 1) // 2
+        dx = np.sqrt(self.dx[0] * self.dx[1])  # type: ignore
+        h = XRayTransform2D._ramp_filter(nvec, 1.0 / dx)
+
+        # Apply ramp filter in the frequency domain, padding to avoid
+        # boundary effects
+        hf = snp.fft.fft(h.reshape(1, -1), n=2 * N - 1, axis=1)
+        yf = snp.fft.fft(y, n=2 * N - 1, axis=1)
+        hy = snp.fft.ifft(hf * yf, n=2 * N - 1, axis=1)[
+            :, (N - 1) // 2 : -(N - 1) // 2
+        ].real.astype(snp.float32)
+
+        x = (snp.pi / y.shape[0]) * self.back_project(hy)
+        # Mask out the invalid region of the reconstruction
+        gi, gj = snp.mgrid[: x.shape[0], : x.shape[1]]
+        x = snp.where(
+            snp.sqrt((gi - x.shape[0] / 2) ** 2 + (gj - x.shape[1] / 2) ** 2) < min(x.shape) / 2,
+            x,
+            0.0,
+        )
+        return x
+
+    @staticmethod
+    def _ramp_filter(x: ArrayLike, tau: float) -> snp.Array:
+        """Compute coefficients of ramp filter used in FBP.
+
+        Compute coefficients of ramp filter used in FBP, as defined in
+        (61) in :cite:`kak-1988-principles`.
+
+        Args:
+            x: Sampling locations at which to compute filter coefficients.
+            tau: Sampling rate.
+
+        Returns:
+            Spatial-domain coefficients of ramp filter.
+        """
+        # The (x == 0) term in x**2 * np.pi**2 * tau**2 + (x == 0)
+        # is included to avoid division by zero warnings when x == 1
+        # since np.where evaluates all values for both True and False
+        # branches.
+        return snp.where(
+            x == 0,
+            1.0 / (4.0 * tau**2),
+            snp.where(x % 2, -1.0 / (x**2 * np.pi**2 * tau**2 + (x == 0)), 0),
+        )
+
     @staticmethod
     @partial(jax.jit, static_argnames=["ny"])
     def _project(
diff --git a/scico/test/linop/xray/test_xray.py b/scico/test/linop/xray/test_xray.py
index cd7c0dcd..4aab2e92 100644
--- a/scico/test/linop/xray/test_xray.py
+++ b/scico/test/linop/xray/test_xray.py
@@ -6,6 +6,7 @@
 
 import scico
 from scico.linop.xray import XRayTransform2D, XRayTransform3D
+from scico.metric import psnr
 
 
 @pytest.mark.filterwarnings("error")
@@ -71,6 +72,22 @@ def test_apply_adjoint():
     assert y.shape[1] == det_count
 
 
+@pytest.mark.parametrize("dx", [0.5, 1.0 / np.sqrt(2)])
+@pytest.mark.parametrize("det_count_factor", [1.02 / np.sqrt(2.0), 1.0])
+def test_fbp(dx, det_count_factor):
+    N = 256
+    x_gt = np.zeros((256, 256), dtype=np.float32)
+    x_gt[64:-64, 64:-64] = 1.0
+
+    det_count = int(det_count_factor * N)
+    n_proj = 360
+    angles = np.linspace(0, np.pi, n_proj)
+    A = XRayTransform2D(x_gt.shape, angles, det_count=det_count, dx=dx)
+    y = A(x_gt)
+    x_fbp = A.fbp(y)
+    assert psnr(x_gt, x_fbp) > 28
+
+
 def test_3d_scaling():
     x = jnp.zeros((4, 4, 1))
     x = x.at[1:3, 1:3, 0].set(1.0)